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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: 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. Good morning, everybody, Happy Tuesday. We have

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Speaker 1: an amazing show for everybody today. What do we have, Crystal?

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Speaker 1: Indeed we do, Boy oh boy, do we have a

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Speaker 1: perfect story for you about the deep state and the FBI.

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Speaker 1: One of the people who was charged with investigating Trump

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Speaker 1: over Russian collusion has now himself been indicted for Russian collusion,

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Speaker 1: so we've got all those details for amazing story. We

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Speaker 1: also have a new chief of staff in coming to

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Speaker 1: the White House and he actually kind of sucks, so

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Speaker 1: we'll break those details down for you. We also have

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Speaker 1: a big deal being done between open ai Chat, Open

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Speaker 1: AI's Chat, GBT and Microsoft, so what is that going

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Speaker 1: to mean for the future of that We also have

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Speaker 1: a challenger to Kuirson Cinema in the Arizona Senate race,

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Speaker 1: Reuben Diego. He's a member of Congress right now, this

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Speaker 1: was expected, but he officially announced yesterday put out a

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Speaker 1: video that I think was pretty solid. So we'll break

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Speaker 1: all of that down for you. And we are tracking

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Speaker 1: rumors that Jeff Bezos may be selling the Washington Post

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Speaker 1: and buying the Washington Commanders. Interesting now right now he's

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Speaker 1: saying no, but the rumors are flying. As I said,

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Speaker 1: Sager is taking a look at the history of federal debt.

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Speaker 1: I am taking a look at JP Morgan Chase admitting

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Speaker 1: they were scammed for one hundred and seventy five million

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Speaker 1: dollars by basically like a fake company. Amazing story. We

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Speaker 1: also have Derek Thompson back to talk about the Mass

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Speaker 1: Tech layoffs. But we did want to start with this

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Speaker 1: FBI story, which is just incredible. It sounds like it

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Speaker 1: was honestly made up. Let's put the Fox News hair

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Speaker 1: sheet up on the screen here. So two indictments released

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Speaker 1: yesterday regarding this top FBI counterintelligence agent guy by the

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Speaker 1: name of Charles McGonagall, former Special Agent in charge of

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Speaker 1: FBI's Counterintelligence Division in New York. He retired back in

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Speaker 1: twenty eighteen. While he was at the FBI, he spent

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Speaker 1: a lot of time supervising and participating in investigations of

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Speaker 1: Russian oligarchs, including a guy you might remember from Russiagate

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Speaker 1: by the name of Oleg dere Paska Derek Poskat a

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Speaker 1: relationship with Paul Manniford. This is part of how the

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Speaker 1: whole Russiagate investigation gets kicked off. Charles McGonagall is there

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Speaker 1: at the very beginning, at the origins of the Russiagate investigation,

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Speaker 1: and lo and behold McGonagall after he leaves the FBI,

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Speaker 1: gets caught, is accused by the government. Of course, I'm

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Speaker 1: sure proclaims his innocence, but what the government is saying

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Speaker 1: here is that he was taking money rum olig Dereapasca,

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Speaker 1: which is illegal because he is a sanctioned individual and

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Speaker 1: has been for a number of years now, both to

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Speaker 1: help him try to get off of that sanctions list

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Speaker 1: of process they call delisting, and also to investigate another

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Speaker 1: rival oligarch on his behalf, apparently Deripaska and this other

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Speaker 1: oligarch that they don't name in the indictment we're in

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Speaker 1: some sort of dispute over control of some sort of

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Speaker 1: Russian company. So our FBF former FBI dude was being

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Speaker 1: paid a lot of money through a bunch of shell

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Speaker 1: companies and using fake names and fake email addresses to

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Speaker 1: try to hide his tracks in order to investigate this

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Speaker 1: other oligarch, to see if he had hidden assets abroad,

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Speaker 1: if he had a different non Russian passport, and to

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Speaker 1: see what was going on with this like fight that

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Speaker 1: they were involved in for this Russian company. So truly,

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Speaker 1: truly incredible. Let me give you a few of the

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Speaker 1: specific details about mcgonagall's involvement with the Trump Russiagate investigation,

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Speaker 1: because this seems important. Let's put this next piece up

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Speaker 1: on the screen. Here from the Washington Free Beacon, they say,

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Speaker 1: plot twist. Ex FBI agent involved in Trump Russia probe

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Speaker 1: indicted for violating Russia sanctions. Charles McGonagall is accused of

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Speaker 1: conspired to live sanctions off Russian businessman with ties to

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Speaker 1: Vladimir Putin. So he was one of the first individuals

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Speaker 1: that FBI, they say, to learn that a Trump campaign advisor,

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Speaker 1: I think that was George Papadopolis had discussed Hillary Clinton's

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Speaker 1: emails with a foreign diplomat that helped to cause the

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Speaker 1: FBI to open its investigation of the Trump campaign based

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Speaker 1: on that conversation. Later found no evidence of collusion between

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Speaker 1: the Trump campaign and Russia. He also when he left

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Speaker 1: the FBI in twenty eighteen, he was involved in the

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Speaker 1: bureau's probe of Trump campaign advisor Carter Page, according to

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Speaker 1: text messages that were released by Senate Republicans, and they

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Speaker 1: go on to say, it's unclear what other involvement McGonagall

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Speaker 1: had in the Trump Russia probe, and just let's go

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Speaker 1: out and put the press release here from the Southern

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Speaker 1: District of New York where they tout this indictment. And

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Speaker 1: then last night there was actally a second indictment that

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Speaker 1: was filed. This one was in Southern District of New York,

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Speaker 1: this first one regarding Derek Paska, but the second one

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Speaker 1: was filed in DC and had to do with while

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Speaker 1: he was still in the FBI, taking about two hundred

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Speaker 1: and fifty thousand dollars in cash from a former Albanian spy.

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Speaker 1: And you know, I think he had set upshell companies

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Speaker 1: to do this sort of work as well. So dude,

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Speaker 1: was corp while I was in the FBI corrupt. When

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Speaker 1: he was out of the FBI, he was actually involved

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Speaker 1: given a heads up of which individuals will be on

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Speaker 1: the sanction list. So he certainly can't plead innocence here.

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Speaker 1: And reportedly a lot of people who are high up

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Speaker 1: in the FBI really freaked out about this because of

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Speaker 1: the high level of sensitive information this man had access

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Speaker 1: to while he was at the bureau. Look, he can

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Speaker 1: contest his innocence. Yeah, that's as right as an American

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Speaker 1: citizen that said, you read this indictment. I mean they

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Speaker 1: pretty much have him dead to run. Yes, Like he

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Speaker 1: was creating new Jersey based shell companies while he was

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Speaker 1: specially aged in charge of investigating these Russian oligarchs, specifically

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Speaker 1: to create a money laundering scheme through which Deripaska and

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Speaker 1: this Albanian official could fundy money to funnel money to

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Speaker 1: him personally. At one point, Deripasco was paying him some

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Speaker 1: forty two thousand dollars per month through this new Jersey

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Speaker 1: based shell company. So he's guilty or appears to be

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Speaker 1: guilty of not only money laundering but straight up violating

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Speaker 1: US sanctions to the tune of potentially hundreds and hundreds

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Speaker 1: of thousands of dollars, and when you read it, what

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Speaker 1: comes through we're talking here not even just about a

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Speaker 1: run of the mill FBI agent. He was a special

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Speaker 1: agent in charge charged with our nation's highest, most sensitive

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Speaker 1: secrets whenever it comes to Russian oligarchs, who literally was

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Speaker 1: working for Russian oligarchs. What's also crazy, crystal in the

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Speaker 1: indictment is he went to great lengths to appease some

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Speaker 1: of these Russian spies who he supposedly was supposed to

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Speaker 1: be investigating. You know, one of them was an official.

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Speaker 1: He got his daughter a internship at the NYPD in

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Speaker 1: his International Liaisons office, and the NYPD was kind of

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Speaker 1: sketched out, even though they gave her VIP treatment, saying

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Speaker 1: that the girl was bragging about how she had connections

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Speaker 1: to FBI agents and had seen some very top secret

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Speaker 1: intelligence like what the daughter of a Russian spy working

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Speaker 1: at the NYPD, who says and told open in their

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Speaker 1: counterintellig Division in the counter Intelligence Division of the New

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Speaker 1: York City Police Department. So we have that, I mean,

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Speaker 1: the secondary part though the scheme, in terms of being

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Speaker 1: hired by Darrek Pasca and no, So let's let's be

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Speaker 1: clear here. Like first he was working at least for

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Speaker 1: a cutout law firm where his checks were coming from.

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Speaker 1: Eventually he just started straight up working for the guy.

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Speaker 1: Also in terms of how they refer to him in emails,

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Speaker 1: it's so funny. It's like out of a movie. They're

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Speaker 1: like the big guy, you know who, the rich Russian guy.

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Speaker 1: It's like, yeah, I wonder, I wonder who you're talking about.

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Speaker 1: He flew to Vienna to go or to London and

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Speaker 1: to Vienna just to go meet with him, Derrek Paska

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Speaker 1: at his house. There. Straight corruption, outrageous, and here's a

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Speaker 1: question how many more people they got in these things?

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Speaker 1: I mean, it's funny too, because you know, I guess

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Speaker 1: it's probably par for the course in Russia, you bribe

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Speaker 1: the security States to investigate your rival business opponents. But

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Speaker 1: it was like, now this is coming here to New York,

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Speaker 1: to the top division of the FBI, and you can't ignore.

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Speaker 1: As my friend Chuck Ross wrote in The Free Beacon

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Speaker 1: that he was intimately involved in the Trump Russia investigation

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Speaker 1: in the first place. So it seems that the person

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Speaker 1: most guilty of Russian collusion is one of the FBI

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Speaker 1: agents investigating Russian who appears to have helped to spark, yes,

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Speaker 1: the Russiagate investigation. I mean again, we really don't have

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Speaker 1: a lot of insight into how directly he was involved

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Speaker 1: throughout the entirety of the Russiagate investigation, but he was

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Speaker 1: definitely there at the beginning. He was the person who

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Speaker 1: initially reported those conversations between with Papadopoulos and that helped

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Speaker 1: to spark this whole thing, and was involved in the

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Speaker 1: investigation of Carter Page, which you know, was a shameful

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Speaker 1: episode in and of itself. So it's just it could

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Speaker 1: not make it up. You could not make it up.

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Speaker 1: And I do find the details here extremely fascinating. It

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Speaker 1: wasn't just McGonagall that was indicted. There was another dude,

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Speaker 1: Sergei Shstikov, a former Soviet and Russian diplomat who later

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Speaker 1: became US citizen Russian interpreter for courts and government offices.

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Speaker 1: He was apparently mcgonagall's sort of partner in all of this,

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Speaker 1: and he's charged with these same crimes as McGonagall with

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Speaker 1: regards to Derek Paska, and you know, violating the sanctions

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Speaker 1: that were levied on Derea Pasca after Russia's invasion of

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Speaker 1: Ukraine during it was the Obama administration that initially listed

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Speaker 1: Deripaska as a sanctioned individual, So he's charged with those

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Speaker 1: same things as well. But the day before the FBI

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Speaker 1: came and I don't know arrested them or rated them

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Speaker 1: or whatever, this other associate Sheskita Shstakov sat with FBI

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Speaker 1: agents and totally lied to Oh, no, we don't do

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Speaker 1: any business, no, not now Whatso I barely know Charles McGonagall.

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Speaker 1: Of course we're not in business together. So he also

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Speaker 1: is charged with lying to the FEDS. So look, that's

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Speaker 1: the longest short of it. Again, the details here are fascinating,

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Speaker 1: and it does raise the question of like how common

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Speaker 1: is this? You know, it's so brazen that you thought

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Speaker 1: you were going to get away with this for and

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Speaker 1: hundreds of thousands of dollars, especially given the all the

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Speaker 1: scrutiny that's on Russian oligarchs right now. Derek Paska is

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Speaker 1: an interesting figure in his own right. He became an

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Speaker 1: oligarch by winning what was called the Aluminum Wars back

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Speaker 1: right after the dissolution of the Soviet Union, so he's

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Speaker 1: able to gain control of this incredibly valuable resource. So

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Speaker 1: he's a big industrialist, incredibly wealthy man, ends up being

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Speaker 1: one of the wealthiest men in the world. It was

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Speaker 1: also reportedly one of the closest advisors, like really tight

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Speaker 1: buddies with Putin. Since this Ukraine invasion has happened, though,

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Speaker 1: he has made some critical comments on telegram, calling it

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Speaker 1: a war, what you're not supposed to do, calling for peace,

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Speaker 1: et cetera, et cetera. So he's you know, he's kind

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Speaker 1: of a fascinating figure in his own right, but just

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Speaker 1: an extraordinary turn of events here. Yeah, well he's probably

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Speaker 1: saying that because all of his yachts and houses and yeah,

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Speaker 1: a god, what they really live for are the London

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Speaker 1: houses and the houses here. Yeah, it's right, I mean

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Speaker 1: in New York. Also, he's a famous doyen apparently of Davos.

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Speaker 1: He used to pair allegedly used to throw some of

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Speaker 1: the best parties in Davos with Dom perry On. Yeah,

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Speaker 1: so he's sad he couldn't make it to Davos this year,

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Speaker 1: were banned. You got to feel for the rough forum,

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Speaker 1: you know there for an oligar, it's really rough out

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Speaker 1: there to be a multi billionaire. But here's what I

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Speaker 1: take away. There is no such thing as former FBI

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Speaker 1: and former CIA. You may not have a badge anymore.

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Speaker 1: If anything, it is worse because he used the badge

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Speaker 1: effectively to sell to the highest bidder. How many other people,

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Speaker 1: former FBI agents, former CIA, maybe some that you see

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Speaker 1: on TV, are on the take to the people that

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Speaker 1: they were supposed to investigating him. It's a disgusting practice that. Look,

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Speaker 1: the only reason we know about this is because Oleg

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Speaker 1: Deripaska is on the sanctions list, and otherwise none of

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Speaker 1: this would be illegal. Guy, he probably would have gotten

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Speaker 1: away with the Albanian cash grab, the two hundred and

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Speaker 1: fifty K he got for this, like Albanians brug probably

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Speaker 1: would have gotten away with that. The only reasons because

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Speaker 1: Oleg is on the sanctions list. How many people in

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Speaker 1: this town within walking distance of where we're shooting this

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Speaker 1: show are on the take to the Kingdom of Saudi Arabia,

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Speaker 1: to Dubai, Oman, Brunei, Thailand, China. I mean, look, those

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Speaker 1: people may not be sanctions, but in many ways they

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Speaker 1: are much richer than Oleg Derripaska. And there's an army,

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Speaker 1: a cadre of former spooks officials and law enforcement officials

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Speaker 1: who are all on the take for them. So just

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Speaker 1: think like this is the one that we know about.

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Speaker 1: It's just the tip of the eyes. So the richest

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Speaker 1: people in this town are the ones who are willing

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Speaker 1: to do the skimmiest work that came out from Manafort.

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Speaker 1: Remember Manafort's pulling twenty five million dollars wasn't just Russians.

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Speaker 1: He was working for his work, the highest spider anybody.

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Speaker 1: So we could buy all these Persian books other like

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Speaker 1: out it was like an Ostrich jacket or whatever. One

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Speaker 1: of my proudest pieces that I wrote at the Daily

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Speaker 1: Caller was interview with Roger Stone, specifically only about Paul

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Speaker 1: Maniford's wardrobe. Fashion Yeah, Rogers fashion critic hated he thought

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Speaker 1: it was gross, even though they were friends in former

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Speaker 1: former business colleagues. He was like, this is quite possibly

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Speaker 1: the worst wardrobe I've ever seen, So, yeah, it's a

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Speaker 1: fun piece. Wish we could have Roger Stone critique Kirsten

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Speaker 1: Cinema's outfit at Dovos this year, dressed as a sheep,

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Speaker 1: might be interested to know what his thoughts were. She

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Speaker 1: literally looked like Corella devellop there. It was amazing. We'll

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Speaker 1: show you later. We got that later in the show.

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Speaker 1: I will save my fashion content for later in the show.

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Speaker 1: Let's go ahead to the next piece here, all right,

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Speaker 1: the next chief of staff at the White House. We

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Speaker 1: now know who that person is going to be. Let's

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Speaker 1: go and put this up there on the screen. Biden

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Speaker 1: is going to tap Jeff Zeines as his next White

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Speaker 1: House Chief is staff to replace ron Klain, who's been

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Speaker 1: in charge of the White House now for two years.

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Speaker 1: Claan apparently, I mean it's normal in terms of the

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Speaker 1: life cycle of chief of staff wants a step down

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Speaker 1: sometime after the State of the Union. By the way,

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Speaker 1: we will have special coverage for that here on the show.

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Speaker 1: And what does that mean? Zenz has worked in the

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Speaker 1: White House both as a deputy chief of Staff, but

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Speaker 1: really made his bones and got Biden's trust by working

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Speaker 1: as the COVID czar while he was there, so coordinating

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Speaker 1: the vaccine response, policy response and some of the American

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Speaker 1: rescue plan in the early days. Now, why does any

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Speaker 1: of that matter. It's not just about his work product.

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Speaker 1: It's actually about who he worked for before he was

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Speaker 1: in the White House and how exactly. He leveraged his

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Speaker 1: position from the Obama administration to then become a very,

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Speaker 1: very wealthy man before he went to work for Biden.

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Speaker 1: Let's put this up there on the screen. The incoming

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Speaker 1: chief of Staff in twenty twenty one was worth eighty

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Speaker 1: nine point three million dollars according to his own disclosure,

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Speaker 1: and built his wealth through healthcare companies that were forced

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Speaker 1: to pay tens of millions to settle allegations of Medicare

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Speaker 1: and Medicaid fraud. For those who've been watching us for

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Speaker 1: quite some time, this is not and this is not

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Speaker 1: a news story. This is somebody who we focused on

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Speaker 1: a lot in the early days of the Biden administration,

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Speaker 1: specifically when he was tapping aids. Specifically, remember this report.

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Speaker 1: Let's put it up there on the screen from December

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Speaker 1: of twenty twenty. It's kind of a mini biography of

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Speaker 1: what he did. I mean, this is a person who

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Speaker 1: was working for the Obama administration, worked in the economic departments,

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Speaker 1: and was a key advisor who leaves the White House

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Speaker 1: with key knowledge and with connections and leverages it crystal

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Speaker 1: into private equity behemoth, working in some cases at Bain Capital.

