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Speaker 1: Hey, guys, ready or not, twenty twenty four is here,

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Speaker 1: and we here at Breaking Points, are already thinking of

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Speaker 1: ways we can up our game for this critical election.

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Speaker 1: We rely on our premium subs to expand coverage, upgrade

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Speaker 1: the studio ad staff give you, guys, the best independent

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Speaker 1: coverage that is possible. If you like what we're all about,

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Speaker 1: it means the absolute world. To have your support. What

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Speaker 1: are you waiting for? Become a premium subscriber today at

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Speaker 1: Breakingpoints dot com. Well, welcome to the first edition of

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Speaker 1: Counterpoints in twenty twenty three. We're happy to see everybody today. Ryan,

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Speaker 1: how you doing good? Happy New Year, Happy New Year

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Speaker 1: to everybody out there. So Ryan is rocking the Andrew

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Speaker 1: Ross Sorkin look today totally. If you're watching this, you'll

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Speaker 1: see it's tropical. I think what happened is Sager went

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Speaker 1: to Tuloom and we decided to set the temperature here

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Speaker 1: to to Loom levels. So that's what we're doing. It's

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Speaker 1: also climate change. It's like sixty five degrees or so

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Speaker 1: in Washington, d C. And so the buildings just don't

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Speaker 1: know how to deal with it. They have four seasons

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Speaker 1: that they're prepared for. When winter comes and the climate

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Speaker 1: wants to give you spring or fall. They just blasted

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Speaker 1: heat anyway. And the funny thing is Ryan called a

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Speaker 1: head and said, I'm wearing a wool jacket today. Please

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Speaker 1: set the studios set preferred temperature. Set it's super hot

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Speaker 1: me listen. No, actually, it was really hot in here.

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Speaker 1: It still is really hot in here. That said, Ryan

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Speaker 1: actually has been. Ryan has reason whether or not it's

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Speaker 1: hot in the studio. You have reason to be a

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Speaker 1: sort of gruff and like an ink stained wretch today

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Speaker 1: because you were all over Capitol Hill yesterday. Well, it

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Speaker 1: was a lot of fun. And so later in the

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Speaker 1: show we're going to talk to Matt Matt Tayibi. Well,

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Speaker 1: we've got a lot to get into. But the drama,

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Speaker 1: of course, was on Capitol Hill, yes and yeah, so

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Speaker 1: I was in the I was in the house press

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Speaker 1: gallery for that watching it unfold, which there was almost

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Speaker 1: more drama in the kind of build up than in

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Speaker 1: the actual unfolding of it, because nothing changed except for

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Speaker 1: one vote, so to So to back it up, n

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Speaker 1: Kevin McCarthy has been eyeing the speakership since pribably since

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Speaker 1: he was four or five years old. I never didn't

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Speaker 1: want to be president, just wanted to be bigger of

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Speaker 1: the House. And so the man gets in position and

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Speaker 1: there are now there's this rump group of right wing

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Speaker 1: Republicans who are running a kind of never Kevin type

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Speaker 1: of campaign. His campaign is called only Kevin. He has

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Speaker 1: given up. It's middle school basically, basically yes, because he's

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Speaker 1: given up that. He's given up so many different concessions

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Speaker 1: to them that it seems like the only thing left

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Speaker 1: that he could give would be himself, Like they just

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Speaker 1: don't want him at this point. They don't trust him

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Speaker 1: the problem. So they had three votes yesterday. He won

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Speaker 1: two hundred and three votes the first time, which is

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Speaker 1: fifteen short of the two eighteen he needed. Second time,

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Speaker 1: he won two o three Again. The third time he

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Speaker 1: won two two, so that he has twenty opponents. So

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Speaker 1: at this point it's very hard to see what his

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Speaker 1: path is to the speakership. Yet at the same time,

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Speaker 1: the opponents don't have a path the victory either, so

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Speaker 1: they're just kind of staring at each other. They adjourned.

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Speaker 1: They didn't have to, but Democrats agreed to adjourn last night,

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Speaker 1: and all the parties agreed to adjourn. So now they're

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Speaker 1: going to do it again today, still with no path forward.

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Speaker 1: You were talking about how kind of Tucker Carlson, who,

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Speaker 1: if anybody's going to mediate this me Tucker Carlson throughout

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Speaker 1: some ideas was what was his take on what McCarthy's

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Speaker 1: path is? Right? So Tucker obviously comes on at night

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Speaker 1: after all of these negotiations had failed, basically and said,

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Speaker 1: all right, how about this. Kevin McCarthy agrees to release

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Speaker 1: all files related to January sixth, and agrees to put

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Speaker 1: Thomas Massey in charge of this church like committee into

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Speaker 1: the intelligence community that Kevin McCarthy has promised to convene. Personally.

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Speaker 1: I'm fine with both of those points. There's a question

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Speaker 1: though as to whether and you probably had a chance

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Speaker 1: to chat with some folks on the Hill yesterday, that's

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Speaker 1: enough to say to your point, they're already saying we

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Speaker 1: don't trust Kevin McCarthy. The problem is not, you know.

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Speaker 1: So it's like, how much more stuff can we get

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Speaker 1: from him? Because he made a concent on one of

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Speaker 1: the biggest possible things that he could concede on, which

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Speaker 1: is called the motion to vacate. It sounds like a

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Speaker 1: dumb procedural Beltway thing, and it is, but it's hugely

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Speaker 1: consequential because it's what ousted John Bayner. It's what Mark

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Speaker 1: Medows used to force John Bayner out of leadership years ago.

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Speaker 1: Nancy Pelosi, the shrewd tactician that she is, immediately was like,

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Speaker 1: hell no to this, got rid of it. They wanted

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Speaker 1: to bring it back. Kevin McCarthy agreed to bring it back,

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Speaker 1: meaning five members of his caucus could basically at any

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Speaker 1: time be like bye and stage a coup, which is

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Speaker 1: really a huge concession from him. They've also from the

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Speaker 1: perspective of the establishment Republicans, that was just a massive concession.

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Speaker 1: He's agreed to impeaching. Having impeachment hearing for Alejandro mayorcis

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Speaker 1: one thing he told me he wouldn't do in September.

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Speaker 1: He then by the time December rolled around was like, Yeah,

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Speaker 1: let's do it. He's given them a lot, and they

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Speaker 1: have a lot to be happy with. They should really

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Speaker 1: be taking the w But because it's more about personality

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Speaker 1: than policy in this case, they think personality is policy.

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Speaker 1: Because it's more about that. Nothing is going to satisfy it.

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Speaker 1: So does the Tucker point satisfy it? I don't know

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Speaker 1: right now, with Tucker saying it rather than McCarthy's saying it,

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Speaker 1: maybe that makes it a little bit easier for them.

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Speaker 1: Can we put up a five actually exist? This has

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Speaker 1: Kevin McCarthy basically saying that he's talking about Matt Gates

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Speaker 1: here and Matt Gates and others that they basically made

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Speaker 1: the same attempted to make the same deal at Tucker

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Speaker 1: is suggesting, so let's roll a five here. Last night,

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Speaker 1: I was presented the only way to have two hundred

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Speaker 1: and eighteen votes if I provided certain members with certain positions,

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Speaker 1: certain gavels to take over to Church Committee, to have

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Speaker 1: certain budgets. And they even came to the position where

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Speaker 1: one Matt Gate said, I don't care if we go

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Speaker 1: to plurality and we elect Hakeem Jeffries and it hurts

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Speaker 1: the new frontline members not to get re elected. So,

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Speaker 1: first of all, he's talking about the Church Committee there,

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Speaker 1: which is this subcommittee that would have investigatory powers, usually

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Speaker 1: a deep state committee. The Church. The Church Committee is

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Speaker 1: a reference to Senator Frank Church back in the seventies,

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Speaker 1: who who exposed all sorts of malfeasance on the part

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Speaker 1: of the deep state. If you want to call them

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Speaker 1: that at the time that I don't know if that

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Speaker 1: term was much in circulation at the time, but he

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Speaker 1: exposed all sorts of different crimes that the CIA, FBI,

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Speaker 1: other elements of the kind of government apparatus had been

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Speaker 1: committing post World War two up up through then. And

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Speaker 1: so there's been this this, and it's in the rules

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Speaker 1: if you if you read the rules package, there is

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Speaker 1: a committee in there for that type of oversight. Look,

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Speaker 1: it sounds like they were making a demand for who

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Speaker 1: would run it. Tucker wants Thomas Massey to run it, right,

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Speaker 1: he wants Thomas Massey, and Thomas Massey is a like

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Speaker 1: super interesting maverick libertarian, great for journalism running that. I'm

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Speaker 1: all for it. And you know what, they need somebody

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Speaker 1: who's going to basically take no prisoner, and that would

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Speaker 1: be the person that you would think of, would be

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Speaker 1: a Thomas Massey, somebody who doesn't care whether he's voting

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Speaker 1: with the party line or not. So I think Tucker's

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Speaker 1: suggestion is not a bad one. But again the question

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Speaker 1: is does that move votes when with a Matt Gates,

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Speaker 1: for instance, Like, here is your sense that what they're

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Speaker 1: doing right now is trying to take this as far

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Speaker 1: as they possibly can, to the point where they have

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Speaker 1: squeezed every last concession out of Kevin McCarthy that they

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Speaker 1: possibly can. And then they'll say, okay, or is it

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Speaker 1: just we don't care, blow it all up if Hakeem

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Speaker 1: Jeffries gets in, Hakeem Jeffries gets in, or here's the

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Speaker 1: third option. It's a combination of everything, and nobody really

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Speaker 1: knows what the plan is. I think it's I think

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Speaker 1: it's the latter, Yeah, because yeah, I don't think that

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Speaker 1: they have thought like I don't think they have a

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Speaker 1: one two, three, four five, here's how we get to

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Speaker 1: exactly where we are. I think it's more of a

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Speaker 1: kind of Trump style approach to it, that we're just

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Speaker 1: going to live to fight another day. Well, and if

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Speaker 1: it can give into something we don't want, and then

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Speaker 1: we're going to keep pushing and if it gets blown up,

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Speaker 1: who cares? And then if it gets blown up, who cares?

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Speaker 1: What did you think? Do you believe that Gates did

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Speaker 1: tell him that he's okay with Hakeem Jefferys taking over.

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Speaker 1: First of all, that's not going to happen, Like it's

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Speaker 1: basically impossible for that to happen be hilarious. This is

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Speaker 1: if this clip is being played a couple of weeks,

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Speaker 1: when they can give him a speaker, it's virtually impossible

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Speaker 1: for it to happen. I could see Gates saying it

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Speaker 1: as a bluff, like to demonstrate to him how little

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Speaker 1: he cares about whether or not McCarthy survives or not

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Speaker 1: one hundred percent. And I've said a couple of times

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Speaker 1: on our podcast, Federal Radio Hour, and maybe he was

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Speaker 1: listening that all Republicans should just like cast if you

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Speaker 1: really want to do a protest vote, you should just

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Speaker 1: get as many Republicans as possible to vote for Ilhan

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Speaker 1: Omar and just stick it to them leadership Republican leadership

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Speaker 1: at the same time in such a narrowly divided House

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Speaker 1: of Representatives, and just say, screw it, it's your Omar. Yes,

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Speaker 1: if you're really trying to blow it all up, then

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Speaker 1: do it. Then do it. But at this point it's

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Speaker 1: such a muddled strategy. They've gotten so much. They really

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Speaker 1: have gotten so much out of Kevin McCarthy. Kevin McCarthy

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Speaker 1: sort of sees himself as someone who stuck his neck

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Speaker 1: out for Jim Jordan put him on oversight when a

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Speaker 1: lot of establishment Republicans didn't. He's friendly with Marjorie Taylor Green,

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Speaker 1: which is in DC Republican circles, unheard of that you

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Speaker 1: would have an establishment a denizen of the sort of

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Speaker 1: Washington establishment also be on personally friendly terms with a

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Speaker 1: person of the sort of positioning and the party that

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Speaker 1: Marjorie Taylor greenholds. And I think he was really more

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Speaker 1: confident because of that. I don't think he was ever

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Speaker 1: perfectly confident, but because it was always like this, what

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Speaker 1: are they going to do? They're gonna throw bombs, and

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Speaker 1: that's exactly what they ended up doing. But they really

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Speaker 1: have it. They have a w to take. The w's

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Speaker 1: on the table. It's there and if we can put

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Speaker 1: if we can put up a two. I'm curious for

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Speaker 1: your take on Trump's role in this entire thing. So

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Speaker 1: somebody called up Donald Trump yesterday and asked him, Hey,

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Speaker 1: are you sticking by your endorsement of your Kevin that's

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Speaker 1: one of his best nicknames, Mike Kevin. And Trump, who

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Speaker 1: is anything if not the least loyal person on the planet, said,

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Speaker 1: you know what, Let's see what do you say. Let's

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Speaker 1: see how it goes. We'll see what happens. We'll see

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Speaker 1: how it all works out. Yeah, So does Trump have

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Speaker 1: a role in all of us? It sounds like according

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Speaker 1: to him, he's got some of these renegades calling him,

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Speaker 1: maybe some of the others calling him, maybe Mike Kevin

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Speaker 1: is calling him Kent Or does he just not have

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Speaker 1: enough juice left to move the needle inside the Republican conference.

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Speaker 1: I don't think he's moving the needle inside the Republican conference,

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Speaker 1: which is very interesting actually to watch because that was

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Speaker 1: the expectation, is that Donald Trump would be sort of

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Speaker 1: the king maker. But he's billy been on the sideline

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Speaker 1: of this particular battle. If he had again positioned himself differently,

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Speaker 1: if he weren't, for instance, attacking pro life voters on

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Speaker 1: True Social this week, maybe easier for him to like

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Speaker 1: actually have a say right now. But I think they're

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Speaker 1: doing most of this of their own accord. And that's

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Speaker 1: what's particularly interesting now. Kevin McCarthy something interesting about him

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Speaker 1: as if you contrast his relationship with Trump with Paul

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Speaker 1: Ryan's relationship with Trump, it's totally a study in contrast,

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Speaker 1: and like he learned from Paul Ryan. That's something I

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Speaker 1: talked to him about he learned from those kind of mistakes.

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Speaker 1: He let Paul Ryan go before him take the arrows

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Speaker 1: and then sort of realized that maybe there's a different

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Speaker 1: way to do this, and that's one of the big

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Speaker 1: differences between the two of them. It's one of the

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Speaker 1: things that helped him build relationships with people like say

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Speaker 1: Marjorie Taylor Green. So he's been trude about it, but

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Speaker 1: after January sixth, they're split. You seem to be not

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Speaker 1: great And I want to get to the intrigue with

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Speaker 1: Matt Gates and AOC on the floor in a second,

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Speaker 1: but I'm curious for your take on this split between

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Speaker 1: Bolbert and Marjorie Taylor Green, because Green has been kind

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Speaker 1: of Pearl McCarthy for a while. In December, she was

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Speaker 1: tweeting like, look, McCarthy has promised to impeach Biden if

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Speaker 1: there's evidence to impeach him, and she listed all her

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Speaker 1: reasons why they should work with the Kevin that they

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Speaker 1: have rather than try to get a new Kevin, whereas

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Speaker 1: Bolbert to this day is still resisting him. What are they?

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Speaker 1: Are they anything of a squad? Have they ever been?

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Speaker 1: What's their situation? How did they get to a place

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Speaker 1: where they're kind of publicly attacking each other over this strategy. Yeah,

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Speaker 1: I think this says way more about the Freedom Caucus

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Speaker 1: than it does about Kevin McCarthy, because again, that's the

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Speaker 1: Freedom Caucus has in the past been extremely successful because

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Speaker 1: they have been tightly knit. They've been able to sort

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Speaker 1: of rally around the cause of opposition to leadership, opposition

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Speaker 1: to the establishment, which is a really strong unifier. That's

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Speaker 1: something that the Squad has kind of grappled with as well.

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Speaker 1: They're always interesting parallels between the Freedom Caucus and the Squad,

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Speaker 1: But the fact that they're on a different page about

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Speaker 1: something like this, I think is it's not It shows

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Speaker 1: that this is not a clean litmus test. You know,

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Speaker 1: if you vote for Kevin McCarthy here, it doesn't mean

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Speaker 1: you are, as publicans might say, a squish or not.

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Speaker 1: It's not that clean litmus test, even though some people

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Speaker 1: are trying to make it into one. Because for people

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Speaker 1: like Lauren Bobert, I think she sees her cross benefit analysis.

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Speaker 1: She's saying, I can get, first of all, a lot

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Speaker 1: of attention, a lot of publicity, and sort of be

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Speaker 1: seen as a star in the same way that Mark

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Speaker 1: Meadows and Jim Jordan became stars in the fight against

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Speaker 1: John Bayner. I think she sees this moment as that,

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Speaker 1: and Marjorie Taylor Green sees this moment as saying, well,

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Speaker 1: I can consolidate support, I can really build on my

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Speaker 1: support with McCarthy, my relationship with McCarthy. I can win

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Speaker 1: a lot of points right here for future negotiations and

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Speaker 1: policymaking down the line and maybe get more w's out

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Speaker 1: of it from that sense. So those are I think

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Speaker 1: it's just two sides of that coin. And you've seen

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Speaker 1: members taking swipes at Lauren Bobert saying, you know, when

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Speaker 1: you can win a deep red district by more than

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Speaker 1: like fifty votes, yeah, then you can come here to

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Speaker 1: dictate to us. You know how we ought to run things,

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Speaker 1: but until then, no thanks. And so the intrigue that

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Speaker 1: did break through, that we did learn about what was

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Speaker 1: between Matt Gates and Alexandro Cosio Cortez, we can play

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Speaker 1: that vio here. So basically, Matt Gates literally crossed the aisle,

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Speaker 1: goes over talks to AOC because apparently he believes actually

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Speaker 1: that AOC runs a democratic party that's not just something

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Speaker 1: like he hears on Fox and that he says he

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Speaker 1: maybe actually believes it to be true, and I love

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Speaker 1: that in him, like it's there's some hope there that's

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Speaker 1: actually true. But so he walks over. There's a lot

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Speaker 1: of speculation about what they said. Ok. I reported in

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Speaker 1: the intercept, We could put that up here that basically

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Speaker 1: what Matt Gates was saying was that, hey, Kevin McCarthy

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Speaker 1: has been he's been making a bunch of threats against us,

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Speaker 1: and he has been telling us that he believes he's

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Speaker 1: going to be able to get a deal with Democrats,

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Speaker 1: that a lot that enough Democrats are going to vote

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Speaker 1: present that it's going to allow him to win with

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Speaker 1: his two two or two three or five or even

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Speaker 1: two D and ten votes. And so therefore all of

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Speaker 1: you people who are fighting me are out on a

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Speaker 1: limb here, and you're going to get annihilated, you know,

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Speaker 1: once Democrats start to fold and kind of bail me

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Speaker 1: out of this jam. Then AOC tells him I don't

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Speaker 1: think that that's the case. Then she goes back to

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Speaker 1: party leadership, and I think you can see it at

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Speaker 1: the end saying I'll get back to you or something

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Speaker 1: like that. So she goes back to party leadership and

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Speaker 1: she's like, look, there's no bailing out of Kevin McCarthy here,

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Speaker 1: is there. Because McCarthy's telling his members that Democrats are

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Speaker 1: going to bail him out, and the party leadership tells AOC, no,

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Speaker 1: absolutely not, Like he's on his own. Nobody here is

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Speaker 1: voting present, nobody here is leaving. Like that was another

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Speaker 1: wishful thinking thing that started circulating kind of from the

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Speaker 1: McCarthy camp, that Democrats were just going to get bored

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Speaker 1: and leave, right, which lowers the threshold, which then would

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Speaker 1: lower the threshold, which to me suggests that they're out

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Speaker 1: of plays, Like if they're just hoping that Democrats are

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Speaker 1: going to get bored, like, that's not how legislating tends

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Speaker 1: to work. But although we're in unshot of territory, who knows.

