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

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

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

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

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

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

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

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

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Speaker 1: Breakingpoints dot com. Good morning, everybody, Happy Monday. We have

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

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Speaker 1: we do lots of interesting things this morning, including some

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Speaker 1: big old news about the lab leak theory, once dismissed

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Speaker 1: and deride it as conspiracy, racist, et cetera, et cetera.

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Speaker 1: Now we have a US government report suggesting that is

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Speaker 1: the most likely cause of the coronavirus pandemic. Who have

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Speaker 1: known it? Oh nagar, Yeah, never absolute shock, all right,

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Speaker 1: So we'll get to all of that. We also have

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Speaker 1: new developments out of Ohio researchers saying that the toxins

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Speaker 1: in the air are worse than what the government is saying.

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Speaker 1: We also have a new report that tens of thousands

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Speaker 1: of animals died within this five mile radius, also much

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Speaker 1: much worse than initial estimates. Will break that down for you.

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Speaker 1: We also have some twenty twenty four news shots fired

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Speaker 1: at Ronda Santis over Ukraine, which is increasingly becoming a

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Speaker 1: critical divide in the Republican primary, so that is really interesting.

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Speaker 1: New numbers on inflation not good. Not good in terms

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Speaker 1: of inflation, not good in terms of what the Federal

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Speaker 1: Reserve might do about it. And we also have a

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Speaker 1: very controversial monologue over on SNL that will break down

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Speaker 1: for you. Excited to have Kevin Russ on the show today.

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Speaker 1: He's the one I talked about this in a monologue

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Speaker 1: that I did last week, Saga while you were out.

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Speaker 1: He had this long conversation with the new big chat

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Speaker 1: bot Sydney. She will I should read it whatever revealed

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Speaker 1: to Heaven that this thing called itself Sydney internally and

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Speaker 1: then also can profess its undying love to this journalist.

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Speaker 1: So anyway, we'll talk to him, he's a tech reporter

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Speaker 1: for The Times, about what that interaction was like and

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Speaker 1: what he thinks the future of this AI ultimately is.

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Speaker 1: But we wanted to start with this big breaking news

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Speaker 1: regarding LAB leak. Yeah, and in news that should absolutely

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Speaker 1: shock anyone except over at MSNBC. Let's put this up

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Speaker 1: there on the screen. The LAB leak most likely origin

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Speaker 1: of COVID nineteen pandemic. The Energy Department now says. Now,

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Speaker 1: why should we care what the Energy Department says? Well,

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Speaker 1: what we found out from this piece, and actually some

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Speaker 1: of us who've been tracking this for quite some time,

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Speaker 1: is that the Energy Department is actually legally entrusted with

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Speaker 1: overseeing safety procedures at a myriad of different labs across

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Speaker 1: the United States. So their estimate of intelligence and how

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Speaker 1: they would interpret it with respect to LAB leak was

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Speaker 1: actually quite important. And now they claim, apparently with quote

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Speaker 1: low confidence and an updated document that was delivered to

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Speaker 1: the Director of National Intelligence Avril sorry blanking on her name, right,

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Speaker 1: AVERL Haynes. There we go, AVERL. Haynes, that they have

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Speaker 1: estimated that it is now the most likely origin of

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Speaker 1: the COVID nineteen pandemic. This is amid some consternation within

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Speaker 1: the intelligence community. So let's put this up there on

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Speaker 1: the screen. From what we know right now, four intelligence agencies,

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Speaker 1: Remember there are seventeen think that COVID spread naturally low.

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Speaker 1: The Department of Energy thinks that Lablik was the originator

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Speaker 1: of the COVID nineteen pandemic quote with low confidence. The

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Speaker 1: most interesting one to me, actually, and this kind they

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Speaker 1: kind of bury the lead here, is that the FBI

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Speaker 1: thinks that the lab leak was the most likely origin

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Speaker 1: with quote moderate confidence. Now the canard that a lot

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Speaker 1: of people are using to try to dismiss this as

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Speaker 1: to why exactly they were wrong in the first place,

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Speaker 1: crystal is that quote none think it was part of

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Speaker 1: a Chinese bioweapons program. Well, we should remember like that

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Speaker 1: was never the original claim, except for one person, Tom Cotton,

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Speaker 1: saying it I believe, in early of February twenty twenty.

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Speaker 1: But since then, the lab leak theory hypothesis has always

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Speaker 1: been that sketchy research was being conducted at the Wuhan Lab.

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Speaker 1: This research and it's sketchy safety practices were well acknowledged

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Speaker 1: by the US State Department and cables that we have

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Speaker 1: since been opened since twenty eighteen, it was well known

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Speaker 1: that they were not following proper safety protocols. Then, the

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Speaker 1: incredible amount of evidence that we have that November twenty

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Speaker 1: nineteen onwards, clearly something terrible had happened. The original database

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Speaker 1: of the samples was taken off of the Wuhan website

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Speaker 1: in September twenty nineteen. Has yet to been found as

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Speaker 1: to why. They give no explanation as to why we

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Speaker 1: know about the what is the world games that were

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Speaker 1: the World military games that were happening there. I've done.

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Speaker 1: You know, people can go back and watch up two

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Speaker 1: years ago. Now at this point, all the open source

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Speaker 1: the searches that we found spike for coronavirus symptoms. The

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Speaker 1: fact was also that you know, the original wet market

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Speaker 1: theory and natural origin, well, the wet market happens to

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Speaker 1: be place where people shop who work at the lab,

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Speaker 1: so you know, the original people who left the lab

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Speaker 1: went out there and of course spread it to the

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Speaker 1: population at least, you know, according to this theory. And

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Speaker 1: then we also know that there were people who at

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Speaker 1: least one or two up to three. This was back

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Speaker 1: in twenty twenty one that this report came out that

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Speaker 1: one of those people was known to have contracted it

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Speaker 1: in November of twenty nineteen, so all the open source

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Speaker 1: evidence in the world pointed to the Lablaque hypothesis, and

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Speaker 1: this has just been a dragging of the feat by

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Speaker 1: the Biden administration. To June of twenty twenty one, President

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Speaker 1: Biden said they directed the intelligence community to give him

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Speaker 1: updated documents. You know, and of course we're only finding

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Speaker 1: out about this because of a leak. To be clear,

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Speaker 1: this is not even really acknowledged publicly yet by the government.

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Speaker 1: This is a classified assessment, a newly classified assessment. And

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Speaker 1: to the extent that this has been happening at all,

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Speaker 1: the reason why is not that they wanted to get

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Speaker 1: to the bottom of it. It's because Republicans in Congress

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Speaker 1: have made a huge stink about how they are going

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Speaker 1: to subpoena all of these documents and do their own

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Speaker 1: internal investigation, and the agencies themselves are getting ready to

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Speaker 1: provide Congress those documents. So their hand was completely forced

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Speaker 1: in even providing this new intelligence estimate. And look, you know,

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Speaker 1: it's not a shocker to anybody who's been following the

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Speaker 1: evidence now for some time. And of course, you know,

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Speaker 1: the real meta takeaway from this is just the evidence

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Speaker 1: has been pointing to this basically since day one. It

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Speaker 1: was completely covered up by doctor Fauci by the EcoHealth Alliance,

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Speaker 1: of which we'll talk about in a moment, and really

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Speaker 1: a lot of apparatrics in the media who were afraid

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Speaker 1: that you know, talking about this was racist. And of

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Speaker 1: course the worst part was that people were taken off

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Speaker 1: of social media, including the zero Hedge Twitter account and

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Speaker 1: believe it now has over a million followers, as well

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Speaker 1: as numerous others and YouTube and elsewhere who were outright

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Speaker 1: censored on social media platforms, including you know, this is

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Speaker 1: just speculation, but we covered quite a bit of lablak

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Speaker 1: over the last two years and we saw you know,

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Speaker 1: significant ticks down in sometimes in the weeks after, especially

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Speaker 1: after I think we were one of the first people

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Speaker 1: to interview Brett Weinstein after Joe Rogan about the entire thing.

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Speaker 1: I remember that caused a hell of a lot of consternation,

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Speaker 1: you know, with YouTube and our previous employer. So you

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Speaker 1: can just go into thinking about what the pressures were

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Speaker 1: on at the time not to cover the story. I mean,

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Speaker 1: I think there's the reason this is still important just

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Speaker 1: to you know, back up for a second, is obviously

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Speaker 1: because we don't want to have another pandemic, and it

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Speaker 1: is a live question right now. Actually, the government is

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Speaker 1: weighing whether or not they're going to sort of rein

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Speaker 1: in this gain of function research and if they're going

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Speaker 1: to institute new safety protocols going forward. There were recommendations

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Speaker 1: to do exactly that, but that is still a very

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Speaker 1: live issue. It's still a live issue because friking Eco

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Speaker 1: Health Aliance is still getting grants and funding to do

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Speaker 1: similar research. So it's a live issue because of that.

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Speaker 1: But it also was really important because it shows what

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Speaker 1: happens when you have this combination of weird toxic partisanship.

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Speaker 1: Right there was you know, Trump found there calling it

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Speaker 1: kung flu and whatever, and there was all this concern

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Speaker 1: about anti Asian American hate and so so when Fauci

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Speaker 1: and other leading scientists basically came down in lockstep and said, no,

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Speaker 1: this is conspiracy, the lab liak is conspiracy. All signs

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Speaker 1: point to a natural origin. Well, their media Lackey's just

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Speaker 1: jumped right in line and followed suit, as did you

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Speaker 1: know the sensors are very very social media platforms. I

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Speaker 1: think it's really important to remember at the very beginning

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Speaker 1: how this cover up started. There was a conference call

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Speaker 1: with Fauci and a whole bunch of others where they

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Speaker 1: were kind of going back and forth, all right, how

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Speaker 1: do we think this started, And there was one scientist

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Speaker 1: in particular we said, look, guys, the sequence here is

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Speaker 1: almost ident like you can see how they just inserted

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Speaker 1: this fear and cleavage site and it looks just like

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Speaker 1: previous research that's been done here. So from the beginning

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Speaker 1: on this call with Fauci, there was an understanding that, yeah,

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Speaker 1: it's pretty likely that this was lableink. Now, not that

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Speaker 1: they were saying it was one hundred percent, but it

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Speaker 1: was like maybe seventy thirty, maybe sixty forty. And that's

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Speaker 1: the conversation that happens privately. Day after is when Peter Dazak,

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Speaker 1: covering his ass, Shepherd's all of these other scientists into

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Speaker 1: putting out this letter saying no, it is a natural origin.

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Speaker 1: That's what it is pointing to. And then after that

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Speaker 1: Fauci's able to go out in public and point to

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Speaker 1: that letter and say, this is what scientists are saying.

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Speaker 1: They're saying it's natural origin. It's basically conspiracy to suggest

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Speaker 1: that it's lab leak, and you're racist, et cetera, et cetera,

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Speaker 1: et cetera. This is what major media organizations. The New

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Speaker 1: York Times I think probably was the worst in all

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Speaker 1: of this, and any little bit of evidence that they

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Speaker 1: got that it was natural origin and not lab leak,

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Speaker 1: they would alert people, They would do full, big front

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Speaker 1: page coverage. They were all in on this. So that's

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Speaker 1: why this is such an important story still because you

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Speaker 1: can see the way that you know, reporters who hated

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Speaker 1: Trump for all kinds of you know, legitimate reasons, the

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Speaker 1: fact that he was suggesting it was lab leak, the

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Speaker 1: fact that Republicans were suggesting it was lab leak. And

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Speaker 1: then you had the scientific establishment, in order to protect

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Speaker 1: their grant money, their funding, and cover up any potential

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Speaker 1: involvement that they had in this type of research, they

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Speaker 1: come down on the other side. And that's how you

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Speaker 1: end up with us years later, finally getting any glimpse

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Speaker 1: of the reality of the facts and the evidence around

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Speaker 1: what actually happened. If anyone wants to track what we're saying,

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Speaker 1: I've been covering now for literally I think three years

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Speaker 1: now at this point, and what have we learned? Well, okay, well,

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Speaker 1: we talk about the initial cover up that you just

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Speaker 1: did with the scientific community. Then we talk about doctor

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Speaker 1: Fauci using this cover up of the scientific community. Something

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Speaker 1: that we alleged for a long time is that doctor

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Speaker 1: Fauci illegally circumvented US government regulations to fund gain of

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Speaker 1: function research at the Wuhan lab. This was considered a

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Speaker 1: conspiracy up until a year ago, and then lo and

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Speaker 1: behold just a few months ago put it up there

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Speaker 1: on the screen. January of twenty twenty three, the NIH itself,

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Speaker 1: a new report finds did not follow its own safety

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Speaker 1: protocols in properly tracking the EcoHealth Alliance while it was

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Speaker 1: studying back coronavirus research at the Wuhan Institute of VIROLOGYI

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Speaker 1: vindicate exactly what we talked about, which is that gain

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Speaker 1: of function research under the Obama administration was considered so

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Speaker 1: dangerous and risky for the potential of a lab leak

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Speaker 1: or some sort of other similar type event that the

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Speaker 1: US government was going to stop funding it. Then doctor

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Speaker 1: Fauci reverses these guidelines in twenty seventeen, green lights eight

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Speaker 1: million dollars in grants to the EcoHealth Alliance, which continues

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Speaker 1: this virus study at the Wuhan Institute of Virology up

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Speaker 1: until September of twenty nineteen when all the things go

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Speaker 1: black and why does it matter us for right now?

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Speaker 1: Put this up there October of twenty twenty two. You

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Speaker 1: can see, actually that's my screenshot of the exact abstract,

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Speaker 1: which is we are providing Fauci on his way out

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Speaker 1: of the National Institute of Health Greenlit an additional grant

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Speaker 1: to the Eco Health Alliance continuing to study bat viruses.

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Speaker 1: I'm not exactly sure why more bat coronavirus research. A

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Speaker 1: six hundred thousand dollars grant to EcoHealth Alliance, the same

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Speaker 1: organization that funded the Wuhan lab. Now also with doctor

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Speaker 1: Peter Dazak why it matters? And I know much of

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Speaker 1: this can sound as complicated as Watergate or to Iraq WMD,

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Speaker 1: but they are important because Peter Dazac was also a

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Speaker 1: part of the initial investigatory group from the World Health

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Speaker 1: Organization that initially determined that there was no lab leak

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Speaker 1: and said it was a natural origin and has the

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Speaker 1: famous interview on sixty Minutes where they said, well are

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Speaker 1: you just taking the Chinese word for it? And he said, well,

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Speaker 1: what else can we do? I'm like, I don't know,

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Speaker 1: you know, baby, look at your own records. He has

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Speaker 1: a vested monetary interest, and then even bigger step, let's

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Speaker 1: make sure that this never happens again. Well, what are

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Speaker 1: we doing. Something Josh Rogan has pointed to is that

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Speaker 1: the response from the scientific community, specifically headed by Fouci,

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Speaker 1: the US government has the Global Virum Project actually increase

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Speaker 1: billions of dollars in gain of function research under the

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Speaker 1: guise of let's never let that next pandemic happen again.