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Speaker 1: The former Mitt Romney firm, which you can't even make

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Speaker 1: that up. That Obama runs against Romney, bashes him and

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Speaker 1: really wins on a message of this person would bankrupt

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Speaker 1: you the way he bankrupted all these companies. And then

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Speaker 1: one of his top deputies leaves and goes works for

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Speaker 1: in terms of private equity and then starts doing like

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Speaker 1: leveraged activity whenever it comes to the healthcare yeactor, it's

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Speaker 1: as scummy as a guest, and no one in liberal

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Speaker 1: media bats an eye. No nobody even noticed, like, hey,

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Speaker 1: this guy worked at Baden Capital. Weren't you just saying

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Speaker 1: that the work they were doing was evil? And like

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Speaker 1: the American people actually really agreed with you on that.

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Speaker 1: Nobody really said a word about that. I mean, it

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Speaker 1: does represent somewhat of an evolution I think post Obama era,

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Speaker 1: when there was very little room on the left of

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Speaker 1: the spectrum to criticize anything that was happening within a

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Speaker 1: democratic administration. At least now you have a little bit

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Speaker 1: of a rump caucus that will say, hey, maybe don't

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Speaker 1: bring in the dude that got five millions of dollars

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Speaker 1: and made a bunch of money off of like defrauding medicare.

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Speaker 1: Tiny A few little news items will be there at

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Speaker 1: least a few lonely voices will say something about it

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Speaker 1: at least But not only does he have that ugly

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Speaker 1: past in terms of making tens of millions of dollars

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Speaker 1: off of effectively healthcare fraud, he also was one of

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Speaker 1: the Obama administration's chief liaisons to executives and lobbyists when

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Speaker 1: anger at Wall Street over the seven A crisis was

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Speaker 1: at its peak. Top lobbyists such as the Business Roundtable

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Speaker 1: and the US Chamber of Commerce have praised mister Science

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Speaker 1: as someone who heard them mount. So that's who we're

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Speaker 1: bringing in now as Biden's chief of staff, someone who

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Speaker 1: probably belongs in prison given the level of fraud that

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Speaker 1: he is accused of year in which he had his

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Speaker 1: company's affiliated companies had to pay fines for and instead

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Speaker 1: being elevated to White House chief of staff, one of

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Speaker 1: the most powerful positions in the entire country. Yeah, here's

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Speaker 1: an interesting one. He also owns five million dollars in

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Speaker 1: gold bars and twenty five million dollars in a security

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Speaker 1: that's actually tied to the value of gold, somewhat ironic

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Speaker 1: because that's obviously an inflation hedge, and he is actually

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Speaker 1: now going to be Joe Biden's literal chief of staff.

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Speaker 1: Absolutely kind of hilarious. So anyway, why does this matter

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Speaker 1: Exactly because this person is now going to be the

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Speaker 1: most powerful person in the White House. And let's also

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Speaker 1: not forget this Biden. He's an old man to the

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Speaker 1: extent that he controls his calendar, legislation, meetings, and all

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Speaker 1: of that, the actual day to day operations of the

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Speaker 1: White House. Who knows and if anything, you know, chiefs

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Speaker 1: of staff have been incredibly powerful under Trump, who was

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Speaker 1: also a disinterested president, and now under Biden as well.

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Speaker 1: I mean, Claine was a top negotiator for a lot

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Speaker 1: of the bills that were happening. He basically controls the

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Speaker 1: president's schedule, which events and all these things go. He

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Speaker 1: really can't underestimate the power of these bureaucratic positions, and

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Speaker 1: now this person is going to hold that. And Christal,

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Speaker 1: as you were saying, Jeff Science named in Politico, let's

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Speaker 1: put this up there on the screen. Do you want

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Speaker 1: to read your favorite quote from Yeah, well, I love

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Speaker 1: the concerns that they raised here in this article about science. Now,

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Speaker 1: to be fair, they do have a quote from the

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Speaker 1: Revolving door project expressing concerns over his corporate background, but

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Speaker 1: they also end with this zience selection is also likely

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Speaker 1: to disappoint some Democrats who saw Clan's up exit as

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Speaker 1: a prime opportunity for buying to appoint a woman person

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Speaker 1: of color as his top aid. So leave it to

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Speaker 1: Politico to make the most surface level critique and observation

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Speaker 1: and not go an inch deeper than that level of analysis.

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Speaker 1: So there you go. Just couldn't resist putting that one in.

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Speaker 1: It's just like, come on, like, are we really being

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Speaker 1: serious here about what's happening? Like this guy, any objection

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Speaker 1: to him, it should not have to do. I don't

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Speaker 1: even care. You know, if it could be a black woman,

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Speaker 1: if they were also a former Bane consultant, and you

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Speaker 1: know at least ran a company that was guilty of

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Speaker 1: medicare fraud again that they paid fines and fully acknowledged

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Speaker 1: what they were doing, I think that would be the

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Speaker 1: same criticism hundred percent. I mean, to be sure to

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Speaker 1: political they're actually right that this would be the critique

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Speaker 1: that some Democrats wh level. Whereas if it was you know,

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Speaker 1: a minority person or some sort of like trailblazing figure

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Speaker 1: who had this exact same terrible track record. They'd be Oh,

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Speaker 1: this is amazing, this is a step forward, most diverse

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Speaker 1: administration history, whatever, and it's like, okay, that's good. But

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Speaker 1: what would be more important is to have someone who's

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Speaker 1: going to be committed to you know, the common person

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Speaker 1: in America and not just cozy cozy with business and

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Speaker 1: doesn't have a track record coming in of basically defrauding

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Speaker 1: the government. So that's what we're looking at. And Sager

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Speaker 1: to your point about how important this position is under

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Speaker 1: any presidency. It matters a lot what ends up in

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Speaker 1: front of the president, and it especially matters in a

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Speaker 1: presidency where you have a declining eighty year old man

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Speaker 1: who is really dependent on his staff to know what

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Speaker 1: is going on and what's important and to frame issues

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Speaker 1: for him. And so this individual, who has again a

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Speaker 1: very troubling, very closely tied to both Corporate America and

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Speaker 1: Wall Street in his past, is being brought into a

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Speaker 1: position of extraordinary power. Absolutely correct. All right, let's go

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Speaker 1: on chat GPT. Even more stuff that's going on there.

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Speaker 1: We'll talk about tech recessions with Derek Thompson a little

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Speaker 1: bit later. But a big business announcement of which there's

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Speaker 1: actually a lot to say. Let's put this up there

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Speaker 1: on the screen. Microsoft is announcing a ten billion dollar

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Speaker 1: investment into open Ai, specifically to fund the chat GPT platform,

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Speaker 1: and it's a partnership where they want to quote accelerate

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Speaker 1: breakthroughs in AI and help both companies commercialize the advanced technologies.

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Speaker 1: So this is the real test for what is the

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Speaker 1: future of chat GPT, And actually I think it goes

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Speaker 1: to a lot of questions around the Internet. So right

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Speaker 1: now it's twenty twenty three, we came off the heels

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Speaker 1: of a lot of hype around Web three and like

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Speaker 1: what that would look like, crypto ethereum, et cetera. But

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Speaker 1: fundamentally it came from this. There was a frustration that

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Speaker 1: the promises of Web two, of the Facebook, Google and

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Speaker 1: all that they became centralized, felt like innovation was stifling

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Speaker 1: and it didn't feel like using the Internet was fun

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Speaker 1: or cool anymore. I haven't seen that level of enthusiasm

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Speaker 1: around technology and specifically online then basically until now with

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Speaker 1: chat GPT. But then, just like with those questions of

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Speaker 1: what we should have asked with Facebook and with Google

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Speaker 1: about centralized monopolized control over ad space around the tension

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Speaker 1: and commodities. We got to ask the same questions too

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Speaker 1: around AI and what exactly the quote unquote uses for

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Speaker 1: this are going to be. Are these going to be

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Speaker 1: open source? You know? Open ai says that they want

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Speaker 1: to have that mission, but Microsoft clearly is eyeing this

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Speaker 1: for what do they do best? Enterprise business control? And

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Speaker 1: there's a lot Look, there's some cool things that could

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Speaker 1: come out of it, right, maybe you're using your Microsoft

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Speaker 1: Teams and it will draft a response for you to

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Speaker 1: an annoying email or in a chat. That sounds cool,

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Speaker 1: But what if they also use it to automate somebody's job,

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Speaker 1: or what if they also use it to, let's say,

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Speaker 1: like predict exactly how many emails you should be sending

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Speaker 1: in a day and then benchmarking and then stacking everybody

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Speaker 1: up against each other. Like there are all kinds of

429
00:23:08,320 --> 00:23:11,880
Speaker 1: dystopian corporate ways that you could push these things as

430
00:23:11,920 --> 00:23:15,720
Speaker 1: well as what the innovations could be. And clearly that's

431
00:23:15,720 --> 00:23:18,160
Speaker 1: where they see the business opportunity. Yeah, that's why they're

432
00:23:18,200 --> 00:23:22,000
Speaker 1: investing ten billion dollars into this. Yes, so open ai

433
00:23:22,119 --> 00:23:25,520
Speaker 1: they built this thing, but they really they aren't really

434
00:23:25,560 --> 00:23:28,960
Speaker 1: in the business of figuring out how to make it profitable,

435
00:23:29,280 --> 00:23:32,560
Speaker 1: it make money, and so they've basically partnered with Microsoft

436
00:23:32,600 --> 00:23:35,000
Speaker 1: so that Microsoft can figure out how this thing will

437
00:23:35,000 --> 00:23:38,159
Speaker 1: make money for them. And you know, that always is

438
00:23:38,440 --> 00:23:41,600
Speaker 1: a direction that starts to take me nervous, because what

439
00:23:41,720 --> 00:23:44,480
Speaker 1: is going to make money for a gigantic corporation, a

440
00:23:44,520 --> 00:23:48,879
Speaker 1: gigantic monopoly like Microsoft, is not necessarily what is in

441
00:23:48,920 --> 00:23:53,040
Speaker 1: the best interest of society. I am not wise enough

442
00:23:53,040 --> 00:23:56,520
Speaker 1: to predict what all of those applications could ultimately be.

443
00:23:57,080 --> 00:23:59,080
Speaker 1: I will just say it makes me wary. I mean,

444
00:23:59,160 --> 00:24:03,720
Speaker 1: some of the things that have been predicted or I

445
00:24:03,720 --> 00:24:06,280
Speaker 1: guess are in the sort of rumor mill. Are like

446
00:24:06,600 --> 00:24:09,479
Speaker 1: including chat GPT as part of the office Suite. Yes,

447
00:24:09,720 --> 00:24:12,560
Speaker 1: so when you're like writing or microsoftware document, you might

448
00:24:12,600 --> 00:24:15,520
Speaker 1: have access to the chat GPT tools right there where

449
00:24:15,560 --> 00:24:17,840
Speaker 1: you can help use it to write whatever you're writing,

450
00:24:18,200 --> 00:24:21,480
Speaker 1: or ask questions or learn from it. They also might

451
00:24:21,480 --> 00:24:25,680
Speaker 1: incorporate it into their search tool to help sort of

452
00:24:25,720 --> 00:24:28,480
Speaker 1: like you know, garner better results or more useful answers

453
00:24:28,480 --> 00:24:30,960
Speaker 1: to questions that people are asking there. So those are

454
00:24:30,960 --> 00:24:34,040
Speaker 1: some of the obvious directions which are not from the

455
00:24:34,160 --> 00:24:37,480
Speaker 1: jump deeply troubling We've talked about some of the concerns

456
00:24:37,480 --> 00:24:42,240
Speaker 1: which I think are justified among academics about like students cheating. Ultimately,

457
00:24:42,320 --> 00:24:43,879
Speaker 1: I think that ship is sale, though you're just going

458
00:24:43,920 --> 00:24:46,080
Speaker 1: to have to grapple with this as a totally new world.

459
00:24:46,520 --> 00:24:50,600
Speaker 1: And part of why there is a real discomfort with

460
00:24:50,720 --> 00:24:54,720
Speaker 1: chat GPT, which again I think is understandable, is the

461
00:24:54,800 --> 00:24:58,560
Speaker 1: fact that now automation is coming for knowledge worker and

462
00:24:58,640 --> 00:25:03,440
Speaker 1: even creative type people like journalists for example, won't be

463
00:25:03,520 --> 00:25:06,840
Speaker 1: needed in as large quantities if they get this working

464
00:25:06,920 --> 00:25:10,800
Speaker 1: really well where they can actually spit out articles or

465
00:25:10,840 --> 00:25:15,119
Speaker 1: explainers that are accurate and useful to the public. I

466
00:25:15,119 --> 00:25:16,760
Speaker 1: will say that just this morning, I sent you an

467
00:25:16,840 --> 00:25:20,280
Speaker 1: article what was it the Verge that was experimenting already

468
00:25:20,480 --> 00:25:23,360
Speaker 1: with using chat GPT to create a lot of these

469
00:25:23,400 --> 00:25:26,240
Speaker 1: like explainers and basic articles, and they had to stop

470
00:25:26,280 --> 00:25:29,639
Speaker 1: the project because a number of the articles contain significant

471
00:25:29,680 --> 00:25:32,520
Speaker 1: factual errors. And they did disclose that they were written

472
00:25:32,560 --> 00:25:35,280
Speaker 1: by chat GPT, but apparently some of the you know,

473
00:25:35,320 --> 00:25:38,199
Speaker 1: some of the journalists at the Verge felt like it

474
00:25:38,320 --> 00:25:41,920
Speaker 1: wasn't disclosed sufficiently, and so and listen and people are

475
00:25:41,920 --> 00:25:44,399
Speaker 1: reading through this article, they're assuming that it's accurate, so

476
00:25:44,400 --> 00:25:45,760
Speaker 1: they sort of had to pull the plug on that

477
00:25:45,800 --> 00:25:47,879
Speaker 1: because the technology is just not quite there yet. It

478
00:25:47,920 --> 00:25:50,399
Speaker 1: was seen it, but yeah, I'm see which has a

479
00:25:50,440 --> 00:25:52,880
Speaker 1: partnership with bank Rate. But I mean, look, that also

480
00:25:52,920 --> 00:25:55,280
Speaker 1: highlights like we're not there yet, but also it's not

481
00:25:55,359 --> 00:25:58,320
Speaker 1: that far away. It also shows you where a lot

482
00:25:58,359 --> 00:26:01,080
Speaker 1: of the big dollars are going to be invested by

483
00:26:01,119 --> 00:26:04,000
Speaker 1: the tech companies in the coming years. So I would

484
00:26:04,000 --> 00:26:06,000
Speaker 1: say that the two frontiers where they're pumping the most

485
00:26:06,000 --> 00:26:09,080
Speaker 1: amount of money is AI and then obviously the metaverse.

486
00:26:09,119 --> 00:26:13,679
Speaker 1: Apple rumor to release its virtual reality headset sometime in

487
00:26:13,720 --> 00:26:16,679
Speaker 1: the next year or so that's going to rival the

488
00:26:17,200 --> 00:26:20,120
Speaker 1: Oculus or whatever the new name is for it over

489
00:26:20,200 --> 00:26:23,879
Speaker 1: at Meta Then Microsoft is investing this ten billion. Specifically,

490
00:26:23,920 --> 00:26:26,000
Speaker 1: it seems to try and get an edge on Google

491
00:26:26,080 --> 00:26:29,760
Speaker 1: by possibly incorporating chat GPT into the bing search engine.

492
00:26:29,800 --> 00:26:32,800
Speaker 1: But at the same time, Google itself is already ramping

493
00:26:32,880 --> 00:26:35,760
Speaker 1: up and has had a long decades long project on

494
00:26:35,880 --> 00:26:39,439
Speaker 1: artificial intelligence and maybe rolling out their own competitor to

495
00:26:39,560 --> 00:26:42,360
Speaker 1: this soon. So this does seem like the next frontier,

496
00:26:42,400 --> 00:26:44,320
Speaker 1: and I do think it's just really important. There's a

497
00:26:44,359 --> 00:26:46,560
Speaker 1: lot of questions around this stuff, right, which is already

498
00:26:46,600 --> 00:26:48,240
Speaker 1: you know, if you go when you ask chat GPT

499
00:26:48,440 --> 00:26:52,000
Speaker 1: like sensitive social political questions, Oh shocker, you know, it's

500
00:26:52,080 --> 00:26:55,199
Speaker 1: got bias or whatever in some directions. So even the

501
00:26:55,200 --> 00:26:57,160
Speaker 1: way that these things get programmed, I mean, it has

502
00:26:57,160 --> 00:26:59,000
Speaker 1: a lot of impact on the way that people might

503
00:26:59,000 --> 00:27:02,479
Speaker 1: think about things, especially if we become very reliant on

504
00:27:02,560 --> 00:27:05,439
Speaker 1: that technology, and even put the social aspects aside, Like

505
00:27:05,480 --> 00:27:07,720
Speaker 1: what is this going to look like at an enterprise level?

506
00:27:07,920 --> 00:27:11,560
Speaker 1: That is the real question, and that's why Microsoft, Remember,

507
00:27:11,640 --> 00:27:13,840
Speaker 1: is a huge This is a big corporate somehow we

508
00:27:13,880 --> 00:27:16,240
Speaker 1: never really talk about it, but it's a multi billion

509
00:27:16,280 --> 00:27:18,800
Speaker 1: dollar behemoth. I think it's like a trillion dollar or

510
00:27:18,840 --> 00:27:22,920
Speaker 1: something company. And they're openly also not only competing against Google,

511
00:27:22,960 --> 00:27:26,400
Speaker 1: but also Amazon, which wants to incorporate AI into its

512
00:27:26,440 --> 00:27:29,200
Speaker 1: web suite. So I think overall, like this is that

513
00:27:29,400 --> 00:27:32,560
Speaker 1: this really is one of the next frontiers, and if

514
00:27:32,560 --> 00:27:34,600
Speaker 1: they've been working on it for quite some time, but

515
00:27:34,840 --> 00:27:37,320
Speaker 1: this does show you like the moment is here, and

516
00:27:37,359 --> 00:27:38,600
Speaker 1: I think we're going to be covering this for the

517
00:27:38,640 --> 00:27:41,080
Speaker 1: next couple of years. Yeah, no doubt about it. I

518
00:27:41,160 --> 00:27:44,840
Speaker 1: listened to a long interview with Sam Altman, who is

519
00:27:44,920 --> 00:27:47,480
Speaker 1: the CEO of open Ai. There are the makers of

520
00:27:47,560 --> 00:27:49,920
Speaker 1: chat GPT. There was a lot there that was interesting

521
00:27:49,960 --> 00:27:52,080
Speaker 1: from him. I mean, one thing, he was kind of

522
00:27:52,080 --> 00:27:55,440
Speaker 1: shooting down rumors that the next iteration of chat GPT

523
00:27:55,560 --> 00:27:58,520
Speaker 1: would really be this dramatic step forward. I mean, obviously

524
00:27:58,520 --> 00:28:01,840
Speaker 1: it'll be improvement, but they're people out there theoryizing that

525
00:28:01,880 --> 00:28:04,280
Speaker 1: it would be, you know, a sort of like difference

526
00:28:04,280 --> 00:28:07,240
Speaker 1: in kind where you're getting close to that organic they

527
00:28:07,240 --> 00:28:10,720
Speaker 1: call it AGI. What does that stand for? Artificial something

528
00:28:10,760 --> 00:28:14,679
Speaker 1: something intelligence? Right? Anyway, clearly I'm just new learning this,

529
00:28:14,880 --> 00:28:16,800
Speaker 1: but he says, listen, it's not going to be everything

530
00:28:16,840 --> 00:28:19,040
Speaker 1: that people are selling it, sort of downplaying that. He

531
00:28:19,080 --> 00:28:23,160
Speaker 1: also talked about how surprised he was that the reception

532
00:28:23,359 --> 00:28:27,439
Speaker 1: to chat GPT has been what it is like that

533
00:28:27,520 --> 00:28:30,719
Speaker 1: it has been so like people so interested in and

534
00:28:30,800 --> 00:28:32,879
Speaker 1: so freaked down about it, and sparked this whole national

535
00:28:32,880 --> 00:28:36,800
Speaker 1: conversation because he felt like they had sort of intentionally

536
00:28:36,840 --> 00:28:39,480
Speaker 1: released things step by step by step, where this was

537
00:28:39,520 --> 00:28:44,320
Speaker 1: the logical next step. But something about this particular iteration

538
00:28:44,480 --> 00:28:48,080
Speaker 1: and people's ability to really interact with it, you know,

539
00:28:48,200 --> 00:28:51,120
Speaker 1: it really kind of shocked people and so he was.