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Speaker 1: So what did you make of Gates kind of using

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Speaker 1: AOC to get intel from Democrats. Well, it was a

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Speaker 1: great report because everybody on Twitter, I mean, this was

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Speaker 1: so many people in tour were speculating what happened, when happened,

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Speaker 1: wondering what happened in Rehn's Like, oh I got this,

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Speaker 1: I asked Gates, I asked car she's the only one

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Speaker 1: that responded, Yeah, no, it was really interesting. And so

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Speaker 1: I think that's also to the point about them being

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Speaker 1: out of plays. There's reporting suggesting right now that's the

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Speaker 1: play for today. That's one of the plays that Kevin

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Speaker 1: McCarthy is perhaps banking on today, just run out the

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Speaker 1: clock getting Democrats to vote present, which lowers the threshold

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Speaker 1: so that he doesn't need the two eighteen that you

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Speaker 1: can bring it down, and that there's precedent for that.

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Speaker 1: I mean, what didn't Nancy Pelosi went with two sixteen.

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Speaker 1: So it's not as though that's a crazy idea. And

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Speaker 1: maybe if you're a certain type of Democrat, there's logic

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Speaker 1: to be had in manipulating and maybe taking you have

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Speaker 1: to give what would he give that ceiling? What would

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Speaker 1: like that? There's a lot he could give, but it

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Speaker 1: hasn't really been on the table so far, so I

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Speaker 1: don't know exactly what it is. But like debt ceiling

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Speaker 1: is a great example, there could be something like that.

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Speaker 1: There could be committee stuff. There's all kinds of stuff

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Speaker 1: potentially out there. But then it screws up as calculus

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Speaker 1: for because he can't give away something that he gave

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Speaker 1: to a Republican in order to win their vote. Yeah, exactly,

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Speaker 1: So look, good luck boy. We'll be watching this all day. Yeah,

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Speaker 1: it's a pickle, I mean quite like. It is actually

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Speaker 1: a pickle. You've got a runner between second and third

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Speaker 1: and is going back and forth. So we'll be paying

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Speaker 1: attention to Ryan. You're heading back to the hill today, Yes, yeah,

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Speaker 1: I wouldn't miss it. Jacket. Oh yeah, you catch You

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Speaker 1: can't be there without a jacket, that's right, all right. Well,

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Speaker 1: there was also big news yesterday on the Twitter front

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Speaker 1: because we had another Twitter files dump. Matt Taibia, as

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Speaker 1: Ryan said, will be with us later in the show.

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Speaker 1: He had sort of a two part Twitter files dump yesterday.

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Speaker 1: We're going to start with the first part here, which

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Speaker 1: I think, personally I think contained one of the more

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Speaker 1: interesting revelations of the entire one of the more important too,

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Speaker 1: Not just interesting, but one of the more important revelations

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Speaker 1: of the entire Twitter files disclosure process. Let's start with

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Speaker 1: B one and put the tear sheet up on the screen.

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Speaker 1: There you see the headline from Taybe's substack, why Twitter

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Speaker 1: let the intelligence community in? We can then go over

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Speaker 1: here to be three because Tayibi posted this Twitter thread

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Speaker 1: in addition to his substack an incredible long thread basically

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Speaker 1: showing the push and pull between our government, between the

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Speaker 1: FBI and all of them going back and forth, and

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Speaker 1: the media trying to figure out where the pressure points

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Speaker 1: are inside of Twitter. Basically, you have reporters, you have

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Speaker 1: the government pushing Twitter to find Russian bots where there

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Speaker 1: aren't Russian bots, and so Tayib has these Twitter threads

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Speaker 1: where people are has this thread where you have emails

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Speaker 1: from inside Twitter showing, hey, you know, the government has

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Speaker 1: just sent us all of these accounts that they're saying

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Speaker 1: our bots that I mean like that, and Mark Warner

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Speaker 1: is telling us we have to go find the Russian

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Speaker 1: infiltration of Twitter. And inside Twitter they're telling, you know,

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Speaker 1: constantly keep pushing reporters to Facebook, this is really a

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Speaker 1: Facebook's problem. But they're also saying, listen, we can't find this.

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Speaker 1: This is this is not what we have on our end.

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Speaker 1: And why I thought that was so important if we

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Speaker 1: could put a B four again on the screen. Personally,

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Speaker 1: I thought it was so important because there's this line

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Speaker 1: from an email where it says, reporters now know this

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Speaker 1: is a model that works. That's an email from inside Twitter.

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Speaker 1: What do they mean by that? So they're saying, given

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Speaker 1: we've now suspended all accounts, we will take a hit

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Speaker 1: in the press that moves from BuzzFeed to more establishment publications,

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Speaker 1: We'll work to contain it. So BuzzFeed's working on a story.

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Speaker 1: This is an email from some one inside Twitter. Relatedly,

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Speaker 1: we can expect more investigation of accounts that are ten

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Speaker 1: eventually associated with the IRA handover to the US committees.

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Speaker 1: Buoyed by academic brand names, reporters now know this is

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Speaker 1: a model that works. A model as in like that

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Speaker 1: for reporting a story of the format, like we found

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Speaker 1: a bot, they didn't take it down, We told Twitter

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Speaker 1: about it, they took it down, and boom, we have

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Speaker 1: a story. Or basically, we can either take we can

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Speaker 1: fabricate a problem inside Twitter based on you know, maybe

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Speaker 1: one bot account, or based on allegations that are fuzzy

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Speaker 1: from the intelligence community. We can sort of fabricate a

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Speaker 1: story and then force it to become a story by

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Speaker 1: making Twitter respond. And that's a model I've seen that

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Speaker 1: happen all the time. Like I see that as a

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Speaker 1: broader model of cancel culture. It happened a lot in

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Speaker 1: different spaces right, Yeah, because canceled this bot right, powerful

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Speaker 1: people realized that it could be exploited and they were

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Speaker 1: happy to exploit it. So Twitter, terrified of the pr backlash,

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Speaker 1: has to respond to this because they see buzzfeedworking on

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Speaker 1: the story. And this actually happened over at the Federalists

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Speaker 1: and a reporter at NBC News said that our comments

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Speaker 1: were in some way like beyond the pale, even though

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Speaker 1: there are plenty of websites that have comments beyond the

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Speaker 1: pale and went to I think it was Google ad

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Speaker 1: Services at the time, Google ad Services basically monopoly and said,

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Speaker 1: you know, what are you going to do about this?

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Speaker 1: And Google was like, oh, maybe we'll have to kick

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Speaker 1: Federalists off of our ad platform because a reporter went

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Speaker 1: to them and fabricated this entire news cycle. And it's

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Speaker 1: just a waste of everyone's time. But it also shows

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Speaker 1: how I think powerful people have exploited the media, and

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Speaker 1: how journalists have allowed themselves to be exploited in the

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Speaker 1: service of really powerful government and business interests. And so

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Speaker 1: we do know that there is this little sub covert

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Speaker 1: agency whatever you want to call it, the Internet Research Agency,

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Speaker 1: basically basically Kremlin type operation. The same, you know, very

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Speaker 1: similar to what a lot of other governments do. I

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Speaker 1: think Russia's probably, you know, on the Russia in the

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Speaker 1: United States, China probably on the leading edge of this

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Speaker 1: of this type of thing. But for all of the

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Speaker 1: investigations that have been done, they seem to have uncovered

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Speaker 1: a couple hundred thousand dollars worth of spending on we know,

428
00:22:13,160 --> 00:22:17,879
Speaker 1: on Facebook, which when you compare it to a multi

429
00:22:17,920 --> 00:22:21,800
Speaker 1: billion dollar presidential campaign, is really hard to see as

430
00:22:22,119 --> 00:22:25,399
Speaker 1: anything significant. If if somebody pitches you or me on

431
00:22:25,480 --> 00:22:27,479
Speaker 1: a story and they and we said, well, how much

432
00:22:27,520 --> 00:22:29,280
Speaker 1: you spending on this campaign and they were like wow,

433
00:22:29,280 --> 00:22:32,119
Speaker 1: one hundred and fifty thousand dollars, Like yeah, you know,

434
00:22:32,560 --> 00:22:35,920
Speaker 1: like sorry, Like we were covering much more important things

435
00:22:35,920 --> 00:22:38,800
Speaker 1: than that, So it wouldn't even be worth a single article,

436
00:22:39,080 --> 00:22:42,320
Speaker 1: probably unless it was Russia that pitched it. We're like, actually,

437
00:22:42,320 --> 00:22:44,400
Speaker 1: that's illegal, so we'll do a little story about how

438
00:22:44,440 --> 00:22:47,479
Speaker 1: you can't legally do that. But what's interesting and if

439
00:22:47,480 --> 00:22:49,280
Speaker 1: we could put up B two here, this is the

440
00:22:49,320 --> 00:22:53,040
Speaker 1: one I found really fascinating, where you have a Twitter

441
00:22:53,040 --> 00:22:55,200
Speaker 1: employee saying that the TLDR is that quote. We have

442
00:22:55,600 --> 00:22:59,560
Speaker 1: found suspicious accounts which demonstrate our investigation strategy is working. However,

443
00:23:00,080 --> 00:23:02,800
Speaker 1: we see no evidence of a coordinative approach. All of

444
00:23:02,800 --> 00:23:05,800
Speaker 1: the accounts found seem to be lone wolf type activity,

445
00:23:06,040 --> 00:23:09,679
Speaker 1: different timing, spend, targeting, less than ten thousand dollars in

446
00:23:09,760 --> 00:23:12,760
Speaker 1: ADS spending. And so what they're saying there is that

447
00:23:13,240 --> 00:23:16,040
Speaker 1: they actually are finding, you know, plenty of bots and

448
00:23:16,080 --> 00:23:19,720
Speaker 1: there's no no necessarily a shortage of bots, but they're

449
00:23:19,760 --> 00:23:23,119
Speaker 1: not able to find that this is coordinated state activity

450
00:23:23,520 --> 00:23:26,000
Speaker 1: which you can find and they have found which does right.

451
00:23:26,040 --> 00:23:28,439
Speaker 1: If it was, it would be clear that it was.

452
00:23:28,520 --> 00:23:31,639
Speaker 1: So this these particular bots that were getting flagged were not.

453
00:23:32,200 --> 00:23:34,800
Speaker 1: And it just seems like both the senators and the

454
00:23:34,800 --> 00:23:37,480
Speaker 1: House members and the intelligence community, some of them I think,

455
00:23:37,520 --> 00:23:40,960
Speaker 1: were operating in bad faith and wanted to impute this,

456
00:23:41,520 --> 00:23:45,199
Speaker 1: you know, these campaigns to Russia. I think others probably

457
00:23:45,240 --> 00:23:48,359
Speaker 1: just see Russia everywhere, yeah, and so believe that. And

458
00:23:48,400 --> 00:23:52,399
Speaker 1: then others, I think watch their mentions fill up with

459
00:23:52,640 --> 00:23:56,359
Speaker 1: trash and criticism and everybody just assumes it's a bot.

460
00:23:56,720 --> 00:24:00,159
Speaker 1: Like everyone criticized cop, Yeah, it's a cope. Everyone who

461
00:24:00,200 --> 00:24:02,640
Speaker 1: gets criticized online is like, clearly that's a bot. There's

462
00:24:02,640 --> 00:24:05,280
Speaker 1: no real human who have bought who could read my

463
00:24:05,440 --> 00:24:11,040
Speaker 1: take and find it objectionable. Sometimes I think, right, yeah,

464
00:24:11,359 --> 00:24:15,760
Speaker 1: if I'm objecting to something you send, then yes, I totally.

465
00:24:16,000 --> 00:24:19,679
Speaker 1: People always think that their opponents are bots now, but

466
00:24:20,280 --> 00:24:23,480
Speaker 1: they think they're state coordinated bots. What they forget is

467
00:24:23,480 --> 00:24:26,760
Speaker 1: that there is money to be made by producing what

468
00:24:27,440 --> 00:24:31,159
Speaker 1: we called in twenty fifteen fake news, which was literally

469
00:24:31,200 --> 00:24:35,919
Speaker 1: fake news, yes, getting written in Romania or somewhere Moldova.

470
00:24:36,280 --> 00:24:40,840
Speaker 1: You won't believe which seventies child star has cancer or

471
00:24:40,920 --> 00:24:44,480
Speaker 1: like or like four bodies found in Hillary Clinton's trunk,

472
00:24:44,840 --> 00:24:47,760
Speaker 1: and it would be ABC Newsgo go dot com or

473
00:24:47,800 --> 00:24:50,000
Speaker 1: something like that, and it would look like an ABC

474
00:24:50,080 --> 00:24:54,480
Speaker 1: News site but it wasn't. And then you would then

475
00:24:54,480 --> 00:24:58,200
Speaker 1: buy ads. Those Moldovans would then buy ads on Facebook

476
00:24:58,240 --> 00:25:01,760
Speaker 1: and Twitter because the money that they could make by

477
00:25:01,760 --> 00:25:04,000
Speaker 1: going viral was more than it would cost them to

478
00:25:04,000 --> 00:25:06,679
Speaker 1: make ads. So it was just a business proposition. And

479
00:25:06,720 --> 00:25:09,760
Speaker 1: so what you're that's probably what they're finding here, less

480
00:25:09,760 --> 00:25:14,120
Speaker 1: than ten thousand dollars lone wolf type stuff. What they

481
00:25:14,160 --> 00:25:16,720
Speaker 1: realize is that they can just set up a fake

482
00:25:16,760 --> 00:25:22,520
Speaker 1: website right outlandish things that the American News consumer will

483
00:25:22,960 --> 00:25:25,080
Speaker 1: gobble up. And the way that they get over the

484
00:25:25,160 --> 00:25:28,159
Speaker 1: hump of not having legitimate news operations, they would spend

485
00:25:28,160 --> 00:25:31,159
Speaker 1: money on Facebook or Twitter, just a small amount of

486
00:25:31,200 --> 00:25:33,040
Speaker 1: money just to get it moving and then hope that

487
00:25:33,040 --> 00:25:36,680
Speaker 1: it would go viral. And so that is a problem,

488
00:25:36,760 --> 00:25:38,520
Speaker 1: Like that's something that I think all of us would

489
00:25:38,520 --> 00:25:41,959
Speaker 1: prefer is not part of our discourse. But it doesn't

490
00:25:42,000 --> 00:25:44,880
Speaker 1: mean that this is Vladimir Putin doing this. No, And

491
00:25:44,920 --> 00:25:50,000
Speaker 1: it was Democrats conflating a serious problem with something that

492
00:25:50,200 --> 00:25:54,080
Speaker 1: was I mean questionable at best, to push reports in media,

493
00:25:54,200 --> 00:25:56,359
Speaker 1: to push reports all over the place. And it's just

494
00:25:56,400 --> 00:26:01,560
Speaker 1: a great look back into how I think paralyzed for Democrats,

495
00:26:01,720 --> 00:26:05,440
Speaker 1: the specter of Russian collusion was for such a long time. Yeah,

496
00:26:05,480 --> 00:26:07,160
Speaker 1: And it's the macro version of what we were talking

497
00:26:07,200 --> 00:26:10,800
Speaker 1: about it. You always think that any critic of yours

498
00:26:10,840 --> 00:26:13,200
Speaker 1: must be a bot. Well, they lost an election, Yes,

499
00:26:13,320 --> 00:26:16,200
Speaker 1: we couldn't have been rejected like it must be all bought.

500
00:26:16,320 --> 00:26:18,880
Speaker 1: It was exactly that. And Hillary Clinton to this day

501
00:26:19,440 --> 00:26:23,560
Speaker 1: uses the Russia collusion cope and I think that's completely true.

502
00:26:24,080 --> 00:26:27,280
Speaker 1: And it's a good glimpse. These the documents SABEE released

503
00:26:27,320 --> 00:26:29,439
Speaker 1: were it was sort of like a time machine, like

504
00:26:29,480 --> 00:26:33,600
Speaker 1: I'd been transported back to twenty seventeen and you were

505
00:26:33,640 --> 00:26:37,439
Speaker 1: in the midst, in the throes of this obsession. And

506
00:26:37,480 --> 00:26:42,200
Speaker 1: it's another great reminder of how the fevered freak out

507
00:26:42,240 --> 00:26:46,240
Speaker 1: of a Russian collusion really really deserved a whole lot

508
00:26:46,280 --> 00:26:49,320
Speaker 1: more skepticism from reporters like the ones at BuzzFeed who

509
00:26:49,359 --> 00:26:51,480
Speaker 1: carried water for Mark Warner in this case and for

510
00:26:51,600 --> 00:26:55,480
Speaker 1: Democrats in Congress, rather than taking both sides of the story.

511
00:26:55,480 --> 00:27:00,680
Speaker 1: Here Twitter side the Mark Warner side and adding a

512
00:27:00,680 --> 00:27:06,720
Speaker 1: little skepticism of both sides. Former ft X founder the

513
00:27:06,800 --> 00:27:09,440
Speaker 1: disgrace that Sam Bankman free arrived in court yesterday. Let's

514
00:27:09,520 --> 00:27:26,520
Speaker 1: roll some video of that. I gotta get it well.

515
00:27:29,880 --> 00:27:34,560
Speaker 1: He had the courtesy of wearing a jacket, Rightack, I'm

516
00:27:34,560 --> 00:27:37,040
Speaker 1: doing work today. I love the I love the news

517
00:27:37,040 --> 00:27:40,000
Speaker 1: cliche of disgraced though it's just it just it just

518
00:27:40,240 --> 00:27:44,119
Speaker 1: it goes into every piece of copy. It just becomes

519
00:27:44,160 --> 00:27:49,240
Speaker 1: part of your name. Disgrace to intercept DC beer one day,

520
00:27:49,560 --> 00:27:52,120
Speaker 1: one day, I will get I will get there. If

521
00:27:52,160 --> 00:27:54,600
Speaker 1: you live long enough in this business, you wind up

522
00:27:54,680 --> 00:27:58,120
Speaker 1: the disgrace, someone will call you disgrace. Absolutely can't wait

523
00:27:58,160 --> 00:27:59,840
Speaker 1: for that. So what stands out to you from the

524
00:28:00,040 --> 00:28:03,000
Speaker 1: video and from SBF's decision, you're to plead not guilty.

525
00:28:03,119 --> 00:28:05,840
Speaker 1: Backpack is classic. You know he's still he's keeping it real.

526
00:28:06,160 --> 00:28:08,200
Speaker 1: So he kept it real by two strapping, Yeah, two

527
00:28:08,280 --> 00:28:11,639
Speaker 1: straps right, unafraid, So he goes in there, he's pleads

528
00:28:11,720 --> 00:28:16,159
Speaker 1: not guilty. Two of his underlings, including his sort of

529
00:28:16,960 --> 00:28:21,840
Speaker 1: former girlfriend, have turn state's evidence and are now going

530
00:28:21,880 --> 00:28:25,320
Speaker 1: to testify against bangrafury. She may Caroline Ellison, she may

531
00:28:25,359 --> 00:28:29,760
Speaker 1: face up to what five years or so, which which

532
00:28:29,800 --> 00:28:31,960
Speaker 1: suggests that if she was willing to plead guilty and

533
00:28:32,040 --> 00:28:34,960
Speaker 1: plead for five years, that they have enough evidence that

534
00:28:35,640 --> 00:28:37,439
Speaker 1: her lawyers were able to persuade her that she was

535
00:28:37,440 --> 00:28:40,880
Speaker 1: looking at decades if she didn't do that, And so therefore,

536
00:28:41,960 --> 00:28:45,120
Speaker 1: is bankman freed looking at decades? You think I would

537
00:28:45,120 --> 00:28:50,200
Speaker 1: think so absolutely. And there's a big question mark here

538
00:28:51,200 --> 00:28:54,880
Speaker 1: when he's pleading not guilty. I'm curious when you think

539
00:28:54,920 --> 00:28:59,720
Speaker 1: of this, what does he think? How does he think

540
00:29:00,080 --> 00:29:03,880
Speaker 1: if Carolyn Ellison has flint and all indications point to

541
00:29:03,920 --> 00:29:06,360
Speaker 1: get that's absolutely what's happening, which means, by the way,

542
00:29:06,400 --> 00:29:09,640
Speaker 1: other people probably will be very eager to cooperate as well,

543
00:29:11,080 --> 00:29:14,880
Speaker 1: what on earth does he think he can argue against

544
00:29:14,920 --> 00:29:17,760
Speaker 1: all of that? Well, maybe, I mean, maybe he can

545
00:29:17,800 --> 00:29:21,400
Speaker 1: get some charges knocked down. Maybe maybe there's some type

546
00:29:21,400 --> 00:29:23,360
Speaker 1: of guilty plea that he can enter. But he can't.