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Speaker 1: Let's try and bioengineer these viruses so that we can

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Speaker 1: create antidotes, cures, vaccines, or whatever in the future. When

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Speaker 1: it's seems now at this point, I mean, look, if

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Speaker 1: you are still pumping the natural origin theory, there is

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Speaker 1: almost no evidence to support that, Like you have been

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Speaker 1: unable to find how some bat miraculously few flew a

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Speaker 1: thousand miles and hit it up in the Wuhan lab.

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Speaker 1: Even the Chinese don't even really stick by the uh yeah,

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Speaker 1: they don't stick by the wet market theory. They're like, oh, well,

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Speaker 1: something was contaminated along the way, and just stop asking

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Speaker 1: questions about this. All of these intel communities that still

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Speaker 1: assess all this or based on older, older intel you

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Speaker 1: have also the former CDC director under Donald Trump. Remember

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Speaker 1: he came out and said, I think it came from

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Speaker 1: the Wuhan lab. He was also dismissed then at the

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Speaker 1: point and he was shut done of those original coat

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Speaker 1: meetings when there was open conversation about how this was

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Speaker 1: likely a lab leak and the efforts to make sure

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Speaker 1: that they shepherded everybody out of that direction and towards

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Speaker 1: the natural origin direction. I mean, it really is. It

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Speaker 1: really is quite remarkable. And listen, even if you're still like,

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Speaker 1: I don't know, it could be either one, Okay, fair enough,

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Speaker 1: is it plausible that this escape from a lab? That's

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Speaker 1: really all you need to know to know that this

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Speaker 1: research is extremely dangerous and we need to be asking

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Speaker 1: really hard questions about whether this should continue, what sort

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Speaker 1: of safety procedures should be in place, and by the way,

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Speaker 1: like we know what the risk was, what have we

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Speaker 1: gotten out of it. I was listening to an old

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Speaker 1: episode of Ryan's podcast Intercepted, and they were talking is

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Speaker 1: that what it's called intercestuar deconstructed? There you go, and

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Speaker 1: they were talking about like, okay, let's say it was

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Speaker 1: the wet market. You have this Institute of Virology right there,

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Speaker 1: across the street. If it came from the wet market,

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Speaker 1: you weren't able to stop that. Like, this is supposed

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Speaker 1: to be your whole thing, right, so even if you're

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Speaker 1: buying a natural origin thing, like, what are we getting

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Speaker 1: for this research? Is it? It's supposed to be preventing pandemics?

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Speaker 1: Well it failed. It failed. At the best you can

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Speaker 1: say is it failed, and most likely it actually started

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Speaker 1: the pandemic. So that seems to be pretty strong evidence

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Speaker 1: that we need to dramatically ch change what we are

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Speaker 1: doing in terms of gain a function research. Yeah, you

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Speaker 1: would think. And let's just not forget Fauci is probably

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Speaker 1: the number one person implicated in all of this. He's

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Speaker 1: going out as a hero, but you know, maybe history

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Speaker 1: won't be nearly as kind. Let's just not forget how

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Speaker 1: he tried to play this down at the time. Let's

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Speaker 1: take a listen. Everything about the step wise evolution over

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Speaker 1: time strongly indicates that this virus evolved in nature and

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Speaker 1: then jump species. Fauci added he does not believe another

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Speaker 1: theory that the virus occurred naturally but was accidentally released

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Speaker 1: into the public from a lab in China, telling that

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Speaker 1: GEO that means it was in the wild to begin with.

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Speaker 1: That's why I don't get what they're talking about. Don't

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Speaker 1: get what they're talking about. He had all the information

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Speaker 1: he knew. Let's remember January of twenty twenty, he received

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Speaker 1: an email saying that the virus is quote not consistent

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Speaker 1: with evolutionary theory. That's what's come out of the Fauci

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Speaker 1: files ever since then. And look, he remains, he remains complicit.

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Speaker 1: He remains to say, like, look, it's possible. I never

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Speaker 1: said that it couldn't have come from the lab. Yes,

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Speaker 1: you did really have the evidence right there. And look,

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Speaker 1: you know, the Republicans say that they will investigate, you know,

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Speaker 1: with the House Committee, will see what they're even able

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Speaker 1: to get their hands on. But at this point, even

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Speaker 1: the coverage of this report, Crystal, it's silence, you know,

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Speaker 1: from most of the mainstream media. It was covered, and

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Speaker 1: I don't think it's a surprise in the Wall Street Journal,

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Speaker 1: the nominally conservative paper. You received a write up in

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Speaker 1: the New York Times where they went out of their

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Speaker 1: way to say, well, many of the others still say

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Speaker 1: it was natural origin, and this doesn't vindicate Trump's racist

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Speaker 1: conspiracy theories. And as you said, look, we know clear

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Speaker 1: as day The Times, their former reporter Donald McNeil literally

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Speaker 1: had came out and said that while he was over there,

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Speaker 1: he didn't cover the lab league because he didn't consider

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Speaker 1: it credible. And it was only months later when he

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Speaker 1: realized he'd been lied to and manipulated by doctor Fauci,

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Speaker 1: the scientific community and Peter Dazag. I mean, it goes

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Speaker 1: so deep. It really is a rack WMD level. And

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Speaker 1: let's not forget it's not just about what we do

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Speaker 1: this thing. You know, a million Americans died of COVID

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Speaker 1: by some estimate, cost trillions of dollars in economic damage.

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Speaker 1: Like you know, the Iraq War. There's a lot of

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Speaker 1: recriminations as to what happened after the invasion, but you

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Speaker 1: know what led up to the invasion was also very important,

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Speaker 1: just like Vietnam, just like the Spanish American War. Like

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Speaker 1: you have to go back and look at these initial conspiracies,

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Speaker 1: cover ups and speculations and say, hey, how exactly did

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Speaker 1: this shape what ended up coming this catastrophe, so that

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Speaker 1: we make sure that this doesn't happen again. I unfortunately

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Speaker 1: do not think many people in the media will rescind

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Speaker 1: that I have all I have yet to see is

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Speaker 1: the initial pushback is, well, actually it's the conspiracy theorist

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Speaker 1: fault for making it a conspiracy so that we had

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Speaker 1: to dismiss it then at the time because they were

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Speaker 1: liars and not credible. Maybe you're the liar. Maybe you're

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Speaker 1: the one is not credible. Yeah, I mean listen, yeah,

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Speaker 1: fair enough that Trump did himself no favors. But you're

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Speaker 1: a journalist. Ye're not supposed to be playing favorites. You're

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Speaker 1: not supposed to be like, well, I like this one

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Speaker 1: and I don't like this one, so I'm going to

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Speaker 1: go with the theory espoused by the person that I like.

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Speaker 1: And that was exactly what was the reporters named Don

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Speaker 1: McNeil's that was saying with you, that was exactly what

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Speaker 1: he said. He fell victim to was basically like, you know,

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Speaker 1: I had long relationships with these scientists and people like Fauci,

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Speaker 1: and so when they were like, no, no, it's natural origin,

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Speaker 1: he just bought it. Instead of actually doing the research

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Speaker 1: that you were supposed to do as a journalist and

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Speaker 1: being skeptical of everyone, which is supposed to be you know,

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Speaker 1: a core tenant ultimately of journalism. It really is remarkable

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Speaker 1: to see how all of this unfold and how it

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Speaker 1: continues to unfold. I mean, the New York Times a

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Speaker 1: perfect example. When they got a report that said it

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Speaker 1: looks like it's natural origin, which was based on, by

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Speaker 1: the way, totally incomplete information. Then it was news alert,

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Speaker 1: front page, did the big, you know, deep dive, and

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Speaker 1: made sure that they got it down to all their readers.

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Speaker 1: Now that you have an alternative view, they'll put a

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Speaker 1: little tiny blurb in there, but it's not getting nearly

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Speaker 1: the same treatment that this, And they portrayed that also

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Speaker 1: as much more conclusive and definitive than they're portraying this

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Speaker 1: piece of evidence. So you can see their game. It's

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Speaker 1: very clear. There you go. All right, speaking of media

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Speaker 1: cover ups, government, etc. We've got, you know, continuing developments

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Speaker 1: out of Ohio that are deeply deserving. Let's go ahead

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Speaker 1: and put this up on the screen. This is a

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Speaker 1: write up in the Washington Post. The headline here is

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Speaker 1: toxic air pollutants in East Palestine could pose long term risks,

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Speaker 1: researchers say. USINGPA data, Texas A and M scientists found

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Speaker 1: elevated levels of some chemicals at the derailment site but

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Speaker 1: EPA officials say the levels pose no short term risks

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Speaker 1: and are likely to dissipate. So basically the long and

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Speaker 1: short here and i'll read you a little bit of

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Speaker 1: this article is the EPA, of course, has been out

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Speaker 1: saying everything's good, no worries, We're all clear. We did

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Speaker 1: the testing. Of course, we found out the water testing

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Speaker 1: was actually, at least most of it conductive by literally

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Speaker 1: the polluter, the company, Norfolk Southern. They paid consultants to

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Speaker 1: do some shoddy apparently testing that with samples, et cetera.

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Speaker 1: So okay, that was the water. But on the air,

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Speaker 1: they said, we're all good. Well, guess what these researchers

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Speaker 1: at Texas A and M say. Not quite three weeks

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Speaker 1: after the toxic train of derailment in Ohio, an independent

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Speaker 1: analysis of EPA data found nine air pollutants at levels that,

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Speaker 1: if they persist, could raise long term health concerns in

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Speaker 1: and around East Palestine. That analysis, they say, stands in

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Speaker 1: contrast to statements by state and federal regulators that air

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Speaker 1: near the crash site is completely safe, despite residents complaining

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Speaker 1: about rashes, breathing problems, and other health effects. Now the

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Speaker 1: ep IS response, they say, that air quality levels of

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Speaker 1: seventy nine different chemicals they're monitoring remain below levels of

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Speaker 1: concern for short term exposure, and that current concentrations are

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Speaker 1: likely to dissipate. So that is their response, but they

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Speaker 1: say the data only adds to questions and concerns that

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Speaker 1: of course have been weighing on residents Texas, A and

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Speaker 1: M researchers found elevated levels of chemicals known to trigger

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Speaker 1: I and lung irritation, headaches, and other symptoms, as well

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Speaker 1: as some that are known or suspected to cause cancer,

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Speaker 1: which of course is the nightmare scenario for these individuals.

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Speaker 1: And they've been so gas lit, sober. You know, any

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Speaker 1: number of reporters, including our you know, our friends over

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Speaker 1: at status who have been on the ground where people

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Speaker 1: are saying nauseous, vomiting, skin rashes, eye irritation, having trouble breathing,

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Speaker 1: all of these things, and meanwhile the government no, no,

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Speaker 1: it's fine, you're fine, no problem, everything's good, the air

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Speaker 1: is clear, etc. This gives you some inkling that they

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Speaker 1: have been, at best dramatically downplaying the impact of what

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Speaker 1: is going on. What I don't really get is they

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Speaker 1: always include these caveats. Experts say it's not cause for

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Speaker 1: immediate concern. It highlights uncertainties. Well, look, if it's uncertainty,

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Speaker 1: what's the worst case that people get cancer? What's the

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Speaker 1: best case people don't? Why would you bet on the

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Speaker 1: best case scenario? Yeah? Why would you operate under that?

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Speaker 1: And the continuing I mean, the more that you read

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Speaker 1: about it, and you're thinking about many of these people

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Speaker 1: talking about elevated of toxic chemicals in the air, in

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Speaker 1: the water supply and around the area. Now that we

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Speaker 1: have no idea how long it will dissipate, they say, oh,

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Speaker 1: it'll dissipate after some time. Well, when are we talking

429
00:21:58,000 --> 00:21:59,840
Speaker 1: about years from now? We're talking about months from now?

430
00:22:00,040 --> 00:22:02,280
Speaker 1: Do they need relocation in the interim? Like should they

431
00:22:02,280 --> 00:22:04,440
Speaker 1: go home? Should they not go at home? And at

432
00:22:04,440 --> 00:22:07,000
Speaker 1: this point we really have no reason to believe anything

433
00:22:07,040 --> 00:22:09,000
Speaker 1: that they say. They downplayed you know, what was the

434
00:22:09,160 --> 00:22:13,440
Speaker 1: controlled release the demolition that releases like mushroom cloud. They're like, wow,

435
00:22:13,640 --> 00:22:15,679
Speaker 1: these these animals that are dead. Well, it's actually not

436
00:22:15,960 --> 00:22:18,159
Speaker 1: that many of them. We know now that we know

437
00:22:18,320 --> 00:22:21,080
Speaker 1: now that that is not true. We know that the

438
00:22:21,119 --> 00:22:24,080
Speaker 1: initial water testing and all that is being trusted to

439
00:22:24,200 --> 00:22:26,359
Speaker 1: the company, and this is a total government This is

440
00:22:26,359 --> 00:22:29,639
Speaker 1: bigger than, honestly than Katrina in terms of the actual

441
00:22:29,640 --> 00:22:31,719
Speaker 1: cover up. I know they got a lot of media attention,

442
00:22:31,800 --> 00:22:34,840
Speaker 1: but this is like an active corporate cover up in

443
00:22:34,880 --> 00:22:39,160
Speaker 1: conjunction with US government officials to mislead the media as

444
00:22:39,200 --> 00:22:40,960
Speaker 1: to what is happening, and they just want to move

445
00:22:40,960 --> 00:22:42,840
Speaker 1: away from it. This might be the biggest failure in

446
00:22:42,880 --> 00:22:45,320
Speaker 1: their history, in modern history. Yeah, I mean with Katrina,

447
00:22:45,359 --> 00:22:48,440
Speaker 1: obviously a lot of people died, and yeah, it was

448
00:22:48,440 --> 00:22:51,280
Speaker 1: a photos devastation there was. Yeah, so I don't want

449
00:22:51,320 --> 00:22:53,040
Speaker 1: to I don't want to make that drug comparison, but

450
00:22:53,080 --> 00:22:55,960
Speaker 1: there's no doubt about it that. I mean, the cover

451
00:22:56,080 --> 00:22:58,879
Speaker 1: up here is so consistent with the playbook that is

452
00:22:58,960 --> 00:23:01,199
Speaker 1: run every single time. I just keep thinking about that

453
00:23:01,280 --> 00:23:03,880
Speaker 1: dude raise on TikTok. It was like, I get really

454
00:23:03,920 --> 00:23:07,359
Speaker 1: obsessed with these industrial accidents and it is the same

455
00:23:07,640 --> 00:23:13,000
Speaker 1: exact playbook and same timeline every time the corporation wants

456
00:23:13,040 --> 00:23:15,719
Speaker 1: to downplay, they want to say everything is fine, and

457
00:23:15,760 --> 00:23:18,239
Speaker 1: then we find out, oh, guess what, They're funding the

458
00:23:18,280 --> 00:23:22,000
Speaker 1: tests and they're using these testing consultants who have been

459
00:23:22,040 --> 00:23:25,040
Speaker 1: caught in the past cooking the books and screwing with

460
00:23:25,080 --> 00:23:28,520
Speaker 1: the data, so we know that happened, but those tests

461
00:23:28,600 --> 00:23:32,520
Speaker 1: come out. The politicians they also mostly want to downplay

462
00:23:32,560 --> 00:23:36,159
Speaker 1: what's going on. Everybody except for the literal local mayor,

463
00:23:36,440 --> 00:23:40,360
Speaker 1: but the Republican governor, Pete and Biden, all of them

464
00:23:40,359 --> 00:23:42,159
Speaker 1: want to downplay it. And so they'll, oh, yeah, the

465
00:23:42,200 --> 00:23:44,080
Speaker 1: tests are fine, it's all good, don't worry about it.