540
00:28:51,320 --> 00:28:54,760
Speaker 1: He was surprised by that. He also talked about the

541
00:28:54,840 --> 00:28:59,160
Speaker 1: Microsoft partnership. Now they had already had a partnership. This

542
00:28:59,280 --> 00:29:02,160
Speaker 1: latest announcement is an additional tranche of money and a

543
00:29:02,200 --> 00:29:05,520
Speaker 1: deepening of that partnership. So this conversation happened between before

544
00:29:05,560 --> 00:29:08,400
Speaker 1: the latest announcement, but these comments from Sam still give

545
00:29:08,440 --> 00:29:10,360
Speaker 1: you a sense of how they are ultimately thinking about it.

546
00:29:10,440 --> 00:29:12,360
Speaker 1: Listake a listen. Can you talk a little bit about

547
00:29:12,360 --> 00:29:16,080
Speaker 1: your partnership with microsofticgus, how it's going, how they're using

548
00:29:16,080 --> 00:29:18,400
Speaker 1: your tech. It's great. They're the only tech company out

549
00:29:18,400 --> 00:29:20,840
Speaker 1: there that I think I'd be excited to partner with

550
00:29:20,880 --> 00:29:23,760
Speaker 1: this deeply. I think Sata is an amazing CEO, but

551
00:29:23,800 --> 00:29:26,600
Speaker 1: more than that, human being and understands so do Kevin

552
00:29:26,600 --> 00:29:29,120
Speaker 1: Scott and mckil who we work with closely as well,

553
00:29:29,280 --> 00:29:32,360
Speaker 1: like understand the stakes of what AGI means and why

554
00:29:32,360 --> 00:29:34,000
Speaker 1: we need to have all the weirdness we do in

555
00:29:34,040 --> 00:29:37,239
Speaker 1: our structure and our agreement with them, and so I

556
00:29:37,280 --> 00:29:39,200
Speaker 1: really feel like it's a very values aligned company. And

557
00:29:39,480 --> 00:29:42,200
Speaker 1: there's some things they're very good at, like building very

558
00:29:42,240 --> 00:29:44,560
Speaker 1: large supercomputers and the instruction we operate on and putting

559
00:29:44,560 --> 00:29:46,320
Speaker 1: the technology into products. There's things we're very good at,

560
00:29:46,440 --> 00:29:49,400
Speaker 1: like doing research, and it's been a great partnership. So

561
00:29:49,840 --> 00:29:52,120
Speaker 1: he's saying, listen, I mean, as we kind of lay

562
00:29:52,120 --> 00:29:53,920
Speaker 1: it out, what we're good at is building this thing,

563
00:29:54,400 --> 00:29:56,520
Speaker 1: they're good at figuring out how to market it and

564
00:29:56,560 --> 00:29:58,800
Speaker 1: incorporate in products in order to make money. And he

565
00:29:58,840 --> 00:30:01,440
Speaker 1: feels comfortable with the Partnershi. So that's sort of his view.

566
00:30:01,440 --> 00:30:03,840
Speaker 1: And by the way, it's artificial general intelligence was the

567
00:30:03,840 --> 00:30:07,480
Speaker 1: word that I was looking for there. At the same time,

568
00:30:07,880 --> 00:30:11,040
Speaker 1: in terms of education, this thing continues to make head roads.

569
00:30:11,080 --> 00:30:13,440
Speaker 1: Let's put this up there on the screen. The CHAT

570
00:30:13,480 --> 00:30:17,200
Speaker 1: GPT actually passed I believe it was a ten question

571
00:30:17,560 --> 00:30:22,520
Speaker 1: test on the MBA exam at the Wharton School of Pennsylvania.

572
00:30:22,560 --> 00:30:26,400
Speaker 1: It earned a quote solid grade and outperformed some humans

573
00:30:26,400 --> 00:30:29,040
Speaker 1: on the Wharton course. So what they did is they

574
00:30:29,080 --> 00:30:31,800
Speaker 1: took one of their test exams. I think it was

575
00:30:31,840 --> 00:30:34,560
Speaker 1: ten questions. And then it's in his white paper it's like,

576
00:30:34,600 --> 00:30:38,880
Speaker 1: would chat GPT get a Wharton MBA GPT would receive

577
00:30:38,920 --> 00:30:41,520
Speaker 1: a B to B minus grade on the exam, so

578
00:30:41,800 --> 00:30:44,320
Speaker 1: obviously that's still a passing grade. Now I'm not saying

579
00:30:44,320 --> 00:30:47,520
Speaker 1: they obviously would have passed the entire curricula, but it

580
00:30:47,560 --> 00:30:50,560
Speaker 1: does highlight like, wow, this is going to really just

581
00:30:50,640 --> 00:30:54,200
Speaker 1: test the grounds of knowledge working what it means. I mean,

582
00:30:54,240 --> 00:30:58,240
Speaker 1: we talked previously about what level of memorization possibly that

583
00:30:58,320 --> 00:31:01,640
Speaker 1: this could free up, maybe in some students, not even

584
00:31:01,640 --> 00:31:04,760
Speaker 1: necessarily for this, but I did see one where chatch

585
00:31:04,800 --> 00:31:07,920
Speaker 1: ept actually worked on. It was one of the medical

586
00:31:07,960 --> 00:31:11,080
Speaker 1: school exams. And one of the reasons why it was

587
00:31:11,240 --> 00:31:14,240
Speaker 1: so powerful and was able to do well is a

588
00:31:14,240 --> 00:31:16,840
Speaker 1: lot of people in medicine I believe it from what

589
00:31:16,880 --> 00:31:18,840
Speaker 1: I've told and some of the doctors and med students

590
00:31:18,920 --> 00:31:21,400
Speaker 1: who I know, they have to memorize a lot of chemistry,

591
00:31:21,480 --> 00:31:23,560
Speaker 1: a lot of like rote facts and other things they

592
00:31:23,560 --> 00:31:25,280
Speaker 1: have to be able to recall off the top of

593
00:31:25,280 --> 00:31:29,200
Speaker 1: their head. Obviously, chatch EPT, with the availability of information

594
00:31:29,640 --> 00:31:33,200
Speaker 1: that they have at their disposable at their disposal, would

595
00:31:33,200 --> 00:31:35,400
Speaker 1: be it'd be much easier for them to have that

596
00:31:35,480 --> 00:31:38,200
Speaker 1: on a general knowledge type test. And then that comes

597
00:31:38,240 --> 00:31:39,920
Speaker 1: to the question of like, well, how is that possibly

598
00:31:39,920 --> 00:31:43,200
Speaker 1: going to manifest in terms of the actual room, Like, right,

599
00:31:43,240 --> 00:31:45,280
Speaker 1: you could have a future doctor who may not have

600
00:31:45,320 --> 00:31:47,520
Speaker 1: to memorize all that stuff and could rely on the

601
00:31:47,560 --> 00:31:50,040
Speaker 1: technology be generally familiar and be able to type in

602
00:31:50,400 --> 00:31:52,800
Speaker 1: something if they need to have a recall and then

603
00:31:52,960 --> 00:31:57,760
Speaker 1: use that knowledge for future diagnosis or future course of action.

604
00:31:57,920 --> 00:32:01,320
Speaker 1: So the actual disruption here to edge and to a

605
00:32:01,360 --> 00:32:04,360
Speaker 1: lot of these feels, I'm finding that really really fascinating.

606
00:32:04,720 --> 00:32:08,760
Speaker 1: How does that absolutely? I mean, many management consultants looking

607
00:32:08,840 --> 00:32:12,200
Speaker 1: at these results and sort of quaking, wondering if they're

608
00:32:12,240 --> 00:32:14,320
Speaker 1: going to become you know, the peat Boodhagites of the

609
00:32:14,320 --> 00:32:17,640
Speaker 1: world are going to ultimately become irrelevant here, I mean

610
00:32:17,680 --> 00:32:20,840
Speaker 1: in playing with the tool as it exists now, Like yesterday,

611
00:32:20,880 --> 00:32:22,560
Speaker 1: I was playing with it. I asked it to write

612
00:32:22,640 --> 00:32:25,360
Speaker 1: a monologue in the style of Crystal Ball about chat

613
00:32:25,400 --> 00:32:28,880
Speaker 1: GPT and it, you know, it put out a thing

614
00:32:29,000 --> 00:32:31,920
Speaker 1: that was reasonably good, but it was kind of surface level.

615
00:32:32,080 --> 00:32:34,680
Speaker 1: It's not bad, you know, I mean, you can definitely

616
00:32:34,760 --> 00:32:39,520
Speaker 1: tell the difference. It's lacking some like soul to it.

617
00:32:39,760 --> 00:32:42,880
Speaker 1: You can tell. And there's also they talked about with

618
00:32:42,920 --> 00:32:45,960
Speaker 1: regard to this NBA test, there were certain things that

619
00:32:46,040 --> 00:32:48,200
Speaker 1: it was really good at. You know, the sentences are

620
00:32:48,240 --> 00:32:52,040
Speaker 1: perfectly structured, the grammar is precise, you know, the factual

621
00:32:52,120 --> 00:32:55,880
Speaker 1: references are totally you know, totally there and thorough, et cetera.

622
00:32:56,280 --> 00:32:59,440
Speaker 1: But actually on some just sort of elementary math, they

623
00:32:59,440 --> 00:33:02,520
Speaker 1: said it was a bismal, just like totally incapable. Now

624
00:33:02,680 --> 00:33:04,640
Speaker 1: that seems like the sort of thing that maybe even

625
00:33:04,680 --> 00:33:06,680
Speaker 1: in the next iteration they're going to be able to fix.

626
00:33:06,760 --> 00:33:09,880
Speaker 1: But I do think it's a long way to go

627
00:33:10,320 --> 00:33:16,440
Speaker 1: before these chat ai or other similar technology like the

628
00:33:16,480 --> 00:33:20,480
Speaker 1: image generation or whatever. Before it's really in a position

629
00:33:20,680 --> 00:33:23,360
Speaker 1: where you can't tell the difference between the output put

630
00:33:23,400 --> 00:33:28,480
Speaker 1: from chat GPT and the output from a creative, thoughtful

631
00:33:28,800 --> 00:33:33,200
Speaker 1: human beings. So don't worry human beings. You're not irrelevant yet.

632
00:33:33,280 --> 00:33:36,360
Speaker 1: I guess this is my bottom line. Not yet. Also, though,

633
00:33:36,480 --> 00:33:38,600
Speaker 1: you know, in terms of education, what we're talking about here,

634
00:33:38,600 --> 00:33:41,040
Speaker 1: put this up there on the screen. The Stanford Daily

635
00:33:41,120 --> 00:33:45,640
Speaker 1: did a did a survey of its students and asked

636
00:33:45,680 --> 00:33:49,240
Speaker 1: how many of you use chat GPT on your final exams.

637
00:33:49,480 --> 00:33:52,640
Speaker 1: They have a quarterly system at Stanford, I believe, and

638
00:33:53,120 --> 00:33:56,360
Speaker 1: the results said that all hell of a lot of

639
00:33:56,400 --> 00:33:59,600
Speaker 1: people were using chat GPT in the fall quarter of

640
00:33:59,640 --> 00:34:02,280
Speaker 1: twenty two, twenty two. Now, to what extent did you

641
00:34:02,400 --> 00:34:04,800
Speaker 1: use it? So it was about seventeen percent of all

642
00:34:04,840 --> 00:34:08,040
Speaker 1: stand for students admitted in an anonymous poll to using it.

643
00:34:08,120 --> 00:34:10,400
Speaker 1: So I don't know how many of them actually did it.

644
00:34:10,440 --> 00:34:12,880
Speaker 1: I would presume and probably be a little bit higher.

645
00:34:12,920 --> 00:34:16,879
Speaker 1: So how did they poll? Yeah, right, not scientific or whatever.

646
00:34:16,960 --> 00:34:19,120
Speaker 1: It's not like perfect mind whatever you stand for daily

647
00:34:19,160 --> 00:34:20,560
Speaker 1: good for them, they gads sense, Yeah, we did a

648
00:34:20,560 --> 00:34:22,839
Speaker 1: good job here. So to what extent did you use

649
00:34:22,920 --> 00:34:26,160
Speaker 1: chat ept in your finals? They said, brainstorming, outlining, and

650
00:34:26,200 --> 00:34:28,359
Speaker 1: forming ideas. That was sixty percent. That's kind of what

651
00:34:28,400 --> 00:34:31,040
Speaker 1: I talked about before in terms of getting people started

652
00:34:31,160 --> 00:34:34,040
Speaker 1: answering multiple choice questions with the help of chat ept.

653
00:34:34,400 --> 00:34:38,680
Speaker 1: Thirty percent submitted written material from chat GPT with edits

654
00:34:38,880 --> 00:34:42,400
Speaker 1: seven point three percent submitted written material from chat GPT

655
00:34:42,719 --> 00:34:45,920
Speaker 1: without edits five point five percent. I want to meet

656
00:34:45,920 --> 00:34:48,239
Speaker 1: some of those people. Those that's a bold move yet

657
00:34:48,360 --> 00:34:51,320
Speaker 1: straight up copy of books, especially after some of the

658
00:34:51,360 --> 00:34:53,839
Speaker 1: stories and all of that that has come out. Can't

659
00:34:53,880 --> 00:34:56,160
Speaker 1: remember if it was. I think it was this article

660
00:34:56,360 --> 00:34:59,319
Speaker 1: where one of the professors had caught a student using it,

661
00:34:59,640 --> 00:35:01,719
Speaker 1: and he it was like it was pretty easy to

662
00:35:01,800 --> 00:35:05,160
Speaker 1: tell because it's said in whatever the essay was like

663
00:35:05,800 --> 00:35:10,120
Speaker 1: as a artificial intelligence chat creator or something like that.

664
00:35:10,239 --> 00:35:12,560
Speaker 1: And the person clearly didn't even just like read through

665
00:35:13,239 --> 00:35:16,160
Speaker 1: to pull out any self references that this was in

666
00:35:16,200 --> 00:35:18,719
Speaker 1: fact or by chat GPT. So come on, guys, if

667
00:35:18,719 --> 00:35:20,520
Speaker 1: you're going to use it, don't be that lazy. At

668
00:35:20,600 --> 00:35:22,960
Speaker 1: least like reread it and make a few edits for yourself.

669
00:35:23,000 --> 00:35:25,320
Speaker 1: Here's the other interesting thing. If you ask the students,

670
00:35:25,360 --> 00:35:28,160
Speaker 1: you're like, do you think that using chat GPT is

671
00:35:28,200 --> 00:35:30,359
Speaker 1: a violation of the academic honor code? So they had

672
00:35:30,440 --> 00:35:32,799
Speaker 1: over five thousand results here, right, So they said yes,

673
00:35:32,960 --> 00:35:36,000
Speaker 1: if used for more than just ideas, thirty one percent, yes,

674
00:35:36,120 --> 00:35:39,080
Speaker 1: if used it all, twenty two percent, yes. If submitted

675
00:35:39,120 --> 00:35:42,799
Speaker 1: with no edits twenty one percent, no, thirteen percent, twelve

676
00:35:43,280 --> 00:35:46,040
Speaker 1: twelve percent said that they were unsure. So a lot

677
00:35:46,080 --> 00:35:48,520
Speaker 1: of people. It's funny because twenty percent or so of

678
00:35:48,520 --> 00:35:51,880
Speaker 1: them are using it, but thirty vast majorities seem to

679
00:35:51,880 --> 00:35:54,759
Speaker 1: say that if you use it at least in some capacity,

680
00:35:54,840 --> 00:35:57,680
Speaker 1: it is a violation. Yeah, the academic all people kind

681
00:35:57,680 --> 00:35:59,719
Speaker 1: of divided though, Yeah, no, they are divided. I mean

682
00:35:59,719 --> 00:36:02,040
Speaker 1: this and seems like sort of mixed on it. As

683
00:36:02,080 --> 00:36:05,160
Speaker 1: the professors that we've been reading about here and under like,

684
00:36:05,239 --> 00:36:07,759
Speaker 1: I get that for sure. I just feel like you're

685
00:36:07,800 --> 00:36:10,600
Speaker 1: fighting an uphill battle if you're not thinking of ways

686
00:36:10,680 --> 00:36:13,960
Speaker 1: to you know, test your students and build the skills

687
00:36:13,960 --> 00:36:16,880
Speaker 1: that they're going to need. Given that chat GPT is

688
00:36:16,960 --> 00:36:19,960
Speaker 1: now here and is not going away, not going anywhere,

689
00:36:20,000 --> 00:36:21,839
Speaker 1: and is only going to get better from here on out.

690
00:36:21,920 --> 00:36:25,600
Speaker 1: So I definitely get the angst around it, and it's

691
00:36:25,640 --> 00:36:28,560
Speaker 1: going to be a difficult transition. But ultimately, I think

692
00:36:28,640 --> 00:36:31,480
Speaker 1: the most fruitful thinking is, Okay, how do we how

693
00:36:31,480 --> 00:36:33,680
Speaker 1: do we use this tool and then build onto it?