547
00:29:24,480 --> 00:29:26,440
Speaker 1: You can't just enter a guilty plea right off the

548
00:29:26,440 --> 00:29:31,560
Speaker 1: bat without without any deal being offered, because what do

549
00:29:31,600 --> 00:29:33,520
Speaker 1: you have to lose at that point? Yeah, you might

550
00:29:33,600 --> 00:29:35,120
Speaker 1: as you might as well take it a trial and

551
00:29:35,200 --> 00:29:37,480
Speaker 1: see if you can. So you don't think he's going

552
00:29:37,520 --> 00:29:39,360
Speaker 1: to do this to the bitter end. You think this

553
00:29:39,400 --> 00:29:41,920
Speaker 1: is part of a give and take. Possibly, you know,

554
00:29:41,960 --> 00:29:45,080
Speaker 1: it's hard to tell because this guy that you know,

555
00:29:45,120 --> 00:29:48,160
Speaker 1: there was this reporting that Michael Lewis was at his

556
00:29:48,240 --> 00:29:51,880
Speaker 1: house while he was on out on bail talking about,

557
00:29:52,080 --> 00:29:53,280
Speaker 1: you know, what they're going to do with the film,

558
00:29:53,280 --> 00:29:55,400
Speaker 1: and you know, probably doing some more interviews about like,

559
00:29:55,560 --> 00:29:58,440
Speaker 1: you know, what was it like getting indicted, filing for

560
00:29:58,480 --> 00:30:02,320
Speaker 1: bankruptcy Because Michael lewis author of The Great Short of

561
00:30:02,440 --> 00:30:05,920
Speaker 1: the Big Short It was great, and a ton of

562
00:30:06,240 --> 00:30:08,840
Speaker 1: a ton of incredible books. He started out with Liar's Poker.

563
00:30:09,240 --> 00:30:11,600
Speaker 1: He was a trader back in the nineteen eighties. And

564
00:30:11,640 --> 00:30:15,080
Speaker 1: then ends up writing about Layman, which then ends up

565
00:30:15,120 --> 00:30:19,160
Speaker 1: fueling the financial crisis in two thousand and seven, two

566
00:30:19,160 --> 00:30:21,080
Speaker 1: thousand and eight with the very mortgage back securities that

567
00:30:21,120 --> 00:30:24,120
Speaker 1: he wrote about in Liar's Poker, which is an incredible book.

568
00:30:24,760 --> 00:30:27,640
Speaker 1: And that's before you get the moneyball blindside. He's just

569
00:30:27,720 --> 00:30:31,000
Speaker 1: had this run of just incredible both luck and talent,

570
00:30:31,760 --> 00:30:34,560
Speaker 1: and the luck lands again where he spent the last

571
00:30:34,600 --> 00:30:37,880
Speaker 1: six months or year or whatever with FTX, he was

572
00:30:37,880 --> 00:30:39,920
Speaker 1: able to see a good story. I don't think where

573
00:30:39,960 --> 00:30:41,480
Speaker 1: it was crump. I don't think he knew it was

574
00:30:41,480 --> 00:30:43,720
Speaker 1: going to be this good, but he knew it was

575
00:30:43,760 --> 00:30:46,600
Speaker 1: going to be good. And so for Sam Bagmen freed

576
00:30:46,880 --> 00:30:51,000
Speaker 1: to basically have carried on as if nothing has really changed,

577
00:30:51,360 --> 00:30:56,880
Speaker 1: dming with reporters, doing Twitter spaces, doing interviews, just hanging

578
00:30:56,920 --> 00:31:00,760
Speaker 1: out and being a dude, incriminating himself, incriminating himself constantly.

579
00:31:00,880 --> 00:31:03,600
Speaker 1: And that's what I don't understand about this. So the

580
00:31:03,600 --> 00:31:06,640
Speaker 1: two possibilities here, give and take possibility. You know, he's

581
00:31:06,680 --> 00:31:08,920
Speaker 1: he's this is part of a way to sort of

582
00:31:09,600 --> 00:31:13,160
Speaker 1: negotiate with law enforcement, or he rides this out to

583
00:31:13,200 --> 00:31:16,280
Speaker 1: the bitter end. I'm curious as to whether he's actually

584
00:31:16,280 --> 00:31:17,800
Speaker 1: going to ride this out to the bitter end, precisely

585
00:31:17,840 --> 00:31:20,680
Speaker 1: because of what you just said, the hubris that he

586
00:31:20,840 --> 00:31:25,160
Speaker 1: has and sort of chatting with reporters, incriminating himself doing interviews,

587
00:31:26,200 --> 00:31:30,960
Speaker 1: looking so casual and relaxed about the whole thing. I

588
00:31:30,960 --> 00:31:33,320
Speaker 1: don't know. I mean, I think maybe he thinks he

589
00:31:33,360 --> 00:31:36,760
Speaker 1: can muddy the waters hire really good lawyers, and I

590
00:31:36,800 --> 00:31:40,560
Speaker 1: believe he has one of Gillayne Maxwell's defense attorneys and

591
00:31:40,720 --> 00:31:45,320
Speaker 1: just work his way through the situation because the law

592
00:31:45,720 --> 00:31:49,040
Speaker 1: has been too loose around crypto period that it's not

593
00:31:49,280 --> 00:31:52,520
Speaker 1: you know, that is the law actually there to say

594
00:31:52,560 --> 00:31:56,880
Speaker 1: this is a slam dunk for decades in prison. I

595
00:31:56,880 --> 00:32:00,320
Speaker 1: don't know, right. I have heard some experts who will say, look,

596
00:32:00,400 --> 00:32:03,320
Speaker 1: there are so few rules around crypto that they're gonna

597
00:32:03,360 --> 00:32:06,840
Speaker 1: have to get him on on tiki tax stuff like

598
00:32:06,880 --> 00:32:12,360
Speaker 1: al Capone style taxication stuff like in other words, there's

599
00:32:12,360 --> 00:32:14,800
Speaker 1: gonna be a ton of wire fraud, which is just

600
00:32:15,840 --> 00:32:18,600
Speaker 1: it's a crime that everybody's committing all the time. Your

601
00:32:18,640 --> 00:32:21,680
Speaker 1: favorite Real housewife Teresa ju Days, that's right. That was

602
00:32:21,680 --> 00:32:23,640
Speaker 1: one of the charges that they that they hit her with.

603
00:32:24,440 --> 00:32:27,160
Speaker 1: They hit her with the kind of the pencil whip

604
00:32:27,200 --> 00:32:29,440
Speaker 1: in your mortgage thing where you're like, oh, yeah, my

605
00:32:29,520 --> 00:32:31,320
Speaker 1: house is like which Trump does all the time, which

606
00:32:31,360 --> 00:32:33,280
Speaker 1: is like you're going to a bank for a loan,

607
00:32:33,320 --> 00:32:36,400
Speaker 1: what's your place worth? Eh? That house worth eight hundred

608
00:32:36,400 --> 00:32:38,720
Speaker 1: thousand dollars, but you know it's worth like six hundred

609
00:32:38,720 --> 00:32:41,520
Speaker 1: thousand dollars. And her actually was her husband doing it.

610
00:32:42,480 --> 00:32:46,000
Speaker 1: She got railroaded. He also got railroaded. They deported the guy.

611
00:32:46,040 --> 00:32:48,479
Speaker 1: It's so ridiculous. He lives in the Bahamas now not

612
00:32:48,520 --> 00:32:51,000
Speaker 1: with FTX, but is he in the Bahamas? I thought

613
00:32:51,520 --> 00:32:53,120
Speaker 1: he was in Italy for a while. He was, and

614
00:32:53,120 --> 00:32:54,680
Speaker 1: then he went to the Bahamas. You have not been

615
00:32:54,760 --> 00:32:58,640
Speaker 1: keeping I have not the Joe Judicce chronicles. Well can

616
00:32:58,680 --> 00:33:00,720
Speaker 1: we put Stu up on the screen because this adds

617
00:33:00,720 --> 00:33:02,720
Speaker 1: a wrinkle to the conversation from a sort of thirty

618
00:33:02,760 --> 00:33:05,480
Speaker 1: thousand foot perspective that I found really interesting. This is

619
00:33:05,520 --> 00:33:08,360
Speaker 1: a column from John Tamney over in Real Clare Markets,

620
00:33:08,520 --> 00:33:11,640
Speaker 1: who wrote the Sam Bankman freed collapse as a paradoxical

621
00:33:11,680 --> 00:33:15,480
Speaker 1: sign of progress. Now Tamney is super libertarian, and he's

622
00:33:15,640 --> 00:33:19,400
Speaker 1: writing and he references one of his own columns from

623
00:33:19,640 --> 00:33:23,040
Speaker 1: June twenty twenty one that said the headline will know

624
00:33:23,160 --> 00:33:26,920
Speaker 1: crypto is for real when it's coins start collapsing. John

625
00:33:27,000 --> 00:33:29,040
Speaker 1: is the writer that I respect because he's totally consistent

626
00:33:29,080 --> 00:33:31,960
Speaker 1: on his libertarianism. You know, it's not like you're not

627
00:33:32,120 --> 00:33:34,440
Speaker 1: sort of like just trying to piece together an ideology

628
00:33:34,480 --> 00:33:36,360
Speaker 1: from what's happening in the news. As you can see

629
00:33:36,360 --> 00:33:37,720
Speaker 1: that from the fact he wrote that in June of

630
00:33:37,800 --> 00:33:40,520
Speaker 1: twenty twenty one, it's been consistent. What do you think

631
00:33:40,520 --> 00:33:44,280
Speaker 1: about that take that I've heard it increasingly from crypto

632
00:33:44,360 --> 00:33:46,400
Speaker 1: quarters as well over the last couple of weeks and

633
00:33:46,440 --> 00:33:49,040
Speaker 1: since the FTX stuff started to come into full bloom,

634
00:33:49,200 --> 00:33:51,960
Speaker 1: is that basically these are the grown, growing pains that

635
00:33:51,960 --> 00:33:55,760
Speaker 1: suggest crypto is here to stay. What is here to

636
00:33:55,760 --> 00:33:59,400
Speaker 1: stay is what I would ask, like, what is crypto?

637
00:34:00,120 --> 00:34:02,719
Speaker 1: What is like? What is it? Post gold standard? What

638
00:34:02,880 --> 00:34:06,760
Speaker 1: is money? Right? If crypto is money, then it's done.

639
00:34:06,840 --> 00:34:09,680
Speaker 1: Then it's done for because it's not money and it's

640
00:34:09,680 --> 00:34:12,080
Speaker 1: not circulating in the way that money is. If crypto

641
00:34:12,160 --> 00:34:15,799
Speaker 1: are securities, which that they're now being regulated or they're

642
00:34:15,800 --> 00:34:19,000
Speaker 1: now being hinted at being regulated as securities, that means

643
00:34:19,040 --> 00:34:22,200
Speaker 1: a security as in a basically a stock certificate that

644
00:34:22,239 --> 00:34:26,040
Speaker 1: represents a company. Okay, what does the company make? For

645
00:34:26,080 --> 00:34:28,000
Speaker 1: a while you can say, wow, oh, it's this blockchain

646
00:34:28,040 --> 00:34:31,920
Speaker 1: technology that's going to revolutionize everything, and then it reveals

647
00:34:31,960 --> 00:34:35,840
Speaker 1: actually the blockchain technology isn't actually that great and nothing

648
00:34:35,880 --> 00:34:39,640
Speaker 1: that super innovative about it, and it doesn't scale, and

649
00:34:39,680 --> 00:34:42,440
Speaker 1: so it's very hard to see how that is a

650
00:34:42,440 --> 00:34:47,120
Speaker 1: product that this stock certificate is related to. So then

651
00:34:47,200 --> 00:34:49,960
Speaker 1: what is it? And as Bankmin Freed described it, it

652
00:34:50,040 --> 00:34:53,920
Speaker 1: is somebody's belief that it is worth something. And as

653
00:34:53,960 --> 00:34:57,359
Speaker 1: long as that belief maintains and it continues to be

654
00:34:57,400 --> 00:34:59,560
Speaker 1: worth something, and as long as you can continue to

655
00:34:59,680 --> 00:35:03,320
Speaker 1: say it attracts people in with advertising and with offers

656
00:35:03,360 --> 00:35:07,879
Speaker 1: of nine percent interest rate, then then people's belief can

657
00:35:07,920 --> 00:35:10,719
Speaker 1: stay in that. But what Bankmin Freed was doing was

658
00:35:10,760 --> 00:35:14,520
Speaker 1: describing a pyramid scheme that as long as money keeps

659
00:35:14,520 --> 00:35:16,479
Speaker 1: coming in, then you can keep putting some money out

660
00:35:17,040 --> 00:35:20,279
Speaker 1: without actually producing any product. And so I haven't seen

661
00:35:20,320 --> 00:35:25,080
Speaker 1: anybody in the crypto space argue for what the kind

662
00:35:25,080 --> 00:35:28,400
Speaker 1: of use case is for it that would justify the

663
00:35:28,480 --> 00:35:32,400
Speaker 1: valuation of it. If it's not money, and it's so

664
00:35:32,520 --> 00:35:34,640
Speaker 1: far it is not money, and there's no and I

665
00:35:34,640 --> 00:35:36,759
Speaker 1: don't see any evidence that it's going to tip over

666
00:35:36,800 --> 00:35:39,640
Speaker 1: into money, and so I think that it's a little

667
00:35:39,640 --> 00:35:42,480
Speaker 1: bit too convenient to say that. Well, the collapse and

668
00:35:42,480 --> 00:35:46,520
Speaker 1: the failure of this project shows that some of it

669
00:35:46,600 --> 00:35:48,799
Speaker 1: is going to work. That's not enough, like you have

670
00:35:48,840 --> 00:35:51,799
Speaker 1: to show Okay, fine, you're getting rid of the scoundrels,

671
00:35:52,840 --> 00:35:57,360
Speaker 1: but what is it about the ones that are still there, Like,

672
00:35:57,440 --> 00:36:00,319
Speaker 1: what fundamentally is it about them that is going to

673
00:36:00,360 --> 00:36:04,520
Speaker 1: survive a tight money situation? And that is producing value

674
00:36:05,320 --> 00:36:09,560
Speaker 1: to society, which then translates into wealth, because you have

675
00:36:09,640 --> 00:36:14,040
Speaker 1: to have some value. Yeah, otherwise it's just a pyramid scheme.

676
00:36:14,160 --> 00:36:17,160
Speaker 1: And John's argument hinges on that because he compares this specifically.

677
00:36:17,280 --> 00:36:20,120
Speaker 1: He says history is repeating itself. He compares it to cars.

678
00:36:20,160 --> 00:36:22,160
Speaker 1: He says, in the early parts of the twentieth century,

679
00:36:22,200 --> 00:36:24,319
Speaker 1: thousands of car makers or would be car makers were

680
00:36:24,320 --> 00:36:27,080
Speaker 1: matched with capital. Just about every single company created failed.

681
00:36:27,120 --> 00:36:29,720
Speaker 1: Fast forward to the the twentieth century, something similar happened

682
00:36:29,760 --> 00:36:33,400
Speaker 1: with the internet. And to your point, both of those

683
00:36:33,480 --> 00:36:38,359
Speaker 1: are predicated on a tangible service, especially cars. I mean,

684
00:36:38,360 --> 00:36:40,720
Speaker 1: that's even more obvious. But the Internet in ways that

685
00:36:40,800 --> 00:36:44,240
Speaker 1: were maybe a hasy I just combined fuzzy and hazy

686
00:36:44,560 --> 00:36:47,359
Speaker 1: one word at the time, but are now very clear

687
00:36:47,400 --> 00:36:49,920
Speaker 1: to us the tangibility of the Internet as a product.

688
00:36:50,160 --> 00:36:54,480
Speaker 1: So yes, if you're trying to add crypto into that mix,

689
00:36:54,960 --> 00:36:56,680
Speaker 1: it's easier to see what the difference is. One of

690
00:36:56,719 --> 00:36:59,240
Speaker 1: these things is not quite like the other. Right, pets

691
00:36:59,239 --> 00:37:02,040
Speaker 1: dot com might have been a ridiculous company that wasn't

692
00:37:02,040 --> 00:37:05,680
Speaker 1: worth a hundred billion dollars, But you can imagine a

693
00:37:05,719 --> 00:37:09,759
Speaker 1: website that sold pet related products as being a company

694
00:37:10,000 --> 00:37:13,240
Speaker 1: that like made money. Yeah, the service that people will buy. Yeah, probably,

695
00:37:13,880 --> 00:37:15,640
Speaker 1: I mean that that exists now. I don't know if

696
00:37:16,120 --> 00:37:19,880
Speaker 1: is it pets dot com? What happened to pets dot com?

697
00:37:20,320 --> 00:37:22,399
Speaker 1: But what did you did you? Did you find anything

698
00:37:22,440 --> 00:37:27,720
Speaker 1: in that argument to cling to? No, I mean, crypto folks,

699
00:37:28,640 --> 00:37:33,279
Speaker 1: the pet smart, Pet Smart, it's really funny. No, I mean,

700
00:37:33,320 --> 00:37:36,440
Speaker 1: I think I always enjoyed the sort of sincerely argued

701
00:37:36,760 --> 00:37:39,279
Speaker 1: Devil's advocate point to media, friends is because it's not

702
00:37:39,320 --> 00:37:41,280
Speaker 1: a point anybody's making in the media. But I think

703
00:37:41,600 --> 00:37:44,360
Speaker 1: in other sectors of the economy, perhaps like social media,

704
00:37:44,400 --> 00:37:46,600
Speaker 1: for instance, where the bubble might be popping. It's an

705
00:37:46,719 --> 00:37:49,279
Speaker 1: argument that's sort of well worth considering that maybe this

706
00:37:49,440 --> 00:37:51,759
Speaker 1: is growing pains that you know, it's hard to see

707
00:37:51,760 --> 00:37:54,680
Speaker 1: the forest for the trees when you're in the middle

708
00:37:54,719 --> 00:37:58,399
Speaker 1: of something. So but no, when it comes to Crypto again,

709
00:37:58,440 --> 00:38:00,560
Speaker 1: it's very, very hard to say, we're all this goes.

710
00:38:01,200 --> 00:38:03,160
Speaker 1: Maybe there's a remote possibility that this turns out to

711
00:38:03,200 --> 00:38:05,520
Speaker 1: be correct. I agree more with you. I think they

712
00:38:05,600 --> 00:38:09,120
Speaker 1: kind of tried to make a forest without trees. Turns

713
00:38:09,160 --> 00:38:11,520
Speaker 1: out you need both and you can't have one without

714
00:38:11,520 --> 00:38:14,319
Speaker 1: the other. That would be my take. We're moving on

715
00:38:14,440 --> 00:38:17,120
Speaker 1: now to the tragedy out of Idaho that continues to

716
00:38:17,120 --> 00:38:20,080
Speaker 1: develop to develop as we learn more about the twenty

717
00:38:20,080 --> 00:38:23,359
Speaker 1: eight year old suspect Brian Coburger, who was arrested at

718
00:38:23,360 --> 00:38:28,000
Speaker 1: his parents' home in Pennsylvania after he drove miles and miles,

719
00:38:28,000 --> 00:38:32,000
Speaker 1: thousands of miles with his father across the country from

720
00:38:32,080 --> 00:38:36,400
Speaker 1: where he committed the crime allegedly too then Pennsylvania and

721
00:38:36,640 --> 00:38:39,480
Speaker 1: a part right country Stroudsburg. Oh, yeah, that's right. It's

722
00:38:39,480 --> 00:38:41,680
Speaker 1: a district that Ryan has paid very close attention to.