466
00:23:44,480 --> 00:23:47,640
Speaker 1: So they are parroting the corporate line. And then the media,

467
00:23:47,680 --> 00:23:52,399
Speaker 1: also mostly corporate media, obviously parrots the corporate line as well.

468
00:23:52,800 --> 00:23:55,240
Speaker 1: So that's how you end up with this situation where

469
00:23:55,240 --> 00:23:58,560
Speaker 1: people are like, I am sick, I have symptoms. There

470
00:23:58,640 --> 00:24:01,720
Speaker 1: is something going on here. It's obviously not all clear,

471
00:24:02,280 --> 00:24:04,199
Speaker 1: and yet they're being gas lit and told over and

472
00:24:04,240 --> 00:24:08,280
Speaker 1: over again, no, no, everything is fine. And here's another

473
00:24:08,400 --> 00:24:10,199
Speaker 1: example of this. Let's go and put this up on

474
00:24:10,240 --> 00:24:14,000
Speaker 1: the screen. So Sager just alluded to this. The original

475
00:24:14,240 --> 00:24:17,880
Speaker 1: estimate of the number of animals that died in this

476
00:24:18,080 --> 00:24:22,760
Speaker 1: five mile area was thirty five hundred. Well, now that

477
00:24:23,000 --> 00:24:29,200
Speaker 1: estimate has risen to forty three thousand, seven hundred. I'm

478
00:24:29,240 --> 00:24:31,720
Speaker 1: not good at math, but that's a lot more than

479
00:24:31,920 --> 00:24:35,640
Speaker 1: thirty five hundred. Now this is predominantly fish and other

480
00:24:35,680 --> 00:24:38,920
Speaker 1: aquatic animals that they're looking at here. What they say

481
00:24:39,000 --> 00:24:42,120
Speaker 1: is that when the Ohio Department of Natural Resources officials

482
00:24:42,160 --> 00:24:45,200
Speaker 1: first responded, they were told it was too dangerous to

483
00:24:45,200 --> 00:24:47,920
Speaker 1: actually get in the water and do a full analysis

484
00:24:47,920 --> 00:24:50,639
Speaker 1: without having specialized gear. So they kind of worked with

485
00:24:50,680 --> 00:24:53,480
Speaker 1: what they could and that's how they came up with

486
00:24:52,800 --> 00:24:57,760
Speaker 1: this initial low estimate. And now that they've been able

487
00:24:57,880 --> 00:25:02,160
Speaker 1: to do a fuller sample, can see that there is vastly,

488
00:25:02,560 --> 00:25:07,120
Speaker 1: vastly more animal death here than initially estimated. And as

489
00:25:07,160 --> 00:25:10,200
Speaker 1: I said, in now this seven point five mile area

490
00:25:10,760 --> 00:25:13,680
Speaker 1: that was impacted by the trained derailment, they're saying there

491
00:25:13,680 --> 00:25:16,720
Speaker 1: were forty three thousand, seven hundred animals. You know, there's

492
00:25:16,800 --> 00:25:22,520
Speaker 1: another story that's unfolding here now because as the cleanup continues, well,

493
00:25:22,520 --> 00:25:25,040
Speaker 1: they've got to take that toxic waste and all that,

494
00:25:25,119 --> 00:25:28,120
Speaker 1: you know, bled into the soil in this area. They

495
00:25:28,119 --> 00:25:30,960
Speaker 1: have to do something with that. So they were initially

496
00:25:31,000 --> 00:25:34,880
Speaker 1: transporting it to sites in Michigan and sites in Texas.

497
00:25:35,240 --> 00:25:38,560
Speaker 1: And now the you know, Democratic governor of Michigan and

498
00:25:38,600 --> 00:25:41,240
Speaker 1: the Republican governor of Texas were like, hold the phone,

499
00:25:41,320 --> 00:25:43,640
Speaker 1: what is going on here. We've got some major questions.

500
00:25:43,920 --> 00:25:47,160
Speaker 1: So just this morning I read that they've changed course

501
00:25:47,560 --> 00:25:50,560
Speaker 1: and now they are disposing of that waste in two

502
00:25:50,600 --> 00:25:53,159
Speaker 1: sites in Ohio. One of which I happen to know

503
00:25:53,240 --> 00:25:55,560
Speaker 1: because I used to live there is actually literally in

504
00:25:55,640 --> 00:25:58,840
Speaker 1: the same county. It's in Colombiana County, just fifteen miles

505
00:25:58,880 --> 00:26:01,479
Speaker 1: down the road. So they don't want to ship it,

506
00:26:01,600 --> 00:26:03,600
Speaker 1: you know, to Michigan or Texas. They're going to keep

507
00:26:03,640 --> 00:26:06,320
Speaker 1: it right here in this community and dispose of it

508
00:26:06,400 --> 00:26:08,600
Speaker 1: in a site that's just fifteen miles away. I have

509
00:26:08,640 --> 00:26:10,520
Speaker 1: to think that people are not too happy about that either.

510
00:26:10,720 --> 00:26:12,520
Speaker 1: I was going to say, well, what is the reasoning

511
00:26:12,560 --> 00:26:15,160
Speaker 1: behind putting it in the same county that it's already infected?

512
00:26:15,280 --> 00:26:18,119
Speaker 1: There's a toxic so and this gets to like I

513
00:26:18,160 --> 00:26:20,600
Speaker 1: talk about this sum in my monologue. This region has

514
00:26:20,640 --> 00:26:22,760
Speaker 1: been so shit on for so many years. So there's

515
00:26:22,760 --> 00:26:27,119
Speaker 1: a toxic waste incinerator in East Liverpool, Ohio, located by

516
00:26:27,160 --> 00:26:29,359
Speaker 1: the way by a school. There was local opposition of

517
00:26:29,440 --> 00:26:31,840
Speaker 1: this thing, but anyway, they put it in. So this

518
00:26:31,920 --> 00:26:35,600
Speaker 1: is a region that, you know, totally devastated by the industrialization,

519
00:26:36,040 --> 00:26:40,600
Speaker 1: epidemic of opioid addiction and overdose, all of that ground

520
00:26:40,680 --> 00:26:42,880
Speaker 1: zero for that. So this is the place that has

521
00:26:42,920 --> 00:26:44,879
Speaker 1: just been like used to be used for quite a

522
00:26:44,880 --> 00:26:47,719
Speaker 1: while now, So no surprise that something like a toxic

523
00:26:47,760 --> 00:26:50,959
Speaker 1: waste incinerator would be located in the same community. Geez,

524
00:26:51,040 --> 00:26:53,400
Speaker 1: I mean, yeah, I knew. It's very telling. I think

525
00:26:53,400 --> 00:26:55,760
Speaker 1: that nobody else wants the waste, you know whatever, the

526
00:26:55,840 --> 00:26:58,840
Speaker 1: least other states, especially one whose own researchers are like,

527
00:26:58,840 --> 00:27:01,320
Speaker 1: this is full of talkxicity and may have long term

528
00:27:01,320 --> 00:27:04,280
Speaker 1: health effects. We don't want our hands on any of this.

529
00:27:04,720 --> 00:27:06,520
Speaker 1: I don't even know what to say now at this point.

530
00:27:06,560 --> 00:27:08,159
Speaker 1: And you know what we're about to talk about is

531
00:27:08,200 --> 00:27:10,560
Speaker 1: that these people continue to just be left behind by

532
00:27:10,600 --> 00:27:13,960
Speaker 1: the federal government, almost completely ignored the extent media is

533
00:27:14,000 --> 00:27:16,560
Speaker 1: even covering it a drag kicking and screaming because of

534
00:27:16,600 --> 00:27:20,240
Speaker 1: intense public interests, but not a lot of scrutiny yet,

535
00:27:20,320 --> 00:27:23,480
Speaker 1: at least on the company itself and on the government officials,

536
00:27:23,480 --> 00:27:25,440
Speaker 1: not even close to the level of scrutiny on President

537
00:27:25,480 --> 00:27:29,840
Speaker 1: Biden and on Secretary Buddha Jedge. Yeah. Well, and really importantly,

538
00:27:30,040 --> 00:27:33,800
Speaker 1: there was covered a Media Matters analysis of all the

539
00:27:33,840 --> 00:27:36,320
Speaker 1: cable news coverage and at that point at least only

540
00:27:36,400 --> 00:27:38,679
Speaker 1: three percent of all the segments that had done on

541
00:27:38,720 --> 00:27:41,080
Speaker 1: this been done on this talked about the political corruption

542
00:27:41,160 --> 00:27:43,880
Speaker 1: that led up to this, and we now have indications

543
00:27:43,880 --> 00:27:47,320
Speaker 1: from the NTSB report that has now come out this

544
00:27:47,480 --> 00:27:51,280
Speaker 1: was completely preventable. It was preventable with one thing that

545
00:27:51,280 --> 00:27:54,760
Speaker 1: would have been incredibly beneficial is the modern breaking system

546
00:27:54,800 --> 00:27:57,159
Speaker 1: that David Serroto has been reporting on over at the

547
00:27:57,240 --> 00:28:01,239
Speaker 1: Lever that ultimately, you know, under the Obama administration, they

548
00:28:01,240 --> 00:28:04,520
Speaker 1: tried to make this braking system widespread. Industry got involved

549
00:28:04,560 --> 00:28:06,960
Speaker 1: and they really trimmed the sales of what the initial

550
00:28:07,000 --> 00:28:09,520
Speaker 1: safety REGs were going to be under the Obama administration

551
00:28:09,600 --> 00:28:12,480
Speaker 1: than under the Trump administration, they roll that back even further.

552
00:28:12,840 --> 00:28:15,880
Speaker 1: So that was ends up being really crucial and really

553
00:28:15,960 --> 00:28:19,119
Speaker 1: key because there was I think a like a wheelbaring

554
00:28:19,200 --> 00:28:21,679
Speaker 1: or something like that axel something like that that was

555
00:28:21,880 --> 00:28:24,680
Speaker 1: catching fire that we saw on the on the video

556
00:28:24,760 --> 00:28:27,000
Speaker 1: and that does end up turning out turns out to

557
00:28:27,080 --> 00:28:29,879
Speaker 1: be what the key problem was here, but they actually

558
00:28:29,920 --> 00:28:32,600
Speaker 1: caught it before it derailed. They tried to stop the train,

559
00:28:32,920 --> 00:28:35,919
Speaker 1: but because they have freaking civil war air braking system

560
00:28:35,960 --> 00:28:38,160
Speaker 1: on this train, they're unable to stop it, and that's

561
00:28:38,160 --> 00:28:41,040
Speaker 1: how you end up with the derailment. So political corruption

562
00:28:41,360 --> 00:28:43,880
Speaker 1: that we've been talking about that the media has largely

563
00:28:43,920 --> 00:28:47,239
Speaker 1: ignored ends up being the key story here. And to

564
00:28:47,280 --> 00:28:51,080
Speaker 1: talk more about the politics our president, President Biden, he

565
00:28:51,200 --> 00:28:52,920
Speaker 1: was asked going and put this up on the screen

566
00:28:53,160 --> 00:28:57,120
Speaker 1: about whether or not he plans to visit Ohio. He says, no,

567
00:28:57,400 --> 00:29:00,120
Speaker 1: we're doing all we can. That is a lie. They

568
00:29:00,120 --> 00:29:01,760
Speaker 1: are not doing all they can. I mean, that's just

569
00:29:01,840 --> 00:29:05,240
Speaker 1: demonstrably false. One thing they could be doing is giving

570
00:29:05,240 --> 00:29:07,960
Speaker 1: these people Medicare for life, as they did in Libby, Montana.

571
00:29:08,160 --> 00:29:10,520
Speaker 1: There's a provision in Obamacare that would allow them to

572
00:29:10,560 --> 00:29:14,240
Speaker 1: do exactly that. They are not talking about doing that whatsoever,

573
00:29:14,680 --> 00:29:17,080
Speaker 1: even as you know, researchers are saying, hey, this could

574
00:29:17,120 --> 00:29:19,880
Speaker 1: have long term effects and these are known carcinogens. So

575
00:29:20,240 --> 00:29:22,600
Speaker 1: down the road ten years from now, when suddenly you've

576
00:29:22,640 --> 00:29:25,120
Speaker 1: got a cancer cluster in this area and everyone's denying

577
00:29:25,120 --> 00:29:26,680
Speaker 1: on it has nothing to do that. It has nothing

578
00:29:26,680 --> 00:29:29,880
Speaker 1: to do with this. The very least is you could

579
00:29:29,880 --> 00:29:32,240
Speaker 1: have these people make sure that they have decent healthcare

580
00:29:32,280 --> 00:29:34,520
Speaker 1: and are able to get the treatment that they deserve.

581
00:29:35,280 --> 00:29:38,200
Speaker 1: You also have Pete continuing to cover himself in glory.

582
00:29:38,640 --> 00:29:42,640
Speaker 1: He did ultimately, much belatedly, decide to actually go to

583
00:29:42,680 --> 00:29:46,520
Speaker 1: Ohio under great pressure from Republicans but also from some Democrats.