694
00:36:33,719 --> 00:36:35,920
Speaker 1: What are the pieces that human beings still bring to

695
00:36:35,960 --> 00:36:39,960
Speaker 1: the table that are additive to this existing technology? That's right,

696
00:36:40,280 --> 00:36:43,120
Speaker 1: all right, guys. Big political story. So, as you all know,

697
00:36:43,320 --> 00:36:46,919
Speaker 1: Kirsten Cinema, with much fanfare at least in her own mind,

698
00:36:46,920 --> 00:36:50,759
Speaker 1: declared her independence. Of course, she has never been truly independent.

699
00:36:50,800 --> 00:36:53,080
Speaker 1: She is fully beholden to her corporate donors. While more

700
00:36:53,080 --> 00:36:56,560
Speaker 1: in that moment, but that opened up a big question of, Okay, well,

701
00:36:56,560 --> 00:36:58,920
Speaker 1: what are Democrats going to do in terms of this

702
00:36:59,040 --> 00:37:02,280
Speaker 1: next Senate cycle, Because Kirsten Cinema is up for reelection

703
00:37:03,040 --> 00:37:06,040
Speaker 1: this next cycle, this next time around, and you could

704
00:37:06,080 --> 00:37:08,120
Speaker 1: see how they have a bit of a problem here.

705
00:37:08,560 --> 00:37:10,080
Speaker 1: She was not going to be able to win a

706
00:37:10,080 --> 00:37:13,920
Speaker 1: Democratic primary because Democrats basically hate her guts. Now, so

707
00:37:13,960 --> 00:37:17,120
Speaker 1: she declares that she's going to run as an independent.

708
00:37:17,520 --> 00:37:19,480
Speaker 1: So you have her in the race, a Republican in

709
00:37:19,480 --> 00:37:22,480
Speaker 1: the race, and you would assume that she would probably

710
00:37:22,560 --> 00:37:25,399
Speaker 1: take away from any Democrat who jumps in the race.

711
00:37:25,400 --> 00:37:27,279
Speaker 1: So there was this question or Democrats just going to

712
00:37:27,400 --> 00:37:29,359
Speaker 1: let her run and sort of tacit lead back her,

713
00:37:29,719 --> 00:37:31,719
Speaker 1: or are they going to run their own candidate, And

714
00:37:31,760 --> 00:37:33,360
Speaker 1: now we seem to have a pretty clear answer, and

715
00:37:33,400 --> 00:37:35,680
Speaker 1: it was always likely to go in this direction, which

716
00:37:35,800 --> 00:37:37,839
Speaker 1: I think is a good thing because I think democracy

717
00:37:37,880 --> 00:37:40,040
Speaker 1: is a good thing and more choices are more better.

718
00:37:40,719 --> 00:37:44,839
Speaker 1: Ruben Diego, who is a progressive member of Congress and

719
00:37:45,239 --> 00:37:48,960
Speaker 1: a rock War veteran. He has officially announced four Senate

720
00:37:49,040 --> 00:37:52,000
Speaker 1: and put out his big announcement video yesterday. Let's take

721
00:37:52,000 --> 00:37:54,200
Speaker 1: a look at a little bit of that. Growing up poor,

722
00:37:54,440 --> 00:37:56,319
Speaker 1: the only thing I really had was the American dream,

723
00:37:57,480 --> 00:38:00,920
Speaker 1: an opportunity. So one thing that we give every American,

724
00:38:01,480 --> 00:38:03,920
Speaker 1: no matter where they are born in life, kinds of

725
00:38:03,960 --> 00:38:06,000
Speaker 1: step up and be about a figure to my three

726
00:38:06,040 --> 00:38:10,040
Speaker 1: sisters and skipping my teenage years. I really did feel

727
00:38:10,480 --> 00:38:13,359
Speaker 1: that I owed the country something and we got sent

728
00:38:13,400 --> 00:38:19,080
Speaker 1: to Iraq, losing all my friends, consistently being shot at

729
00:38:19,320 --> 00:38:21,120
Speaker 1: and people are trying to blow you up all the time.

730
00:38:26,680 --> 00:38:29,719
Speaker 1: You never really fully come back from the war. I

731
00:38:29,760 --> 00:38:32,960
Speaker 1: will be challenging kirston cinema for the United States Senate

732
00:38:33,000 --> 00:38:39,520
Speaker 1: and I need all of your support. Most families feel

733
00:38:39,560 --> 00:38:42,120
Speaker 1: that they are one or two pit chicks away from

734
00:38:42,120 --> 00:38:45,720
Speaker 1: going under. That is not the way that we should

735
00:38:45,719 --> 00:38:48,680
Speaker 1: be living in this country. Very smart way, I think

736
00:38:48,719 --> 00:38:53,360
Speaker 1: to announce emphasizing the background military service of course genuinely

737
00:38:53,400 --> 00:38:56,280
Speaker 1: heroic in terms of for those who were just listening.

738
00:38:56,360 --> 00:38:58,279
Speaker 1: It actually put up on the screen that his marine

739
00:38:58,360 --> 00:39:02,080
Speaker 1: units suffered one of the highest casual He's actually casualty rates,

740
00:39:02,120 --> 00:39:04,879
Speaker 1: I believe while he was deployed in Iraq. And then

741
00:39:05,000 --> 00:39:08,240
Speaker 1: with the economics there at the end, to really punch

742
00:39:08,560 --> 00:39:11,960
Speaker 1: home the message, combined with the bio, it's a strong ad.

743
00:39:12,239 --> 00:39:14,399
Speaker 1: I think it's a very smart play. Yeah. I thought

744
00:39:14,400 --> 00:39:17,880
Speaker 1: it was well done too. You know, it was emotional.

745
00:39:17,960 --> 00:39:20,320
Speaker 1: He talks about growing up in poverty, what that meant,

746
00:39:20,360 --> 00:39:22,839
Speaker 1: how he had to basically you know, be a kind

747
00:39:22,840 --> 00:39:25,760
Speaker 1: of a leader or father figure to his three sisters.

748
00:39:26,080 --> 00:39:29,759
Speaker 1: You know, his mom struggling with paying bills, and he

749
00:39:29,800 --> 00:39:32,080
Speaker 1: would throw in. It didn't seem gratuitous, but there were

750
00:39:32,080 --> 00:39:34,239
Speaker 1: a few lines that were thrown in Spanish to kind of,

751
00:39:34,239 --> 00:39:36,840
Speaker 1: you know, for Arizona, like make clear to voters that

752
00:39:36,920 --> 00:39:40,080
Speaker 1: he's also fluent in Spanish and culturally connected. So yeah,

753
00:39:40,120 --> 00:39:43,000
Speaker 1: I thought it was I thought it was ultimately very effective,

754
00:39:43,120 --> 00:39:45,200
Speaker 1: and it's going to be interesting to see how this

755
00:39:45,320 --> 00:39:49,920
Speaker 1: all ultimately plays out. Because if it was just Ruben

756
00:39:50,120 --> 00:39:53,200
Speaker 1: versus whoever the Republicans put up, and there's already talk

757
00:39:53,200 --> 00:39:56,360
Speaker 1: of Blake Masters running again, there's already carry Lake running again.

758
00:39:56,600 --> 00:39:58,360
Speaker 1: I think he'd have a pretty good shot at that.

759
00:39:58,719 --> 00:40:01,080
Speaker 1: But it is complicated by the fact that Kreson Cinema

760
00:40:01,239 --> 00:40:03,840
Speaker 1: is like hanging out there and you know, likely to

761
00:40:03,920 --> 00:40:06,600
Speaker 1: run as an independent. She's not going to be able

762
00:40:06,600 --> 00:40:10,359
Speaker 1: to win as an independent. But again, which pot does

763
00:40:10,400 --> 00:40:12,560
Speaker 1: she pull from more You would have to think it's

764
00:40:12,560 --> 00:40:15,560
Speaker 1: the Democratic side. So this is going to be complex.

765
00:40:15,600 --> 00:40:17,719
Speaker 1: It's hard to predict exactly what way this is all

766
00:40:17,760 --> 00:40:20,280
Speaker 1: going to go because she does not have high favorability ratings.

767
00:40:20,320 --> 00:40:23,719
Speaker 1: Among any group in the state of Arizona, not among Democrats,

768
00:40:23,760 --> 00:40:28,040
Speaker 1: not among Republicans, not among independents. But if she's going

769
00:40:28,080 --> 00:40:30,400
Speaker 1: to pull votes from somewhere, you would think, since she

770
00:40:30,640 --> 00:40:32,960
Speaker 1: was a former Democrat, it might come more from the

771
00:40:32,960 --> 00:40:36,920
Speaker 1: Democratic side. We'll see at the same time. You know,

772
00:40:37,040 --> 00:40:39,960
Speaker 1: we cover Davos last week and all the elites gathering

773
00:40:40,000 --> 00:40:42,879
Speaker 1: at the World Economic Forum, and low and behold, guess

774
00:40:42,920 --> 00:40:46,160
Speaker 1: who was there hob nobbing with a bunch of bankers

775
00:40:46,280 --> 00:40:49,680
Speaker 1: and other global elites. Kirsen Cinema, herself, real woman of

776
00:40:49,680 --> 00:40:51,319
Speaker 1: the people. Let's take to listen to a little bit

777
00:40:51,360 --> 00:40:53,520
Speaker 1: of what she had to say there. So, as folks know,

778
00:40:53,560 --> 00:40:56,160
Speaker 1: I have declared, formally declared my independence from what I

779
00:40:56,120 --> 00:40:58,439
Speaker 1: considered to be a deeply broken to party system. Those

780
00:40:58,440 --> 00:41:00,680
Speaker 1: who know me know that I was always an independent

781
00:41:00,760 --> 00:41:02,160
Speaker 1: voice and always have been for the things that I

782
00:41:02,200 --> 00:41:03,880
Speaker 1: believe in and for my state and for my country.

783
00:41:04,200 --> 00:41:05,920
Speaker 1: But I do think it's important to note that that

784
00:41:06,719 --> 00:41:09,360
Speaker 1: what you've heard about partisanship, I believe is accurate. You know,

785
00:41:09,600 --> 00:41:11,279
Speaker 1: in the last two years, if we think, you know,

786
00:41:11,680 --> 00:41:14,400
Speaker 1: January sixth, which is a horrible day from two years ago,

787
00:41:14,880 --> 00:41:17,880
Speaker 1: created I think concern and fear for every patriotic American

788
00:41:17,920 --> 00:41:20,400
Speaker 1: across the country. But in the resulting two years, the

789
00:41:20,440 --> 00:41:24,600
Speaker 1: Democratic Party shared a narrative that said, we would not

790
00:41:24,719 --> 00:41:27,240
Speaker 1: have any more free and fair elections in this country

791
00:41:27,480 --> 00:41:29,920
Speaker 1: if the United States Congress didn't eliminate the filibuster and

792
00:41:29,960 --> 00:41:32,840
Speaker 1: pass a massive voting rights package. As you as we

793
00:41:32,880 --> 00:41:35,759
Speaker 1: all know, the philibuster was not eliminated. Joe and I

794
00:41:35,840 --> 00:41:39,360
Speaker 1: were not interested in sacrificing that important guardrail for the institution.

795
00:41:40,160 --> 00:41:42,640
Speaker 1: That massive voting rights built was not passed through Congress,

796
00:41:42,840 --> 00:41:44,440
Speaker 1: and then we had a free and fair election all

797
00:41:44,440 --> 00:41:46,600
Speaker 1: across the country. We still don't agree on getting rid

798
00:41:46,640 --> 00:41:49,120
Speaker 1: of the philibuster. That's correct. Thank you well, high five

799
00:41:49,160 --> 00:41:52,040
Speaker 1: there between two of the most corrupt members of the

800
00:41:52,200 --> 00:41:55,279
Speaker 1: entire Senate. I mean, it just drives me crazy. We'll

801
00:41:55,320 --> 00:41:58,800
Speaker 1: fight about the philibuster another day, but it does drives

802
00:41:58,800 --> 00:42:02,120
Speaker 1: me crazy. The way that she postures herself like this, Oh,

803
00:42:02,160 --> 00:42:04,520
Speaker 1: truly independent and she's just speaking up for the voices

804
00:42:04,560 --> 00:42:10,000
Speaker 1: of Arizona. This is total, complete garbage. She is there

805
00:42:10,160 --> 00:42:12,640
Speaker 1: for one reason. It's to serve her donors. And I

806
00:42:12,680 --> 00:42:14,880
Speaker 1: say this because there is a really clear track record

807
00:42:15,080 --> 00:42:17,959
Speaker 1: by the way you know report from CNBC. Of course,

808
00:42:18,000 --> 00:42:20,680
Speaker 1: she and mansion and Chris Coon's one of Biden's type

809
00:42:20,840 --> 00:42:22,799
Speaker 1: top advisors, put this up on the screen. They went

810
00:42:22,800 --> 00:42:25,600
Speaker 1: ahead and met with CEOs and private Davos luncheon for

811
00:42:25,680 --> 00:42:28,359
Speaker 1: World Economic Forum. Again, real woman of the people stuff there.

812
00:42:28,640 --> 00:42:33,040
Speaker 1: And also she has been the top ally of private

813
00:42:33,080 --> 00:42:37,160
Speaker 1: equity keeping their goodies in the tax cud, a gigantic

814
00:42:37,320 --> 00:42:40,400
Speaker 1: loophole giveaway that they get in the tax code. She

815
00:42:40,480 --> 00:42:43,040
Speaker 1: got a bunch of cash from them. She preserved what's

816
00:42:43,040 --> 00:42:46,440
Speaker 1: called the carried interest loophole. She has continued after she

817
00:42:46,760 --> 00:42:49,040
Speaker 1: went to bad for them to take their money and

818
00:42:49,560 --> 00:42:52,399
Speaker 1: received donations even from executives at the private equity firm

819
00:42:52,440 --> 00:42:56,320
Speaker 1: whose founder owns the winery where she interned and fundraised.

820
00:42:56,320 --> 00:43:00,120
Speaker 1: So guys just don't buy her nonsense here about how

821
00:43:00,200 --> 00:43:02,799
Speaker 1: she's so independent minded and she's really there for the

822
00:43:02,840 --> 00:43:05,160
Speaker 1: people because it could not possibly be for their For

823
00:43:05,239 --> 00:43:07,160
Speaker 1: the truth, well, most people don't buy it. I mean,

824
00:43:07,280 --> 00:43:09,520
Speaker 1: that's why she has such low favorabilities. And by the way,

825
00:43:09,560 --> 00:43:11,480
Speaker 1: it's not like it's actually winning amongst Republicans. She has

826
00:43:11,480 --> 00:43:14,120
Speaker 1: a low favorability amongst Republicans. The carry Lake people actually

827
00:43:14,160 --> 00:43:16,520
Speaker 1: hate her guts. She has a low favorability. I believe

828
00:43:16,560 --> 00:43:19,480
Speaker 1: it's under forty percent amongst Arizona Democrats. One of the

829
00:43:19,560 --> 00:43:22,239
Speaker 1: reasons that she went independent, but even independent seemed to

830
00:43:22,320 --> 00:43:25,040
Speaker 1: be underwater there. I think the gyego is going to

831
00:43:25,120 --> 00:43:28,000
Speaker 1: really push her because the only way that I could

832
00:43:28,000 --> 00:43:31,480
Speaker 1: see it is if the DSCC and the Democratic top

833
00:43:31,600 --> 00:43:34,400
Speaker 1: don't or the top party comes in and intervenes on

834
00:43:34,440 --> 00:43:37,640
Speaker 1: her behalf. So, for example, Senator Schumer yesterday would not

835
00:43:37,640 --> 00:43:40,920
Speaker 1: say anything about whether he would endorse Reuben Diego. Neither

836
00:43:40,960 --> 00:43:43,920
Speaker 1: would Dick Durbin. So I believe one of the top

837
00:43:43,960 --> 00:43:45,840
Speaker 1: Democrats I know he's want. I think he's a whip

838
00:43:46,480 --> 00:43:48,719
Speaker 1: who's there. So they wouldn't even come out and say, yeah,

839
00:43:48,760 --> 00:43:51,879
Speaker 1: we might support Guyago. Actually, even Bernie Sanders was asked

840
00:43:51,880 --> 00:43:53,200
Speaker 1: about it and he was like, well, he hasn't asked

841
00:43:53,280 --> 00:43:55,319
Speaker 1: yet for my endorsement, And I was like, I guess,

842
00:43:55,360 --> 00:43:58,640
Speaker 1: to be fair, there are other Democrats who may get

843
00:43:58,640 --> 00:44:00,799
Speaker 1: into this product. That's true, Well, I think it's I

844
00:44:00,840 --> 00:44:03,799
Speaker 1: think it's reasonable to wait and see who else gets

845
00:44:03,800 --> 00:44:06,319
Speaker 1: into the race. But if you're really staring at a

846
00:44:06,360 --> 00:44:11,200
Speaker 1: three way race between Diego, Cinema and whoever the Republicans

847
00:44:11,239 --> 00:44:15,400
Speaker 1: ultimately put up to then stay out is you know,

848
00:44:15,520 --> 00:44:17,840
Speaker 1: I think that's absurd. Meanwhile, Mansion is already saying that

849
00:44:17,880 --> 00:44:20,000
Speaker 1: he would back here cin Cinema. So they've got their

850
00:44:20,120 --> 00:44:23,080
Speaker 1: you know, corrupt uh corrupt bond there I guess made

851
00:44:23,120 --> 00:44:26,439
Speaker 1: of with the with the the folks over at Davos. Yeah,

852
00:44:26,480 --> 00:44:28,880
Speaker 1: it's an important story. Will continue to track it. It

853
00:44:28,880 --> 00:44:31,239
Speaker 1: could add up being one of the hottest primaries and

854
00:44:31,640 --> 00:44:33,799
Speaker 1: probably you know, in terms of her donors, like that

855
00:44:33,800 --> 00:44:35,680
Speaker 1: would come in big for her. This could there could

856
00:44:35,680 --> 00:44:37,200
Speaker 1: be a hell of a lot of money. Yeah, that

857
00:44:37,200 --> 00:44:39,000
Speaker 1: happens in this race. One last thing, just to put

858
00:44:39,000 --> 00:44:42,520
Speaker 1: this in context. I was looking at the first Larry

859
00:44:42,520 --> 00:44:46,640
Speaker 1: Sabadeau crystal Ball ratings for the Senate for the next cycle,

860
00:44:46,840 --> 00:44:49,560
Speaker 1: and Democrats are on defense in a lot of places

861
00:44:49,600 --> 00:44:55,160
Speaker 1: including Arizona, Arizona, Ohio, and Montana. They have labeled as

862
00:44:55,200 --> 00:44:59,399
Speaker 1: toss ups. These are all Democratic health seats West Virginia. Also,

863
00:44:59,560 --> 00:45:01,560
Speaker 1: Joe Mann is up. He hasn't said he's running for

864
00:45:01,560 --> 00:45:03,239
Speaker 1: reelection again, by the way, In fact, he's sort of

865
00:45:03,280 --> 00:45:05,400
Speaker 1: like flirted with running for president. Okay, dude, give me

866
00:45:05,440 --> 00:45:07,880
Speaker 1: a breath. But anyway, they have West Virginia, which is

867
00:45:07,880 --> 00:45:11,479
Speaker 1: currently held by a Democrat as lean Republican. So that's

868
00:45:11,560 --> 00:45:16,440
Speaker 1: four states where Democrats are at best a toss up Republicans.