723
00:38:42,800 --> 00:38:46,400
Speaker 1: The white Hyundai Alantra that police had told everybody to

724
00:38:46,440 --> 00:38:50,200
Speaker 1: be out on the lookout for. This is an interesting

725
00:38:50,239 --> 00:38:52,759
Speaker 1: part of the puzzle. We've learned now that he was

726
00:38:53,000 --> 00:38:56,600
Speaker 1: pulled over twice by Indiana police while he was driving

727
00:38:56,960 --> 00:39:01,000
Speaker 1: from Washington, where he lived very close to Moscow, on

728
00:39:01,040 --> 00:39:03,480
Speaker 1: his way to Pennsylvania with his father. His dad flew

729
00:39:03,520 --> 00:39:06,240
Speaker 1: out to Washington. We're sort of piecing these puzzles together,

730
00:39:06,719 --> 00:39:10,400
Speaker 1: piecing this puzzle together. But that white hounday Launtra that

731
00:39:10,400 --> 00:39:13,680
Speaker 1: police said, yeah, let's roll D two. Police said to

732
00:39:13,760 --> 00:39:15,520
Speaker 1: be on the lookout for this white HONDAYI Lantra. They

733
00:39:15,520 --> 00:39:17,720
Speaker 1: said they didn't know the license plate from it because

734
00:39:17,719 --> 00:39:21,520
Speaker 1: they had footage of the car going sideways in the

735
00:39:21,600 --> 00:39:25,479
Speaker 1: vicinity of the house where the murders happened. They didn't

736
00:39:25,520 --> 00:39:28,480
Speaker 1: know the license plate number. It turns out that Indiana

737
00:39:28,520 --> 00:39:31,720
Speaker 1: police pulled that car over with Coburger and his father

738
00:39:31,920 --> 00:39:37,040
Speaker 1: in it twice for traffic questions on their trip back

739
00:39:37,080 --> 00:39:40,560
Speaker 1: from Pennsylvania. That car was in the driveway of his parents'

740
00:39:40,600 --> 00:39:43,400
Speaker 1: house in the gated community in Pennsylvania when he was

741
00:39:43,560 --> 00:39:47,560
Speaker 1: arrested just several days ago. Indiana police again say they

742
00:39:47,600 --> 00:39:50,799
Speaker 1: did not have the information about the license plate and

743
00:39:50,840 --> 00:39:53,760
Speaker 1: about that car. Obviously, there are many many Ondaia lutras

744
00:39:53,840 --> 00:39:55,760
Speaker 1: from that time period. I think it was twenty thirteen

745
00:39:55,800 --> 00:39:58,960
Speaker 1: through twenty sixteen out on the market. It's too generic.

746
00:39:59,480 --> 00:40:02,520
Speaker 1: But they say that license plate wasn't available in mid

747
00:40:02,560 --> 00:40:07,080
Speaker 1: December when they made these traffic stops. So again you

748
00:40:07,120 --> 00:40:10,640
Speaker 1: have a situation incredibly frustrating. I can imagine for the

749
00:40:10,680 --> 00:40:14,279
Speaker 1: families where this guy was driving across country with his father.

750
00:40:14,320 --> 00:40:16,520
Speaker 1: We know he was a criminology student. He has a

751
00:40:16,600 --> 00:40:21,439
Speaker 1: master's degree in what is a criminal studies or criminology

752
00:40:21,480 --> 00:40:25,200
Speaker 1: from the Sales University. We're learning more details about his

753
00:40:25,320 --> 00:40:31,320
Speaker 1: life Ryan. These new developments are just peeling back predictable

754
00:40:31,360 --> 00:40:34,160
Speaker 1: layers of an onion, and there's obviously a lot of

755
00:40:34,200 --> 00:40:37,440
Speaker 1: intrigues surrounding this. He appears to be a pretty textbook

756
00:40:37,560 --> 00:40:40,600
Speaker 1: case of a suspect in a tragedy like this. What

757
00:40:40,640 --> 00:40:42,319
Speaker 1: do you make of it? What he says he looks

758
00:40:42,320 --> 00:40:44,319
Speaker 1: forward to or his lawyer says that he looks forward

759
00:40:44,400 --> 00:40:48,800
Speaker 1: to exonerating himself. But the way that he got arrested,

760
00:40:49,080 --> 00:40:56,239
Speaker 1: you know, raises real questions about our current approach to

761
00:40:56,400 --> 00:41:01,080
Speaker 1: DNA DNA collection and the way that government authorities are

762
00:41:01,120 --> 00:41:04,920
Speaker 1: able to access that DNA, that DNA database and so

763
00:41:05,000 --> 00:41:08,200
Speaker 1: it turns out that it wasn't that he himself had

764
00:41:08,200 --> 00:41:11,000
Speaker 1: done an ancestry or twenty three and meter or whichever,

765
00:41:11,080 --> 00:41:14,399
Speaker 1: the ones that are more willing to give up up. Yeah,

766
00:41:14,400 --> 00:41:16,000
Speaker 1: we can put D we can put D one there.

767
00:41:17,480 --> 00:41:21,880
Speaker 1: But there was some relative of his great grandfather or something.

768
00:41:21,920 --> 00:41:24,600
Speaker 1: Basically like you were saying, all you need is a

769
00:41:24,640 --> 00:41:27,680
Speaker 1: close relative. So if you're out there saying like, wow,

770
00:41:27,719 --> 00:41:31,520
Speaker 1: this isn't a problem for me because A, I don't

771
00:41:31,520 --> 00:41:35,160
Speaker 1: commit murder and B I have never submitted my DNA

772
00:41:35,239 --> 00:41:37,880
Speaker 1: test anywhere, well hopefully you don't ever commit murder. But

773
00:41:37,920 --> 00:41:40,080
Speaker 1: also it doesn't matter if you have submitted your own

774
00:41:40,160 --> 00:41:42,960
Speaker 1: DNA to twenty three and me or any of these sites.

775
00:41:43,040 --> 00:41:44,759
Speaker 1: It's out of your hands, out of your hands because

776
00:41:44,760 --> 00:41:48,840
Speaker 1: if somebody in your family, and we're talking distant, distant family,

777
00:41:48,920 --> 00:41:51,480
Speaker 1: somebody that you've never met, would would qualify for this.

778
00:41:52,320 --> 00:41:55,520
Speaker 1: They were able to then track, you know, track the

779
00:41:55,600 --> 00:42:01,000
Speaker 1: DNA that way. And so the the article that you

780
00:42:01,040 --> 00:42:04,120
Speaker 1: were mentioning raises all of the privacy implications of this.

781
00:42:04,520 --> 00:42:06,399
Speaker 1: And I wonder if this is something where the horse

782
00:42:06,440 --> 00:42:11,719
Speaker 1: is out of the barn it is, and what kind

783
00:42:11,800 --> 00:42:15,040
Speaker 1: of rules at this point could you put in place,

784
00:42:15,040 --> 00:42:19,520
Speaker 1: because you also have government authorities, including Chinese government authorities,

785
00:42:19,800 --> 00:42:23,759
Speaker 1: who are who get around kind of court orders, subpoenas

786
00:42:23,840 --> 00:42:26,600
Speaker 1: and other restrictions and civilities by just straight up buying it.

787
00:42:26,960 --> 00:42:30,440
Speaker 1: So they'll say, hey, look this is for sale. Like this,

788
00:42:30,440 --> 00:42:33,600
Speaker 1: this evidence is for sale to the public. So this

789
00:42:33,680 --> 00:42:36,279
Speaker 1: is just like us reading your your tweets. If you

790
00:42:36,320 --> 00:42:39,839
Speaker 1: put your tweets out on a public timeline, then we're

791
00:42:39,840 --> 00:42:41,799
Speaker 1: going to read those. Now, there are actually restrictions on

792
00:42:41,920 --> 00:42:43,960
Speaker 1: how that whether the FBI is able to collect and

793
00:42:44,000 --> 00:42:45,920
Speaker 1: analyze them. They're not. They're not supposed to do that.

794
00:42:45,960 --> 00:42:48,360
Speaker 1: They can read them, but they can't collect and analyze whatever.

795
00:42:48,800 --> 00:42:51,560
Speaker 1: The point is that once all of this stuff is

796
00:42:51,560 --> 00:42:56,720
Speaker 1: in the public domain, governments are just going to exploit

797
00:42:56,840 --> 00:43:00,680
Speaker 1: access no matter what the what the laws are the problem,

798
00:43:00,680 --> 00:43:04,520
Speaker 1: and then the further problem becomes every victim of this,

799
00:43:04,680 --> 00:43:07,239
Speaker 1: so to speak, is the least sympathetic person you could

800
00:43:07,280 --> 00:43:11,920
Speaker 1: possibly imagine, who, of course, like, assuming that the allegations

801
00:43:11,920 --> 00:43:14,520
Speaker 1: are true, this is somebody who killed four innocent people

802
00:43:14,960 --> 00:43:22,759
Speaker 1: for absolutely no just derange, no reason like so to

803
00:43:22,840 --> 00:43:25,120
Speaker 1: then say, well, it's a shame that the way he

804
00:43:25,239 --> 00:43:27,520
Speaker 1: was caught. It's a shame that way. It doesn't make

805
00:43:27,600 --> 00:43:30,880
Speaker 1: you sound like a very sympathetic actor in the argument,

806
00:43:30,920 --> 00:43:33,400
Speaker 1: and it doesn't move the chains in policy world at all, right, period,

807
00:43:33,440 --> 00:43:35,040
Speaker 1: And that's what you get to what member of Congress

808
00:43:35,040 --> 00:43:36,279
Speaker 1: you need to get to be able to stand up

809
00:43:36,480 --> 00:43:38,799
Speaker 1: at this point? Yeah, well, and this is sort of

810
00:43:38,800 --> 00:43:40,719
Speaker 1: famously used in the case of the Golden State killer.

811
00:43:40,760 --> 00:43:42,400
Speaker 1: But my colleague give you to Duffy who wrote that

812
00:43:42,480 --> 00:43:45,240
Speaker 1: article that was up on the screen. She actually reminded

813
00:43:45,239 --> 00:43:47,640
Speaker 1: me of another case, she writes. In twenty nineteen, ged

814
00:43:47,880 --> 00:43:50,359
Speaker 1: match helped solve the assault case of a seventy one

815
00:43:50,440 --> 00:43:52,759
Speaker 1: year old woman who was strangled while practicing the organ

816
00:43:52,800 --> 00:43:55,640
Speaker 1: alone in a church. Per ged matches terms of service,

817
00:43:55,680 --> 00:43:57,880
Speaker 1: the site would only share users DNA with law enforcement

818
00:43:57,960 --> 00:44:00,320
Speaker 1: in the case of sexual assault or homicide. Since the

819
00:44:00,400 --> 00:44:03,160
Speaker 1: Utah victim was not sexually assaulted and survived the attack,

820
00:44:03,520 --> 00:44:06,719
Speaker 1: ged match was not supposed to share users data to

821
00:44:06,760 --> 00:44:09,520
Speaker 1: solve the case. However, the company's founder decided to hand

822
00:44:09,520 --> 00:44:13,360
Speaker 1: over DNA data anyway. The offender was substantly subsequently caught,

823
00:44:13,640 --> 00:44:17,759
Speaker 1: and ged match users privacy was breached. And again, what

824
00:44:17,800 --> 00:44:19,560
Speaker 1: do you do to put the genie back in the bottle.

825
00:44:19,640 --> 00:44:23,240
Speaker 1: Absolutely nothing. There's nothing to be done in that case, period,

826
00:44:23,719 --> 00:44:25,960
Speaker 1: And how do you argue against it? How do you

827
00:44:26,440 --> 00:44:29,360
Speaker 1: it's the same difficulty. It's it's similar to the difficulty

828
00:44:29,400 --> 00:44:31,640
Speaker 1: people have in defending free speech, especially in the sort

829
00:44:31,680 --> 00:44:35,040
Speaker 1: of social media climate where there's so much like finger

830
00:44:35,080 --> 00:44:38,520
Speaker 1: pointing and there's so much bad faith argumentation. If you're

831
00:44:38,800 --> 00:44:43,880
Speaker 1: champion of free speech is let's say the ACLU, for instance,

832
00:44:43,960 --> 00:44:46,480
Speaker 1: used to put up press releases about defending the rights

833
00:44:46,480 --> 00:44:50,520
Speaker 1: of awful Nazis to march in Skokie, Illinois. They don't

834
00:44:50,520 --> 00:44:52,080
Speaker 1: want to come anywhere near that stuff with a ten

835
00:44:52,120 --> 00:44:55,040
Speaker 1: foot full, ten foot poll anymore. Because we exist in

836
00:44:55,040 --> 00:44:57,520
Speaker 1: the social media world, right, Yeah, who wants to make

837
00:44:57,560 --> 00:45:00,719
Speaker 1: the case that well, hey, look, the one was only

838
00:45:00,840 --> 00:45:04,360
Speaker 1: nearly murdered and not murdered according to your terms of service,

839
00:45:04,400 --> 00:45:06,880
Speaker 1: you should not have, you know, a loud access to

840
00:45:06,920 --> 00:45:09,760
Speaker 1: this DNA. Yeah, you look like a ghoul making that argument.

841
00:45:10,239 --> 00:45:14,120
Speaker 1: And if there isn't a kind of kind of cultural

842
00:45:14,160 --> 00:45:17,640
Speaker 1: respect for the value of privacy and for civil liberties,

843
00:45:18,120 --> 00:45:20,399
Speaker 1: then you don't have anything to stand on. You don't

844
00:45:20,440 --> 00:45:22,520
Speaker 1: have anything to embed yourself in when you're making that

845
00:45:22,760 --> 00:45:27,440
Speaker 1: argument and you just look like a freak. Yeah, And tragically,

846
00:45:27,480 --> 00:45:29,920
Speaker 1: this story looks like it's headed straight to the sort

847
00:45:29,960 --> 00:45:34,719
Speaker 1: of true crime books, like the stacks of true crime

848
00:45:34,760 --> 00:45:36,440
Speaker 1: books that have been written, but in a way that

849
00:45:36,560 --> 00:45:41,560
Speaker 1: is I think sensationalist and overly sort of drenched an

850
00:45:41,560 --> 00:45:44,880
Speaker 1: intrigue that distracts from the victims and is more focused

851
00:45:44,920 --> 00:45:49,000
Speaker 1: on the suspect who may eventually be convicted as the

852
00:45:49,080 --> 00:45:54,800
Speaker 1: killer himself, because it's such a it's such a crazy story.

853
00:45:54,800 --> 00:45:57,719
Speaker 1: If you have a criminal justice student who are criminology

854
00:45:57,760 --> 00:46:03,440
Speaker 1: student who's studying serial killers, studying murder is on Reddit,

855
00:46:03,719 --> 00:46:08,800
Speaker 1: we now know, asking survey questions to people who have

856
00:46:08,840 --> 00:46:13,680
Speaker 1: actually been in prison about topics like these. So it's

857
00:46:13,719 --> 00:46:15,919
Speaker 1: headed straight in that direction, being one of the sort

858
00:46:15,960 --> 00:46:20,719
Speaker 1: of infamous serial killers that deserves really no part of

859
00:46:20,760 --> 00:46:24,680
Speaker 1: the infamy, no part of the fame in the infamy

860
00:46:24,760 --> 00:46:29,520
Speaker 1: question at all. Although the person seems the twist being

861
00:46:29,560 --> 00:46:32,960
Speaker 1: the denial. Yeah, well that'll be yeah, that'll be great. Sure,

862
00:46:33,000 --> 00:46:36,600
Speaker 1: maybe maybe just a weird case of mistake in DNA identity,

863
00:46:36,600 --> 00:46:39,759
Speaker 1: who knows, But like it certainly doesn't seem that way,

864
00:46:40,200 --> 00:46:43,080
Speaker 1: It doesn't seem that way they've pinpointed. According to police,

865
00:46:43,080 --> 00:46:46,280
Speaker 1: This is according to police, they have pinpointed location data

866
00:46:46,600 --> 00:46:50,120
Speaker 1: and they've pieced together they say, they've pieced together a

867
00:46:50,120 --> 00:46:55,160
Speaker 1: pretty plausible explanation. You can't do for murders anymore with

868
00:46:55,719 --> 00:46:59,200
Speaker 1: iPhones and with your easy pass and everything else they've

869
00:46:59,200 --> 00:47:01,040
Speaker 1: got on you. And then to get nailed with the

870
00:47:01,120 --> 00:47:04,879
Speaker 1: DNA on top of that. Yeah, So if you're thinking

871
00:47:04,880 --> 00:47:07,319
Speaker 1: of doing for murders, like I would suggest, you don't

872
00:47:07,360 --> 00:47:08,799
Speaker 1: do them because you're you're not going to get away

873
00:47:08,800 --> 00:47:10,640
Speaker 1: with those anymore. Well, that's one of the interesting parts

874
00:47:10,680 --> 00:47:12,400
Speaker 1: of this is they say again that and I have

875
00:47:12,440 --> 00:47:15,440
Speaker 1: no problem whatsoever with true crime intrigue at all. I mean,

876
00:47:15,480 --> 00:47:19,040
Speaker 1: I love it myself, but they're obviously I have no

877
00:47:19,040 --> 00:47:22,560
Speaker 1: problem with I love committing crime. Yet no, no no, no,

878
00:47:23,080 --> 00:47:25,839
Speaker 1: I love all the true crime seriessly I have zero

879
00:47:25,880 --> 00:47:27,640
Speaker 1: problem with that. But obviously I have a problem with

880
00:47:27,680 --> 00:47:30,560
Speaker 1: the times that it's it's so sensationalist. And we've seen

881
00:47:30,600 --> 00:47:32,400
Speaker 1: a lot of that and the coverage you're from the

882
00:47:32,400 --> 00:47:34,239
Speaker 1: New York Posts and Daily Mail and others who have

883
00:47:34,280 --> 00:47:36,160
Speaker 1: some really good reporting, but the way they frame it

884
00:47:36,360 --> 00:47:38,719
Speaker 1: I think is unfortunate sometimes. But that is one of

885
00:47:38,719 --> 00:47:41,799
Speaker 1: the interesting case cases here. Cops say they have a

886
00:47:41,880 --> 00:47:46,160
Speaker 1: location data of him pinging really close to the victims

887
00:47:46,680 --> 00:47:49,280
Speaker 1: through a long period of time, so it was perhaps

888
00:47:49,360 --> 00:47:51,960
Speaker 1: following them. They also say though that there's perhaps he

889
00:47:52,040 --> 00:47:56,600
Speaker 1: was wearing gloves in a gloves gloves that that should

890
00:47:56,680 --> 00:47:58,879
Speaker 1: protect you when when your phone is trying to get

891
00:47:58,920 --> 00:48:01,040
Speaker 1: onto the Wi Fi of your victims. As long as

892
00:48:01,040 --> 00:48:02,759
Speaker 1: you've got gloves, you're going to be fine. There were

893
00:48:02,800 --> 00:48:05,640
Speaker 1: some perhaps there was some effort made while he was

894
00:48:05,640 --> 00:48:08,840
Speaker 1: following them to conceal his identity. We again, all of

895
00:48:08,880 --> 00:48:12,600
Speaker 1: the stuff is preliminary, but certainly a case that will follow,

896
00:48:12,680 --> 00:48:16,840
Speaker 1: especially the DNA angle. But prayers go out to the victims,

897
00:48:16,840 --> 00:48:19,680
Speaker 1: families and the entire community, small community that has just

898
00:48:19,719 --> 00:48:24,440
Speaker 1: been in totally uprooted for weeks. Not only are four

899
00:48:24,480 --> 00:48:31,080
Speaker 1: lives ended, four families changed forever, the entire community will

900
00:48:31,120 --> 00:48:38,280
Speaker 1: reel from this for decades. The school, Yeah, absolutely, all right, Brian,

901
00:48:38,960 --> 00:48:42,160
Speaker 1: What are you looking at today? What is I love

902
00:48:42,239 --> 00:48:44,799
Speaker 1: asking this? What is your point? I don't have any

903
00:48:44,800 --> 00:48:49,080
Speaker 1: point today. My point today I'm looking at the usual

904
00:48:49,160 --> 00:48:52,680
Speaker 1: unusual whales who runs a great Twitter account that has

905
00:48:52,760 --> 00:48:57,400
Speaker 1: been exposing kind of in real time, along with a

906
00:48:57,400 --> 00:48:59,680
Speaker 1: lot of other great reporting that's been done. Business Insider

907
00:49:00,239 --> 00:49:03,799
Speaker 1: made it, I think, didn't they do like a year

908
00:49:03,840 --> 00:49:08,440
Speaker 1: long investigation into this, and finally pressure has been building

909
00:49:09,000 --> 00:49:12,520
Speaker 1: on Congress to do something about it, and then Nancy

910
00:49:12,520 --> 00:49:15,440
Speaker 1: Pelosi kind of just sabotaged the talks at the very

911
00:49:15,560 --> 00:49:18,719
Speaker 1: end and stepped down as speaker. Now Kevin McCarthy has

912
00:49:18,760 --> 00:49:21,520
Speaker 1: sworn that he is going to ban this, like he

913
00:49:21,600 --> 00:49:25,400
Speaker 1: said this to kind of embarrass Pelosi. So we'll see

914
00:49:25,480 --> 00:49:27,920
Speaker 1: if Kevin McCarthy does become speaker, We'll see if he

915
00:49:27,960 --> 00:49:30,840
Speaker 1: does this as one of his first acts or not.