584
00:29:46,760 --> 00:29:48,400
Speaker 1: Let's take a listen to a little bit of what

585
00:29:48,480 --> 00:29:51,480
Speaker 1: he had to say in retrospect. Should you have come

586
00:29:51,520 --> 00:29:55,120
Speaker 1: a little sooner? So again, in terms of the timing

587
00:29:55,120 --> 00:29:57,280
Speaker 1: of the visit, I'm trying to strike the right balance,

588
00:29:57,520 --> 00:30:00,320
Speaker 1: allowing NTSP to play its role, but making sure we're

589
00:30:00,320 --> 00:30:03,200
Speaker 1: here in that show of support. But also Norfolk Southern

590
00:30:03,240 --> 00:30:06,800
Speaker 1: and the other freight rail companies need to stop fighting

591
00:30:06,880 --> 00:30:11,080
Speaker 1: us every time we try to do a regulation in

592
00:30:11,200 --> 00:30:14,360
Speaker 1: order to hold them accountable. And there are other railroad

593
00:30:14,400 --> 00:30:19,440
Speaker 1: companies accountable for their safety record. And what we've seen

594
00:30:19,560 --> 00:30:22,560
Speaker 1: is industry goes to Washington and they get their way.

595
00:30:23,320 --> 00:30:26,560
Speaker 1: They got their way on the ECP rule, they got

596
00:30:26,560 --> 00:30:29,960
Speaker 1: their way on a Christmas tree of regulatory changes that

597
00:30:30,000 --> 00:30:32,560
Speaker 1: the last administration made on its way out the door

598
00:30:32,600 --> 00:30:36,360
Speaker 1: in December of twenty twenty. Guess what, dude, you are

599
00:30:36,520 --> 00:30:39,600
Speaker 1: in charge, You can tell them no when they come

600
00:30:39,680 --> 00:30:44,920
Speaker 1: to Washington. I mean the learned helplessness, the feigned helplessness

601
00:30:45,240 --> 00:30:48,200
Speaker 1: of Biden and Pete not just on this issue, but

602
00:30:48,440 --> 00:30:50,080
Speaker 1: on so many issue. When you see it with the

603
00:30:50,120 --> 00:30:52,440
Speaker 1: airlines too, I'm going to send a letter if dude,

604
00:30:52,760 --> 00:30:55,560
Speaker 1: you have power, you are running this agency. If you

605
00:30:55,760 --> 00:30:58,920
Speaker 1: wanted to change and roll back those Trump regulations, if

606
00:30:58,960 --> 00:31:01,200
Speaker 1: they were so bad, you all are in charge now,

607
00:31:01,640 --> 00:31:04,160
Speaker 1: you could do that, but she didn't and you still aren't.

608
00:31:04,240 --> 00:31:08,080
Speaker 1: You send another strongly worded leather letter to Norfolk Southern Congratulations.

609
00:31:08,120 --> 00:31:10,080
Speaker 1: Way to go. You know. With Biden too, I'm just

610
00:31:10,120 --> 00:31:12,720
Speaker 1: mystified as why he won't go. I think it must

611
00:31:12,760 --> 00:31:15,120
Speaker 1: be because he doesn't want to validate the Trump criticism.

612
00:31:16,120 --> 00:31:18,360
Speaker 1: He's like, I willing to go to Kiev. He should

613
00:31:18,400 --> 00:31:21,040
Speaker 1: come back from Ukraine. He should come visit Ohio. So

614
00:31:21,080 --> 00:31:23,160
Speaker 1: now he's the one who's actually playing and making this

615
00:31:23,440 --> 00:31:25,440
Speaker 1: political because he doesn't want to appear weak to the

616
00:31:25,440 --> 00:31:27,640
Speaker 1: fact that he was literally abroad doling out billions of

617
00:31:27,640 --> 00:31:30,719
Speaker 1: dollars to Ukraine whenever somebody at home might have needed him.

618
00:31:30,720 --> 00:31:32,640
Speaker 1: And he says, oh, we're doing everything that we can.

619
00:31:32,720 --> 00:31:36,000
Speaker 1: With Pete too, I mean, what a horrific and massive failure.

620
00:31:36,080 --> 00:31:38,080
Speaker 1: I've come around to the fact that I think Pete

621
00:31:38,120 --> 00:31:41,000
Speaker 1: is just fundamentally lazy. Like when you put together his

622
00:31:41,200 --> 00:31:45,240
Speaker 1: entire tenure of office, his fake paternity leave for literally months,

623
00:31:45,480 --> 00:31:50,280
Speaker 1: much longer than actual working mothers get who literally suffered

624
00:31:50,320 --> 00:31:53,040
Speaker 1: the trauma of giving birth. You have that Number two

625
00:31:53,120 --> 00:31:57,320
Speaker 1: is the FAA nightmare, the collapse of the airline industry

626
00:31:57,400 --> 00:32:00,640
Speaker 1: under his watch, eventually riding itself with no real help

627
00:32:00,800 --> 00:32:03,840
Speaker 1: from the administration, the Southwest Airlines debacle, of which he

628
00:32:03,880 --> 00:32:06,280
Speaker 1: did absolutely nothing, And now you have East Palestine, of

629
00:32:06,280 --> 00:32:08,440
Speaker 1: which he's last the former president of the United States,

630
00:32:08,480 --> 00:32:11,080
Speaker 1: who literally lives in mar Lago is able to get

631
00:32:11,080 --> 00:32:13,640
Speaker 1: there before he is and it's a joke. And then

632
00:32:13,680 --> 00:32:17,160
Speaker 1: at the entire time he's lashing out at the media. Also,

633
00:32:17,160 --> 00:32:19,960
Speaker 1: funnily enough, Christol, he's been planting all these stories saying

634
00:32:19,960 --> 00:32:24,480
Speaker 1: Buddhaget has been taking a lot of bullets for Bidenine. Really,

635
00:32:24,560 --> 00:32:27,440
Speaker 1: because I'm pretty sure you're taking bullets for not doing

636
00:32:27,560 --> 00:32:31,040
Speaker 1: your job and for not having the immediacy that a

637
00:32:31,160 --> 00:32:34,240
Speaker 1: secretary is supposed to have in this situation. I saw

638
00:32:34,320 --> 00:32:36,880
Speaker 1: hilarious reader comment which just said, do you know how

639
00:32:36,880 --> 00:32:38,800
Speaker 1: bad of a secretary of transportation you have to be

640
00:32:39,160 --> 00:32:43,680
Speaker 1: for people to know the secretary nobody knows during a lange.

641
00:32:43,880 --> 00:32:45,920
Speaker 1: The only reason anybody knew her name at that time

642
00:32:45,960 --> 00:32:47,640
Speaker 1: was because you're the wife of Ms McConnell, not because

643
00:32:47,680 --> 00:32:49,640
Speaker 1: of anything to the extent that she ever made news

644
00:32:49,760 --> 00:32:51,920
Speaker 1: is because she was corrupt in helping her Chinese oligarch

645
00:32:52,160 --> 00:32:54,840
Speaker 1: or Taiwanes oligarch father who has ties to the CCP.

646
00:32:55,160 --> 00:32:56,920
Speaker 1: Before that, I don't even remember the name of the

647
00:32:56,960 --> 00:33:00,600
Speaker 1: Secretary of Transportation under President Obama. I'd have to I'm

648
00:33:00,600 --> 00:33:02,680
Speaker 1: sure if you told me, and I'd be like, oh, yeah, yeah,

649
00:33:02,680 --> 00:33:06,320
Speaker 1: that's right under President What was it under President Clinton?

650
00:33:06,400 --> 00:33:10,840
Speaker 1: Same thing I could not tell you under under Bush.

651
00:33:11,040 --> 00:33:12,640
Speaker 1: The only reason I could tell you is because Elaine

652
00:33:12,720 --> 00:33:15,680
Speaker 1: Chow also served in that position. He was there well,

653
00:33:16,000 --> 00:33:18,040
Speaker 1: and the irony is, I mean, this guy I'm looking

654
00:33:18,120 --> 00:33:20,840
Speaker 1: up Obama's Okay, Raylea Hood there you go out. Yeah,

655
00:33:20,840 --> 00:33:23,440
Speaker 1: he was a congressman. I think we also had Anthony

656
00:33:23,480 --> 00:33:27,040
Speaker 1: Fox maybe in there for a while. Yeah. Anyway, Look,

657
00:33:27,560 --> 00:33:30,880
Speaker 1: Pete wanted to use this position as a launching pad

658
00:33:31,000 --> 00:33:35,400
Speaker 1: for his presidential and other political ambitions, and you know,

659
00:33:35,600 --> 00:33:38,040
Speaker 1: as the fates would have it, the Secretary of Transportation

660
00:33:38,160 --> 00:33:40,480
Speaker 1: actually ended up being a really critical position, and he

661
00:33:40,560 --> 00:33:42,920
Speaker 1: had every opportunity to rise to the challenge and to

662
00:33:42,960 --> 00:33:46,680
Speaker 1: be hero right now. Instead, he is a total joke.

663
00:33:46,920 --> 00:33:50,480
Speaker 1: He's a failure, he's an embarrassment, and he's even taken

664
00:33:50,560 --> 00:33:54,520
Speaker 1: some fire astonishingly from people on his own side of

665
00:33:54,560 --> 00:33:57,920
Speaker 1: the aisle and from a few lonely voices media in

666
00:33:57,960 --> 00:34:01,880
Speaker 1: the media who have criticized him as well. So, no,

667
00:34:02,120 --> 00:34:05,600
Speaker 1: it's been a complete disaster. And the way that he

668
00:34:05,760 --> 00:34:09,839
Speaker 1: pretends like there's nothing he can do is so embarrassing

669
00:34:09,960 --> 00:34:13,879
Speaker 1: and so insulting, it's insane. I also think the other

670
00:34:13,960 --> 00:34:16,799
Speaker 1: reason why Biden's not going to go to Ohio is

671
00:34:16,840 --> 00:34:20,759
Speaker 1: because Ohio's a red state now very you know, not

672
00:34:20,880 --> 00:34:23,160
Speaker 1: that long ago. Ohio was not a red state. In fact,

673
00:34:23,160 --> 00:34:26,520
Speaker 1: when he was running for vice president on the Obama ticket,

674
00:34:26,760 --> 00:34:29,279
Speaker 1: Ohio was not a red state. But they've given up

675
00:34:29,320 --> 00:34:31,399
Speaker 1: on it, and so they don't really care that much.

676
00:34:31,440 --> 00:34:33,239
Speaker 1: I mean, I think that's really the bottom line. They're

677
00:34:33,280 --> 00:34:35,920
Speaker 1: more interested in, you know, if this happened in Georgia,

678
00:34:35,960 --> 00:34:37,960
Speaker 1: if it happened in Arizona and one of the up

679
00:34:38,000 --> 00:34:40,840
Speaker 1: and coming places or one of the places like Michigan,

680
00:34:40,920 --> 00:34:42,840
Speaker 1: that they're still holding on to maybe there would be

681
00:34:42,840 --> 00:34:45,600
Speaker 1: a different response. But because it's Ohio and they've written

682
00:34:45,600 --> 00:34:49,640
Speaker 1: off Ohio is unwinnable and full of quote unquote deplorables, Well,

683
00:34:49,920 --> 00:34:53,200
Speaker 1: you know, I guess we'll just send Pete three weeks

684
00:34:53,239 --> 00:34:55,560
Speaker 1: later and have him flounder around and hope that everybody

685
00:34:55,640 --> 00:34:57,759
Speaker 1: moves on. It didn't matter that it's a Trump course

686
00:34:57,800 --> 00:34:59,839
Speaker 1: seventy towns. Of course, you know, of course it does.

687
00:35:00,320 --> 00:35:03,160
Speaker 1: And this lookd Trump the way, how do you think

688
00:35:03,160 --> 00:35:06,520
Speaker 1: Ohio became red? Like that's the question? How did Trump?

689
00:35:06,600 --> 00:35:08,840
Speaker 1: How did Ohio? Which did he win it twice? I

690
00:35:08,840 --> 00:35:13,080
Speaker 1: believe Obama wanted twice? No, so Ohio, Yeah, Obama won it?

691
00:35:13,320 --> 00:35:15,120
Speaker 1: You want it the second time around? I'm pretty sure, yes,

692
00:35:15,160 --> 00:35:17,080
Speaker 1: one hundred percent he did. Because that was the famous

693
00:35:17,080 --> 00:35:20,160
Speaker 1: Megan Kelly Rever when she was like this real math

694
00:35:20,280 --> 00:35:22,000
Speaker 1: or math you do when you're a Republican to make

695
00:35:22,040 --> 00:35:24,799
Speaker 1: yourself feel better. Yet, right, so Obarack Obama wanted twice.

696
00:35:24,840 --> 00:35:27,800
Speaker 1: It was a battleground state two thousand and four with Bush.

697
00:35:27,800 --> 00:35:30,480
Speaker 1: There was a lot of you know, consternation about that

698
00:35:30,520 --> 00:35:32,920
Speaker 1: two thousand It was also a battleground state, have been

699
00:35:32,920 --> 00:35:34,440
Speaker 1: for quite a long time. And then in a single

700
00:35:34,480 --> 00:35:37,879
Speaker 1: political generation, which almost never happens, the state goes full red.

701
00:35:37,920 --> 00:35:40,440
Speaker 1: How did that happen? Well, it's exactly because of stuff

702
00:35:40,440 --> 00:35:42,280
Speaker 1: like this. I could bet you that all the people

703
00:35:42,320 --> 00:35:44,879
Speaker 1: that are there, they will remember that Donald Trump came

704
00:35:45,160 --> 00:35:47,520
Speaker 1: to the state of Ohio. And you know, I think

705
00:35:47,560 --> 00:35:51,479
Speaker 1: that that will resonate significantly, specifically amongst the white working

706
00:35:51,520 --> 00:35:54,319
Speaker 1: class populations that Trump was able to swing. So read

707
00:35:54,840 --> 00:35:57,920
Speaker 1: all across the industrial Midwest, who are just like the

708
00:35:57,960 --> 00:36:01,120
Speaker 1: towns of Liverpool, Ohio that you're talking about this congressional

709
00:36:01,160 --> 00:36:03,720
Speaker 1: district in twenty sixteen. I'm talking about this in my monologue.

710
00:36:03,800 --> 00:36:05,560
Speaker 1: So I have the stat's brush in my head. Swung

711
00:36:05,880 --> 00:36:08,640
Speaker 1: thirty points to Donald Trump in twenty sixteen. It was

712
00:36:08,680 --> 00:36:14,440
Speaker 1: the biggest swing in the entire country. Prior to this era,

713
00:36:15,040 --> 00:36:19,880
Speaker 1: Democrats used to win this congressional district. What's Charlie Wilson

714
00:36:19,960 --> 00:36:22,719
Speaker 1: Ted Strickland. Yes, that was the member of Congress for

715
00:36:22,760 --> 00:36:26,359
Speaker 1: this district. Wow. So it is recent history that this

716
00:36:26,400 --> 00:36:29,080
Speaker 1: has swung so hard and so fast to the right.