869
00:45:16,560 --> 00:45:19,480
Speaker 1: Every single Republican seat that is held right now is

870
00:45:19,560 --> 00:45:24,160
Speaker 1: effectively safe for them as the writings stand today, looking

871
00:45:24,200 --> 00:45:28,080
Speaker 1: forward to the next Senate election cycle. So this state

872
00:45:28,200 --> 00:45:30,799
Speaker 1: is going to be extremely key if Democrats have any

873
00:45:30,800 --> 00:45:33,080
Speaker 1: prayer of holding on to their control of the Senate.

874
00:45:33,200 --> 00:45:36,080
Speaker 1: There you go, all right, let's move on. Final. The

875
00:45:36,200 --> 00:45:39,359
Speaker 1: rumors are flying here in Washington basically have been ever

876
00:45:39,400 --> 00:45:41,759
Speaker 1: since Dan Snyder said that he might be open to

877
00:45:41,800 --> 00:45:45,640
Speaker 1: a sale of the Washington Commanders. So the richest duienne

878
00:45:45,640 --> 00:45:48,439
Speaker 1: of Washington, our richest resident, Jeff Bezos, let's go ahead

879
00:45:48,440 --> 00:45:51,320
Speaker 1: and put this up there on the screen, may sell,

880
00:45:51,360 --> 00:45:53,880
Speaker 1: and this is according to sources from the New York Post,

881
00:45:54,160 --> 00:45:57,879
Speaker 1: may sell the Washington Post to buy the Commanders. Now

882
00:45:58,200 --> 00:46:01,759
Speaker 1: they're immediately this report denied by Bezos, saying that the

883
00:46:01,840 --> 00:46:04,640
Speaker 1: Washington Post is not for sale. But what the New

884
00:46:04,719 --> 00:46:06,600
Speaker 1: York Posts learned from a source that they say that

885
00:46:06,680 --> 00:46:09,319
Speaker 1: was close to the situation, said that Bezos had told

886
00:46:09,320 --> 00:46:11,920
Speaker 1: the paper senior staff in private meetings that while he

887
00:46:12,000 --> 00:46:14,120
Speaker 1: had no plans to sell the paper, that there were

888
00:46:14,200 --> 00:46:17,320
Speaker 1: others who he had reportedly discussed the actual sale of

889
00:46:17,360 --> 00:46:19,759
Speaker 1: the Post to do so. The only reason why I

890
00:46:19,800 --> 00:46:22,400
Speaker 1: think that this might be dubious is a man is

891
00:46:22,440 --> 00:46:25,719
Speaker 1: worth what two hundred billion dollars? Does he really need

892
00:46:25,760 --> 00:46:29,200
Speaker 1: the He bought the Post for two fifty million. Does

893
00:46:29,200 --> 00:46:32,600
Speaker 1: he really need the one billion maybe that he would

894
00:46:32,640 --> 00:46:35,080
Speaker 1: get for the Washington Post, which is a failing business

895
00:46:35,080 --> 00:46:37,840
Speaker 1: and has yet to turn a profit and is currently

896
00:46:38,120 --> 00:46:41,640
Speaker 1: undergoing layoffs. Does he really need that to fund let's

897
00:46:41,640 --> 00:46:44,000
Speaker 1: say at max is like what a five to ten

898
00:46:44,080 --> 00:46:47,640
Speaker 1: billion dollar purchase for the Washington Commanders. It could be

899
00:46:47,760 --> 00:46:49,560
Speaker 1: And I think this is actually more likely the case

900
00:46:49,600 --> 00:46:51,640
Speaker 1: that if he does sell the Posts, one of the

901
00:46:51,640 --> 00:46:53,920
Speaker 1: reasons would be he just lost interest. I mean, remember

902
00:46:53,960 --> 00:46:56,200
Speaker 1: he bought the Post in twenty thirteen. That was kind

903
00:46:56,239 --> 00:46:59,359
Speaker 1: of his respectability era. That's why he came in here

904
00:46:59,360 --> 00:47:02,880
Speaker 1: to Washington. He built the largest house here in Washington,

905
00:47:02,880 --> 00:47:06,360
Speaker 1: which is truly a site to behold. At the same time,

906
00:47:06,400 --> 00:47:09,480
Speaker 1: he started attending all of these major political dinners like

907
00:47:09,520 --> 00:47:13,240
Speaker 1: the Gridiron Dinner and others. The White House correspondence dinner.

908
00:47:13,800 --> 00:47:16,319
Speaker 1: But then his life all kind of changed after he

909
00:47:16,400 --> 00:47:20,000
Speaker 1: started dating his girlfriend and started taking TRT and getting

910
00:47:20,120 --> 00:47:22,920
Speaker 1: jacked and hanging out on yachts at Saint Bart's and

911
00:47:23,160 --> 00:47:26,359
Speaker 1: in the Caribbean. It's like he kind of resigned as

912
00:47:26,400 --> 00:47:29,440
Speaker 1: the Amazon CEO. He's now like the chairman of Amazon,

913
00:47:29,480 --> 00:47:32,160
Speaker 1: not involved as much in the day to day operations.

914
00:47:32,200 --> 00:47:34,600
Speaker 1: It seems like his interests have gone much more in

915
00:47:34,640 --> 00:47:37,680
Speaker 1: the Hollywood pop culture direction. So that's why it would

916
00:47:37,680 --> 00:47:39,960
Speaker 1: make more sense for him to be one of the

917
00:47:39,960 --> 00:47:42,960
Speaker 1: deans of America's billionaires, an NFL owner, right, Like that's

918
00:47:42,960 --> 00:47:45,080
Speaker 1: as good as it gets for a lot of these

919
00:47:45,120 --> 00:47:48,279
Speaker 1: people in pop culture, whereas with the Washington Post, that's

920
00:47:48,320 --> 00:47:49,759
Speaker 1: more something that you want to own if you want

921
00:47:49,800 --> 00:47:52,799
Speaker 1: to influence the political process. That's the only reason I

922
00:47:52,800 --> 00:47:54,640
Speaker 1: think he might sell it. There were a couple of

923
00:47:54,719 --> 00:47:57,759
Speaker 1: other things that people were theorizing about why he might

924
00:47:57,800 --> 00:48:00,080
Speaker 1: want to get rid of the Post. In particular. I

925
00:48:00,120 --> 00:48:01,799
Speaker 1: don't know if you guys remember he got into the

926
00:48:01,920 --> 00:48:06,680
Speaker 1: spat with Joe Biden over corporate tax rates. Yeah, and

927
00:48:07,320 --> 00:48:10,640
Speaker 1: obviously when you own the Washington Post, like this is

928
00:48:10,680 --> 00:48:13,479
Speaker 1: no longer just sort of your opinion being put out there.

929
00:48:13,719 --> 00:48:15,759
Speaker 1: People that are going to look at the op eds

930
00:48:15,840 --> 00:48:17,880
Speaker 1: and the articles that are being written at the Post

931
00:48:18,200 --> 00:48:20,600
Speaker 1: and take a look at whether they are being reflective

932
00:48:20,680 --> 00:48:22,880
Speaker 1: of the view that you have publicly put forward like

933
00:48:23,400 --> 00:48:26,040
Speaker 1: you now are no longer just like a neutral observer

934
00:48:26,160 --> 00:48:29,160
Speaker 1: out there have in your take, it has huge implications

935
00:48:29,160 --> 00:48:31,680
Speaker 1: for what is happening at the newspaper that you own

936
00:48:31,840 --> 00:48:35,040
Speaker 1: and pay the salaries at. So some people were saying, Okay,

937
00:48:35,080 --> 00:48:38,000
Speaker 1: maybe he realized like he didn't like being muzzled this way.

938
00:48:38,600 --> 00:48:42,800
Speaker 1: And then there's another little sort of drama with regards

939
00:48:42,840 --> 00:48:46,200
Speaker 1: to any potential interest he might have in purchasing the

940
00:48:46,320 --> 00:48:49,360
Speaker 1: Washington Commanders, which is that the current owner, Dan Snyder,

941
00:48:49,360 --> 00:48:51,440
Speaker 1: who's got to be the worst owner in all of

942
00:48:51,680 --> 00:48:54,839
Speaker 1: NFL football history, as a long suffering Washington sports fan,

943
00:48:55,880 --> 00:48:59,520
Speaker 1: he hates Bethos and he hates the Washing Post. Why

944
00:49:00,040 --> 00:49:02,800
Speaker 1: because they were the ones that did those investigations into

945
00:49:02,840 --> 00:49:07,120
Speaker 1: all sorts of allegations of impropriety and sexual harassment rife

946
00:49:07,200 --> 00:49:13,480
Speaker 1: throughout the now Washington Commanders organization, and Snyder, they said,

947
00:49:13,560 --> 00:49:18,560
Speaker 1: suspects that Bezos encouraged that tough coverage in a bid

948
00:49:18,600 --> 00:49:21,400
Speaker 1: to force him to sell the team so that he

949
00:49:21,400 --> 00:49:24,239
Speaker 1: could then sort of pick up the pieces. So you know,

950
00:49:24,360 --> 00:49:27,680
Speaker 1: Snyder would have to agree to sell it to Bezos,

951
00:49:28,120 --> 00:49:33,640
Speaker 1: and he apparently reportedly hayes his guests. It's another complicating factor.

952
00:49:33,719 --> 00:49:36,239
Speaker 1: Five billion dollars is a real easy way to make

953
00:49:36,320 --> 00:49:38,480
Speaker 1: that stomach. And again, ultimately, these guys are all just

954
00:49:38,480 --> 00:49:40,640
Speaker 1: remember of the Green Party. They care about the money. Yeah,

955
00:49:40,640 --> 00:49:44,320
Speaker 1: put this up there on the screen. Bezos immediately denied

956
00:49:44,400 --> 00:49:47,560
Speaker 1: the report, but he's not ruling out buying the commanders.

957
00:49:47,600 --> 00:49:51,200
Speaker 1: I'd seen other speculation that he was considering bringing in outside.

958
00:49:51,200 --> 00:49:52,200
Speaker 1: I know that this is the way a lot of

959
00:49:52,239 --> 00:49:54,120
Speaker 1: people do it now. They'll do like a conglomerate, like

960
00:49:54,160 --> 00:49:57,040
Speaker 1: a couple of famous people or whatever, with some investments

961
00:49:57,040 --> 00:49:58,799
Speaker 1: and then buy it the team as a whole. I

962
00:49:58,840 --> 00:50:00,560
Speaker 1: know they do that in the NBA. I'm not quite

963
00:50:00,600 --> 00:50:02,600
Speaker 1: sure if they do that in the NFL. You're looking

964
00:50:02,640 --> 00:50:05,520
Speaker 1: at a real NFL fan here. People knows quite a bit.

965
00:50:05,600 --> 00:50:09,000
Speaker 1: But I do think that it is important because for me,

966
00:50:09,280 --> 00:50:13,680
Speaker 1: it just tracks with Bezos's evolution clearly in his in

967
00:50:13,760 --> 00:50:16,520
Speaker 1: his in way he sees himself. At first, he was

968
00:50:16,520 --> 00:50:18,720
Speaker 1: donating all this money to the Air and Space Museum

969
00:50:18,960 --> 00:50:22,920
Speaker 1: one hundred million dollars, the Obama Foundation, naming the auditorium

970
00:50:23,000 --> 00:50:25,719
Speaker 1: or whatever he was going for the respectability game. Did

971
00:50:26,080 --> 00:50:28,200
Speaker 1: Jones get a bunch of cash in that? Yes? Yeah,

972
00:50:28,200 --> 00:50:31,920
Speaker 1: oh yeah, like he listen, there's the Bezos Genius Grants,

973
00:50:31,920 --> 00:50:34,400
Speaker 1: which just happened to fund some of the non geniuses

974
00:50:34,960 --> 00:50:38,480
Speaker 1: in our society. Though he now appears to be much

975
00:50:38,520 --> 00:50:43,600
Speaker 1: more interested in Amazon Studios, going to the Oscars, hanging

976
00:50:43,640 --> 00:50:46,719
Speaker 1: out with the Rock, getting a bridge disassembled in order

977
00:50:46,760 --> 00:50:50,960
Speaker 1: to get his buying a super yacht. You know what?

978
00:50:51,040 --> 00:50:53,560
Speaker 1: Did he got into that weird thing with Leo DiCaprio

979
00:50:54,120 --> 00:50:57,200
Speaker 1: where he like joked about Leo stealing his girlfriend and

980
00:50:57,239 --> 00:51:01,040
Speaker 1: now buying the NFL. So this seems to be the

981
00:51:01,080 --> 00:51:05,439
Speaker 1: next evolution of bezos ken. I mean, you know, life

982
00:51:05,440 --> 00:51:08,520
Speaker 1: seems fun. Yeah, it's like real midlife crisis kind of

983
00:51:08,560 --> 00:51:11,440
Speaker 1: vibes too, especially that photo of him with his Miami

984
00:51:11,520 --> 00:51:13,560
Speaker 1: shirt like bawling out like I think he was literally

985
00:51:13,680 --> 00:51:16,040
Speaker 1: he literally looked like he was in Miami. Makes sense,

986
00:51:16,040 --> 00:51:18,439
Speaker 1: He's from there, so maybe you could take the boy out,

987
00:51:18,480 --> 00:51:22,120
Speaker 1: but that's something that clearly sticks with you anyway. That's

988
00:51:22,200 --> 00:51:27,839
Speaker 1: Bezos's next evolution. All right, Zachar, what are you looking at? Well,

989
00:51:28,160 --> 00:51:30,480
Speaker 1: the debt is one of those things that instantly evokes

990
00:51:30,560 --> 00:51:32,920
Speaker 1: a lot of emotion in people. It's something that has

991
00:51:32,960 --> 00:51:35,600
Speaker 1: animated American politics since the founding of the Republic, and

992
00:51:35,640 --> 00:51:37,719
Speaker 1: the fight's over it, the increase in it, and the

993
00:51:37,719 --> 00:51:39,879
Speaker 1: way that we manage it actually tells us a lot

994
00:51:39,880 --> 00:51:42,399
Speaker 1: about the times that we're in. In the twenty tens,

995
00:51:42,400 --> 00:51:44,000
Speaker 1: it seemed that we were living in the era of

996
00:51:44,040 --> 00:51:46,840
Speaker 1: debt politics, the rise of the Tea Party and Greece,

997
00:51:46,880 --> 00:51:49,600
Speaker 1: and then the immense fights that were picked with President Obama.

998
00:51:49,880 --> 00:51:51,920
Speaker 1: Trump seems to have wiped some of that away, but

999
00:51:52,000 --> 00:51:55,720
Speaker 1: with this temporary disappearance that we're back back to what though,

1000
00:51:55,920 --> 00:51:58,719
Speaker 1: exactly what is debt? Should we even care about it

1001
00:51:58,760 --> 00:52:01,440
Speaker 1: at all? How did he even get like this? Anyways?

1002
00:52:01,680 --> 00:52:04,120
Speaker 1: The modern history of US debt is actually fascinating and

1003
00:52:04,160 --> 00:52:06,840
Speaker 1: it tells us a lot about American elites and their priorities,

1004
00:52:06,880 --> 00:52:09,600
Speaker 1: both in the last thirty years and throughout our entire history.

1005
00:52:09,800 --> 00:52:11,799
Speaker 1: So let's go all the way back in time to

1006
00:52:11,880 --> 00:52:15,040
Speaker 1: the very beginning. When did we first incur debt? The

1007
00:52:15,120 --> 00:52:18,080
Speaker 1: first debt incurred by the sovereign United States. Technically happened

1008
00:52:18,160 --> 00:52:20,719
Speaker 1: during the Revolutionary War, but as we understood it today,

1009
00:52:20,760 --> 00:52:23,759
Speaker 1: after Alexander Hamilton structured the debt to the tune of

1010
00:52:23,800 --> 00:52:27,600
Speaker 1: nearly seventy seventy million dollars in seventeen ninety, debt continued

1011
00:52:27,640 --> 00:52:31,560
Speaker 1: to increase throughout the early nineteenth century with the Louisiana Purchase.

1012
00:52:31,760 --> 00:52:34,279
Speaker 1: Despite attempts at the time to pay it down, it

1013
00:52:34,440 --> 00:52:38,200
Speaker 1: especially ballooned during the War of eighteen twelve to approximately

1014
00:52:38,320 --> 00:52:41,280
Speaker 1: one hundred and twenty seven million dollars. Now, a familiar

1015
00:52:41,400 --> 00:52:44,000
Speaker 1: story begins to emerge in US history. We get a

1016
00:52:44,040 --> 00:52:48,080
Speaker 1: lot of debt during territorial expansion and during war, followed

1017
00:52:48,080 --> 00:52:50,319
Speaker 1: by a period of kind of paying it off but

1018
00:52:50,360 --> 00:52:53,640
Speaker 1: not really. President Andrew Jackson, in his campaign against the

1019
00:52:53,680 --> 00:52:56,080
Speaker 1: National Bank of the United States, actually reduced the debt

1020
00:52:56,120 --> 00:52:58,880
Speaker 1: to approximately zero in eighteen thirty five, only for it

1021
00:52:58,920 --> 00:53:02,239
Speaker 1: to then explode a again during the Mexican War. From

1022
00:53:02,239 --> 00:53:06,320
Speaker 1: that period onward, internal strife, territorial expansion, and more continued

1023
00:53:06,360 --> 00:53:09,480
Speaker 1: the debt upwards until it blew up during the American

1024
00:53:09,520 --> 00:53:13,239
Speaker 1: Civil War. There again we saw an increase historic rise

1025
00:53:13,320 --> 00:53:16,280
Speaker 1: in the debt, crossing the billion dollar threshold and peaking

1026
00:53:16,320 --> 00:53:19,520
Speaker 1: at approximately two point eight billion in eighteen sixty five.