916
00:49:32,360 --> 00:49:35,399
Speaker 1: So I wanted to run through unusual. Wales put out

917
00:49:35,400 --> 00:49:38,440
Speaker 1: a put out its annual report on twenty twenty two spending.

918
00:49:38,520 --> 00:49:41,160
Speaker 1: We can put up that first element there, twenty twenty

919
00:49:41,200 --> 00:49:45,439
Speaker 1: two stock trading report, and so I think he runs

920
00:49:45,480 --> 00:49:46,919
Speaker 1: through a lot of the numbers and has a bunch

921
00:49:46,920 --> 00:49:50,279
Speaker 1: of kind of helpful little bars that we can talk

922
00:49:50,280 --> 00:49:54,120
Speaker 1: about here. So, first of all, it does appear that

923
00:49:54,200 --> 00:49:57,520
Speaker 1: I think two things combined to reduce the amount of

924
00:49:57,880 --> 00:49:59,640
Speaker 1: trading that was done in Congress. I think one is

925
00:49:59,680 --> 00:50:03,040
Speaker 1: the pub like condemnation of it that it just became

926
00:50:03,600 --> 00:50:06,160
Speaker 1: kind of the jew's not worth the squeeze anymore. But

927
00:50:06,280 --> 00:50:11,799
Speaker 1: I also when the market is receding, you're making less money,

928
00:50:11,800 --> 00:50:14,640
Speaker 1: you're less inclined to play around in it. Everybody thinks

929
00:50:14,640 --> 00:50:16,920
Speaker 1: they're a genius when the market is rising because their

930
00:50:16,920 --> 00:50:20,600
Speaker 1: trades are all paying off, and then people really quickly

931
00:50:20,640 --> 00:50:24,120
Speaker 1: realize that they're not geniuses at all when the market

932
00:50:24,200 --> 00:50:26,960
Speaker 1: is retreating. And we're going to get to how Pelosi's

933
00:50:26,960 --> 00:50:30,440
Speaker 1: family lost twenty percent, which actually lost to the market,

934
00:50:30,520 --> 00:50:34,480
Speaker 1: which is fascinating in a minute, But we put up

935
00:50:34,480 --> 00:50:37,000
Speaker 1: this second one so that what that shows you right

936
00:50:37,040 --> 00:50:41,680
Speaker 1: there is that the number of the amount of trading

937
00:50:41,719 --> 00:50:45,439
Speaker 1: went down, and so for the next one it goes

938
00:50:45,480 --> 00:50:49,200
Speaker 1: to that one right there. This compares a total value

939
00:50:49,239 --> 00:50:52,880
Speaker 1: traded by members of Congress. One of the few people

940
00:50:52,920 --> 00:50:57,360
Speaker 1: that increased significantly the amount of trading Rocana, who I

941
00:50:57,400 --> 00:51:03,480
Speaker 1: think his spouse is super super wealthy, and the spouse

942
00:51:04,000 --> 00:51:07,799
Speaker 1: spouse trading gets thrown into this as well. That's the

943
00:51:07,800 --> 00:51:11,839
Speaker 1: case with with Pelosi. Her her husband Paul is a

944
00:51:11,840 --> 00:51:15,920
Speaker 1: literal trader, like that's his that's his job. And then

945
00:51:15,960 --> 00:51:18,279
Speaker 1: you have then you have Josh Gottheimer down after Michael

946
00:51:18,320 --> 00:51:21,239
Speaker 1: McCall ar, Josh Gottheimer, who we're going to get to later,

947
00:51:21,320 --> 00:51:26,000
Speaker 1: traded sixty four million dollars in options. Call call is

948
00:51:26,040 --> 00:51:29,160
Speaker 1: buying and selling call options. But here he has he's

949
00:51:29,200 --> 00:51:31,279
Speaker 1: down from one hundred and thirty three million trading down

950
00:51:31,320 --> 00:51:35,759
Speaker 1: to fifty two million in stock trading this year. Rick

951
00:51:36,600 --> 00:51:41,400
Speaker 1: Rick Scott not surprising right that he's one of the

952
00:51:41,440 --> 00:51:46,360
Speaker 1: biggest traders out there. Bill Haggerty, that one surprise you, no, no, no,

953
00:51:46,480 --> 00:51:51,480
Speaker 1: Tommy Tuomerville got himself caught up in some really sketchy trades.

954
00:51:51,719 --> 00:51:54,319
Speaker 1: Nobody has kind of confused him for a stock market

955
00:51:54,400 --> 00:51:56,399
Speaker 1: genius either. And he's like, wait a minute, Tommy, where

956
00:51:56,440 --> 00:52:01,360
Speaker 1: you where'd you get these ideas? And then Rick Blumenthal, Connecticut,

957
00:52:01,800 --> 00:52:04,480
Speaker 1: which is like the land of the traders, And so

958
00:52:05,440 --> 00:52:08,840
Speaker 1: it wouldn't be surprising that Richard Blumenthal also think red

959
00:52:09,080 --> 00:52:11,640
Speaker 1: where'd you hear that? On the on the on the

960
00:52:11,640 --> 00:52:16,200
Speaker 1: flight back to Hartford train the train like. He has

961
00:52:16,239 --> 00:52:19,919
Speaker 1: also done a bunch of good reporting on the relationship

962
00:52:19,960 --> 00:52:24,040
Speaker 1: between uh, the amount of lobbying that's done and then

963
00:52:24,080 --> 00:52:26,760
Speaker 1: the amount of trading that's done. So if you're lobbied

964
00:52:27,560 --> 00:52:30,760
Speaker 1: by a particular industry. There you're more likely to trade

965
00:52:31,280 --> 00:52:35,400
Speaker 1: stocks of that industry. And you can imagine how that's happening.

966
00:52:35,880 --> 00:52:39,279
Speaker 1: So somebody's coming in, they're telling you what they what

967
00:52:39,280 --> 00:52:41,040
Speaker 1: they want to do on this what they want on

968
00:52:41,080 --> 00:52:44,279
Speaker 1: this particular bill, and then they're also saying, hey, and

969
00:52:44,280 --> 00:52:46,359
Speaker 1: by the way, it would be really great for us

970
00:52:46,400 --> 00:52:48,640
Speaker 1: because you know one of our drugs is about to

971
00:52:48,680 --> 00:52:51,760
Speaker 1: be approved for stage three is you know, phase three FDA,

972
00:52:53,160 --> 00:52:56,120
Speaker 1: and this is why that's important for us to have

973
00:52:56,200 --> 00:52:59,640
Speaker 1: this particular type of regulatory relief. And the member of

974
00:52:59,719 --> 00:53:02,040
Speaker 1: Kinress like, what did you say again about that Phase three?

975
00:53:02,239 --> 00:53:04,239
Speaker 1: When's that? When's that hit? When's that news hitting? Oh?

976
00:53:04,280 --> 00:53:08,160
Speaker 1: October fifteenth, circle, just just happy to share this information

977
00:53:08,200 --> 00:53:11,759
Speaker 1: with you, and then boom, October thirteenth, you see this

978
00:53:11,920 --> 00:53:14,000
Speaker 1: memory of Congress buys a bunch of stock and you

979
00:53:14,040 --> 00:53:15,640
Speaker 1: know that they were lobby. You don't know what happened,

980
00:53:15,640 --> 00:53:18,240
Speaker 1: but you can put you can put two and two together.

981
00:53:18,280 --> 00:53:21,239
Speaker 1: So then thet can't prove it. Well, you can only

982
00:53:21,280 --> 00:53:24,480
Speaker 1: prove it when you get when you who's phoned it,

983
00:53:24,560 --> 00:53:27,120
Speaker 1: they bug like they actually caught some people like actually

984
00:53:27,640 --> 00:53:32,319
Speaker 1: h Chris Chris Cox, No, no, no, not in New York. Yeah,

985
00:53:32,400 --> 00:53:34,520
Speaker 1: New York, New York congressman who actually get like they

986
00:53:34,680 --> 00:53:40,160
Speaker 1: like godlike on the phone. Although yeah, anyway, And they

987
00:53:40,200 --> 00:53:42,800
Speaker 1: also always say it's in a blind trust. Oh it

988
00:53:42,920 --> 00:53:45,080
Speaker 1: is blind blind trust. I couldn't see it. I was

989
00:53:45,160 --> 00:53:48,360
Speaker 1: just talking about it. And so let's let's put the

990
00:53:48,360 --> 00:53:51,440
Speaker 1: one that says top ten stocks bought by house reps

991
00:53:51,440 --> 00:53:53,520
Speaker 1: in twenty twenty two. I thought this one was interesting too.

992
00:53:54,120 --> 00:53:59,680
Speaker 1: Look at Tesla Democrats. Of the six million dollars worth

993
00:53:59,680 --> 00:54:03,400
Speaker 1: of tests less stock purchased, almost all of it was

994
00:54:03,440 --> 00:54:08,399
Speaker 1: by Democrats. First of all, they took a path on that, right.

995
00:54:08,480 --> 00:54:11,160
Speaker 1: I mean, that's six million dollars is now worth about

996
00:54:11,160 --> 00:54:13,960
Speaker 1: a million dollars or two million dollars depending on when

997
00:54:13,960 --> 00:54:15,600
Speaker 1: they depending on when they bought it. Well, see what

998
00:54:15,640 --> 00:54:19,480
Speaker 1: happens next year. Yes, So it looks like Republicans were

999
00:54:19,520 --> 00:54:23,120
Speaker 1: smart not to be buying Tesla in twenty twenty two.

1000
00:54:25,120 --> 00:54:28,440
Speaker 1: You also see the same partisan divide here with Apple

1001
00:54:28,680 --> 00:54:32,840
Speaker 1: and Disney and Google and MVDA. I don't even know

1002
00:54:32,840 --> 00:54:38,040
Speaker 1: what that one is. So what does you think of that?

1003
00:54:38,040 --> 00:54:41,120
Speaker 1: That Tesla breakdown? How funny is that? It's also it

1004
00:54:41,120 --> 00:54:43,239
Speaker 1: could just be and the problem, and he makes this

1005
00:54:43,320 --> 00:54:48,040
Speaker 1: point it could just be Pelosi or like Rocon and

1006
00:54:48,080 --> 00:54:50,399
Speaker 1: a bunch of his stats, he just pulled Pelosi out.

1007
00:54:50,480 --> 00:54:53,120
Speaker 1: It's because she ruins all the data because you've traded

1008
00:54:53,200 --> 00:54:55,759
Speaker 1: so much, or her husband trades so much. So what

1009
00:54:55,880 --> 00:54:59,640
Speaker 1: is the what is the breakdown you mentioned the g

1010
00:54:59,840 --> 00:55:04,160
Speaker 1: high timer breakdown? What is interesting about his trading because Tesla,

1011
00:55:04,280 --> 00:55:06,480
Speaker 1: to me is a good reminder that like Elon Musk,

1012
00:55:06,719 --> 00:55:08,919
Speaker 1: especially with Tesla, has been trying to do this sort

1013
00:55:08,920 --> 00:55:12,279
Speaker 1: of private public public private partnership thing for years. Where

1014
00:55:12,280 --> 00:55:14,880
Speaker 1: he is, he has been very heavily subsidized, and Tesla's

1015
00:55:14,880 --> 00:55:17,640
Speaker 1: are very heavily subsidized. They're only at the price point

1016
00:55:17,760 --> 00:55:22,040
Speaker 1: they are because of like very heavy subsidies from Democrats

1017
00:55:22,040 --> 00:55:26,160
Speaker 1: that have been so interested in this transition to renewables

1018
00:55:26,880 --> 00:55:29,120
Speaker 1: and grain energy, and that's why they've always been generally

1019
00:55:29,200 --> 00:55:31,960
Speaker 1: very supportive of Tesla. And it wasn't a toxic stock.

1020
00:55:32,040 --> 00:55:34,120
Speaker 1: It wasn't a toxic company to be in the mix

1021
00:55:34,239 --> 00:55:37,080
Speaker 1: with at all until Elon must started flirting with buying

1022
00:55:37,160 --> 00:55:41,719
Speaker 1: Twitter basically, which again then Tesla stock has gone down

1023
00:55:41,719 --> 00:55:43,960
Speaker 1: over the course of the year. But what's the deal

1024
00:55:44,000 --> 00:55:46,640
Speaker 1: with Gottheimer, So yeah, put so put the next one up.

1025
00:55:46,680 --> 00:55:52,040
Speaker 1: This is options contracts traded traded in Congress. And you

1026
00:55:52,160 --> 00:55:55,719
Speaker 1: see Pelosi as expected, like, so this is like her

1027
00:55:55,800 --> 00:55:58,920
Speaker 1: husband's a trader. He's going to be trading options. Uh,

1028
00:55:58,960 --> 00:56:03,400
Speaker 1: but Josh Gonnhai Timer, So sixty four million dollars in

1029
00:56:03,920 --> 00:56:07,760
Speaker 1: call options in twenty twenty one, twenty three million dollars

1030
00:56:07,800 --> 00:56:14,000
Speaker 1: in call options in twenty twenty two, just an extraordinary

1031
00:56:14,080 --> 00:56:17,600
Speaker 1: amount of options trading. Now, Gottheimer represents a lot of

1032
00:56:17,600 --> 00:56:19,480
Speaker 1: the bankers because the bankers, a lot of them live

1033
00:56:19,520 --> 00:56:25,960
Speaker 1: in northern New Jersey, which he represents. The report points

1034
00:56:25,960 --> 00:56:27,960
Speaker 1: out that a lot of his options trading was done

1035
00:56:28,000 --> 00:56:31,200
Speaker 1: in Microsoft, and he used to work for Microsoft under

1036
00:56:31,239 --> 00:56:35,879
Speaker 1: Mark Penn if you remember they did that. Mark Penn

1037
00:56:35,920 --> 00:56:38,920
Speaker 1: was leading their kind of anti Google and also like

1038
00:56:38,960 --> 00:56:41,640
Speaker 1: this very kind of cutthroat DC operation that they were

1039
00:56:41,719 --> 00:56:45,080
Speaker 1: running for a while and Gotdheimer was a part of that.

1040
00:56:46,840 --> 00:56:50,000
Speaker 1: So to be trading, you know, tens of millions of

1041
00:56:50,040 --> 00:56:52,759
Speaker 1: dollars worth of options, a lot of them related to

1042
00:56:52,800 --> 00:56:55,759
Speaker 1: a company that you used to work for be an

1043
00:56:55,800 --> 00:57:01,359
Speaker 1: executive in while serving in Congress, representing probably as many

1044
00:57:02,320 --> 00:57:07,480
Speaker 1: stockbrokers and other bankers. As Richard Blumenthal represents in Connecticut,

1045
00:57:08,120 --> 00:57:10,560
Speaker 1: it is just a real kind of thumb in the

1046
00:57:10,560 --> 00:57:13,640
Speaker 1: face of the American public. It feels like, yeah, and

1047
00:57:13,719 --> 00:57:17,400
Speaker 1: this is all happening post yeah, maybe something, This is

1048
00:57:17,440 --> 00:57:21,160
Speaker 1: all happening post Stock Act, all Caps Stock Act, which

1049
00:57:21,320 --> 00:57:23,120
Speaker 1: was something which is why, which is why we know

1050
00:57:23,280 --> 00:57:25,720
Speaker 1: about the trading, And yeah, that's true, and it was

1051
00:57:25,720 --> 00:57:28,240
Speaker 1: something that Pelosi sort of championed. But it's a good

1052
00:57:28,280 --> 00:57:33,120
Speaker 1: example of reluctantly, reluctantly but then eventually like she's talked

1053
00:57:33,160 --> 00:57:35,560
Speaker 1: about it since it's passed and says, we've got it

1054
00:57:35,600 --> 00:57:38,800
Speaker 1: on the books. But this is the point, is that

1055
00:57:38,840 --> 00:57:41,720
Speaker 1: there's no shame. And one of the interesting things from

1056
00:57:41,760 --> 00:57:45,240
Speaker 1: the recent Pelosi documentary that her daughter Alexandra did is

1057
00:57:45,560 --> 00:57:49,400
Speaker 1: how frequently Paul Pelosi is in the room during those

1058
00:57:49,400 --> 00:57:53,360
Speaker 1: phone calls. There's no connection made at all to this,

1059
00:57:53,440 --> 00:57:56,600
Speaker 1: of course in Alexandra's documentary, but from if you put

1060
00:57:56,600 --> 00:57:58,960
Speaker 1: two and two together with this topic that we're discussing

1061
00:57:59,040 --> 00:58:01,720
Speaker 1: right now, and you just see how often he's in

1062
00:58:01,840 --> 00:58:06,520
Speaker 1: the office, how often there's there's business in their condo,

1063
00:58:06,680 --> 00:58:10,560
Speaker 1: there's business in the car. He's always around, and you

1064
00:58:10,560 --> 00:58:13,960
Speaker 1: can there's no possible way that you can have what

1065
00:58:14,080 --> 00:58:17,040
Speaker 1: the Stock Act. Sure, but unless you totally ban trading,

1066
00:58:17,280 --> 00:58:20,000
Speaker 1: which Kevin McCarthy can say he'll do. I don't believe

1067
00:58:20,040 --> 00:58:24,120
Speaker 1: anyone is going to ban trading. But unless you ban trading,

1068
00:58:25,000 --> 00:58:28,040
Speaker 1: if spouse trading too, I don't see how you can

1069
00:58:28,520 --> 00:58:32,600
Speaker 1: truly truly make this fair because I mean you just

1070
00:58:32,640 --> 00:58:36,200
Speaker 1: look at that. He's he's there everywhere, he's hearing everything.

1071
00:58:36,600 --> 00:58:39,280
Speaker 1: And if you're a smart trainer, you can you can

1072
00:58:39,280 --> 00:58:41,960
Speaker 1: figure it out. And we know although they lost this year,

1073
00:58:42,000 --> 00:58:44,440
Speaker 1: although they lost you yeah, actually put up this next one.

1074
00:58:44,480 --> 00:58:47,280
Speaker 1: This is this is average stock returns in twenty twenty two,

1075
00:58:47,360 --> 00:58:50,320
Speaker 1: And can you break down why that doesn't that why

1076
00:58:50,360 --> 00:58:54,000
Speaker 1: that doesn't just sort of negate the question of corruption. Well, right,

1077
00:58:54,080 --> 00:58:58,640
Speaker 1: just because you failed at getting rich in one particular year,

1078
00:58:59,240 --> 00:59:02,240
Speaker 1: and also it's the it's the appearance of it, and

1079
00:59:02,280 --> 00:59:04,920
Speaker 1: you could I think that's all you need right there.