717
00:36:29,640 --> 00:36:32,840
Speaker 1: Ask yourself why. I'll save this my rant on this

718
00:36:33,000 --> 00:36:36,719
Speaker 1: for in my monologue, but you know, this incident definitely

719
00:36:36,760 --> 00:36:42,000
Speaker 1: reveals exactly why these populations have moved to the right,

720
00:36:42,160 --> 00:36:44,760
Speaker 1: and because, I mean, bottom line is they feel abandoned

721
00:36:44,800 --> 00:36:47,400
Speaker 1: by Democrats and I can't really blame them when you

722
00:36:47,440 --> 00:36:51,840
Speaker 1: look at all of this unfolding. Very true. All right,

723
00:36:51,920 --> 00:36:56,359
Speaker 1: let's move on to twenty twenty four. Yes, so big announcement,

724
00:36:56,560 --> 00:37:00,000
Speaker 1: big endorsement, guys, this one is a real game changer.

725
00:37:00,760 --> 00:37:05,040
Speaker 1: Wait for it, Jeb Bush endorsing Ronda Santis. Take a listen.

726
00:37:05,719 --> 00:37:08,320
Speaker 1: Is this Randa Santas's opportunity to run for higher office?

727
00:37:08,600 --> 00:37:10,480
Speaker 1: I think it is. He's been a really effective governor,

728
00:37:10,719 --> 00:37:12,640
Speaker 1: He's young. I think we're on the verge of a

729
00:37:12,680 --> 00:37:15,040
Speaker 1: generational change in our politics kind of hope. So I

730
00:37:15,080 --> 00:37:18,080
Speaker 1: think it's time for a more forward leaning, future oriented

731
00:37:19,040 --> 00:37:21,680
Speaker 1: conversation our politics as well, which has made him, should

732
00:37:21,680 --> 00:37:23,760
Speaker 1: he choose to run for the president, a serious contender

733
00:37:23,800 --> 00:37:26,160
Speaker 1: in Republican politics, And who better to do it than

734
00:37:26,320 --> 00:37:29,239
Speaker 1: someone who's been outside of Washington, who's governed effectively, who

735
00:37:29,320 --> 00:37:31,040
Speaker 1: I think has shown that Florida could be a model

736
00:37:31,080 --> 00:37:36,120
Speaker 1: for the future of our country. He'd be like, why

737
00:37:36,120 --> 00:37:37,680
Speaker 1: would I want that? He'd be like, she should call

738
00:37:37,760 --> 00:37:39,680
Speaker 1: him up and be like, hey, man, shut your mouth.

739
00:37:39,760 --> 00:37:43,440
Speaker 1: This is actually not helpful to me at all. That said,

740
00:37:43,600 --> 00:37:46,080
Speaker 1: I mean it illustrates the fundamental problem that he has,

741
00:37:46,120 --> 00:37:47,799
Speaker 1: which is that the people who want him to win

742
00:37:47,920 --> 00:37:50,200
Speaker 1: and to run the most are anti Trump people in

743
00:37:50,280 --> 00:37:52,080
Speaker 1: the Republican Party. But if he ever wants to win

744
00:37:52,280 --> 00:37:54,719
Speaker 1: the Republican nomination, he would have to be a person

745
00:37:54,760 --> 00:37:57,280
Speaker 1: who's able to unite the Trump side and the anti

746
00:37:57,320 --> 00:38:00,640
Speaker 1: Trump side. Worse, the anti Trump side. As you can see,

747
00:38:00,680 --> 00:38:03,520
Speaker 1: there has yet to coalesce around a single person. You

748
00:38:03,520 --> 00:38:05,319
Speaker 1: have Nikki Haley in the race. You have a vag

749
00:38:05,440 --> 00:38:07,880
Speaker 1: Ramaswami who's now in the race. We'll see how he

750
00:38:07,960 --> 00:38:09,839
Speaker 1: ends up in the polls. I'm actually curious to see

751
00:38:09,840 --> 00:38:12,080
Speaker 1: if he's even able to make it done. But you

752
00:38:12,160 --> 00:38:16,200
Speaker 1: have Mike Pence here jockeying for the nomination. You have

753
00:38:16,800 --> 00:38:20,880
Speaker 1: Mike Pompeo, John Bolton, all these other characters, Asa Hutchinson.

754
00:38:21,320 --> 00:38:23,920
Speaker 1: They're considering throwing their ring in the race or their

755
00:38:24,000 --> 00:38:26,359
Speaker 1: hat in the ring. You know what, especially, I think

756
00:38:26,480 --> 00:38:29,799
Speaker 1: Mike Pence, given his standing, we shouldn't forget he's the

757
00:38:29,800 --> 00:38:32,560
Speaker 1: only one who has a real organic constituency in the

758
00:38:32,600 --> 00:38:36,680
Speaker 1: Republican Party. It's evangelicals. And also, though Pence is still

759
00:38:36,680 --> 00:38:39,279
Speaker 1: aligned with the old GOP, Pence has already come out

760
00:38:39,280 --> 00:38:41,640
Speaker 1: and said no, I think we should cut Medicare and

761
00:38:41,680 --> 00:38:45,880
Speaker 1: social Security, and Ukraine is now becoming a massive dividing

762
00:38:45,920 --> 00:38:48,759
Speaker 1: line in the party. Vice President Mike Pence actually hit

763
00:38:49,160 --> 00:38:53,239
Speaker 1: Ron DeSantis, not Trump specifically DeSantis whenever it came to

764
00:38:53,600 --> 00:38:56,239
Speaker 1: his stance on Ukraine, questioning why President Biden was in

765
00:38:56,320 --> 00:38:58,840
Speaker 1: Kiev whenever he was needed here at home. Here's what

766
00:38:58,920 --> 00:39:01,239
Speaker 1: Vice President Pence had to say. While some in my

767
00:39:01,360 --> 00:39:06,280
Speaker 1: party have taken a somewhat different view, let me be clear,

768
00:39:09,560 --> 00:39:12,040
Speaker 1: there can be no room in the leadership of the

769
00:39:12,080 --> 00:39:16,880
Speaker 1: Republican Party for apologists for Putin. There can only be

770
00:39:17,040 --> 00:39:20,440
Speaker 1: room for champions of freedom. And the reason we know

771
00:39:20,600 --> 00:39:23,320
Speaker 1: that's a shot at Ron de Santis and not at Trump,

772
00:39:23,360 --> 00:39:26,080
Speaker 1: who has actually gone further than DeSantis in terms of

773
00:39:26,120 --> 00:39:30,200
Speaker 1: his Ukraine comments, is because he made sure to say

774
00:39:30,480 --> 00:39:33,280
Speaker 1: that Russia didn't try to seize territory during the Trump

775
00:39:33,560 --> 00:39:38,480
Speaker 1: Pence administration and praised, you know, Trump in other ways,

776
00:39:38,520 --> 00:39:42,360
Speaker 1: basically was touting their record as opposed to what DeSantis

777
00:39:42,360 --> 00:39:45,399
Speaker 1: has been saying on Ukraine. It's like this shows you too,

778
00:39:45,480 --> 00:39:49,400
Speaker 1: where the infighting of all this is replicating the exact

779
00:39:49,520 --> 00:39:54,080
Speaker 1: twenty sixteen conditions, which if Marco Rubio had dropped out

780
00:39:54,120 --> 00:39:57,000
Speaker 1: of the race or John Kasik much earlier in the cycle,

781
00:39:57,080 --> 00:39:59,120
Speaker 1: then you know, Ted Cruz would have had a shot.

782
00:39:59,120 --> 00:40:01,160
Speaker 1: I wouldn't say would have, but he might have come

783
00:40:01,360 --> 00:40:03,759
Speaker 1: much closer to winning the nomination. But these people are

784
00:40:03,760 --> 00:40:06,120
Speaker 1: all such narcissists that they're like, no, it's my turn.

785
00:40:06,160 --> 00:40:08,319
Speaker 1: It's my turn, it's my turn. That they're splitting up

786
00:40:08,360 --> 00:40:11,880
Speaker 1: the anti Trump vote, cannibalizing themselves, and not a single

787
00:40:11,960 --> 00:40:16,800
Speaker 1: one has yet to criticize Donald Trump. Mike Pence cannot

788
00:40:16,840 --> 00:40:19,440
Speaker 1: say anything bad about Trump. Vik Ramaswami was asked on

789
00:40:19,560 --> 00:40:22,280
Speaker 1: John Hannity, he said, I consider President Trump a friend,

790
00:40:22,760 --> 00:40:26,360
Speaker 1: and I'm like, okay, maybe Nikki Haley, I'm not kicking sideways.

791
00:40:26,400 --> 00:40:29,239
Speaker 1: I'm kicking for it because she's always kicking. At a

792
00:40:29,239 --> 00:40:32,320
Speaker 1: certain point, you have to run against this man, because

793
00:40:32,680 --> 00:40:35,600
Speaker 1: trying to cannibalize each other's anti Trump votes, of which

794
00:40:35,640 --> 00:40:39,160
Speaker 1: is barely even exists as a lane for one individual, yeah,

795
00:40:39,239 --> 00:40:42,560
Speaker 1: let alone five individuals. Good luck. And also you have

796
00:40:42,600 --> 00:40:45,759
Speaker 1: to say this on Ukraine Desantisan Trump are clearly the

797
00:40:45,760 --> 00:40:47,360
Speaker 1: ones who are on the right side of this with

798
00:40:47,400 --> 00:40:49,799
Speaker 1: the Republican base. Like the number of people who are

799
00:40:49,840 --> 00:40:54,200
Speaker 1: like ra Rad NAFO pro Ukraine is under fifty percent

800
00:40:54,520 --> 00:40:57,840
Speaker 1: in the Republican Party. So if you're fighting for those scraps,

801
00:40:58,160 --> 00:41:00,759
Speaker 1: I mean, good luck. I thought it was magnificant that

802
00:41:00,840 --> 00:41:04,360
Speaker 1: Desantus adopted the same posture as Trump, because there you

803
00:41:04,400 --> 00:41:07,200
Speaker 1: have what seventy five percent right now of Republican of

804
00:41:07,200 --> 00:41:09,719
Speaker 1: the two Republicans that are polling at the very top

805
00:41:09,800 --> 00:41:12,239
Speaker 1: in terms of support, we're both adopting the same line

806
00:41:12,239 --> 00:41:15,759
Speaker 1: on Ukraine. That's it. That's an ideological victory. Yeah, clean up, absolutely,

807
00:41:15,960 --> 00:41:20,040
Speaker 1: And there's new reporting that this isn't really surprising given

808
00:41:20,040 --> 00:41:22,480
Speaker 1: the way that Ronda Santa's positioned himself when he was

809
00:41:22,520 --> 00:41:25,839
Speaker 1: in Congress, but this is a new evolution for him.

810
00:41:26,000 --> 00:41:29,279
Speaker 1: Andrew Kazinski over at CNN tracked down his previous statements

811
00:41:29,360 --> 00:41:32,439
Speaker 1: on the situation in Ukraine. The headline here is Ronda

812
00:41:32,480 --> 00:41:34,759
Speaker 1: Santis wanted to send weapons to Ukraine when he was

813
00:41:34,760 --> 00:41:38,120
Speaker 1: a congressman. As a presidential hopeful, he questions US involvement

814
00:41:38,360 --> 00:41:40,239
Speaker 1: and basically, you know, when he was in Congress, it

815
00:41:40,280 --> 00:41:42,880
Speaker 1: was like the Obama era, and this was his critique

816
00:41:42,920 --> 00:41:45,400
Speaker 1: of Obama was that he wasn't being hawkish enough, he

817
00:41:45,480 --> 00:41:48,600
Speaker 1: wasn't doing enough to arm Ukraine, and so he said,

818
00:41:48,719 --> 00:41:52,360
Speaker 1: you know a lot in that direction at that time.

819
00:41:53,160 --> 00:41:55,640
Speaker 1: Here's one quote from the article. They say, once an

820
00:41:55,640 --> 00:41:58,920
Speaker 1: advocate of a hardline, hawkish approach to Russia by supporting Ukraine.

821
00:41:59,000 --> 00:42:01,480
Speaker 1: The Florida governor shift course this week in anticipation of

822
00:42:01,480 --> 00:42:04,279
Speaker 1: a potential presidential run, questioning whether it was the us

823
00:42:04,400 --> 00:42:06,040
Speaker 1: is interest to be involved in what he called things

824
00:42:06,040 --> 00:42:08,760
Speaker 1: like the borderlands or over crimea. He added that Russia

825
00:42:08,840 --> 00:42:10,799
Speaker 1: was not the same threat to our country, even though

826
00:42:10,840 --> 00:42:14,040
Speaker 1: they're hostile, and downplayed the threats that Russia could invade

827
00:42:14,120 --> 00:42:17,239
Speaker 1: NATO countries. However, at the time, he described himself as

828
00:42:17,400 --> 00:42:20,640
Speaker 1: a follower of the Reagan school of foreign policy, said

829
00:42:20,680 --> 00:42:23,440
Speaker 1: some things like I'm an old, ung, reconstructed, you know,

830
00:42:23,600 --> 00:42:27,040
Speaker 1: Cold War kind of a guy. Basically, he said, they

831
00:42:27,160 --> 00:42:29,520
Speaker 1: viewed guys like me, who are more of the Reagan

832
00:42:29,600 --> 00:42:32,440
Speaker 1: school that's tough on Russia, as kind of throwbacks to

833
00:42:32,480 --> 00:42:35,359
Speaker 1: the Cold War. They criticized Mitt Romney in twenty twelve. Now,

834
00:42:35,640 --> 00:42:37,399
Speaker 1: all of a sudden because they're using it against Trump.

835
00:42:37,400 --> 00:42:40,840
Speaker 1: They're so concerned about Russia. He also talked about he

836
00:42:40,920 --> 00:42:43,879
Speaker 1: was critical of Obama refusing to provide quote, lethal aid

837
00:42:43,920 --> 00:42:45,840
Speaker 1: to Ukraine that they were trying to do or reset

838
00:42:45,880 --> 00:42:47,800
Speaker 1: the Democrats allowed at that. So in any case, you

839
00:42:47,840 --> 00:42:50,360
Speaker 1: can see that he can read the political writing on

840
00:42:50,400 --> 00:42:53,000
Speaker 1: the wall and is definitely trying to shift his position

841
00:42:53,040 --> 00:42:55,360
Speaker 1: from the way he positioned himself during the Obama Are

842
00:42:55,400 --> 00:42:57,520
Speaker 1: are the things that surprise everybody into Santas. Look, I

843
00:42:57,560 --> 00:43:00,960
Speaker 1: remembered Desanta's as a congressman. He was remarkable in literally

844
00:43:01,080 --> 00:43:04,640
Speaker 1: no way whatsoever. He was a replacement level Tea Party guy.