1027
00:53:19,880 --> 00:53:22,759
Speaker 1: From there, the wild economic ups and downs of the

1028
00:53:22,760 --> 00:53:25,640
Speaker 1: Gilded Age kept us in debt, combined with the Spanish

1029
00:53:25,680 --> 00:53:30,200
Speaker 1: American War and other attempts at establishing the American Empire. Overall,

1030
00:53:30,320 --> 00:53:33,759
Speaker 1: the debt remained relatively stable, approximately consistent at two to

1031
00:53:33,800 --> 00:53:37,799
Speaker 1: three billion dollars until you guessed it. Another war, the

1032
00:53:37,880 --> 00:53:40,839
Speaker 1: First World War. It exploded American debt from two point

1033
00:53:40,920 --> 00:53:44,160
Speaker 1: nine billion in nineteen thirteen to five point seven billion

1034
00:53:44,200 --> 00:53:46,960
Speaker 1: when the war ended, and somehow it began ballooning to

1035
00:53:47,200 --> 00:53:50,879
Speaker 1: twenty five billion in nineteen twenty. The Roaring Twenties helped

1036
00:53:50,920 --> 00:53:52,759
Speaker 1: pay down some of America's debt, or at least keep

1037
00:53:52,760 --> 00:53:56,160
Speaker 1: it stable, dropping below sixteen billion, but going down until

1038
00:53:56,239 --> 00:54:00,080
Speaker 1: somewhat until the Great Depression. The election of FDR, the

1039
00:54:00,160 --> 00:54:04,160
Speaker 1: establishment of social welfare programs unprecedent at the time, exploded

1040
00:54:04,200 --> 00:54:06,879
Speaker 1: the debt again, beginning in nineteen thirty three at twenty

1041
00:54:06,920 --> 00:54:09,839
Speaker 1: two billion dollars and ending after World War Two at

1042
00:54:09,880 --> 00:54:13,480
Speaker 1: a whopping two hundred and fifty eight billion. Here again

1043
00:54:13,520 --> 00:54:16,600
Speaker 1: we see a new normal. America is the world leader.

1044
00:54:16,800 --> 00:54:19,440
Speaker 1: It has the expansion programs. These things seemed to be

1045
00:54:19,520 --> 00:54:23,400
Speaker 1: okay right up until nineteen sixty five, with the simultaneous

1046
00:54:23,440 --> 00:54:27,080
Speaker 1: expansion of medicare combined with the Vietnam War, pushed the

1047
00:54:27,120 --> 00:54:29,840
Speaker 1: debt up to some three hundred and fifty billion before

1048
00:54:29,840 --> 00:54:33,760
Speaker 1: it just kept blowing up even more exponentially. By nineteen eighty,

1049
00:54:33,800 --> 00:54:36,520
Speaker 1: we began teetering on the edge of one trillion dollars.

1050
00:54:36,680 --> 00:54:39,720
Speaker 1: And when Reagan came into office, he did two simultaneous things.

1051
00:54:39,960 --> 00:54:44,000
Speaker 1: He massively expanded the military budget and he cut taxes,

1052
00:54:44,280 --> 00:54:46,200
Speaker 1: and from there we are off to the races. The

1053
00:54:46,239 --> 00:54:49,200
Speaker 1: debt was increasing fivefold under Ronald Reagan and George W.

1054
00:54:49,280 --> 00:54:52,080
Speaker 1: Bush under Clint or George H. W. Bush. Under Clinton,

1055
00:54:52,239 --> 00:54:54,719
Speaker 1: we had a very brief respite, mostly because of the

1056
00:54:54,760 --> 00:54:58,520
Speaker 1: dot com boom, which massively increased federal tax revenues, and

1057
00:54:58,560 --> 00:55:01,560
Speaker 1: because finally we weren't fight any more damn wars. We

1058
00:55:01,640 --> 00:55:04,400
Speaker 1: reduced our military spending and we actually had a budget

1059
00:55:04,440 --> 00:55:08,239
Speaker 1: surplus for an extraordinary period, a very rare occurrence in

1060
00:55:08,320 --> 00:55:11,440
Speaker 1: US history. But all things come to an end. We

1061
00:55:11,520 --> 00:55:14,120
Speaker 1: got smacked by the dot com crash then nine to eleven.

1062
00:55:14,360 --> 00:55:16,400
Speaker 1: Bush then did the two things that you shouldn't do

1063
00:55:16,440 --> 00:55:18,879
Speaker 1: if you care about the national debt. He massively cut

1064
00:55:18,920 --> 00:55:22,360
Speaker 1: taxes and he invaded two foreign nations. Doesn't take a

1065
00:55:22,400 --> 00:55:24,840
Speaker 1: genius to figure out what comes next. A doubling of

1066
00:55:24,880 --> 00:55:28,400
Speaker 1: the national debt under George W. Bush to finance programs

1067
00:55:28,480 --> 00:55:32,440
Speaker 1: and disastrous foreign wars to the tune of trillions of

1068
00:55:32,480 --> 00:55:37,600
Speaker 1: dollars with less actual with more actual money. Obama similar story.

1069
00:55:37,800 --> 00:55:40,400
Speaker 1: You keep the war machine going in Afghanistan. In Iraq,

1070
00:55:40,640 --> 00:55:44,040
Speaker 1: debt keeps ballooning a trillion dollars or more so per year,

1071
00:55:44,239 --> 00:55:46,960
Speaker 1: in addition to Obamacare and some moderate expansion of the

1072
00:55:47,000 --> 00:55:50,439
Speaker 1: federal government. Then of course you get Donald Trump, who

1073
00:55:50,760 --> 00:55:53,319
Speaker 1: cut taxes again and for the most part kept the

1074
00:55:53,320 --> 00:55:57,280
Speaker 1: war machine running up until twenty nineteen. Then COVID happens

1075
00:55:57,400 --> 00:56:02,160
Speaker 1: and that accelerated the game even more unprecedented bailouts, stimulus checks, etc.

1076
00:56:02,640 --> 00:56:05,200
Speaker 1: So here we are thirty one trillion dollars in debt

1077
00:56:05,320 --> 00:56:07,840
Speaker 1: today counting. What do we do about it? If you

1078
00:56:07,880 --> 00:56:09,920
Speaker 1: look back at the story I just told, there are

1079
00:56:09,960 --> 00:56:13,000
Speaker 1: three major components that actually contribute the most overtime to

1080
00:56:13,040 --> 00:56:16,200
Speaker 1: our debt. One is social spending, which is enormously popular

1081
00:56:16,239 --> 00:56:20,400
Speaker 1: and effectively untouchable. Two is foreign wars since our founding,

1082
00:56:20,680 --> 00:56:24,239
Speaker 1: wars have historically ballooned the federal debt and expanded the

1083
00:56:24,320 --> 00:56:26,880
Speaker 1: role of government to levels that we never quite seem

1084
00:56:26,960 --> 00:56:30,440
Speaker 1: to ever get away from. Three is major economic downturn.

1085
00:56:30,840 --> 00:56:34,880
Speaker 1: Every major economic downturn has ballooned the debt, requiring in

1086
00:56:34,920 --> 00:56:38,759
Speaker 1: recent years not only less federal revenue, but bailouts. Now,

1087
00:56:38,800 --> 00:56:41,240
Speaker 1: I'm not against bailouts per se, as long as therefore

1088
00:56:41,320 --> 00:56:43,839
Speaker 1: the average American and not industry, but you can guess

1089
00:56:43,840 --> 00:56:46,400
Speaker 1: where the vast majority of bailout money has gone in

1090
00:56:46,440 --> 00:56:48,799
Speaker 1: the last two centuries, which brings us to the choice

1091
00:56:48,800 --> 00:56:51,160
Speaker 1: that we face today. Our debt has increased by ten

1092
00:56:51,200 --> 00:56:54,360
Speaker 1: trillion dollars just this year in twenty twenty two up

1093
00:56:54,440 --> 00:56:57,320
Speaker 1: to twenty twenty three. Worse, our actual debt servicing payments

1094
00:56:57,360 --> 00:57:00,879
Speaker 1: are going up astronomically because the Federals is now raising

1095
00:57:00,960 --> 00:57:03,160
Speaker 1: rates now to the extent that this is a problem,

1096
00:57:03,239 --> 00:57:05,560
Speaker 1: I think it just highlights how futile much of our

1097
00:57:05,600 --> 00:57:09,400
Speaker 1: discussion around this is. Here is the reality. Sixteen percent

1098
00:57:09,560 --> 00:57:12,080
Speaker 1: of the six point twenty seven trillion dollar federal budget

1099
00:57:12,320 --> 00:57:16,080
Speaker 1: is non military, non entitlement spending. That's basically a rounding

1100
00:57:16,200 --> 00:57:18,640
Speaker 1: error on our debt. We really only have a few

1101
00:57:18,640 --> 00:57:21,760
Speaker 1: good options for actually solving it, and those are stopping

1102
00:57:21,880 --> 00:57:26,520
Speaker 1: military adventurism abroad, ensuring proper federal revenues come in instead

1103
00:57:26,520 --> 00:57:30,720
Speaker 1: of slashing taxes repeatedly for the wealthy, and having a stable,

1104
00:57:31,120 --> 00:57:35,000
Speaker 1: non financialized economy that doesn't crash all the time. I've

1105
00:57:35,080 --> 00:57:37,240
Speaker 1: yet to see anyone actually lay it out this way,

1106
00:57:37,440 --> 00:57:39,640
Speaker 1: And when you look at the story over nearly three

1107
00:57:39,720 --> 00:57:43,440
Speaker 1: hundred years, it actually seems pretty simple. Unfortunately, we know

1108
00:57:43,560 --> 00:57:46,320
Speaker 1: that at every point we almost never learned the right lesson.

1109
00:57:46,600 --> 00:57:48,760
Speaker 1: It's really interesting, actually, and if you want to hear

1110
00:57:48,960 --> 00:57:52,520
Speaker 1: my reaction to Sager's monologue, become a premium subscriber today

1111
00:57:52,560 --> 00:57:57,480
Speaker 1: at Breakingpoints dot Com, Cristsell, what do you take a

1112
00:57:57,520 --> 00:58:00,360
Speaker 1: look at? Well, guys, as you've probably noticedom is this

1113
00:58:00,440 --> 00:58:02,240
Speaker 1: year is how we seem to be living in a

1114
00:58:02,280 --> 00:58:07,040
Speaker 1: golden era of scams, Crypto MLMs, scam influencers, scam members

1115
00:58:07,080 --> 00:58:09,680
Speaker 1: of Congress. Truly feels like today the con man or

1116
00:58:09,720 --> 00:58:12,800
Speaker 1: con woman is king. But it's not just listless young

1117
00:58:12,840 --> 00:58:15,440
Speaker 1: men or dumb rich people or desperate housewives who are

1118
00:58:15,440 --> 00:58:20,040
Speaker 1: being scammed routinely. No the highest echalons of society. They

1119
00:58:20,040 --> 00:58:23,720
Speaker 1: are falling victim to the same charlatan's impredations as everyone else.

1120
00:58:24,080 --> 00:58:28,160
Speaker 1: The latest mark here Wall Street Titan JP Morgan Chase,

1121
00:58:28,360 --> 00:58:31,720
Speaker 1: they have now admitted they got scammed by a fraud

1122
00:58:31,800 --> 00:58:35,000
Speaker 1: business to the tune of one hundred and seventy five

1123
00:58:35,160 --> 00:58:38,320
Speaker 1: million dollars. Now, before I get to all of the

1124
00:58:38,360 --> 00:58:40,000
Speaker 1: details here, and you are going to love some of

1125
00:58:40,040 --> 00:58:42,480
Speaker 1: the details here, let's just set the table. One thing

1126
00:58:42,480 --> 00:58:45,160
Speaker 1: that is core to a successful con is getting the

1127
00:58:45,200 --> 00:58:48,520
Speaker 1: story right, figuring out what it is that your mark

1128
00:58:48,600 --> 00:58:52,000
Speaker 1: wants to hear. What are their vulnerabilities, their wants, their fears,

1129
00:58:52,040 --> 00:58:55,360
Speaker 1: their desires. And like other successful con artists, the young

1130
00:58:55,400 --> 00:58:58,360
Speaker 1: woman at the heart of this Chase scam knew exactly

1131
00:58:58,440 --> 00:59:00,200
Speaker 1: the part to play and the song to say to

1132
00:59:00,280 --> 00:59:03,800
Speaker 1: successfully defraud people who are supposed to be among the

1133
00:59:03,800 --> 00:59:08,480
Speaker 1: most sophisticated investors in the entire world. So here's what happened.

1134
00:59:08,560 --> 00:59:11,320
Speaker 1: It all started with a big announcement JP Morgan Chase,

1135
00:59:11,400 --> 00:59:14,000
Speaker 1: led by fame CEO Jamie Diamond. They issued a big

1136
00:59:14,000 --> 00:59:16,760
Speaker 1: press release crowing that they were buying a company called

1137
00:59:16,840 --> 00:59:19,960
Speaker 1: Frank which supposedly made it easier for college students to

1138
00:59:20,040 --> 00:59:24,320
Speaker 1: apply for financial assistance. Here's how CNBC framed that news.

1139
00:59:24,640 --> 00:59:26,720
Speaker 1: Jamie Diamond declared last year he planned to get more

1140
00:59:26,720 --> 00:59:28,920
Speaker 1: aggressive and seeking takeovers, and he's certainly made good on

1141
00:59:28,920 --> 00:59:31,440
Speaker 1: that promise, with JP Morgan buying another financial startup. This

1142
00:59:31,480 --> 00:59:35,480
Speaker 1: time it's college financial planning platform. Frankc dot COM's he

1143
00:59:35,640 --> 00:59:37,720
Speaker 1: Son as the exclusive details on the story, and he

1144
00:59:37,840 --> 00:59:41,080
Speaker 1: joins us now, Hugh, why this deal? Hey, it's good

1145
00:59:41,080 --> 00:59:43,080
Speaker 1: to be with you, Kelly. So the short answer is,

1146
00:59:43,120 --> 00:59:45,440
Speaker 1: what they're getting is a software platform that's been pretty

1147
00:59:45,440 --> 00:59:48,800
Speaker 1: effective at serving people young people who are heading to

1148
00:59:48,840 --> 00:59:50,320
Speaker 1: college and need to try to pay for it. So

1149
00:59:50,440 --> 00:59:52,400
Speaker 1: it's got a bunch of tools. The biggest, I think

1150
00:59:52,440 --> 00:59:55,560
Speaker 1: the main tool is an automated service that helps students

1151
00:59:55,600 --> 00:59:58,720
Speaker 1: apply for federal aids. It's got five million users, and

1152
00:59:59,160 --> 01:00:00,840
Speaker 1: you know, Japie Morgan wants to get in on that.

1153
01:00:00,880 --> 01:00:03,680
Speaker 1: They want to basically have an affinity program essentially that

1154
01:00:04,120 --> 01:00:06,400
Speaker 1: you know, as you're thinking about attending college, you've got

1155
01:00:06,440 --> 01:00:08,520
Speaker 1: to chase you know, if all goes going to plan,

1156
01:00:08,560 --> 01:00:11,000
Speaker 1: you got to chase bank account. And after that, you know,

1157
01:00:11,040 --> 01:00:12,919
Speaker 1: if you graduate, perhaps you're going to add a credit card,

1158
01:00:13,160 --> 01:00:15,400
Speaker 1: mortgage in auto. So this is their plate to sort

1159
01:00:15,400 --> 01:00:17,680
Speaker 1: of get people hooked into the Chase ecosystem at an

1160
01:00:17,720 --> 01:00:21,560
Speaker 1: early age. So Frank this company specifically claimed to JP

1161
01:00:21,680 --> 01:00:24,840
Speaker 1: Morgan that they had more than four million customers who

1162
01:00:24,880 --> 01:00:27,479
Speaker 1: were already using their tools to apply for financial aid,

1163
01:00:27,760 --> 01:00:30,880
Speaker 1: and as the CNBC analyst coldly observes, this represented an

1164
01:00:31,040 --> 01:00:33,320
Speaker 1: enticing opportunity for Chase to be able to get these

1165
01:00:33,360 --> 01:00:36,640
Speaker 1: young people hooked on their debt products early. Sure starts

1166
01:00:36,680 --> 01:00:39,040
Speaker 1: with a streamlined aid process, but before you know it,

1167
01:00:39,160 --> 01:00:41,760
Speaker 1: you've got these eighteen year olds into credit cards and

1168
01:00:41,840 --> 01:00:44,600
Speaker 1: many other services. Wonderful new way for Chase to profit

1169
01:00:44,640 --> 01:00:47,440
Speaker 1: off of the college affordability crisis while also hooking young

1170
01:00:47,440 --> 01:00:50,200
Speaker 1: people into a whole lifetime of high interest in debtedness.