1080
00:59:05,360 --> 00:59:10,240
Speaker 1: I think the public sees members of Congress trading stocks

1081
00:59:10,400 --> 00:59:14,400
Speaker 1: while also having power over the economy and over these

1082
00:59:14,440 --> 00:59:18,480
Speaker 1: companies as wrong. Like whether they're beating the market or

1083
00:59:18,520 --> 00:59:22,240
Speaker 1: losing to the market isn't really the point. The point

1084
00:59:22,280 --> 00:59:25,280
Speaker 1: is that it just looks wrong and it erodes faith

1085
00:59:26,800 --> 00:59:29,480
Speaker 1: in Congress. Now it turns out that they also do

1086
00:59:29,600 --> 00:59:33,040
Speaker 1: beat the market, and so as this one shows here Republicans,

1087
00:59:33,120 --> 00:59:35,880
Speaker 1: so this is and this is matched to basically like

1088
00:59:35,920 --> 00:59:39,720
Speaker 1: a standard Impoor index, which was down eighteen percent over

1089
00:59:39,720 --> 00:59:44,840
Speaker 1: the year, So everybody was losing money here, But Republicans

1090
00:59:45,720 --> 00:59:48,760
Speaker 1: beat the market by four point six four percent. Democrats

1091
00:59:48,840 --> 00:59:52,160
Speaker 1: beat the market by three point seven percent. Pelosi and

1092
00:59:52,240 --> 00:59:54,439
Speaker 1: I actually, if you take Pelosi out of that, then

1093
00:59:54,920 --> 01:00:01,200
Speaker 1: Democrats did much better because Pelosi lost the Pelosi He's

1094
01:00:01,200 --> 01:00:03,760
Speaker 1: lost twenty percent over the year while the market lost

1095
01:00:03,760 --> 01:00:07,400
Speaker 1: eighteen percent. So you know, they underperformed the market by

1096
01:00:07,960 --> 01:00:13,040
Speaker 1: by two percent. But the fact that year over year

1097
01:00:13,400 --> 01:00:17,720
Speaker 1: on average, members of Congress are outperforming the market, I

1098
01:00:17,760 --> 01:00:19,520
Speaker 1: think is all the proof you need that they have

1099
01:00:19,600 --> 01:00:22,720
Speaker 1: inside information. You've met these people, these these are not

1100
01:00:22,800 --> 01:00:26,640
Speaker 1: geniuses by a stretch of the imagination. Very stable genius,

1101
01:00:26,760 --> 01:00:29,960
Speaker 1: very stable, very stable geniuses. And if they can beat

1102
01:00:29,960 --> 01:00:33,000
Speaker 1: the market, then they have some information. Yeah, there's no

1103
01:00:33,240 --> 01:00:36,360
Speaker 1: like yeah, and unusual Wills is often good as well

1104
01:00:36,360 --> 01:00:38,800
Speaker 1: as pointing out what committees are on and Peter Swizer,

1105
01:00:38,840 --> 01:00:40,880
Speaker 1: so really good reporting on that too. It's just like

1106
01:00:41,000 --> 01:00:43,040
Speaker 1: the most obvious thing in the world. Run through the

1107
01:00:43,080 --> 01:00:48,280
Speaker 1: last two really quick. This is worth worth exploring. If

1108
01:00:48,280 --> 01:00:52,320
Speaker 1: anybody watching feels like digging more into this, these are

1109
01:00:52,320 --> 01:00:56,120
Speaker 1: some breadcrumbs. These are the best best traders. Mike Kelly,

1110
01:00:56,320 --> 01:01:00,960
Speaker 1: a Republican two hundred and thirty nine percent gain match

1111
01:01:02,120 --> 01:01:05,560
Speaker 1: or beat against the spy. And then David Trone, who's

1112
01:01:05,920 --> 01:01:08,280
Speaker 1: one of the richest Democrats. He's the guy that owns

1113
01:01:08,280 --> 01:01:11,760
Speaker 1: Total Wine, which is I don't know how national that

1114
01:01:11,840 --> 01:01:14,480
Speaker 1: chain is, but it's at least in the mid Atlantic.

1115
01:01:14,800 --> 01:01:17,920
Speaker 1: It's this like Walmart sized liquor store. Yeah, I've been

1116
01:01:17,960 --> 01:01:20,360
Speaker 1: in one once and it just blew my mind. It's incredible.

1117
01:01:21,120 --> 01:01:25,240
Speaker 1: It's total wine. Well it's not totally wine, right, it

1118
01:01:25,360 --> 01:01:27,680
Speaker 1: is also beer and if the state allows it, liquor.

1119
01:01:27,960 --> 01:01:29,760
Speaker 1: And then if you go down you see other people

1120
01:01:29,800 --> 01:01:35,640
Speaker 1: who did pretty well, aren't they aren't they lucky? And

1121
01:01:36,240 --> 01:01:39,000
Speaker 1: David Trone, I guess it feels like he deserves this

1122
01:01:39,040 --> 01:01:42,400
Speaker 1: because he also has spent something like ten million dollars

1123
01:01:42,480 --> 01:01:47,960
Speaker 1: every election cycle. His was the I think he I

1124
01:01:48,000 --> 01:01:50,920
Speaker 1: think his was the most expensive primary ever up until

1125
01:01:50,960 --> 01:01:53,439
Speaker 1: Sam bankmin Freed dumped an insane amount of money into

1126
01:01:53,440 --> 01:01:56,880
Speaker 1: a losing district, and so he has spent an enormous

1127
01:01:56,880 --> 01:01:58,560
Speaker 1: amount of money to be a member of Congress. Maybe

1128
01:01:58,560 --> 01:02:01,440
Speaker 1: this is just his way of getting getting a return

1129
01:02:01,480 --> 01:02:04,640
Speaker 1: on his Yeah, that's why it was a good investment.

1130
01:02:04,960 --> 01:02:07,920
Speaker 1: And then finally another more bread comes for people if

1131
01:02:07,960 --> 01:02:10,720
Speaker 1: they want. These the top stocks purchased by Congress with

1132
01:02:10,760 --> 01:02:14,640
Speaker 1: the largest gain since Russia's invasion, and these are of

1133
01:02:14,680 --> 01:02:18,120
Speaker 1: these are a lot of weapons makers of course, surprisingly

1134
01:02:18,400 --> 01:02:20,920
Speaker 1: of course, so you don't think McCarthy is going to

1135
01:02:21,480 --> 01:02:24,720
Speaker 1: follow through on his promise to to do this. No.

1136
01:02:24,760 --> 01:02:27,000
Speaker 1: I think the Stock Act is the best template of

1137
01:02:27,000 --> 01:02:30,000
Speaker 1: how Congress takes care of these things. It's it's a

1138
01:02:30,280 --> 01:02:33,200
Speaker 1: sort of symbolic gesture that takes a tiny, tiny step

1139
01:02:33,200 --> 01:02:36,120
Speaker 1: in the right direction while also shielding people's ability to

1140
01:02:36,160 --> 01:02:40,360
Speaker 1: continue corruption because that's all they're capable of, that's all

1141
01:02:40,360 --> 01:02:41,840
Speaker 1: they have the will to do. So if you can

1142
01:02:41,880 --> 01:02:44,840
Speaker 1: package something that doesn't really I mean, because this is

1143
01:02:44,880 --> 01:02:47,320
Speaker 1: so existential for so many members of Congress, Like you said,

1144
01:02:47,360 --> 01:02:51,160
Speaker 1: return on investment, this is considered you know, they make

1145
01:02:51,480 --> 01:02:53,880
Speaker 1: compared to all the donors they spend all day talking

1146
01:02:53,920 --> 01:02:57,320
Speaker 1: to and the community business leaders they spend all day

1147
01:02:57,320 --> 01:03:01,480
Speaker 1: talking to They're making south of two hundred thousand dollars

1148
01:03:01,520 --> 01:03:05,120
Speaker 1: a year from their congressional salary, and they feel like

1149
01:03:05,160 --> 01:03:07,320
Speaker 1: they're entitled to more. They got to feed their family.

1150
01:03:07,400 --> 01:03:09,080
Speaker 1: Why am I in this room with all these rich people,

1151
01:03:09,120 --> 01:03:12,720
Speaker 1: who are you know, sucking up to me? Exactly? Yet

1152
01:03:12,960 --> 01:03:15,120
Speaker 1: I have less money than them? Just feels wrong. Yeah,

1153
01:03:15,200 --> 01:03:16,680
Speaker 1: or they in general have a lot of money to

1154
01:03:16,680 --> 01:03:20,000
Speaker 1: play with, and they're sort of the entrepreneurial type people

1155
01:03:20,040 --> 01:03:22,520
Speaker 1: that say, I've got this money, I'm gonna play with that.

1156
01:03:22,560 --> 01:03:24,320
Speaker 1: I've got the information, I'm gonna play with it. But

1157
01:03:24,360 --> 01:03:27,040
Speaker 1: it's always just been considered, you know that two hundred

1158
01:03:27,080 --> 01:03:29,280
Speaker 1: k a year isn't enough for me, so obviously I'm

1159
01:03:29,280 --> 01:03:31,760
Speaker 1: going to make money somewhere else. It's really an existential

1160
01:03:31,800 --> 01:03:35,560
Speaker 1: part of their lives. So no, I don't you would

1161
01:03:35,640 --> 01:03:38,440
Speaker 1: take like a Thomas Massey as Speaker of the House

1162
01:03:38,520 --> 01:03:40,480
Speaker 1: to get rid of that. Yeah, And I wonder if

1163
01:03:40,520 --> 01:03:44,120
Speaker 1: they feel like they they deserve it in the sense

1164
01:03:44,160 --> 01:03:47,240
Speaker 1: that let's say a member of Congress is having dinner

1165
01:03:47,280 --> 01:03:50,000
Speaker 1: with somebody that they've known for a very long time.

1166
01:03:51,800 --> 01:03:53,600
Speaker 1: But the person that they've known for a very long

1167
01:03:53,640 --> 01:03:55,200
Speaker 1: time is the head of an oil company, where the

1168
01:03:55,240 --> 01:03:57,920
Speaker 1: head of a construction company, head of a pharmaceutical company,

1169
01:03:58,600 --> 01:04:02,800
Speaker 1: and is so comfortable in their power that they forget

1170
01:04:02,880 --> 01:04:04,560
Speaker 1: that the reason that that person is friends with you,

1171
01:04:04,600 --> 01:04:06,360
Speaker 1: and the reason the person is buying you dinner, and

1172
01:04:06,360 --> 01:04:08,560
Speaker 1: the reason the person is telling you these things about

1173
01:04:08,600 --> 01:04:12,120
Speaker 1: their company is that they're trying to corrupt you. And

1174
01:04:13,080 --> 01:04:15,800
Speaker 1: maybe it just becomes very difficult to see. They also

1175
01:04:15,880 --> 01:04:17,920
Speaker 1: might just not care. It's a little wait, you know,

1176
01:04:17,960 --> 01:04:19,680
Speaker 1: I'm just going for the grab. Yeah. I think some

1177
01:04:19,840 --> 01:04:22,960
Speaker 1: come up with justifications for it, and others just shameless

1178
01:04:22,960 --> 01:04:25,600
Speaker 1: don't care. Yeah. I think, Yes, I think if you're

1179
01:04:25,600 --> 01:04:27,600
Speaker 1: a total narciss you might just think, Oh, it's because

1180
01:04:27,640 --> 01:04:31,280
Speaker 1: they love me. I'm just such an interesting person. Yes,

1181
01:04:31,320 --> 01:04:37,360
Speaker 1: I'm so good at my job. What's your point today? Well, Crystal,

1182
01:04:37,360 --> 01:04:39,880
Speaker 1: I actually talked yesterday about a Wall Street Journal article

1183
01:04:40,000 --> 01:04:45,040
Speaker 1: in which corporate bosses vented their frustrations about workers. Can

1184
01:04:45,080 --> 01:04:47,360
Speaker 1: see that up on the screen now. The New Yorker

1185
01:04:47,880 --> 01:04:51,080
Speaker 1: they published a much better take on so called quiet

1186
01:04:51,200 --> 01:04:54,400
Speaker 1: quitting That was courtesy of Cal Newport, who wrote that

1187
01:04:54,440 --> 01:04:57,200
Speaker 1: the trend is quote the first step of a younger

1188
01:04:57,240 --> 01:05:01,320
Speaker 1: generation taking their turn in developing a more nuanced understanding

1189
01:05:01,400 --> 01:05:03,680
Speaker 1: of the role of work in their lives. These two

1190
01:05:03,680 --> 01:05:06,960
Speaker 1: positions from the Journal and The New Yorker are actually

1191
01:05:07,280 --> 01:05:11,440
Speaker 1: entirely related, and I actually don't doubt. Let's say some

1192
01:05:11,800 --> 01:05:15,840
Speaker 1: fraction of the workforce is actually getting lazier. Can you

1193
01:05:15,880 --> 01:05:21,120
Speaker 1: blame people? Though American work culture has absolutely created immense prosperity,

1194
01:05:21,440 --> 01:05:24,080
Speaker 1: that doesn't mean it's perfect. Plus, when the labor market

1195
01:05:24,120 --> 01:05:27,640
Speaker 1: benefits workers and inflation is high, obviously people are going

1196
01:05:27,680 --> 01:05:30,200
Speaker 1: to demand more, especially when they know they can get

1197
01:05:30,240 --> 01:05:33,320
Speaker 1: a job that perhaps involves working from home with a

1198
01:05:33,360 --> 01:05:38,240
Speaker 1: flexible schedule. And hey, technology is making some folks very rich.

1199
01:05:38,320 --> 01:05:41,680
Speaker 1: Technology that is making some folks very rich is sapping

1200
01:05:41,720 --> 01:05:44,000
Speaker 1: others of their will to live, let alone to work.

1201
01:05:44,200 --> 01:05:46,680
Speaker 1: One twenty six year old British man told common Sense

1202
01:05:46,720 --> 01:05:49,840
Speaker 1: earlier last year, quote, the lowest possible quality of life

1203
01:05:49,920 --> 01:05:52,360
Speaker 1: you can have with the internet is still kind of tolerable.

1204
01:05:52,600 --> 01:05:55,440
Speaker 1: It's not absolutely awful. You can sort of exist in

1205
01:05:55,520 --> 01:05:57,520
Speaker 1: that and there's nothing to give you a kick up

1206
01:05:57,560 --> 01:06:00,280
Speaker 1: the butt because it's not the worst thing, all right.

1207
01:06:00,320 --> 01:06:03,440
Speaker 1: So the labor force participation rate among working age men

1208
01:06:03,600 --> 01:06:06,200
Speaker 1: has dropped a lot. It's gone from about ninety four

1209
01:06:06,240 --> 01:06:08,920
Speaker 1: percent in nineteen forty eight to eighty four percent in

1210
01:06:08,960 --> 01:06:12,120
Speaker 1: twenty fifteen, with most of the drop actually happening since

1211
01:06:12,200 --> 01:06:17,640
Speaker 1: that period in the mid sixties. Women's labor force participation rate, meanwhile,

1212
01:06:17,680 --> 01:06:20,480
Speaker 1: it has actually doubled over that same time period. As

1213
01:06:20,560 --> 01:06:23,720
Speaker 1: Nicholas Eversat told the Fifth Column this fall. Quote, for

1214
01:06:23,960 --> 01:06:26,320
Speaker 1: every twenty five to fifty five year old guy who

1215
01:06:26,360 --> 01:06:28,120
Speaker 1: is out of work and looking for a job in

1216
01:06:28,160 --> 01:06:31,280
Speaker 1: twenty twenty two, there are four guys who are neither

1217
01:06:31,360 --> 01:06:34,800
Speaker 1: working nor looking for work. But we needn't go far

1218
01:06:34,920 --> 01:06:37,720
Speaker 1: too far down that particular rabbit hole. Are people really

1219
01:06:37,720 --> 01:06:40,800
Speaker 1: getting lazier? And if so, is that even morally wrong

1220
01:06:41,040 --> 01:06:44,360
Speaker 1: or logically irrational? As Helen Miron said on this Week's

1221
01:06:44,360 --> 01:06:47,400
Speaker 1: Great Episode of nineteen twenty three, what could humans possibly

1222
01:06:47,480 --> 01:06:50,120
Speaker 1: do with all of the leisure time new inventions like

1223
01:06:50,120 --> 01:06:52,680
Speaker 1: the washing machine and refrigerator have given us in the

1224
01:06:52,760 --> 01:06:55,920
Speaker 1: last century. Well, let's check back in with Cal Newport

1225
01:06:55,960 --> 01:06:58,560
Speaker 1: over at The New Yorker. It's hard to find references

1226
01:06:58,560 --> 01:07:01,160
Speaker 1: to the phrase follow your path in the context of

1227
01:07:01,200 --> 01:07:04,640
Speaker 1: career advice until the nineteen nineties, at which point the

1228
01:07:04,680 --> 01:07:09,800
Speaker 1: adage explodes into common usage. Newport wrote, this passion centric

1229
01:07:09,840 --> 01:07:13,440
Speaker 1: perspective attempted to thread the needle between the extremes that

1230
01:07:13,480 --> 01:07:16,200
Speaker 1: the Boomers had experienced. Get a job, they told their kids,

1231
01:07:16,200 --> 01:07:19,320
Speaker 1: but make it one you love, seek self actualization, but

1232
01:07:19,400 --> 01:07:23,400
Speaker 1: also care about making your mortgage payments. Newport added, quote,

1233
01:07:23,440 --> 01:07:26,640
Speaker 1: before we heap disdain on gen Z's travails, we should

1234
01:07:26,720 --> 01:07:30,000
Speaker 1: remember that we were all once in this same position.

1235
01:07:30,200 --> 01:07:32,520
Speaker 1: For me and my fellow millennials, Newport wrote, it wasn't

1236
01:07:32,520 --> 01:07:34,680
Speaker 1: that long ago that our own parents shook their heads

1237
01:07:34,680 --> 01:07:37,680
Speaker 1: at our confident plans to run an automated business from

1238
01:07:37,720 --> 01:07:40,640
Speaker 1: a laptop into loom. Now, I didn't plan that while

1239
01:07:40,680 --> 01:07:42,400
Speaker 1: Sager was into loom, but I kind of think it's

1240
01:07:42,400 --> 01:07:44,680
Speaker 1: funny that that's where he is right now. There's a

1241
01:07:44,760 --> 01:07:48,320
Speaker 1: lot of debate over the relationship between wages and productivity

1242
01:07:48,360 --> 01:07:51,200
Speaker 1: over time. Jason Furman and the folks over at AEI

1243
01:07:51,360 --> 01:07:53,520
Speaker 1: say it's not a big deal, and that quote Greater

1244
01:07:53,600 --> 01:07:56,840
Speaker 1: productivity growth holds the potential of being the most powerful

1245
01:07:56,880 --> 01:08:02,160
Speaker 1: source of sustained wage growth across the income sam. Productivity inputs, though,

1246
01:08:02,200 --> 01:08:06,560
Speaker 1: are getting harder to measure, as the common eight hour workday,

1247
01:08:06,640 --> 01:08:10,240
Speaker 1: with that hour lunch break fades away. Hourly workers are

1248
01:08:10,280 --> 01:08:12,520
Speaker 1: not immune from this. If a boss sends a text

1249
01:08:12,560 --> 01:08:15,800
Speaker 1: message or an email at any time, pretty much everyone

1250
01:08:15,920 --> 01:08:18,840
Speaker 1: is expected to respond. You can be at a pool party,

1251
01:08:18,920 --> 01:08:21,000
Speaker 1: you could be at the gym, but your phone is

1252
01:08:21,040 --> 01:08:24,400
Speaker 1: always there, and that means there's always a possibility of

1253
01:08:24,439 --> 01:08:29,280
Speaker 1: a dramatic request or a minor request. But it's work related, nonetheless,

1254
01:08:29,360 --> 01:08:31,400
Speaker 1: and that is no small thing. It's not a matter

1255
01:08:31,439 --> 01:08:33,920
Speaker 1: of checking your phone for three seconds and just shooting

1256
01:08:33,920 --> 01:08:36,600
Speaker 1: off a quick reply. It is the psychic toll of

1257
01:08:36,800 --> 01:08:40,040
Speaker 1: never ever disconnecting. And that's not whining. It's a very

1258
01:08:40,080 --> 01:08:42,760
Speaker 1: real thing. For people with zoom jobs, the luxury of

1259
01:08:42,760 --> 01:08:46,280
Speaker 1: flexible schedules means monitoring email and doing little bits of

1260
01:08:46,320 --> 01:08:48,479
Speaker 1: work here and there when you're with your family or

1261
01:08:48,600 --> 01:08:50,800
Speaker 1: just trying to decompress at the end of the day.