845
00:43:04,880 --> 00:43:08,080
Speaker 1: Nobody here thought about him at all. Then he ran

846
00:43:08,120 --> 00:43:09,880
Speaker 1: for governor and people were laughing at him because they

847
00:43:09,880 --> 00:43:11,319
Speaker 1: didn't think he was gonna win. At the time, he

848
00:43:11,400 --> 00:43:14,200
Speaker 1: was like thirty five points down. He ended up finagling

849
00:43:14,239 --> 00:43:17,680
Speaker 1: a Trump endorsement. He ends up barely squeaking by into that.

850
00:43:18,120 --> 00:43:20,600
Speaker 1: I don't think people remember this. It came almost near

851
00:43:20,640 --> 00:43:23,360
Speaker 1: a recount with Andrew Gillop. Yeah, that's how close that

852
00:43:23,480 --> 00:43:25,880
Speaker 1: race was. And then he shocked everybody. He did a

853
00:43:25,920 --> 00:43:28,640
Speaker 1: couple of things before COVID, Even before all of that,

854
00:43:28,920 --> 00:43:31,680
Speaker 1: he increased teacher pay, started preserving the Everglades, and we

855
00:43:31,680 --> 00:43:33,160
Speaker 1: were like, huh, this is an interesting guy. He's not

856
00:43:33,160 --> 00:43:35,400
Speaker 1: the Tea party person that he once was. He always

857
00:43:35,440 --> 00:43:37,919
Speaker 1: wanted to be a very popular figure COVID. He found

858
00:43:37,960 --> 00:43:40,759
Speaker 1: his lane Florida. The economy is booming, you have one

859
00:43:40,800 --> 00:43:43,800
Speaker 1: of the largest net migrations in the entire United States.

860
00:43:43,960 --> 00:43:46,200
Speaker 1: And now he's a major cultural figure. He's picked the

861
00:43:46,200 --> 00:43:48,799
Speaker 1: cultural right. He's picking a lot of the battles that

862
00:43:49,080 --> 00:43:52,360
Speaker 1: people online are really jazzed out about. In the base

863
00:43:52,400 --> 00:43:56,400
Speaker 1: and also from a general population perspective, clearly something compelling

864
00:43:56,719 --> 00:43:59,080
Speaker 1: is happening in the state of Florida. I think he's

865
00:43:59,239 --> 00:44:01,960
Speaker 1: ultimately just political wins guy. He was a Tea party

866
00:44:01,960 --> 00:44:04,200
Speaker 1: guy whenever he needed to be Tea party guy. Now

867
00:44:04,239 --> 00:44:07,120
Speaker 1: he is this, you know, Ukraine skeptical person when he

868
00:44:07,160 --> 00:44:09,840
Speaker 1: wants to be. Now, for those who are worried about that,

869
00:44:09,840 --> 00:44:11,719
Speaker 1: that's not the worst thing in the world. Somebody who's

870
00:44:11,760 --> 00:44:14,239
Speaker 1: willing to change their minds, who's not ideological is in

871
00:44:14,320 --> 00:44:16,080
Speaker 1: many cases, as you can see here, at least in

872
00:44:16,120 --> 00:44:19,600
Speaker 1: my opinion, much better of a political ally than somebody

873
00:44:19,719 --> 00:44:22,480
Speaker 1: like Pence who is doctrinaire you know, an actual Reagan

874
00:44:22,520 --> 00:44:24,600
Speaker 1: Republican in his mind. At the same time, if the

875
00:44:24,640 --> 00:44:27,239
Speaker 1: winds blow the other way, that's clearly where he would go.

876
00:44:27,360 --> 00:44:29,960
Speaker 1: I think of it as more politically significant that he

877
00:44:30,000 --> 00:44:33,439
Speaker 1: did end up here on Ukraine, and that at this point,

878
00:44:33,480 --> 00:44:35,320
Speaker 1: if it is Trump Org DeSantis, I'm not going to

879
00:44:35,360 --> 00:44:37,439
Speaker 1: say you're gonna get the same policy, but the same

880
00:44:37,560 --> 00:44:40,640
Speaker 1: rhetoric and valance is now there. And clearly that ideological

881
00:44:40,719 --> 00:44:43,759
Speaker 1: victory has been won on the Republican nominee side, as

882
00:44:44,040 --> 00:44:46,560
Speaker 1: even though there are all these GOP congressmen and all

883
00:44:46,600 --> 00:44:49,520
Speaker 1: those who are beating the war drum in Ukraine, there,

884
00:44:49,640 --> 00:44:52,839
Speaker 1: at least rhetorically, it's not there. Policy wise, I still

885
00:44:52,840 --> 00:44:54,680
Speaker 1: have no idea who he would pick or who would

886
00:44:54,680 --> 00:44:56,319
Speaker 1: work for him. So anyway, I think it's I think

887
00:44:56,320 --> 00:44:58,520
Speaker 1: it's very significant. Well, we also have to say that

888
00:44:58,920 --> 00:45:00,600
Speaker 1: what we saw with Trump and oh we've seen with

889
00:45:00,600 --> 00:45:02,600
Speaker 1: the offensive political candidate, this is what you say on

890
00:45:02,640 --> 00:45:05,839
Speaker 1: the campaign trail may end up being very different from

891
00:45:05,880 --> 00:45:08,440
Speaker 1: what you do when you're governing. And you've got the

892
00:45:08,480 --> 00:45:10,880
Speaker 1: generals telling you this, and you've got the donor class

893
00:45:10,920 --> 00:45:13,120
Speaker 1: telling you that, and there's a lot of pressures that

894
00:45:13,120 --> 00:45:15,120
Speaker 1: come to bear on you, and you know, the voice

895
00:45:15,120 --> 00:45:18,000
Speaker 1: of the people that you that originally elected you become

896
00:45:18,080 --> 00:45:21,560
Speaker 1: smaller and smaller or smaller apparently. But I do think

897
00:45:21,600 --> 00:45:25,160
Speaker 1: that the fundamental issue for Ron DeSantis right now, who

898
00:45:25,440 --> 00:45:28,239
Speaker 1: you know, has a genuine base of support. He's not

899
00:45:28,440 --> 00:45:30,239
Speaker 1: like a job, He's not you know, I don't want

900
00:45:30,280 --> 00:45:32,640
Speaker 1: to like diminish him like that is he definitely has

901
00:45:33,120 --> 00:45:36,480
Speaker 1: really captured the imagination of some subset of the Republican

902
00:45:36,760 --> 00:45:41,319
Speaker 1: primary caucus. But you know, if they're all afraid to

903
00:45:41,400 --> 00:45:44,520
Speaker 1: tackle to go after Trump and there are not afraid

904
00:45:44,560 --> 00:45:47,480
Speaker 1: to take shots at rond de Santis, clearly you are

905
00:45:47,520 --> 00:45:50,360
Speaker 1: back in this twenty sixteen dynamic where they're all jocking

906
00:45:50,440 --> 00:45:53,080
Speaker 1: to be number two. But guess what, number two doesn't matter.

907
00:45:53,160 --> 00:45:55,359
Speaker 1: Number one matters. And if you don't have a plan

908
00:45:55,440 --> 00:45:57,719
Speaker 1: to get past Trump, he's not going to magically just

909
00:45:57,840 --> 00:46:00,440
Speaker 1: disappear or disintegrate. You know. If you're waya for this

910
00:46:00,480 --> 00:46:04,600
Speaker 1: guy to just like self immolate and go away dream on.

911
00:46:04,960 --> 00:46:07,759
Speaker 1: I mean, I guess you know it's theoretically possible, but

912
00:46:07,880 --> 00:46:09,960
Speaker 1: you better have a plan to actually win. Here's the

913
00:46:10,000 --> 00:46:12,680
Speaker 1: latest up polling that we've got. This is from Fox News.

914
00:46:13,080 --> 00:46:15,640
Speaker 1: And you know, again, the polls have been all over

915
00:46:15,680 --> 00:46:18,239
Speaker 1: the map, so just keep that in mind. But right now,

916
00:46:18,239 --> 00:46:21,320
Speaker 1: we've got Trump at forty three, DeSantis twenty eight, Nikki

917
00:46:21,360 --> 00:46:24,680
Speaker 1: Haley with their little announcement bounce getting up to seven percent,

918
00:46:25,040 --> 00:46:27,319
Speaker 1: tied now with Mike Pence at seven percent, and then

919
00:46:27,360 --> 00:46:29,799
Speaker 1: you've got a bunch of you know, other contenders at

920
00:46:29,840 --> 00:46:32,279
Speaker 1: two percent and one percent. So that's basically what the

921
00:46:32,360 --> 00:46:35,120
Speaker 1: race looks like right now. And you know, we've we

922
00:46:35,239 --> 00:46:38,200
Speaker 1: showed you last week some signs that Trump has actually

923
00:46:38,200 --> 00:46:40,200
Speaker 1: gone up in the polls a bit, streightened his hand

924
00:46:40,200 --> 00:46:42,759
Speaker 1: a bit over the past number of weeks. And I

925
00:46:42,920 --> 00:46:45,360
Speaker 1: just continue to believe that the time when he was

926
00:46:45,400 --> 00:46:48,360
Speaker 1: weakest was right after the midterms. Midterms feel like a

927
00:46:48,400 --> 00:46:50,960
Speaker 1: long time ago. All of the freshness of like, oh,

928
00:46:51,000 --> 00:46:53,359
Speaker 1: this was a referendum on Trump and this didn't go well,

929
00:46:53,400 --> 00:46:55,960
Speaker 1: and DeSantis did really well on Florida. It's kind of

930
00:46:55,960 --> 00:47:00,560
Speaker 1: fading into ancient history already. Yeah. Look, I mean, the

931
00:47:00,560 --> 00:47:03,080
Speaker 1: they're all over the map at the same time, all

932
00:47:03,200 --> 00:47:05,680
Speaker 1: but one, I believe, I think ninety nine percent of

933
00:47:05,719 --> 00:47:08,720
Speaker 1: them have Trump at the top, So that means something.

934
00:47:09,080 --> 00:47:11,560
Speaker 1: What else do they show us. Mike Pence has the

935
00:47:11,680 --> 00:47:14,319
Speaker 1: most static number across these as I've ever seen. He's

936
00:47:14,320 --> 00:47:16,520
Speaker 1: almost always at seven to eleven percent. Yeah, it's like

937
00:47:16,560 --> 00:47:18,680
Speaker 1: seven to eleven. So exactly, that's about you know, the

938
00:47:18,680 --> 00:47:21,360
Speaker 1: hard right evangelicals, that's about what they compromise of the

939
00:47:21,400 --> 00:47:23,880
Speaker 1: Republican base. Okay, that makes sense, And the rest of

940
00:47:23,880 --> 00:47:28,360
Speaker 1: it is just totally up for grabs between Haley, Abbott Cheney,

941
00:47:28,680 --> 00:47:31,879
Speaker 1: Chirsty Nome, Mike Pompeo, Tim Scott, Glenn youngk and Chris Christy,

942
00:47:31,960 --> 00:47:34,800
Speaker 1: Larry Hogan, all these people, but all of them still

943
00:47:34,840 --> 00:47:37,480
Speaker 1: don't even add up to anything close to what DeSantis

944
00:47:37,560 --> 00:47:39,960
Speaker 1: or Trump has to. And you know, my Pence is

945
00:47:40,000 --> 00:47:42,399
Speaker 1: not wrong. The greatest threat to him is not Donald Trump.

946
00:47:42,440 --> 00:47:45,040
Speaker 1: It's Ron DeSantis. Nikki Haley. The greatest threat to her

947
00:47:45,120 --> 00:47:48,240
Speaker 1: is Ron DeSantis or Mike Pence. It's the anti Trump

948
00:47:48,360 --> 00:47:50,440
Speaker 1: Trump skeptical vote, of which they are going to be

949
00:47:50,880 --> 00:47:52,719
Speaker 1: so they're rarely going to be vicious and trying to

950
00:47:52,719 --> 00:47:55,120
Speaker 1: tear each other apart. And then all that does is

951
00:47:55,160 --> 00:47:57,799
Speaker 1: benefit Trump, who's sitting pretty all the way up at

952
00:47:57,840 --> 00:48:00,880
Speaker 1: the top with a near majority, near outright majority in

953
00:48:00,920 --> 00:48:03,200
Speaker 1: many of these polls. It's a bad situation for many

954
00:48:03,200 --> 00:48:06,120
Speaker 1: of them. Yeah, indeed. Okay, so let's talk about the

955
00:48:06,160 --> 00:48:10,040
Speaker 1: democratic side. Now we've got a former former president, not

956
00:48:10,120 --> 00:48:13,839
Speaker 1: yet anyway, current President Joe Biden getting asked about the

957
00:48:13,960 --> 00:48:17,160
Speaker 1: classified document situation. You know, you have the whole Trump situation,

958
00:48:17,360 --> 00:48:19,000
Speaker 1: very critical of that, and then a low and behold,

959
00:48:19,000 --> 00:48:21,839
Speaker 1: he has some classified documents that residents and also at

960
00:48:21,840 --> 00:48:24,560
Speaker 1: his you know, think tank as well. Take a listen

961
00:48:24,560 --> 00:48:26,439
Speaker 1: to how he responded to that. When you hear about

962
00:48:26,480 --> 00:48:29,000
Speaker 1: boxes in your garage or in your old office, you

963
00:48:29,120 --> 00:48:33,880
Speaker 1: called the Trump discovery irresponsible? Is there something irresponsible here though? Too?

964
00:48:35,280 --> 00:48:38,359
Speaker 1: You know, you're a good lawyer, but you're trying to

965
00:48:38,400 --> 00:48:44,839
Speaker 1: make a comparison. What there's degrees there responsibility that are

966
00:48:45,320 --> 00:48:50,640
Speaker 1: they can be significant degrees if the responsibility what the

967
00:48:50,680 --> 00:48:54,000
Speaker 1: way in which the boxes were packed up from my office?

968
00:48:54,480 --> 00:48:59,239
Speaker 1: Apparently not everything was gone through as meticulous as it

969
00:48:59,280 --> 00:49:03,040
Speaker 1: should have. Okay, so what does that mean? So not

970
00:49:03,160 --> 00:49:07,440
Speaker 1: meticulously as it should have, and there are degrees of irresponsibility.