1171
01:00:50,440 --> 01:00:53,560
Speaker 1: The business prospect of getting their hooks into these young

1172
01:00:53,600 --> 01:00:58,200
Speaker 1: people early through a seemingly charitable service was absolutely irresistible,

1173
01:00:58,560 --> 01:01:01,160
Speaker 1: and the young founder of Frank herself, Health Charlie Javis,

1174
01:01:01,320 --> 01:01:04,600
Speaker 1: Like Elizabeth Thernos and Sam mccinfried before her, she had

1175
01:01:04,720 --> 01:01:08,480
Speaker 1: exactly the right personal narrative to sell to elites. As

1176
01:01:08,480 --> 01:01:10,400
Speaker 1: The New York Times put it quite well, I thought,

1177
01:01:10,560 --> 01:01:14,160
Speaker 1: Miss Javis's story is an archetypal tale of late stage

1178
01:01:14,160 --> 01:01:17,840
Speaker 1: startup hustle culture. A teenage prodigy turned Ivy League social

1179
01:01:17,960 --> 01:01:21,800
Speaker 1: enterprise maven and shape shifting savior of higher education, or

1180
01:01:21,880 --> 01:01:24,440
Speaker 1: so she would have the world believe. Let's get a

1181
01:01:24,480 --> 01:01:27,760
Speaker 1: sense of our protagonist here, alleged con woman extraordinaire. Here

1182
01:01:27,840 --> 01:01:30,840
Speaker 1: is Charlie Javis on some show that's sponsored by American Express,

1183
01:01:31,120 --> 01:01:33,960
Speaker 1: answering a question about what she's had to overcome as

1184
01:01:34,000 --> 01:01:37,120
Speaker 1: a female entrepreneur, major roadblock, and you might hear it

1185
01:01:37,160 --> 01:01:38,920
Speaker 1: from other female entrepreneurs who are trying to pitch like

1186
01:01:38,960 --> 01:01:42,560
Speaker 1: tampon companies or like underwear. Who knows, but male investors

1187
01:01:42,640 --> 01:01:45,120
Speaker 1: just don't know about that full segment of the market. Now,

1188
01:01:45,240 --> 01:01:47,520
Speaker 1: try and take a segment of the market where investors

1189
01:01:47,560 --> 01:01:50,040
Speaker 1: who are usually wealthy have inherited it or made their money,

1190
01:01:50,120 --> 01:01:53,000
Speaker 1: have zero exposure to fatso or the financially process, and

1191
01:01:53,040 --> 01:01:54,960
Speaker 1: so it's almost like take odds where you might have

1192
01:01:54,960 --> 01:01:57,320
Speaker 1: fifteen to twenty percent female investors. Now flip it to

1193
01:01:57,360 --> 01:02:00,000
Speaker 1: people where literally maybe one person out of the hundre

1194
01:02:00,040 --> 01:02:02,200
Speaker 1: as I've spoken to has had personal exposure with it,

1195
01:02:02,280 --> 01:02:05,400
Speaker 1: and that's the largest roadblock. In selling Frank, Javis spun

1196
01:02:05,520 --> 01:02:08,680
Speaker 1: elaborate tales of the pain that she had personally suffered

1197
01:02:08,720 --> 01:02:12,040
Speaker 1: in trying to afford college. She recounted difficult stories of

1198
01:02:12,040 --> 01:02:14,400
Speaker 1: her mother weeping as she tried to sort through the

1199
01:02:14,440 --> 01:02:18,280
Speaker 1: complicated financial aid process, months of agony as they waited

1200
01:02:18,320 --> 01:02:21,440
Speaker 1: to get answers. In her telling, Javis started Frank to

1201
01:02:21,520 --> 01:02:24,880
Speaker 1: keep other struggling students from dealing with the excruciating ordeal

1202
01:02:24,960 --> 01:02:28,480
Speaker 1: her own family had supposedly been put through. Course, turned

1203
01:02:28,520 --> 01:02:31,600
Speaker 1: out both her parents had master's degrees. Her dad literally

1204
01:02:31,600 --> 01:02:34,480
Speaker 1: worked on Wall Street for Goldman, Sachs and Merrill Lynch

1205
01:02:34,760 --> 01:02:37,480
Speaker 1: for decades. Hard and Matt and Javis was quite as

1206
01:02:37,520 --> 01:02:41,000
Speaker 1: financially strapped and struggling with complex financial bureaucracy as her

1207
01:02:41,000 --> 01:02:44,120
Speaker 1: corporate origin story would have you believed. But no one

1208
01:02:44,120 --> 01:02:47,120
Speaker 1: in the female empowerment press or business journalists world looked

1209
01:02:47,120 --> 01:02:50,360
Speaker 1: into any of these inconsistencies. So Javis started peering in

1210
01:02:50,480 --> 01:02:53,360
Speaker 1: all the important lists of up and coming entrepreneurs. She

1211
01:02:53,480 --> 01:02:57,080
Speaker 1: made Forbes thirty under thirty, she made Cranes forty under

1212
01:02:57,080 --> 01:02:59,800
Speaker 1: forty she got this weird quote, and forty under forty.

1213
01:02:59,800 --> 01:03:01,240
Speaker 1: By by the way, in this write up, where she

1214
01:03:01,280 --> 01:03:04,880
Speaker 1: says that since women disproportionately hold student loan debt, Frank

1215
01:03:04,960 --> 01:03:09,480
Speaker 1: is quote not as masculine around money, whatever that means.

1216
01:03:09,680 --> 01:03:12,560
Speaker 1: As time went on, academics and even government agencies in

1217
01:03:12,600 --> 01:03:15,200
Speaker 1: the student loan space started growing suspicious of the many

1218
01:03:15,200 --> 01:03:17,919
Speaker 1: claims made by Frank about their services, their customer base,

1219
01:03:17,960 --> 01:03:21,240
Speaker 1: and their purported college partners. But JP Morgan Chase apparently

1220
01:03:21,400 --> 01:03:23,600
Speaker 1: didn't talk to any of them. So when presented this

1221
01:03:23,680 --> 01:03:28,040
Speaker 1: seemingly perfect package, a four million plus receptive young Frank

1222
01:03:28,120 --> 01:03:30,720
Speaker 1: customers that they could market their products to, and a

1223
01:03:30,840 --> 01:03:33,720
Speaker 1: fresh face founder named on all the right list, they

1224
01:03:33,800 --> 01:03:36,560
Speaker 1: jumped right in with that one hundred and seventy five

1225
01:03:36,760 --> 01:03:40,280
Speaker 1: million dollar offer. It was just one little problem. Those

1226
01:03:40,320 --> 01:03:43,240
Speaker 1: four million plus established customers that Frank claimed that they

1227
01:03:43,320 --> 01:03:47,280
Speaker 1: had they were fake. All but a few hundred thousands

1228
01:03:47,320 --> 01:03:50,120
Speaker 1: of of them were invented out of whole cloth in

1229
01:03:50,240 --> 01:03:52,880
Speaker 1: order to get through the JP Mortgan Chase vetting process.

1230
01:03:52,920 --> 01:03:57,480
Speaker 1: I'm talking fake names, fake birthdays, high schools, everything, As

1231
01:03:57,560 --> 01:04:00,200
Speaker 1: Chase put it in their legal filing against Javis quote.

1232
01:04:00,400 --> 01:04:04,080
Speaker 1: In reality, Frank was nearly four million short of its

1233
01:04:04,120 --> 01:04:07,920
Speaker 1: representations to JP Morgan Chase. You see, after her initial

1234
01:04:07,960 --> 01:04:10,320
Speaker 1: pitch to Chase claiming she had four million plus customers,

1235
01:04:10,480 --> 01:04:12,840
Speaker 1: she had to actually put together a fake list in

1236
01:04:12,960 --> 01:04:15,400
Speaker 1: order to pass that vetting process. So to accomplish this,

1237
01:04:15,760 --> 01:04:18,560
Speaker 1: she took a couple of different approaches. First off, she

1238
01:04:18,680 --> 01:04:22,280
Speaker 1: hired a data scientist to generate a quote synthetic list,

1239
01:04:22,520 --> 01:04:25,200
Speaker 1: just totally fabricated data is what that means. That was

1240
01:04:25,320 --> 01:04:27,680
Speaker 1: enough to get her through that deal diligence and get

1241
01:04:27,680 --> 01:04:31,480
Speaker 1: the deal closed. But then Javis had another problem. Chase

1242
01:04:31,520 --> 01:04:35,000
Speaker 1: obviously wanted to market to those four million customers, and

1243
01:04:35,040 --> 01:04:37,320
Speaker 1: they were going to need to send some actual, real

1244
01:04:37,440 --> 01:04:42,400
Speaker 1: email addresses solicitations. So to solve that issue, Javis purchased

1245
01:04:42,440 --> 01:04:45,440
Speaker 1: a list of real college students and their email addresses,

1246
01:04:45,640 --> 01:04:49,040
Speaker 1: hoping that these acquired addresses would be enough to continue

1247
01:04:49,160 --> 01:04:52,360
Speaker 1: tricking JP Morgan. Chase red flag started to pop up

1248
01:04:52,360 --> 01:04:54,720
Speaker 1: with this list, though almost immediately. First of all, this

1249
01:04:54,800 --> 01:04:57,680
Speaker 1: is kind of funny detail. A Chase engineer noticed that

1250
01:04:57,720 --> 01:04:59,520
Speaker 1: the number of lines in one of the files that

1251
01:04:59,520 --> 01:05:04,200
Speaker 1: they were see from Frank suspiciously matched precisely the maximum

1252
01:05:04,320 --> 01:05:06,760
Speaker 1: number of rows that you're allowed in an Excel spreadsheet.

1253
01:05:07,040 --> 01:05:09,960
Speaker 1: The whole cover story was completely blown though. When emails

1254
01:05:09,960 --> 01:05:12,800
Speaker 1: went out to a subset of this new purchased list,

1255
01:05:13,080 --> 01:05:17,040
Speaker 1: more than seventy percent of Chase's marketing emails to this

1256
01:05:17,200 --> 01:05:22,080
Speaker 1: supposed Frank customer list bounced back as undeliverable. Of the

1257
01:05:22,120 --> 01:05:25,400
Speaker 1: emails that did go through, almost no one opened the

1258
01:05:25,400 --> 01:05:29,200
Speaker 1: email or clicked on any link contained therein. That's the

1259
01:05:29,240 --> 01:05:31,400
Speaker 1: sort of result that you might expect from a purchase

1260
01:05:31,480 --> 01:05:33,680
Speaker 1: list of random college students, which is in truth, what

1261
01:05:33,720 --> 01:05:36,960
Speaker 1: this list actually was. Definitely not the result they expected

1262
01:05:36,960 --> 01:05:40,760
Speaker 1: from a cultivated list of students that had supposedly affirmatively

1263
01:05:40,760 --> 01:05:43,000
Speaker 1: signed up with interest in the products and services that

1264
01:05:43,040 --> 01:05:46,320
Speaker 1: Frank and Chase were then offering. After the marketing campaign

1265
01:05:46,320 --> 01:05:50,840
Speaker 1: testra and crashed and burn, whole plot was uncovered quite easily, hilariously,

1266
01:05:50,920 --> 01:05:53,760
Speaker 1: all of Javis's scheming about how to generate these fake

1267
01:05:53,880 --> 01:05:56,200
Speaker 1: names and make it look real, that was all done

1268
01:05:56,240 --> 01:05:59,960
Speaker 1: on Frank email accounts, and once JP Morgan Chase acquired Frank,

1269
01:06:00,360 --> 01:06:03,040
Speaker 1: they also acquired those email accounts and could just go

1270
01:06:03,120 --> 01:06:06,400
Speaker 1: back through the email traffic that documented every step of

1271
01:06:06,440 --> 01:06:09,680
Speaker 1: the fraud in the merger. All told, Charlie Javis pocketed

1272
01:06:09,680 --> 01:06:12,920
Speaker 1: about thirty million dollars in proceed and also secured a

1273
01:06:13,040 --> 01:06:16,680
Speaker 1: twenty million dollar retention bonus. JP Morgan Chase is of

1274
01:06:16,760 --> 01:06:18,720
Speaker 1: course trying to claw all of their money back, and

1275
01:06:18,760 --> 01:06:20,800
Speaker 1: I should add here for the lawyers, Miss Javis of

1276
01:06:20,800 --> 01:06:23,560
Speaker 1: course denies any wrongdoing and is in fact counter sewing

1277
01:06:23,680 --> 01:06:27,160
Speaker 1: JP Morgan Chase. So what is the big takeaway here?

1278
01:06:27,720 --> 01:06:31,080
Speaker 1: Number one? Know yourself and your vulnerabilities, know what stories

1279
01:06:31,080 --> 01:06:33,680
Speaker 1: you would be susceptible to, and don't be an easy mark,

1280
01:06:33,720 --> 01:06:35,919
Speaker 1: because I truly do believe we're living in a moment

1281
01:06:35,960 --> 01:06:39,000
Speaker 1: that is a wash in audacious schemes and audacious fraud.

1282
01:06:39,120 --> 01:06:41,400
Speaker 1: And if JP Morgan Chase can be tricked, you can

1283
01:06:41,480 --> 01:06:45,200
Speaker 1: be too. Number two, it's humiliating that one of the

1284
01:06:45,200 --> 01:06:48,880
Speaker 1: world's most sophisticated financial institutions could be duped by such

1285
01:06:48,920 --> 01:06:52,880
Speaker 1: a brazen scam. Remember all of the supposedly brilliant minds

1286
01:06:52,880 --> 01:06:55,320
Speaker 1: and a lead investors in famous business journals who were

1287
01:06:55,320 --> 01:06:58,400
Speaker 1: fooled by Elizabeth Holmes of Farnos and Sam BigMan Freed

1288
01:06:58,480 --> 01:07:02,240
Speaker 1: of FTX are not half as smart or all knowing

1289
01:07:02,360 --> 01:07:05,400
Speaker 1: as they think that they are. Finally, in these successful

1290
01:07:05,440 --> 01:07:08,280
Speaker 1: cons we can see the outlines of the failures that

1291
01:07:08,360 --> 01:07:10,680
Speaker 1: exist in our society, and that honestly is the part

1292
01:07:10,680 --> 01:07:13,520
Speaker 1: that fascinates me the most. With this story, we catch

1293
01:07:13,520 --> 01:07:16,280
Speaker 1: a glimpse of a criminal higher education system that inflicts

1294
01:07:16,320 --> 01:07:19,320
Speaker 1: deep financial pain on millions to the profit of a few.

1295
01:07:19,720 --> 01:07:23,000
Speaker 1: We see greedy banks licking their lips at the chance

1296
01:07:23,040 --> 01:07:26,000
Speaker 1: to market a lifetime of debt to these young people.

1297
01:07:26,320 --> 01:07:29,920
Speaker 1: We see bio and identity weaponized to gloss over failed

1298
01:07:29,960 --> 01:07:33,680
Speaker 1: products and fake claims. We see how little due diligence

1299
01:07:33,840 --> 01:07:36,800
Speaker 1: any of our higher institutions of media and finance are

1300
01:07:36,920 --> 01:07:39,360
Speaker 1: actually doing, at least when it comes to people who

1301
01:07:39,360 --> 01:07:42,000
Speaker 1: have been vouched for by other elites. If we are

1302
01:07:42,000 --> 01:07:43,920
Speaker 1: in a golden age of con artists, it's because we

1303
01:07:43,960 --> 01:07:46,320
Speaker 1: are in a dark age of societal failures that have

1304
01:07:46,480 --> 01:07:51,800
Speaker 1: left almost everyone vulnerable. And this was a humiliating experience

1305
01:07:51,840 --> 01:07:54,320
Speaker 1: for Chase because they had to pull down the website.

1306
01:07:54,320 --> 01:07:57,040
Speaker 1: And if you want to hear my reaction to Crystal's monologue,

1307
01:07:57,040 --> 01:08:03,000
Speaker 1: become a premium subscriber today at breakingpoints dot com. So

1308
01:08:03,160 --> 01:08:05,760
Speaker 1: joining us now we have the aforementioned Derek Thompson. He's

1309
01:08:05,800 --> 01:08:07,680
Speaker 1: a staff writer at the Atlantic and host of the

1310
01:08:07,760 --> 01:08:11,560
Speaker 1: podcast Plain English, the latest episode of which looks at

1311
01:08:11,560 --> 01:08:13,680
Speaker 1: these tech layoffs. Great to see Derek, Good to see Man.

1312
01:08:14,960 --> 01:08:17,280
Speaker 1: Good to see you guys too. Yeah. Absolutely, so let's

1313
01:08:17,320 --> 01:08:19,280
Speaker 1: go and throw his piece from the Atlantic up on

1314
01:08:19,320 --> 01:08:21,680
Speaker 1: the screen. Here. The headline is what the tech and

1315
01:08:21,760 --> 01:08:24,439
Speaker 1: media layoffs are really telling us about the economy. About

1316
01:08:24,439 --> 01:08:26,599
Speaker 1: one hundred and thirty thousand people have been dismissed from

1317
01:08:26,600 --> 01:08:29,360
Speaker 1: their jobs at large tech and media companies in the

1318
01:08:29,400 --> 01:08:33,160
Speaker 1: past twelve months. Why and what you get at here, Derek,

1319
01:08:33,240 --> 01:08:35,479
Speaker 1: is something that we've been really interested in as well,

1320
01:08:35,800 --> 01:08:38,880
Speaker 1: which is, you know, in previous years you had these

1321
01:08:38,880 --> 01:08:41,320
Speaker 1: sort of knowledge economy and tech workers that were in

1322
01:08:41,479 --> 01:08:43,320
Speaker 1: very high demand and seem to be able to sort

1323
01:08:43,360 --> 01:08:45,280
Speaker 1: of like name their price. And at the other end

1324
01:08:45,320 --> 01:08:47,599
Speaker 1: of the spectrum, we had service sector workers who you know,

1325
01:08:47,640 --> 01:08:50,920
Speaker 1: employers are basically treating as disposable. Well, now you have

1326
01:08:51,040 --> 01:08:54,840
Speaker 1: a very low unemployment rate, but at the same time

1327
01:08:54,880 --> 01:08:58,080
Speaker 1: you see these huge layoffs in the tech sector in particular,

1328
01:08:58,120 --> 01:08:59,719
Speaker 1: but also, as you point out, in the media sector

1329
01:08:59,720 --> 01:09:04,080
Speaker 1: as well. So what is going on here isn't an

1330
01:09:04,120 --> 01:09:08,080
Speaker 1: incredible flipping like in the first part of this century.

1331
01:09:08,280 --> 01:09:11,759
Speaker 1: Is let's say the late twenty twenty tens. The story

1332
01:09:11,920 --> 01:09:14,960
Speaker 1: was that the labor market was terrible and Silicon Valley

1333
01:09:15,000 --> 01:09:17,120
Speaker 1: was the future, and then the pandemic was sort of

1334
01:09:17,160 --> 01:09:20,960
Speaker 1: that narrative on steroids. We had a flash freeze depression

1335
01:09:21,080 --> 01:09:24,920
Speaker 1: in twenty twenty, but the big tech companies continue to

1336
01:09:25,040 --> 01:09:28,320
Speaker 1: hire more and more people. Now the opposite is happening.

1337
01:09:28,360 --> 01:09:31,000
Speaker 1: The overall unemployment rate is three point five percent to

1338
01:09:31,040 --> 01:09:34,160
Speaker 1: the first decimal. That is the lowest unemployment rate of

1339
01:09:34,200 --> 01:09:36,639
Speaker 1: the twenty first century and the lowest unemployment rate going

1340
01:09:36,680 --> 01:09:39,400
Speaker 1: back at least forty fifty years. At the same time,

1341
01:09:39,520 --> 01:09:41,840
Speaker 1: now you have one hundred thirty thousand people laid off

1342
01:09:41,840 --> 01:09:43,840
Speaker 1: from the big tech and media companies. One hundred and

1343
01:09:43,840 --> 01:09:46,000
Speaker 1: thirty thousand people. To give you an impression, that's the

1344
01:09:46,040 --> 01:09:49,960
Speaker 1: size of Apple before the pandemic. That is a lot

1345
01:09:50,120 --> 01:09:51,880
Speaker 1: of people. And yet it's not showing up in the

1346
01:09:51,920 --> 01:09:54,639
Speaker 1: unemployment rate because we are having a kind of jobs

1347
01:09:54,640 --> 01:09:58,240
Speaker 1: recession that is concentrated in big tech. So why is

1348
01:09:58,240 --> 01:10:00,479
Speaker 1: this happening? I run through a bunch of reasons both

1349
01:10:00,520 --> 01:10:03,559
Speaker 1: that article and my podcast Planned English. I'll start with this.

1350
01:10:04,400 --> 01:10:07,720
Speaker 1: People thought that the pandemic was going to be an acceleration.