1262
01:08:51,200 --> 01:08:53,479
Speaker 1: Screen time is bad for our health. We know that,

1263
01:08:53,560 --> 01:08:57,000
Speaker 1: and jobs require more and more screen time in formal

1264
01:08:57,040 --> 01:09:00,240
Speaker 1: work hours nine to five and outside of them, almost

1265
01:09:00,280 --> 01:09:03,120
Speaker 1: impossible to add up all the little seconds here and

1266
01:09:03,160 --> 01:09:07,120
Speaker 1: there people spend working when they're not technically working. So yeah,

1267
01:09:07,240 --> 01:09:09,599
Speaker 1: some people might throw in the towel and others might

1268
01:09:09,640 --> 01:09:13,439
Speaker 1: get a little mad. Plus, Americans are working more hours,

1269
01:09:13,600 --> 01:09:17,000
Speaker 1: more weeks, and even small things like lunch breaks are declined.

1270
01:09:17,240 --> 01:09:19,479
Speaker 1: Declining we have the data on all of that, to

1271
01:09:19,520 --> 01:09:22,559
Speaker 1: my conservative friends, is worth saying. We don't necessarily need

1272
01:09:22,600 --> 01:09:26,000
Speaker 1: new regulations so much as we need fair interpretations of

1273
01:09:26,160 --> 01:09:29,160
Speaker 1: labor laws, defensible labor laws that are already on the books.

1274
01:09:29,520 --> 01:09:31,720
Speaker 1: Not many people paid attention to it, but in the fall,

1275
01:09:31,800 --> 01:09:35,479
Speaker 1: the NLRB issued a memo that announced an investigation into

1276
01:09:35,560 --> 01:09:40,479
Speaker 1: quote unlawful electronic surveillance and automated management practices. As the

1277
01:09:40,600 --> 01:09:44,320
Speaker 1: NLRB said, quote under settled board law, numerous practices employers

1278
01:09:44,400 --> 01:09:47,479
Speaker 1: may engage in using new surveillance and management technologies are

1279
01:09:47,520 --> 01:09:52,280
Speaker 1: already unlawful. Already unlawful. The board pointed to several provisions

1280
01:09:52,280 --> 01:09:56,120
Speaker 1: in the FDR era nlr National Labor Relations Act that

1281
01:09:56,200 --> 01:09:59,960
Speaker 1: are potentially being violated, and crucially, they wrote, electronic monit

1282
01:10:00,120 --> 01:10:03,360
Speaker 1: during and automated management are not always limited to working

1283
01:10:03,479 --> 01:10:06,840
Speaker 1: time after the workday ends. Some employers continue to track

1284
01:10:06,920 --> 01:10:11,879
Speaker 1: employees whereabouts and communications using employer issued phones or wearable

1285
01:10:11,880 --> 01:10:15,439
Speaker 1: devices or apps installed on workers' own devices. Read the memo,

1286
01:10:15,680 --> 01:10:19,040
Speaker 1: and even before they continued the employment relationship begins some

1287
01:10:19,080 --> 01:10:22,920
Speaker 1: employers prying to job applicants private lives by conducting personality

1288
01:10:22,960 --> 01:10:26,679
Speaker 1: tests and scrutinizing applicants social media accounts. This means work

1289
01:10:26,760 --> 01:10:30,200
Speaker 1: is now absolutely everywhere in a way that it never

1290
01:10:30,360 --> 01:10:34,280
Speaker 1: has been before. It's an incredible blending of the private

1291
01:10:34,360 --> 01:10:38,160
Speaker 1: and personal, just as Newport suggested gen Z as experiencing.

1292
01:10:38,560 --> 01:10:41,920
Speaker 1: Of course, that's going to change employees. Of course, it's

1293
01:10:41,960 --> 01:10:46,960
Speaker 1: going to change everything, and it renders employees problems reasonable

1294
01:10:47,280 --> 01:10:50,320
Speaker 1: in a way that many employers are not yet willing

1295
01:10:50,479 --> 01:10:56,080
Speaker 1: to accept. We're excited to be back now with none

1296
01:10:56,160 --> 01:10:59,120
Speaker 1: other than Matt Tayib. You should check out Tayib dot

1297
01:10:59,160 --> 01:11:03,760
Speaker 1: subseac dot com news over there. Matt, welcome back to Counterpoints.

1298
01:11:04,160 --> 01:11:06,599
Speaker 1: Thanks for having me on. Of course, we're really excited.

1299
01:11:06,760 --> 01:11:08,799
Speaker 1: We talked earlier in the show about so the first

1300
01:11:08,880 --> 01:11:11,479
Speaker 1: jump of the two part jump yesterday, there was so

1301
01:11:11,600 --> 01:11:14,240
Speaker 1: much information. We want to talk to you about the

1302
01:11:14,240 --> 01:11:17,000
Speaker 1: belly button and maybe we have a picture of the

1303
01:11:17,040 --> 01:11:23,920
Speaker 1: lovely illustration of the Twitter and the FBI belly button. Now, Matt,

1304
01:11:23,960 --> 01:11:25,840
Speaker 1: you had a thread on this on Twitter, you had

1305
01:11:25,840 --> 01:11:28,080
Speaker 1: a post on it up on TK, which was excellent.

1306
01:11:28,360 --> 01:11:31,240
Speaker 1: Can you start just by telling us some mo people

1307
01:11:31,280 --> 01:11:33,920
Speaker 1: are looking at the lovely graphic, what the hell the

1308
01:11:33,960 --> 01:11:39,280
Speaker 1: FBI belly button is? It's actually really interesting. I know

1309
01:11:39,360 --> 01:11:42,600
Speaker 1: that picture is gross and kind of off putting, but

1310
01:11:43,160 --> 01:11:46,880
Speaker 1: in a way it kind of should be, because one

1311
01:11:46,880 --> 01:11:48,720
Speaker 1: of the things that we were focused on with these

1312
01:11:48,720 --> 01:11:51,160
Speaker 1: documents is trying to figure out the architecture of how

1313
01:11:51,240 --> 01:11:55,679
Speaker 1: information flowed to and from the government with Twitter, And

1314
01:11:55,720 --> 01:12:00,880
Speaker 1: what this thread is really about is how Twitter didn't

1315
01:12:00,880 --> 01:12:03,799
Speaker 1: really want to work with certain agencies for a variety

1316
01:12:03,840 --> 01:12:08,120
Speaker 1: of reasons. They had political disagreements. They thought the State Department,

1317
01:12:08,160 --> 01:12:13,400
Speaker 1: for instance, was too trumpy, and so they were putting

1318
01:12:13,479 --> 01:12:17,880
Speaker 1: up a front about how many different agencies they wanted

1319
01:12:17,920 --> 01:12:21,920
Speaker 1: to give away their chief moderator's phone number two, and

1320
01:12:22,040 --> 01:12:26,200
Speaker 1: ultimately they settled on a system where everything went through

1321
01:12:27,560 --> 01:12:30,439
Speaker 1: the DHS and the FBI. And there's a passage where

1322
01:12:30,520 --> 01:12:33,759
Speaker 1: the FBI agent says, think of us as the belly

1323
01:12:33,760 --> 01:12:40,479
Speaker 1: button for the USG so information essentially. And there's another

1324
01:12:40,520 --> 01:12:44,920
Speaker 1: passage where the agent says, the FBI will take care,

1325
01:12:45,760 --> 01:12:48,680
Speaker 1: you know, will handle the federal and the usi C

1326
01:12:49,520 --> 01:12:52,200
Speaker 1: and DHS will know what's going on in all the states.

1327
01:12:52,760 --> 01:12:56,120
Speaker 1: So that's how they did did up the information. The

1328
01:12:56,160 --> 01:12:59,519
Speaker 1: moderation request came in on the federal and international side

1329
01:12:59,520 --> 01:13:04,439
Speaker 1: through FB and domestically through the Homeland Security And you

1330
01:13:04,479 --> 01:13:08,360
Speaker 1: also talked about this one back and forth around people

1331
01:13:08,439 --> 01:13:12,559
Speaker 1: who were retweeting or zero head or zero hedge articles

1332
01:13:12,640 --> 01:13:15,719
Speaker 1: or on zero hedge had been banned, but then people

1333
01:13:15,760 --> 01:13:18,400
Speaker 1: are still sharing some of the information, and they said

1334
01:13:18,439 --> 01:13:22,400
Speaker 1: it led to quote another flurry of disinformation narratives around

1335
01:13:22,400 --> 01:13:25,280
Speaker 1: the time, zero hedge had been speculating on the origins

1336
01:13:25,320 --> 01:13:27,760
Speaker 1: of COVID, whether whether or not it was a lab

1337
01:13:27,840 --> 01:13:31,680
Speaker 1: leak or natural origin. So what what's going on with

1338
01:13:31,960 --> 01:13:34,519
Speaker 1: like are they are they zeroing in on zero hedge

1339
01:13:34,920 --> 01:13:37,000
Speaker 1: at this point and then trying to make sure that

1340
01:13:37,080 --> 01:13:40,880
Speaker 1: there's nothing emanating remotely from them? And what else did

1341
01:13:40,920 --> 01:13:45,320
Speaker 1: you find around this area? So I think this came

1342
01:13:45,360 --> 01:13:49,160
Speaker 1: from a report by the Global Engagement Center, which is

1343
01:13:49,240 --> 01:13:53,000
Speaker 1: like the fledgling intel arm of the State Department that

1344
01:13:53,040 --> 01:13:56,759
Speaker 1: nobody's ever heard of. It was founded in the Obama

1345
01:13:56,880 --> 01:13:59,760
Speaker 1: years under Hillary Clinton. I think they wanted to make

1346
01:14:00,400 --> 01:14:05,040
Speaker 1: basically like the State Department version of the NSA, And

1347
01:14:05,160 --> 01:14:08,680
Speaker 1: so it's kind of the weak sister of the intelligence community.

1348
01:14:09,240 --> 01:14:13,120
Speaker 1: And they issued a report in February of twenty twenty

1349
01:14:13,880 --> 01:14:18,080
Speaker 1: that basically identified a whole bunch of actors as potential

1350
01:14:18,160 --> 01:14:23,200
Speaker 1: cyber threats. And one of their criteria was sort of

1351
01:14:23,360 --> 01:14:27,080
Speaker 1: arguing that COVID might have come from a lab or

1352
01:14:27,600 --> 01:14:32,280
Speaker 1: retweeting news about zero hedge being banned from Twitter. And

1353
01:14:32,320 --> 01:14:36,000
Speaker 1: what this really offers you a window into is how

1354
01:14:36,240 --> 01:14:40,360
Speaker 1: government agencies decide that this or that account is suspicious,

1355
01:14:40,439 --> 01:14:43,280
Speaker 1: Like they have all these crazy criteria. Everybody thinks it's

1356
01:14:43,320 --> 01:14:47,360
Speaker 1: super sophisticated. It's not. It's really stupid. Like in another

1357
01:14:47,479 --> 01:14:54,360
Speaker 1: area of these reports, they're talking about anybody who retweets

1358
01:14:54,840 --> 01:15:00,719
Speaker 1: two or more Chinese diplomats is now suspect for being

1359
01:15:01,280 --> 01:15:05,680
Speaker 1: for spreading Chinese disinformation, and that included like the Canadian military,

1360
01:15:05,960 --> 01:15:09,240
Speaker 1: like a CNN account. So this just gives you a

1361
01:15:09,240 --> 01:15:12,360
Speaker 1: window into how they're how they're making those decisions. I

1362
01:15:12,360 --> 01:15:14,840
Speaker 1: think it also gives us a window into how And

1363
01:15:14,880 --> 01:15:18,160
Speaker 1: this was another thing your earlier thread yesterday reported on

1364
01:15:18,280 --> 01:15:23,559
Speaker 1: how reporters promulgate and fuel so much of the cycle.

1365
01:15:23,640 --> 01:15:26,479
Speaker 1: Your earlier threads something like reporters now know this model works.

1366
01:15:26,760 --> 01:15:29,320
Speaker 1: That was an email from a Twitter reporter or a

1367
01:15:29,360 --> 01:15:32,479
Speaker 1: Twitter staffer. What did you find as it relates to

1368
01:15:32,680 --> 01:15:36,559
Speaker 1: this question in terms of reporters fueling and working with

1369
01:15:36,880 --> 01:15:42,080
Speaker 1: the government agencies basically in service of censorship. Yeah, so

1370
01:15:42,160 --> 01:15:45,880
Speaker 1: I find this part fascinating. Maybe only other reporters find

1371
01:15:45,920 --> 01:15:50,640
Speaker 1: it interesting, but you know, you might remember back in

1372
01:15:50,680 --> 01:15:55,000
Speaker 1: the WMD episode, there was this famous thing involving Dick

1373
01:15:55,080 --> 01:15:59,720
Speaker 1: Cheney and this thing called stovepiping, where Cheney would reach

1374
01:15:59,800 --> 01:16:03,519
Speaker 1: in to one of the intelligence agencies in grab raw

1375
01:16:03,600 --> 01:16:06,920
Speaker 1: intel that hadn't been vetted, would feed it to a

1376
01:16:06,960 --> 01:16:10,320
Speaker 1: news agency or a newspaper like the New York Times,

1377
01:16:10,360 --> 01:16:13,680
Speaker 1: and then would go on a show like Meet the

1378
01:16:13,720 --> 01:16:15,720
Speaker 1: Press and say, hey, did you hear about that New

1379
01:16:15,800 --> 01:16:21,040
Speaker 1: York Times article? And exactly the same process goes on here. Basically,

1380
01:16:21,680 --> 01:16:25,600
Speaker 1: you have you know, either a government government department like

1381
01:16:25,680 --> 01:16:30,120
Speaker 1: the Senate Intel Committee, or some private researcher that's connected

1382
01:16:30,160 --> 01:16:33,719
Speaker 1: to the government that goes through a reporter and says,

1383
01:16:33,880 --> 01:16:36,080
Speaker 1: here are a bunch of suspect accounts we think are

1384
01:16:36,160 --> 01:16:40,519
Speaker 1: Russia linked. And then they will go to Twitter and

1385
01:16:40,600 --> 01:16:42,880
Speaker 1: say we think these are Russia linked. Will you take

1386
01:16:42,920 --> 01:16:47,040
Speaker 1: action on them. Do you have any comment? And Facebook,

1387
01:16:47,080 --> 01:16:50,680
Speaker 1: not wanting to take the political hit, might suspend a

1388
01:16:50,720 --> 01:16:53,559
Speaker 1: few accounts and then instantly they have a headline like,

1389
01:16:53,640 --> 01:16:58,720
Speaker 1: you know, Facebook uncovers with our help, you know, Russian influence.

1390
01:16:59,320 --> 01:17:01,880
Speaker 1: And that that's what the Twitter stuffer was talking about,

1391
01:17:01,920 --> 01:17:03,640
Speaker 1: that this is a model that works. As soon as

1392
01:17:03,640 --> 01:17:06,559
Speaker 1: you get somebody to sign off on the idea that

1393
01:17:06,920 --> 01:17:10,519
Speaker 1: this or that account is Russia linked, you already you

1394
01:17:10,560 --> 01:17:13,160
Speaker 1: have an automatic news story. And they did that over

1395
01:17:13,240 --> 01:17:15,439
Speaker 1: and over again. And what's really interesting, I thought in

1396
01:17:15,479 --> 01:17:18,120
Speaker 1: these documents is you see the twitter stuffer saying this

1397
01:17:18,200 --> 01:17:20,000
Speaker 1: is going to happen to us over and over again,

1398
01:17:20,400 --> 01:17:23,759
Speaker 1: and it did. I also want to ask you about

1399
01:17:23,760 --> 01:17:26,960
Speaker 1: this your Roth email that's number seventeen in your thread,

1400
01:17:27,000 --> 01:17:29,960
Speaker 1: which I think actually kind of exposes the divide in

1401
01:17:30,000 --> 01:17:34,800
Speaker 1: such a profound way. And it's Rath referring to the

1402
01:17:34,920 --> 01:17:38,880
Speaker 1: DHS and FBI is as a political and he puts generally,

1403
01:17:38,920 --> 01:17:42,759
Speaker 1: he puts generally in parentheses to acknowledge that, Okay, maybe

1404
01:17:42,800 --> 01:17:45,000
Speaker 1: there's a tiny bit of politics going on with DHS

1405
01:17:45,000 --> 01:17:47,280
Speaker 1: and FI but it's an internal email. I feel like

1406
01:17:47,360 --> 01:17:50,560
Speaker 1: when he's writing this, I think he means it. And

1407
01:17:51,600 --> 01:17:53,479
Speaker 1: the problem with this GEC, and you can talk a

1408
01:17:53,479 --> 01:17:56,560
Speaker 1: little bout with GC, is to them that was political.

1409
01:17:56,920 --> 01:17:59,320
Speaker 1: So what do you draw from the fact that, and

1410
01:17:59,400 --> 01:18:01,960
Speaker 1: let's assume he means it that when he thinks of

1411
01:18:02,000 --> 01:18:04,040
Speaker 1: the FBI and DHS, that he sees them as a

1412
01:18:04,120 --> 01:18:07,800
Speaker 1: political What does that tell you about how Twitter was

1413
01:18:08,400 --> 01:18:14,800
Speaker 1: relating to those organizations, those government agencies. Well, clearly, I

1414
01:18:14,840 --> 01:18:18,760
Speaker 1: think they moved. They brought themselves into a place psychologically

1415
01:18:18,800 --> 01:18:23,880
Speaker 1: where they were able to comply with really this unbelievably

1416
01:18:23,960 --> 01:18:28,400
Speaker 1: incessant stream of demands from agencies like the FBI and

1417
01:18:28,439 --> 01:18:34,000
Speaker 1: the DHS to eliminate certain accounts. They justified that in

1418
01:18:34,040 --> 01:18:37,320
Speaker 1: the grounds that all these things were legitimate threats. They

1419
01:18:37,320 --> 01:18:42,200
Speaker 1: were legitimately dangerous. We legitimately had to worry about what

1420
01:18:42,280 --> 01:18:45,120
Speaker 1: he called major risks, which included I guess Donald Trump

1421
01:18:45,160 --> 01:18:49,599
Speaker 1: getting re elected. So when they were presented with an

1422
01:18:49,600 --> 01:18:52,800
Speaker 1: agency that was politically not in the same place as

1423
01:18:52,840 --> 01:18:55,680
Speaker 1: they were, and it happened twice that year. Actually I

1424
01:18:55,680 --> 01:19:00,760
Speaker 1: didn't mention this, but also that summer they were refused

1425
01:19:01,360 --> 01:19:05,720
Speaker 1: to go along with a Pentagon program that involved the

1426
01:19:05,920 --> 01:19:10,680
Speaker 1: Army making fake accounts to cover up for drone attacks.