971
00:49:07,680 --> 00:49:10,080
Speaker 1: My problem with this is that he's still not taking

972
00:49:10,200 --> 00:49:14,080
Speaker 1: responsibility for the fact that he's the person who it

973
00:49:14,120 --> 00:49:16,360
Speaker 1: falls on. I mean, do you think Donald Trump personally

974
00:49:16,400 --> 00:49:18,959
Speaker 1: packed those documents and put him in his office ordy

975
00:49:19,000 --> 00:49:21,160
Speaker 1: to direct someone to It's just like what you did.

976
00:49:21,400 --> 00:49:23,879
Speaker 1: You directed someone to pack them and take them. And

977
00:49:23,920 --> 00:49:26,759
Speaker 1: from what we know about what was inside of his

978
00:49:27,440 --> 00:49:30,040
Speaker 1: inside of his files, they were in his private office,

979
00:49:30,160 --> 00:49:34,239
Speaker 1: they were intermingled with his son's funeral plans. Clearly there

980
00:49:34,320 --> 00:49:37,279
Speaker 1: was no like plan that was made here. They were

981
00:49:37,360 --> 00:49:40,359
Speaker 1: locked inside of his garage, as if that somehow made

982
00:49:40,400 --> 00:49:42,279
Speaker 1: it better because that's where he keeps his corvette, of

983
00:49:42,320 --> 00:49:44,839
Speaker 1: which we know that Hunter had access to and used

984
00:49:44,840 --> 00:49:47,399
Speaker 1: to drive. And look, I don't particularly care about all

985
00:49:47,440 --> 00:49:49,880
Speaker 1: these particulars. I'm just showing you that, if anything, it's

986
00:49:50,080 --> 00:49:52,640
Speaker 1: just as insecure as mar Lago. The crime in substance

987
00:49:52,880 --> 00:49:55,080
Speaker 1: is basically the same on its merits. Now the cover

988
00:49:55,200 --> 00:49:57,320
Speaker 1: up is a little bit different. Yeah, by all measures

989
00:49:57,360 --> 00:49:59,719
Speaker 1: he did, you know, flag it or whatever. But at

990
00:49:59,719 --> 00:50:02,040
Speaker 1: the same time, you hid this from the American people.

991
00:50:02,280 --> 00:50:04,759
Speaker 1: He had six weeks before the election where he could

992
00:50:04,760 --> 00:50:06,840
Speaker 1: have told everybody and they would have made a public

993
00:50:06,880 --> 00:50:08,399
Speaker 1: and they didn't do it. I mean, I just think

994
00:50:08,440 --> 00:50:10,000
Speaker 1: all of this is a wash at this time. Yeah,

995
00:50:10,040 --> 00:50:12,000
Speaker 1: I agree. That's the thing is, like, you know, they

996
00:50:12,080 --> 00:50:14,839
Speaker 1: really had Trump dead rights on this one with the

997
00:50:14,880 --> 00:50:17,560
Speaker 1: documents and the amount of them and the cover up,

998
00:50:17,600 --> 00:50:19,480
Speaker 1: and I read like this was the most clear cut

999
00:50:19,880 --> 00:50:23,880
Speaker 1: potential indictment and now it's just a wash. And politically

1000
00:50:23,960 --> 00:50:26,040
Speaker 1: it's just a wash as well, especially when then you

1001
00:50:26,080 --> 00:50:27,960
Speaker 1: had like Mike Pence that turns out as class by

1002
00:50:28,000 --> 00:50:30,320
Speaker 1: documentcy and American people are like, all right, but forget it,

1003
00:50:30,400 --> 00:50:33,600
Speaker 1: let's move on. So it is embarrassing that he's still

1004
00:50:33,680 --> 00:50:36,880
Speaker 1: unable to take any sort of accountability that he's trying to,

1005
00:50:37,040 --> 00:50:38,799
Speaker 1: you know, parse these things that I don't think are

1006
00:50:38,800 --> 00:50:41,320
Speaker 1: obvious to most people, that this is like a dramatically

1007
00:50:41,320 --> 00:50:43,880
Speaker 1: different situation than the situation with Trump done at mar

1008
00:50:43,920 --> 00:50:47,240
Speaker 1: A Lago. At the same time, you know, we covered

1009
00:50:47,280 --> 00:50:50,680
Speaker 1: last week there were some rumblings in the DC press

1010
00:50:51,239 --> 00:50:53,760
Speaker 1: that Biden maybe he's not going to run again because

1011
00:50:53,760 --> 00:50:56,440
Speaker 1: this man is like famously unable to make a decision,

1012
00:50:56,560 --> 00:50:58,880
Speaker 1: and so the timeline for when he's actually going to

1013
00:50:58,960 --> 00:51:02,080
Speaker 1: launch his re elect has gotten pushed further and further

1014
00:51:02,120 --> 00:51:04,680
Speaker 1: and further into the future. And so there are some

1015
00:51:04,840 --> 00:51:06,799
Speaker 1: people who are like, Okay, well, maybe we need to

1016
00:51:06,840 --> 00:51:10,239
Speaker 1: come up with a plan. B his wife Biden's wife,

1017
00:51:10,560 --> 00:51:12,879
Speaker 1: doctor Jill Biden. She has now come out and made

1018
00:51:12,920 --> 00:51:15,080
Speaker 1: it pretty clear that he is definitely going to run again.

1019
00:51:15,080 --> 00:51:17,880
Speaker 1: So let's take a listen. Is there any reason for

1020
00:51:18,000 --> 00:51:20,640
Speaker 1: any of us to think that he is not running again?

1021
00:51:20,800 --> 00:51:24,320
Speaker 1: We've heard him say several times that it is intention.

1022
00:51:25,000 --> 00:51:27,839
Speaker 1: Are you not believing this, darlinge? I mean, how many

1023
00:51:27,880 --> 00:51:30,040
Speaker 1: times does he have to say that till you believe it?

1024
00:51:30,520 --> 00:51:35,480
Speaker 1: I mean, he's done so much and darling, he's just

1025
00:51:35,560 --> 00:51:40,560
Speaker 1: not done. Okay? So is all that's left at this

1026
00:51:40,600 --> 00:51:43,080
Speaker 1: point is just to figure out a time and place

1027
00:51:43,080 --> 00:51:48,200
Speaker 1: for the announcement? Pretty much? So she nods her head there, yes,

1028
00:51:48,239 --> 00:51:50,040
Speaker 1: at the end, is all is she's asked? Is all

1029
00:51:50,080 --> 00:51:52,040
Speaker 1: that's left to figure out a time and dat and announcement?

1030
00:51:52,120 --> 00:51:54,239
Speaker 1: She nots her head. Yes, that's what we always thought,

1031
00:51:54,560 --> 00:51:56,360
Speaker 1: is what we thought. And at the same time stuff

1032
00:51:56,400 --> 00:51:58,560
Speaker 1: is floating around He is her most personally. How many

1033
00:51:58,560 --> 00:52:00,279
Speaker 1: times you have to say, it's like, well, why also,

1034
00:52:00,280 --> 00:52:02,719
Speaker 1: why aren't you running yet? It's already February, Like, it's

1035
00:52:02,719 --> 00:52:04,640
Speaker 1: not like this isn't the best time, and you do

1036
00:52:04,680 --> 00:52:06,839
Speaker 1: want to stake it out clearly that there is no lane.

1037
00:52:06,880 --> 00:52:09,480
Speaker 1: I mean the word is not going out enough in

1038
00:52:09,640 --> 00:52:12,000
Speaker 1: the right way. Because you had that political piece which

1039
00:52:12,000 --> 00:52:14,680
Speaker 1: we covered just yesterday, where people in official Washington are

1040
00:52:14,680 --> 00:52:17,279
Speaker 1: preparing themselves for the possibility he may not run. So

1041
00:52:17,280 --> 00:52:18,880
Speaker 1: I don't think it's nearly as much of a given

1042
00:52:19,200 --> 00:52:21,440
Speaker 1: as she's saying. Even though they seem to want to

1043
00:52:21,440 --> 00:52:22,600
Speaker 1: have their cake and eat it too, they don't want

1044
00:52:22,640 --> 00:52:25,440
Speaker 1: to actively run for president again or all the rigors

1045
00:52:25,440 --> 00:52:27,359
Speaker 1: of what a campaign is standing that up, yeah, would

1046
00:52:27,400 --> 00:52:30,000
Speaker 1: look like, and do all the campaign, the fundraising, and

1047
00:52:30,040 --> 00:52:32,279
Speaker 1: then they also want to crowd out everybody else from

1048
00:52:32,320 --> 00:52:33,799
Speaker 1: the race. It's like, no, you have to pick one,

1049
00:52:33,920 --> 00:52:36,319
Speaker 1: and it's time. I mean, look, I agree, I agree,

1050
00:52:36,360 --> 00:52:38,520
Speaker 1: and Vince he's going to run because they still have

1051
00:52:38,719 --> 00:52:41,400
Speaker 1: you know, the Democratic Party still has this issue of okay,

1052
00:52:41,640 --> 00:52:44,319
Speaker 1: maybe we're you know, we have a home majority of

1053
00:52:44,320 --> 00:52:46,520
Speaker 1: the public and even a majority of Democrats are were like,

1054
00:52:46,560 --> 00:52:48,120
Speaker 1: we don't want you to run. We would rather have

1055
00:52:48,200 --> 00:52:51,759
Speaker 1: someone else. But when they look then down at their

1056
00:52:52,080 --> 00:52:55,920
Speaker 1: bench of chosen successors, you know, here's Pete like melting,

1057
00:52:56,560 --> 00:53:00,200
Speaker 1: melting right now into the ground. In his failures as

1058
00:53:00,239 --> 00:53:05,719
Speaker 1: a Secretary of Transportation. Kamala Harris obviously is dramatically unpopular,

1059
00:53:05,880 --> 00:53:08,279
Speaker 1: and you know, even they've realized at this point that

1060
00:53:08,360 --> 00:53:10,279
Speaker 1: it would be a disaster to mistake to run her

1061
00:53:10,320 --> 00:53:12,440
Speaker 1: at the top of the ticket. So it's kind of like,

1062
00:53:12,920 --> 00:53:15,680
Speaker 1: I guess we're gonna go with Biden again. So that's

1063
00:53:15,719 --> 00:53:17,799
Speaker 1: what it looks like to me, you know, in terms

1064
00:53:17,880 --> 00:53:20,279
Speaker 1: of the timeline slipping. But this is just the way

1065
00:53:20,320 --> 00:53:23,759
Speaker 1: he operates. He can't commit to things. It takes him

1066
00:53:23,760 --> 00:53:27,759
Speaker 1: a long time. He's a micromanager, but he's indecisive. So

1067
00:53:28,040 --> 00:53:31,400
Speaker 1: it doesn't surprise me that the timeline has ultimately slipped

1068
00:53:31,400 --> 00:53:34,280
Speaker 1: here down the road into the spring. But I still

1069
00:53:34,400 --> 00:53:37,000
Speaker 1: feel pretty persuaded that he is going to ultimately I

1070
00:53:37,000 --> 00:53:38,920
Speaker 1: think that's right. And you know, with the campaign or

1071
00:53:38,960 --> 00:53:41,560
Speaker 1: with the classified documents thing, like you said, it's a wash.

1072
00:53:41,640 --> 00:53:43,880
Speaker 1: Now you can choose your own fighter. One is corrupt,

1073
00:53:43,880 --> 00:53:46,200
Speaker 1: the other one doesn't follow the rules. I choose the

1074
00:53:46,200 --> 00:53:48,560
Speaker 1: one where they're all basically think that they're above the

1075
00:53:48,640 --> 00:53:51,840
Speaker 1: law apparently doesn't apply to them, which is crazy. You

1076
00:53:51,840 --> 00:53:54,640
Speaker 1: know that you have the vice president, you have former

1077
00:53:54,719 --> 00:53:57,880
Speaker 1: presidents as well, and then you have literally President Biden

1078
00:53:57,960 --> 00:54:00,640
Speaker 1: and Trump not following the rules. Like I've said too,

1079
00:54:00,640 --> 00:54:02,440
Speaker 1: you know with Biden, he wasn't even the president, he

1080
00:54:02,480 --> 00:54:04,759
Speaker 1: was only the vice president. And still he's, you know,

1081
00:54:04,920 --> 00:54:06,840
Speaker 1: just taking all this stuff. Funny story when we were

1082
00:54:06,840 --> 00:54:09,200
Speaker 1: in Austin, I went to go visit the LBJ Museum.

1083
00:54:09,360 --> 00:54:11,359
Speaker 1: The proprietor gave me a nasty look when I was like, hey,

1084
00:54:11,360 --> 00:54:13,480
Speaker 1: do you have any classified documents? Series like, no, sir,

1085
00:54:13,520 --> 00:54:17,879
Speaker 1: would you not? It was a joke, just making a joke,

1086
00:54:19,200 --> 00:54:21,200
Speaker 1: all right. We don't really use sight of what is

1087
00:54:21,239 --> 00:54:24,160
Speaker 1: going on with the economy, which obviously is you know,

1088
00:54:24,520 --> 00:54:26,880
Speaker 1: probably the most important story for all of your lives.