1351
01:10:07,960 --> 01:10:09,320
Speaker 1: We said, you know, we're going to be living like

1352
01:10:09,360 --> 01:10:11,840
Speaker 1: this forever. In the future, we're working from home, ordering

1353
01:10:11,880 --> 01:10:14,439
Speaker 1: all of our groceries from online, never going to grocery stores,

1354
01:10:14,520 --> 01:10:17,080
Speaker 1: never going to movies, streaming everything that we watch. But

1355
01:10:17,160 --> 01:10:19,679
Speaker 1: what happened as the pandemic wound down, as we'd gotten

1356
01:10:19,680 --> 01:10:21,919
Speaker 1: too the sort of let's call it late pandemic period,

1357
01:10:22,320 --> 01:10:24,479
Speaker 1: we flipped back. We sort of going at out restaurants,

1358
01:10:24,520 --> 01:10:26,360
Speaker 1: We started seeing movies a little bit more, we sorted

1359
01:10:26,360 --> 01:10:28,479
Speaker 1: commuting to work a little bit more. And as a result,

1360
01:10:28,560 --> 01:10:31,560
Speaker 1: is future that tech thought would materialize and not materialize

1361
01:10:31,600 --> 01:10:34,320
Speaker 1: the exact same way. Obviously, at the same time, inflation

1362
01:10:34,400 --> 01:10:37,400
Speaker 1: went up, rates went up. That means their stock valuations

1363
01:10:37,439 --> 01:10:40,280
Speaker 1: came down, and they said, we hired for a future

1364
01:10:40,360 --> 01:10:43,439
Speaker 1: that did not materialize. That means we have to start

1365
01:10:43,520 --> 01:10:46,080
Speaker 1: laying people off, and that brings you to six percent

1366
01:10:46,439 --> 01:10:48,840
Speaker 1: ten percent layoffs. I just want to throw one more

1367
01:10:48,920 --> 01:10:51,639
Speaker 1: thing in here, and we can discuss that whatever you want.

1368
01:10:51,880 --> 01:10:53,680
Speaker 1: I also think it's an element of what's called in

1369
01:10:53,720 --> 01:10:58,240
Speaker 1: psychology social proof. That is, if Microsoft lays off ten

1370
01:10:58,240 --> 01:11:01,080
Speaker 1: thousand people, it's much easier for Google to then lay

1371
01:11:01,120 --> 01:11:04,720
Speaker 1: off exactly ten thousand people. Much easier for salesforce to

1372
01:11:04,760 --> 01:11:07,720
Speaker 1: then lay off ten thousand people. You can have kind

1373
01:11:07,760 --> 01:11:11,519
Speaker 1: of cordated layoffs or de risked layoffs as a CEO,

1374
01:11:11,880 --> 01:11:14,439
Speaker 1: because you don't expect the media to get as mad

1375
01:11:14,479 --> 01:11:16,880
Speaker 1: at you if everybody is doing it right. It's a

1376
01:11:16,880 --> 01:11:19,240
Speaker 1: little bit like a riot. If the first person throws

1377
01:11:19,280 --> 01:11:22,759
Speaker 1: a rock through window, they're crazy. If the nineteenth person

1378
01:11:22,800 --> 01:11:24,760
Speaker 1: throws a rock through window, they're just a part of

1379
01:11:24,760 --> 01:11:27,000
Speaker 1: the crowd. So I do think there's an economic story

1380
01:11:27,040 --> 01:11:29,680
Speaker 1: to tell here, but there's also a psychology story to

1381
01:11:29,720 --> 01:11:32,719
Speaker 1: tell here with regard to the CEOs that are doing

1382
01:11:32,760 --> 01:11:35,960
Speaker 1: this to juice up their stock valuations. That's a really

1383
01:11:36,000 --> 01:11:38,080
Speaker 1: excellent point, actually, Derek. I've seen a lot of Silicon

1384
01:11:38,160 --> 01:11:41,120
Speaker 1: Valley guys who were looking at the Elon situation. Many

1385
01:11:41,160 --> 01:11:42,599
Speaker 1: of them are like, look, I'm not even a fan

1386
01:11:42,640 --> 01:11:45,400
Speaker 1: of Elon. They're like, but if you can fire you know,

1387
01:11:45,520 --> 01:11:48,679
Speaker 1: seventy percent of your staff and your product still works.

1388
01:11:48,720 --> 01:11:51,400
Speaker 1: And I'm putting aside the ad revenue and the down

1389
01:11:51,720 --> 01:11:54,120
Speaker 1: on that, but the core product itself doesn't appear to

1390
01:11:54,160 --> 01:11:57,240
Speaker 1: have as many problems as predicted. He said, you're an idiot.

1391
01:11:57,280 --> 01:11:59,960
Speaker 1: If you think every Silicon Valley CEO is not paying

1392
01:12:00,040 --> 01:12:01,600
Speaker 1: attention to that, Do you think that that might have

1393
01:12:01,640 --> 01:12:03,880
Speaker 1: played summer role in this in addition to the cheap

1394
01:12:03,880 --> 01:12:07,680
Speaker 1: money discussion. Yeah, it's a really interesting point. Let me

1395
01:12:07,680 --> 01:12:09,519
Speaker 1: say two things here. The first point to make is

1396
01:12:09,520 --> 01:12:12,519
Speaker 1: that you're putting meat on the bones of the social

1397
01:12:12,680 --> 01:12:16,679
Speaker 1: proof theory Elon might have offered in this story, social

1398
01:12:16,760 --> 01:12:19,520
Speaker 1: proof that you can lay off a ton of engineers

1399
01:12:19,520 --> 01:12:22,360
Speaker 1: and computer programmers and people maybe in HR or in

1400
01:12:22,400 --> 01:12:24,880
Speaker 1: some other part of the company, and the company seems

1401
01:12:24,920 --> 01:12:26,960
Speaker 1: to be at least the product seems to be still

1402
01:12:27,040 --> 01:12:31,879
Speaker 1: moving predictably through the rails. Okay, fine, that I understand

1403
01:12:31,880 --> 01:12:33,639
Speaker 1: why that might get some people to say, I thought

1404
01:12:33,640 --> 01:12:36,320
Speaker 1: about laying off five percent of my workforce. Now maybe

1405
01:12:36,320 --> 01:12:38,080
Speaker 1: I can lay off ten percent of my workforce and

1406
01:12:38,120 --> 01:12:40,719
Speaker 1: I won't fundamentally damage the underline products. But you also

1407
01:12:40,720 --> 01:12:43,120
Speaker 1: put your finger on something that's very important. How is

1408
01:12:43,160 --> 01:12:48,080
Speaker 1: Twitter doing as a company? Yes, terribly, but terribly. Their

1409
01:12:48,120 --> 01:12:50,439
Speaker 1: advertising revenue is down forty percent. You go on the

1410
01:12:50,479 --> 01:12:54,320
Speaker 1: platform and it's a bunch of like recommended promoted tweets

1411
01:12:54,360 --> 01:12:58,240
Speaker 1: from people that I don't even follow. The product might

1412
01:12:58,400 --> 01:13:01,880
Speaker 1: basically look the same, but as a business, it is

1413
01:13:01,920 --> 01:13:04,200
Speaker 1: software to the point that Elon now has to scroungejump

1414
01:13:04,200 --> 01:13:06,559
Speaker 1: more money to make that first interest rate payment. So

1415
01:13:07,160 --> 01:13:09,800
Speaker 1: if I'm a CEO, I'm a smart CEO in Silicon Valley,

1416
01:13:10,160 --> 01:13:13,040
Speaker 1: you want to be careful about what lesson to draw

1417
01:13:13,080 --> 01:13:16,839
Speaker 1: from Twitter. Yes, maybe you could argue that your company

1418
01:13:16,880 --> 01:13:19,560
Speaker 1: is overstaffed in terms of computer programmers, and therefore you

1419
01:13:19,560 --> 01:13:21,799
Speaker 1: can lay off some people and you know, whatever Google

1420
01:13:22,040 --> 01:13:25,720
Speaker 1: or Azure whatever can still can still function. But at

1421
01:13:25,720 --> 01:13:28,120
Speaker 1: the same time, I don't think that what Elon Musk

1422
01:13:28,240 --> 01:13:32,000
Speaker 1: is doing to the business of Twitter should offer any

1423
01:13:32,120 --> 01:13:35,720
Speaker 1: kind of roadmap to anybody yet, because Twitter is a

1424
01:13:35,840 --> 01:13:38,920
Speaker 1: dumpster fire in terms of its finances. Right, excellent point.

1425
01:13:39,680 --> 01:13:42,719
Speaker 1: Let's talk a little bit more about the cheap money piece,

1426
01:13:43,439 --> 01:13:46,400
Speaker 1: which has shifted just in the past year, because I

1427
01:13:46,439 --> 01:13:49,599
Speaker 1: find this part really interesting. I was listening to an

1428
01:13:49,640 --> 01:13:53,240
Speaker 1: interview with Sam Altman, who's the CEO of open Ai,

1429
01:13:53,760 --> 01:13:57,880
Speaker 1: and he was saying, counterintuitively, like, listen, I actually think

1430
01:13:57,920 --> 01:14:00,759
Speaker 1: now is the best time to start a business. Yeah,

1431
01:14:00,800 --> 01:14:03,040
Speaker 1: financing is going to be difficult, but everything else is

1432
01:14:03,080 --> 01:14:05,639
Speaker 1: actually easy because you're gonna have an easier time getting people,

1433
01:14:05,760 --> 01:14:08,519
Speaker 1: You're going to have an easier time distinguishing yourself and

1434
01:14:08,560 --> 01:14:11,320
Speaker 1: being able to innovate and create sort of real value.

1435
01:14:11,560 --> 01:14:14,519
Speaker 1: And inherent he put this all very diplomatically, by the way,

1436
01:14:14,600 --> 01:14:17,360
Speaker 1: but inherent in his analysis was the fact that you

1437
01:14:17,439 --> 01:14:20,640
Speaker 1: had a bunch of tech companies that really didn't have

1438
01:14:20,680 --> 01:14:24,120
Speaker 1: a business model that only were continued to able to

1439
01:14:24,160 --> 01:14:28,080
Speaker 1: continue to exist because of the ability to grab some

1440
01:14:28,160 --> 01:14:30,400
Speaker 1: of this cheap cash and sort of like float their

1441
01:14:30,400 --> 01:14:33,080
Speaker 1: debt forever and ever and ever. So I have a

1442
01:14:33,120 --> 01:14:35,200
Speaker 1: lot of compassion for the people who are being laid

1443
01:14:35,200 --> 01:14:38,360
Speaker 1: off right now, Like that's an incredibly traumatic event ultimately,

1444
01:14:38,439 --> 01:14:40,519
Speaker 1: even if you are higher up on the income scale.

1445
01:14:40,800 --> 01:14:43,120
Speaker 1: But do you think there could be a silver lining

1446
01:14:43,160 --> 01:14:45,880
Speaker 1: here where there is more of an emphasis on like

1447
01:14:46,400 --> 01:14:50,360
Speaker 1: actual real business models that generate a profit and include

1448
01:14:50,400 --> 01:14:53,799
Speaker 1: innovation and ways that you know, consumers and others ultimately

1449
01:14:53,840 --> 01:14:58,880
Speaker 1: benefit from. It's certainly possible. You could definitely tell a

1450
01:14:58,880 --> 01:15:02,880
Speaker 1: pretty compelling story that when rates are low and money

1451
01:15:02,960 --> 01:15:06,559
Speaker 1: is easy, hiring is easy, which means hiring the right

1452
01:15:06,600 --> 01:15:09,360
Speaker 1: person's hard because everyone is trying to hire at the

1453
01:15:09,439 --> 01:15:13,200
Speaker 1: exact same time. But it's a different story when rates

1454
01:15:13,240 --> 01:15:16,040
Speaker 1: go up and then hiring is a little bit harder

1455
01:15:16,040 --> 01:15:18,519
Speaker 1: people are laying off rather than adding to their headcount.

1456
01:15:18,720 --> 01:15:21,320
Speaker 1: That means that there are more people that are available

1457
01:15:21,439 --> 01:15:24,600
Speaker 1: to be hired. And as a result, you know, you

1458
01:15:24,640 --> 01:15:26,439
Speaker 1: take the ten thousand people that are being laid off

1459
01:15:26,560 --> 01:15:28,280
Speaker 1: from Google, you add it to the ten thousand people

1460
01:15:28,360 --> 01:15:31,400
Speaker 1: being laid off from Salesforce and Microsoft and Amazon, you think, wow,

1461
01:15:32,000 --> 01:15:35,120
Speaker 1: this could be the kindling for a really interesting startup.

1462
01:15:35,360 --> 01:15:37,439
Speaker 1: So yeah, I think it's important to both have a

1463
01:15:37,479 --> 01:15:39,280
Speaker 1: lot of you know, compassion for people who are losing

1464
01:15:39,320 --> 01:15:41,920
Speaker 1: their jobs right now. Losing your job sucks, even with

1465
01:15:41,960 --> 01:15:45,320
Speaker 1: what I hope would be the fantastic or relatively fantastic

1466
01:15:45,320 --> 01:15:49,080
Speaker 1: severance packages from a place like Alphabet. I also think

1467
01:15:49,120 --> 01:15:52,760
Speaker 1: that we could be primed for pretty interesting period of

1468
01:15:52,880 --> 01:15:56,840
Speaker 1: innovation because now these kind of employees are in the

1469
01:15:56,880 --> 01:15:59,439
Speaker 1: marketplace and they're thinking, Okay, what do I do next?

1470
01:15:59,439 --> 01:16:01,639
Speaker 1: Do I go right back to you know, trade desk,

1471
01:16:01,720 --> 01:16:03,840
Speaker 1: Do I go right back to a shopify, or do

1472
01:16:03,960 --> 01:16:06,040
Speaker 1: I think, you know, I really want to do something

1473
01:16:06,080 --> 01:16:08,320
Speaker 1: that matters to the world. I want to start a

1474
01:16:08,320 --> 01:16:11,040
Speaker 1: company or belong to a startup that's really doing something interesting.

1475
01:16:11,040 --> 01:16:13,320
Speaker 1: For the world, and not just creating a new way

1476
01:16:13,400 --> 01:16:15,640
Speaker 1: of digitized gambling, which I think is what a lot

1477
01:16:15,640 --> 01:16:18,679
Speaker 1: of these crypto startups were. That is an optimistic read

1478
01:16:18,960 --> 01:16:21,120
Speaker 1: to put on this. Of course, it's economics. The most

1479
01:16:21,120 --> 01:16:22,760
Speaker 1: optimistic read that you can put on something is not

1480
01:16:22,760 --> 01:16:26,559
Speaker 1: always the thing that happens, but it certainly could happen theoretically. Yeah,

1481
01:16:26,600 --> 01:16:28,360
Speaker 1: I think that's the most important Derek. I know that

1482
01:16:28,400 --> 01:16:31,519
Speaker 1: you're really interested in the history of innovation. Where do

1483
01:16:31,600 --> 01:16:34,240
Speaker 1: you see things right now? Are you optimistic about this

1484
01:16:34,760 --> 01:16:36,960
Speaker 1: in terms of possibly changing the terms of the way

1485
01:16:37,000 --> 01:16:38,920
Speaker 1: that tech is going to operate, or do you think

1486
01:16:38,960 --> 01:16:45,000
Speaker 1: they were ending more of a lull period. I think

1487
01:16:45,000 --> 01:16:47,800
Speaker 1: that we are entering a period where tech is going

1488
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Speaker 1: to continue to be an absolutely massive part of our lives,

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01:16:51,920 --> 01:16:54,599
Speaker 1: and that we would make a mistake if we think

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01:16:54,840 --> 01:16:57,840
Speaker 1: that one hundred and thirty thousand people being laid off

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01:16:57,880 --> 01:17:00,400
Speaker 1: from big tech is going to change the role Google

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01:17:00,400 --> 01:17:02,760
Speaker 1: in our lives, or Amazon in our lives, or Microsoft

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01:17:02,760 --> 01:17:06,240
Speaker 1: in our lives. These companies continue to be the largest,

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01:17:06,560 --> 01:17:11,800
Speaker 1: most powerful, most profitable com company in the world. Yes,

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01:17:11,840 --> 01:17:14,679
Speaker 1: they laid off six to ten percent of the workforce. Yes,

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Speaker 1: that is in many cases the largest layoff in these

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01:17:17,040 --> 01:17:20,800
Speaker 1: companies history. But these still are the most significant institutions

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01:17:20,920 --> 01:17:24,720
Speaker 1: organizations may be in the world. So if you're a regulator,

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01:17:24,800 --> 01:17:26,960
Speaker 1: or if you're just an analyst, I would say, don't

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01:17:27,040 --> 01:17:30,400
Speaker 1: let up. Continue to scrutinize, continue to look at what

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01:17:30,520 --> 01:17:33,160
Speaker 1: they're doing and criticize what they're doing if it looks

1502
01:17:33,160 --> 01:17:35,679
Speaker 1: like they're abusing their power, because they still have quite

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01:17:35,720 --> 01:17:37,720
Speaker 1: a lot of it, right. I think that is all

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01:17:37,960 --> 01:17:41,200
Speaker 1: very well put. Guys, follow Derek. He does fantastic work

1505
01:17:41,200 --> 01:17:42,920
Speaker 1: and it's going to make you think harder about a

1506
01:17:42,960 --> 01:17:44,840
Speaker 1: lot of different topics. Great to see you, my friend

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01:17:44,880 --> 01:17:47,200
Speaker 1: critzea man, thank you. Thank you guys so much for watching.

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01:17:47,240 --> 01:17:50,439
Speaker 1: Really appreciate it. I'm sure you guys enjoyed our videos

1509
01:17:50,479 --> 01:17:53,400
Speaker 1: on Instagram yesterday modeling some of the new Austin merch

1510
01:17:53,439 --> 01:17:56,000
Speaker 1: You can check that out at the actual Austin show,

1511
01:17:56,040 --> 01:17:58,639
Speaker 1: all made in the USA and in a Union shop,

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01:17:58,640 --> 01:18:00,280
Speaker 1: which we're very proud of here. Thanks so much to

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01:18:00,280 --> 01:18:02,880
Speaker 1: our pre union subscribers who help support the show. Look

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01:18:02,880 --> 01:18:06,000
Speaker 1: out for Counterpoints tomorrow. Yeah, they got some great Suprise planned.

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01:18:06,000 --> 01:18:08,040
Speaker 1: They've got some good stuff. I'm always excited to see

1516
01:18:08,040 --> 01:18:09,760
Speaker 1: what they is. It's just nice to have a continuous

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01:18:09,800 --> 01:18:11,640
Speaker 1: flow throughout the week and then of course we'll be

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01:18:11,680 --> 01:18:30,480
Speaker 1: back here on Thursday. Love y'all, See you Thursday.