1427
01:19:11,560 --> 01:19:17,519
Speaker 1: They basically they decided that you know, a more Trump

1428
01:19:17,640 --> 01:19:23,840
Speaker 1: leaning government agency was quote political, and the FBI, DHS, N,

1429
01:19:23,960 --> 01:19:26,479
Speaker 1: s A, c, i, A, all those other agencies were

1430
01:19:26,520 --> 01:19:30,640
Speaker 1: not political. So that's how they justified dealing with all

1431
01:19:30,680 --> 01:19:35,800
Speaker 1: those those moderation requests. But I think it's pretty transparent

1432
01:19:36,160 --> 01:19:38,479
Speaker 1: what they were doing, and I think people will see

1433
01:19:38,479 --> 01:19:41,320
Speaker 1: it that way too. Follow up on that, real Q,

1434
01:19:41,880 --> 01:19:43,600
Speaker 1: how does that relate to what you found? How does

1435
01:19:43,640 --> 01:19:47,840
Speaker 1: that relate to Lefong's story? But once Trump was in

1436
01:19:49,360 --> 01:19:52,799
Speaker 1: or yeah, can you like what's the connection between Leifan

1437
01:19:52,920 --> 01:19:55,280
Speaker 1: was on our show a couple of weeks ago talking

1438
01:19:55,280 --> 01:19:59,240
Speaker 1: about what he had found in which Twitter was kind

1439
01:19:59,280 --> 01:20:03,519
Speaker 1: of whitelisting a bunch of Pentagon bots and Pentagon accounts

1440
01:20:03,520 --> 01:20:08,439
Speaker 1: that were that were doing propaganda around the globe and

1441
01:20:08,520 --> 01:20:11,160
Speaker 1: around around drone strikes. They did they at some point

1442
01:20:11,240 --> 01:20:16,320
Speaker 1: then you found pushback against some dad they going too far? Yeah,

1443
01:20:16,360 --> 01:20:20,200
Speaker 1: and Lee even wrote about that. The pushback came in

1444
01:20:20,320 --> 01:20:26,320
Speaker 1: June of twenty twenty. It started with Facebook. Actually Facebook

1445
01:20:26,400 --> 01:20:29,200
Speaker 1: was the first company to say no, and then Twitter

1446
01:20:29,280 --> 01:20:35,040
Speaker 1: kind of followed their lead. But essentially the Pentagon and

1447
01:20:36,200 --> 01:20:39,320
Speaker 1: we can put Pentagon in parentheses, because this really we're

1448
01:20:39,320 --> 01:20:45,240
Speaker 1: talking about the NSA. In some cases, they they had

1449
01:20:45,280 --> 01:20:50,439
Speaker 1: been creating local foreign language accounts, so that if, for instance,

1450
01:20:50,520 --> 01:20:53,240
Speaker 1: you did a drone strike in a foreign country where

1451
01:20:53,280 --> 01:20:56,200
Speaker 1: they speak Arabic and there were lots of local news

1452
01:20:56,280 --> 01:20:58,400
Speaker 1: reports saying, oh, they blew up a hospital, all these

1453
01:20:58,479 --> 01:21:01,320
Speaker 1: kids died, suddenly you would see a flurry of fake

1454
01:21:01,880 --> 01:21:04,759
Speaker 1: accounts that would say, oh, no, actually it wasn't that bad.

1455
01:21:05,439 --> 01:21:08,160
Speaker 1: There were no serious casualties, and guess who that was.

1456
01:21:08,200 --> 01:21:10,320
Speaker 1: That was us doing that, right, So those were what

1457
01:21:10,360 --> 01:21:14,479
Speaker 1: we call influence information operations, and they had been doing

1458
01:21:14,479 --> 01:21:17,840
Speaker 1: They've been going along with that for years. But the

1459
01:21:17,840 --> 01:21:20,880
Speaker 1: Pentagon had not had a good relationship with Twitter dating

1460
01:21:20,920 --> 01:21:24,559
Speaker 1: back to twenty seventeen. I was told this explicitly by

1461
01:21:25,439 --> 01:21:30,080
Speaker 1: somebody in the defense community, that they had been basically

1462
01:21:30,160 --> 01:21:33,680
Speaker 1: ghosted by a Twitter dating back to the middle of

1463
01:21:33,720 --> 01:21:38,960
Speaker 1: twenty seventeen. Finally, in twenty twenty, the company's banded together

1464
01:21:39,320 --> 01:21:41,960
Speaker 1: and decided not to do this anymore, and that story

1465
01:21:42,000 --> 01:21:45,320
Speaker 1: didn't come out until twenty twenty two. You might remember

1466
01:21:45,400 --> 01:21:48,360
Speaker 1: it from earlier last fall, when it came out in

1467
01:21:48,400 --> 01:21:53,160
Speaker 1: the Washington Post that they had uncovered a US information operation.

1468
01:21:53,280 --> 01:21:57,200
Speaker 1: It was actually a Pentagon operation. It's really interesting. One

1469
01:21:57,240 --> 01:21:59,439
Speaker 1: thing from all of these, all of the reporting that's

1470
01:21:59,439 --> 01:22:02,000
Speaker 1: happened during the Time files to see how much business

1471
01:22:02,160 --> 01:22:04,920
Speaker 1: is conducted over email and slack. I mean, one thing

1472
01:22:04,920 --> 01:22:09,640
Speaker 1: that's always plagged journalists like to Time like forever basically

1473
01:22:09,840 --> 01:22:11,640
Speaker 1: is that nobody puts things in writing. You know, this

1474
01:22:11,720 --> 01:22:14,040
Speaker 1: is all phone calls or in writing they say we

1475
01:22:14,080 --> 01:22:16,360
Speaker 1: need to move this to a phone call. But so

1476
01:22:16,439 --> 01:22:19,400
Speaker 1: much of this was happening in writing and was etched

1477
01:22:19,439 --> 01:22:23,000
Speaker 1: and so called like digital stone maybe, But Matt, is

1478
01:22:23,040 --> 01:22:25,200
Speaker 1: that have you seen any effort. I'm sure there are

1479
01:22:25,200 --> 01:22:27,120
Speaker 1: a couple of emails where it's like, let's chat over

1480
01:22:27,160 --> 01:22:28,680
Speaker 1: the phone, or I think I've actually even seen any

1481
01:22:28,720 --> 01:22:31,360
Speaker 1: of that. But have you seen any effort as you're

1482
01:22:31,400 --> 01:22:35,640
Speaker 1: going through these thousands, tens of thousands of emails to

1483
01:22:36,120 --> 01:22:39,040
Speaker 1: move conversations to phone. Not that I'm in favor of

1484
01:22:39,400 --> 01:22:42,720
Speaker 1: less transparency, but did they really just think that this

1485
01:22:42,840 --> 01:22:45,040
Speaker 1: was kind of in a vault and was not a

1486
01:22:45,080 --> 01:22:49,799
Speaker 1: liability for them to be talking like this. They clearly

1487
01:22:49,880 --> 01:22:54,799
Speaker 1: thought that they were extremely cavalier in what they were doing.

1488
01:22:55,320 --> 01:22:57,720
Speaker 1: There are moments where they say, maybe this is more

1489
01:22:57,760 --> 01:23:01,120
Speaker 1: of a phone conversation. They also and this is one

1490
01:23:01,160 --> 01:23:03,560
Speaker 1: of the things that came out in the second threat yesterday,

1491
01:23:03,920 --> 01:23:09,120
Speaker 1: the quote unquote industry call, which is involved Twitter, Facebook,

1492
01:23:09,280 --> 01:23:11,240
Speaker 1: some other company, and a whole bunch of other companies,

1493
01:23:11,960 --> 01:23:17,320
Speaker 1: and then the DHS, FBI Director of National Intelligence, and

1494
01:23:17,360 --> 01:23:19,639
Speaker 1: then there were a bunch of other agencies that were

1495
01:23:19,720 --> 01:23:24,040
Speaker 1: sort of auditing. They were in listen mode only. All

1496
01:23:24,080 --> 01:23:28,320
Speaker 1: that was going on in signal, Which is an interesting

1497
01:23:28,400 --> 01:23:31,320
Speaker 1: question because some lawyers have raised to me the issue of,

1498
01:23:32,640 --> 01:23:37,040
Speaker 1: you know, can you really send things an encrypted fashion

1499
01:23:37,320 --> 01:23:40,280
Speaker 1: or can you send documents that are time to disappear,

1500
01:23:40,320 --> 01:23:45,000
Speaker 1: which is what happens through the FBI's teleporter program. If

1501
01:23:45,000 --> 01:23:48,519
Speaker 1: you're a government agency, you're supposed to be recording everything, right,

1502
01:23:49,760 --> 01:23:53,439
Speaker 1: So that's interesting, Like they had, I would say, on

1503
01:23:53,479 --> 01:23:57,120
Speaker 1: the whole really terrible opseec on all this stuff. But

1504
01:23:57,840 --> 01:24:02,559
Speaker 1: in some cases, you know, they're using methods that I'm

1505
01:24:02,560 --> 01:24:08,240
Speaker 1: not sure were legal if their government agency endeavors. So

1506
01:24:08,280 --> 01:24:11,559
Speaker 1: that it's both it's interesting on both fronts. Yeah, that

1507
01:24:11,600 --> 01:24:14,479
Speaker 1: happened here in DC. Government officials were communicating over signal

1508
01:24:14,520 --> 01:24:18,720
Speaker 1: and they had to stop that because it's not appropriate right. Well,

1509
01:24:18,760 --> 01:24:20,799
Speaker 1: that praises a lot of questions. I'm sorry to interrupt,

1510
01:24:20,800 --> 01:24:26,360
Speaker 1: but you had thousands and thousands of moderation requests coming

1511
01:24:26,400 --> 01:24:30,559
Speaker 1: in via signal and teleporter, and you know, I'm not

1512
01:24:30,600 --> 01:24:32,880
Speaker 1: sure how much of that is ever going to be retrieved.

1513
01:24:32,920 --> 01:24:38,559
Speaker 1: And you know, is that okay? I'm not sure? You know? Yeah?

1514
01:24:38,560 --> 01:24:41,720
Speaker 1: And so you and the other other reporters working on

1515
01:24:41,720 --> 01:24:44,759
Speaker 1: the Twitter files have been accused of quote unquote cherry picking,

1516
01:24:45,320 --> 01:24:47,840
Speaker 1: and I saw that you responded to that in one

1517
01:24:47,880 --> 01:24:50,880
Speaker 1: of your recent recent newsletters. Can you can you elaborate

1518
01:24:50,920 --> 01:24:54,960
Speaker 1: on on on your kind of response to that particular

1519
01:24:55,040 --> 01:24:57,600
Speaker 1: line of attack. First of all, I don't even know

1520
01:24:57,640 --> 01:25:00,920
Speaker 1: what that means, like as opposed to what you know,

1521
01:25:01,080 --> 01:25:07,000
Speaker 1: the the the absolutely perfect even representative sample of humanity

1522
01:25:07,040 --> 01:25:09,920
Speaker 1: that you see on other news channels, Like what are

1523
01:25:09,920 --> 01:25:14,000
Speaker 1: they talking about? Uh? And if you see an email

1524
01:25:14,479 --> 01:25:18,479
Speaker 1: in a seat of emails where it says, hey, we're

1525
01:25:18,479 --> 01:25:22,240
Speaker 1: the FBI, all the reports that come in from the

1526
01:25:22,400 --> 01:25:27,479
Speaker 1: US intelligence community, uh, and from US we'll go through

1527
01:25:27,680 --> 01:25:31,679
Speaker 1: our channels and the Homeland Security is going to handle

1528
01:25:31,720 --> 01:25:35,040
Speaker 1: all the states. You're not going to pick that cherry like,

1529
01:25:35,400 --> 01:25:37,160
Speaker 1: you know, as a reporter, I don't I don't really

1530
01:25:37,240 --> 01:25:40,320
Speaker 1: understand what they're arguing here. Yes, of course we're taking

1531
01:25:40,360 --> 01:25:44,240
Speaker 1: the interesting, interesting stuff. But if you're if you want

1532
01:25:44,280 --> 01:25:47,880
Speaker 1: to argue that there's some other emails somewhere that says, oh,

1533
01:25:47,920 --> 01:25:52,479
Speaker 1: we're not doing this, I would feel ethically obligated to

1534
01:25:52,520 --> 01:25:56,599
Speaker 1: put that in there. I haven't seen that, so that's

1535
01:25:56,600 --> 01:25:59,280
Speaker 1: why I'm feeling confident in publishing this stuff. But there

1536
01:25:59,320 --> 01:26:03,200
Speaker 1: there isn't like a contra argument that is not appearing

1537
01:26:03,240 --> 01:26:06,400
Speaker 1: in these emails. One thing that people have said, because

1538
01:26:06,439 --> 01:26:09,680
Speaker 1: I reported this initially, there were requests that came from

1539
01:26:09,720 --> 01:26:14,160
Speaker 1: the Trump administration and we're honored. I was told that

1540
01:26:14,760 --> 01:26:18,320
Speaker 1: pretty solidly by former executives, and I felt obligated to

1541
01:26:18,439 --> 01:26:21,920
Speaker 1: report it. But I haven't seen it in the email record,

1542
01:26:22,320 --> 01:26:25,120
Speaker 1: so that's why people aren't seeing it. It's just that

1543
01:26:25,200 --> 01:26:28,160
Speaker 1: I don't have it in writing. That is really interesting.

1544
01:26:28,200 --> 01:26:30,679
Speaker 1: And I really recommend folks follow your subsect to TK

1545
01:26:30,800 --> 01:26:34,160
Speaker 1: News because it's been helpful sort of flushing this out

1546
01:26:34,200 --> 01:26:36,960
Speaker 1: even more. Just great reads over there, and I hope

1547
01:26:37,000 --> 01:26:40,080
Speaker 1: you're able to get some sleep. Mat I think, thank

1548
01:26:40,120 --> 01:26:43,880
Speaker 1: you both. Of course. All right, Well, that does it

1549
01:26:43,920 --> 01:26:49,080
Speaker 1: for us on Counterpoints today Ryan. Fascinating reporting from Matt. Yeah,

1550
01:26:49,640 --> 01:26:53,120
Speaker 1: it's I hope. I wish every company would just open

1551
01:26:53,200 --> 01:26:55,800
Speaker 1: up its archives, I know, I mean and let people in.

1552
01:26:55,960 --> 01:26:58,120
Speaker 1: One of the interesting things I've seen is that elone

1553
01:26:58,120 --> 01:27:02,080
Speaker 1: should have just dumped all of this wikily style and

1554
01:27:02,400 --> 01:27:04,880
Speaker 1: you know that. I mean, yes, we would all love

1555
01:27:04,920 --> 01:27:08,920
Speaker 1: that from every major corporation, especially publicly traded ones. But

1556
01:27:09,080 --> 01:27:10,800
Speaker 1: it's been I mean, it gives me a lot of

1557
01:27:10,840 --> 01:27:13,840
Speaker 1: confidence when you have Tayibi and Lee Fong going through

1558
01:27:13,840 --> 01:27:16,600
Speaker 1: all of this, and really, even though you can have

1559
01:27:16,640 --> 01:27:19,280
Speaker 1: these accusations of cherry picking flying back and forth, you

1560
01:27:19,320 --> 01:27:22,479
Speaker 1: can tell based on what's included that it's it's a

1561
01:27:22,520 --> 01:27:24,960
Speaker 1: good faith effort to really pull this back and pull

1562
01:27:25,000 --> 01:27:27,800
Speaker 1: back the curtain on what's happened at Twitter. Yeah. And

1563
01:27:27,960 --> 01:27:31,240
Speaker 1: like I said, it's if and we had people should

1564
01:27:31,280 --> 01:27:34,599
Speaker 1: check out our interview with Lee because he talked about

1565
01:27:34,640 --> 01:27:37,320
Speaker 1: the process that you go through in order to do

1566
01:27:37,360 --> 01:27:40,439
Speaker 1: the reporting. Basically, there's there's a lawyer who a Twitter

1567
01:27:40,520 --> 01:27:45,640
Speaker 1: lawyer who is designated to work with the reporters, and

1568
01:27:44,960 --> 01:27:48,519
Speaker 1: the reporters then Lee would or Matt would send a

1569
01:27:48,560 --> 01:27:51,559
Speaker 1: request like I would like everything that matches the keyword

1570
01:27:51,640 --> 01:27:55,919
Speaker 1: of this and then and it comes back from the attorney,

1571
01:27:55,920 --> 01:28:01,240
Speaker 1: which so it's not the ideal process. There's no you

1572
01:28:01,240 --> 01:28:04,599
Speaker 1: can imagine why a lawyer at a company would say,

1573
01:28:04,920 --> 01:28:08,760
Speaker 1: we're not just going to allow a keyword search, and

1574
01:28:09,000 --> 01:28:14,519
Speaker 1: because there are there are privacy laws around different personnel stuff.

1575
01:28:15,040 --> 01:28:19,799
Speaker 1: And also that's not what they're necessarily looking for anyway,

1576
01:28:20,240 --> 01:28:25,040
Speaker 1: not trying to get like into like people's like performance

1577
01:28:25,080 --> 01:28:28,240
Speaker 1: reviews or other like you know, super personal stuff. They

1578
01:28:28,240 --> 01:28:31,320
Speaker 1: want the they want the exchanges that are of public importance.

1579
01:28:32,040 --> 01:28:34,880
Speaker 1: So yeah, it wouldn't it's not it's not perfect. You'd

1580
01:28:34,960 --> 01:28:39,280
Speaker 1: rather have journalists have complete and unfettered access, but that's

1581
01:28:39,280 --> 01:28:42,880
Speaker 1: that just doesn't exist absent a kind of a hack

1582
01:28:43,240 --> 01:28:46,400
Speaker 1: or a leak like Edward Snowden style type of type

1583
01:28:46,400 --> 01:28:49,600
Speaker 1: of dump. And even with those you know, news organizations

1584
01:28:49,600 --> 01:28:53,559
Speaker 1: didn't didn't dump everything that Edward Snowden obtained. Yeah, and

1585
01:28:53,600 --> 01:28:55,479
Speaker 1: it's fascinating to watch the Washington Post you and call

1586
01:28:55,520 --> 01:28:58,400
Speaker 1: people like Matt conservative or yeah, I mean it's just

1587
01:28:58,400 --> 01:29:00,599
Speaker 1: like dismissing change that did you see they did? Yeah,

1588
01:29:00,600 --> 01:29:02,559
Speaker 1: they corrected it. It was like a little insult. Yeah,

1589
01:29:02,560 --> 01:29:04,720
Speaker 1: how do you like that that's an insult. Yeah, I mean, hey,

1590
01:29:04,760 --> 01:29:07,080
Speaker 1: it's been an insult in the corporate press for a

1591
01:29:07,080 --> 01:29:11,080
Speaker 1: long time. But it's just their it's their cope and

1592
01:29:11,120 --> 01:29:15,000
Speaker 1: it's their scapegoat. And but but it shows you that

1593
01:29:15,040 --> 01:29:19,840
Speaker 1: they don't want to give the credibility to genuine left

1594
01:29:19,880 --> 01:29:23,200
Speaker 1: wing journalists that are doing this reporting because they want

1595
01:29:23,240 --> 01:29:25,719
Speaker 1: to somehow excuse it as being biased or cherry picked,

1596
01:29:25,960 --> 01:29:29,320
Speaker 1: when in fact, again Matt and we have absolutely no

1597
01:29:29,360 --> 01:29:33,519
Speaker 1: incentive to care water for rebuttlicans or for the right period.

1598
01:29:34,240 --> 01:29:36,680
Speaker 1: But it's easy to just sort of offhand do the

1599
01:29:36,760 --> 01:29:39,240
Speaker 1: sort of yeah, they're just carrying water for the right thing.

1600
01:29:40,120 --> 01:29:42,880
Speaker 1: It's not reality. But that's the good news here is

1601
01:29:42,880 --> 01:29:45,240
Speaker 1: that lots of people now can see pretty clearly what

1602
01:29:45,280 --> 01:29:48,479
Speaker 1: was happening on the inside. Also, I just noticed, speaking

1603
01:29:48,520 --> 01:29:52,240
Speaker 1: of seeing things pretty clearly, that Counterpoints is now Counterpoints.

1604
01:29:52,280 --> 01:29:56,160
Speaker 1: Look at that nice clean yes logo. You know, well,

1605
01:29:56,280 --> 01:29:58,840
Speaker 1: we will probably be with you, not just on on

1606
01:29:58,960 --> 01:30:02,080
Speaker 1: Fridays in the future. Stay tuned for more information on that.

1607
01:30:02,160 --> 01:30:05,840
Speaker 1: We're excited to keep going. Yep. And so that does

1608
01:30:05,840 --> 01:30:08,760
Speaker 1: it for Counterpoints Wednesday. Now everybody can head on over

1609
01:30:08,760 --> 01:30:10,200
Speaker 1: to c Span, where you can binge that for the

1610
01:30:10,200 --> 01:30:14,080
Speaker 1: next day. They're about to start the fourth vote for speaker,

1611
01:30:14,080 --> 01:30:17,080
Speaker 1: which will probably go about as well as the last did,

1612
01:30:17,200 --> 01:30:21,639
Speaker 1: but we'll see. It's going to get interesting. See us soon.