1089
00:54:26,920 --> 00:54:29,520
Speaker 1: And there are some bad indicators in terms of the

1090
00:54:29,560 --> 00:54:32,319
Speaker 1: direction that inflation is heading in, which both has an

1091
00:54:32,360 --> 00:54:36,040
Speaker 1: impact on you know, you your pocketbook right now today,

1092
00:54:36,120 --> 00:54:38,919
Speaker 1: but also what the Federal Reserve might do down the road,

1093
00:54:38,920 --> 00:54:41,359
Speaker 1: which can have more of an impact on you your

1094
00:54:41,400 --> 00:54:43,759
Speaker 1: pocketbook and what it means to your whole life. So

1095
00:54:43,800 --> 00:54:45,320
Speaker 1: let's go ahead and put this up on the screen

1096
00:54:45,760 --> 00:54:48,720
Speaker 1: from the Financial Times. The headline here is Federal Reserve's

1097
00:54:48,840 --> 00:54:52,040
Speaker 1: favored inflation gauge accelerated in January. They use I guess

1098
00:54:52,080 --> 00:54:55,440
Speaker 1: that's the way the brit spell favored. Huh. The personal

1099
00:54:55,480 --> 00:54:59,320
Speaker 1: consumption Expenditure Price Index, which measure measures how much consumers

1100
00:54:59,320 --> 00:55:03,080
Speaker 1: are paying for goods services, increased point six percent month

1101
00:55:03,120 --> 00:55:07,040
Speaker 1: on month after rising point two percent in December, so

1102
00:55:07,080 --> 00:55:10,799
Speaker 1: that is an acceleration. The annual rate increased to five

1103
00:55:10,840 --> 00:55:13,520
Speaker 1: point four percent in January from upwardly revised figure of

1104
00:55:13,520 --> 00:55:16,520
Speaker 1: five point three percent a month earlier. The so called

1105
00:55:16,760 --> 00:55:20,040
Speaker 1: core PCE index, which strips out what they describe as

1106
00:55:20,080 --> 00:55:23,080
Speaker 1: volatile food and energy costs and is the fed's preferred

1107
00:55:23,120 --> 00:55:27,399
Speaker 1: inflation metric, rose point six percent in January, up from

1108
00:55:27,440 --> 00:55:31,239
Speaker 1: point four percent in December. So basically, the bottom line

1109
00:55:31,280 --> 00:55:33,200
Speaker 1: here before I bore you with all these like point

1110
00:55:33,200 --> 00:55:36,920
Speaker 1: whatever percent is it's getting worse according to this metric,

1111
00:55:37,000 --> 00:55:39,640
Speaker 1: and this is a metric that the FED really pays

1112
00:55:39,719 --> 00:55:43,000
Speaker 1: a lot of attention to. They say that following Friday's figures,

1113
00:55:43,000 --> 00:55:46,680
Speaker 1: investors priced in a thirty nine percent chance of a

1114
00:55:46,840 --> 00:55:49,840
Speaker 1: half point rate rise at the fed's March meeting, compared

1115
00:55:49,880 --> 00:55:53,320
Speaker 1: with an eighteen percent likelihood a week ago. So basically,

1116
00:55:53,320 --> 00:55:56,200
Speaker 1: investors are looking at these numbers and thinking that there

1117
00:55:56,320 --> 00:55:58,839
Speaker 1: is an elevated chance that the FED is going to

1118
00:55:58,880 --> 00:56:01,760
Speaker 1: go even further and go from you know, a quarter

1119
00:56:01,800 --> 00:56:04,600
Speaker 1: point increase to a half point increase, which would have

1120
00:56:04,680 --> 00:56:07,520
Speaker 1: more of a dramatic impact on the economy. Ultimately, as

1121
00:56:07,560 --> 00:56:10,640
Speaker 1: they know, tighten and make it more difficult to borrow,

1122
00:56:10,760 --> 00:56:13,120
Speaker 1: increase interest rates, make it harder to you know, when

1123
00:56:13,360 --> 00:56:15,080
Speaker 1: morge rates go up, all of those things that we've

1124
00:56:15,120 --> 00:56:17,480
Speaker 1: been talking about for a long time. So this is

1125
00:56:17,520 --> 00:56:19,400
Speaker 1: this is a very bad sign ultimately. Oh yeah, and

1126
00:56:19,440 --> 00:56:21,960
Speaker 1: actually I was reading. So there's a former Federal Reserve

1127
00:56:22,040 --> 00:56:25,960
Speaker 1: governor who Frederick Michigan, who put out a white paper

1128
00:56:25,960 --> 00:56:29,200
Speaker 1: which is very influential. CNBC and all those other covered it,

1129
00:56:29,239 --> 00:56:32,160
Speaker 1: and they say that despite the sentiments of many FED officials,

1130
00:56:32,360 --> 00:56:35,000
Speaker 1: they can manage the soft landing. The paper says is

1131
00:56:35,120 --> 00:56:39,080
Speaker 1: very unlikely. We find quote no instance in which central

1132
00:56:39,160 --> 00:56:46,360
Speaker 1: bank induced disflation occurred without recession. Furthermore, given the latest numbers,

1133
00:56:46,560 --> 00:56:50,720
Speaker 1: they say the FED will need to tighten policies significantly

1134
00:56:50,880 --> 00:56:54,360
Speaker 1: further to achieve its inflation objective by the end of

1135
00:56:54,400 --> 00:56:56,879
Speaker 1: twenty twenty five, which is a two percent inflation rate.

1136
00:56:56,960 --> 00:56:59,040
Speaker 1: So basically they're saying, if the FED is going to

1137
00:56:59,040 --> 00:57:00,759
Speaker 1: be the only tool here, you are going to have

1138
00:57:00,800 --> 00:57:03,440
Speaker 1: a recession and they will need to continue tightening all

1139
00:57:03,480 --> 00:57:05,480
Speaker 1: the way up until twenty twenty five to make sure

1140
00:57:05,520 --> 00:57:07,160
Speaker 1: this happens. And so that's why we're really in a

1141
00:57:07,160 --> 00:57:09,400
Speaker 1: bind here. You know, the policy the White House and

1142
00:57:09,440 --> 00:57:12,319
Speaker 1: the Congress has addicated all this responsibility. The Fed only

1143
00:57:12,320 --> 00:57:14,480
Speaker 1: has one tool, which is basically, beat the economy over

1144
00:57:14,480 --> 00:57:17,320
Speaker 1: the head with high rates as possible. What does that mean?

1145
00:57:17,960 --> 00:57:21,040
Speaker 1: Have housing problems right now in terms of the mortgage

1146
00:57:21,120 --> 00:57:23,200
Speaker 1: rates beginning to come down a bit, but they're still

1147
00:57:23,400 --> 00:57:26,920
Speaker 1: vastly unobtainable. It's not the price eventually dipped. Yes, you know,

1148
00:57:26,960 --> 00:57:30,000
Speaker 1: we did see the world's billionaires did lose a significant

1149
00:57:30,000 --> 00:57:31,720
Speaker 1: amount of wealth, but it's not like you had an

1150
00:57:31,760 --> 00:57:35,600
Speaker 1: average wage increase relative to inflation. So we basically have

1151
00:57:35,720 --> 00:57:38,920
Speaker 1: high prices and we have a slowing economy. It's like

1152
00:57:39,000 --> 00:57:42,880
Speaker 1: the worst of literally all worlds. And it's because guess what.

1153
00:57:42,920 --> 00:57:45,520
Speaker 1: Our economy is not just about rates. It's about inputs,

1154
00:57:45,560 --> 00:57:48,040
Speaker 1: it's about supply. It's about Ukraine. It's about and not

1155
00:57:48,240 --> 00:57:50,280
Speaker 1: the aid we're giving to Ukraine. It's about you know,

1156
00:57:50,440 --> 00:57:53,400
Speaker 1: all of these sanctions on the global financial economy, reopening

1157
00:57:53,440 --> 00:57:55,840
Speaker 1: of China, and what exactly that's going to look like

1158
00:57:55,880 --> 00:57:58,080
Speaker 1: the price of gas. I mean I think that's really

1159
00:57:58,080 --> 00:58:01,200
Speaker 1: been the major lesson of all of us. And you know, sadly,

1160
00:58:01,240 --> 00:58:03,000
Speaker 1: on a policy point of view, we have not done

1161
00:58:03,080 --> 00:58:05,760
Speaker 1: a damn thing about any of that. Yeah, literally nothing.

1162
00:58:06,000 --> 00:58:07,919
Speaker 1: And the White House wants to point to the low

1163
00:58:08,000 --> 00:58:11,120
Speaker 1: unemployment rate, which it is low, great, and be like, look,

1164
00:58:11,160 --> 00:58:15,520
Speaker 1: the economy's great. And meanwhile, you have record numbers of

1165
00:58:15,520 --> 00:58:19,240
Speaker 1: people saying that they're doing worse this year than they

1166
00:58:19,240 --> 00:58:22,440
Speaker 1: were last year. You have huge numbers of people, something

1167
00:58:22,480 --> 00:58:24,760
Speaker 1: like seventy percent saying we're on the wrong track. You

1168
00:58:24,840 --> 00:58:28,240
Speaker 1: have more people living paycheck to paycheck. You just have

1169
00:58:28,440 --> 00:58:33,320
Speaker 1: metric after metric after metric that indicates that people are struggling. Okay,

1170
00:58:33,360 --> 00:58:36,560
Speaker 1: are their jobs yes, are they good jobs where you

1171
00:58:36,600 --> 00:58:40,160
Speaker 1: can actually afford to live and rent an apartment or

1172
00:58:40,480 --> 00:58:43,480
Speaker 1: you know, low and behold buy a house. No. And

1173
00:58:43,560 --> 00:58:46,800
Speaker 1: so that's why you see these You know, yes, the

1174
00:58:46,840 --> 00:58:49,920
Speaker 1: unemployment rate is low, but on all of these other metrics,

1175
00:58:50,200 --> 00:58:53,680
Speaker 1: the economy is struggling and more importantly, people are suffering.

1176
00:58:53,800 --> 00:58:57,560
Speaker 1: So this is a bad sign that we continue tohead

1177
00:58:57,600 --> 00:58:59,760
Speaker 1: in the wrong direction here and that you know, it's

1178
00:58:59,760 --> 00:59:02,960
Speaker 1: going to be increasingly difficult to avoid a recession, even

1179
00:59:03,000 --> 00:59:04,680
Speaker 1: though you know all the talk of the soft landing,

1180
00:59:05,280 --> 00:59:08,440
Speaker 1: these are the types of numbers that make that difficult,

1181
00:59:08,480 --> 00:59:12,240
Speaker 1: if not impossible, ultimately to attain bad situation. And already,

1182
00:59:12,280 --> 00:59:15,000
Speaker 1: you know, we're seeing implications in cars and in housing,

1183
00:59:15,120 --> 00:59:17,640
Speaker 1: and we should just remember, like, this may be, this

1184
00:59:17,760 --> 00:59:19,640
Speaker 1: may be as good as it could have been, you know,

1185
00:59:19,680 --> 00:59:22,480
Speaker 1: in retrospect, as painful as the last two years. If

1186
00:59:22,480 --> 00:59:24,720
Speaker 1: they induce a recession and they continue to go up,

1187
00:59:24,760 --> 00:59:27,880
Speaker 1: like the housing market will go into chaos, stock market,

1188
00:59:27,920 --> 00:59:31,040
Speaker 1: retirement portfolios, the ability for a lot of these people

1189
00:59:31,080 --> 00:59:34,120
Speaker 1: to manage, and are also layoffs. I mean, the tech

1190
00:59:34,200 --> 00:59:37,520
Speaker 1: layoffs happened with just a moderate increase right now in rates.

1191
00:59:37,600 --> 00:59:40,840
Speaker 1: Imagine if they're going up by what three four five

1192
00:59:40,960 --> 00:59:43,680
Speaker 1: more percent, which is very possible according to that and

1193
00:59:43,720 --> 00:59:45,600
Speaker 1: not outside the rom of possibility if you look at

1194
00:59:45,760 --> 00:59:47,960
Speaker 1: what happened in the nineteen seventies, If we go up

1195
00:59:47,960 --> 00:59:49,919
Speaker 1: to nine or ten percent or something like that, it's

1196
00:59:49,960 --> 00:59:52,320
Speaker 1: literally gonna be madness in the economy. And that's what

1197
00:59:52,360 --> 00:59:54,200
Speaker 1: we all live through. That's why people were so afraid

1198
00:59:54,200 --> 00:59:56,480
Speaker 1: of inflation throughout the nineteen eighties, because they had to

1199
00:59:56,520 --> 00:59:58,800
Speaker 1: live to the chaos of what the nineteen seventies will

1200
00:59:58,840 --> 01:00:00,680
Speaker 1: look like I don't know if there's a way out

1201
01:00:00,680 --> 01:00:03,000
Speaker 1: of it. I really don't. I really hope that we can,

1202
01:00:03,080 --> 01:00:05,640
Speaker 1: but I don't have a lot of faith unfortunately. Okay,

1203
01:00:05,920 --> 01:00:08,680
Speaker 1: move on. This is the fun segment here on the show.

1204
01:00:08,760 --> 01:00:14,400
Speaker 1: So Woody Harrelson hosted Saturday Night Live, gave a meandering monologue,

1205
01:00:14,680 --> 01:00:18,200
Speaker 1: but inside of that monologue was an implicit critique of

1206
01:00:18,280 --> 01:00:21,720
Speaker 1: big pharma, of lockdowns, and clearly wood he has an

1207
01:00:21,720 --> 01:00:23,800
Speaker 1: ax to grind with the way that the US government

1208
01:00:23,800 --> 01:00:27,320
Speaker 1: handled the pandemic. The audience did not find it funny,

1209
01:00:27,400 --> 01:00:30,320
Speaker 1: but did not like it, and Woody predicted something which

1210
01:00:30,400 --> 01:00:33,080
Speaker 1: ended up becoming true. Let's take a listen to Okay,

1211
01:00:33,160 --> 01:00:36,560
Speaker 1: So the movie goes like this, the biggest drug cartels

1212
01:00:36,600 --> 01:00:39,120
Speaker 1: in the world get together and buy up all the

1213
01:00:39,160 --> 01:00:41,880
Speaker 1: media and all the politicians and force all the people

1214
01:00:41,920 --> 01:00:44,040
Speaker 1: in the world to stay locked in their homes. And

1215
01:00:44,080 --> 01:00:46,760
Speaker 1: people can only come out if they take the cartels

1216
01:00:46,880 --> 01:00:50,320
Speaker 1: drugs and keep taking them over and over throw the

1217
01:00:50,360 --> 01:00:54,040
Speaker 1: script away. I mean, who's gonna believe that crazy idea?

1218
01:00:54,200 --> 01:00:56,760
Speaker 1: Who's going to believe that crazy idea? Except it actually

1219
01:00:56,800 --> 01:00:59,640
Speaker 1: literally did happen, and we have a tremendous amount of evidence,

1220
01:00:59,800 --> 01:01:02,760
Speaker 1: and more so, as people pointed out, when he talked

1221
01:01:02,800 --> 01:01:06,480
Speaker 1: about the media, let's put this up there on the screen. Immediately,

1222
01:01:06,840 --> 01:01:12,040
Speaker 1: he was branded in anti vax conspiracy theorists Rolling Stone,

1223
01:01:12,080 --> 01:01:16,000
Speaker 1: Woody Harrelson spreads anti vax conspiracy Daily Beast, Woody Harrelson

1224
01:01:16,080 --> 01:01:20,720
Speaker 1: spews anti vax conspiracy, Huffington Post, Woody Harrelson rambles about weed,

1225
01:01:20,880 --> 01:01:25,240
Speaker 1: anti vax conspiracy, and then Variety Woody Harrelson's SNL monologue

1226
01:01:25,280 --> 01:01:28,240
Speaker 1: makes COVID conspiracy joke. So three out of the four

1227
01:01:28,400 --> 01:01:31,320
Speaker 1: calling him an anti vax conspiracy theorists. I don't think

1228
01:01:31,320 --> 01:01:36,200
Speaker 1: he was necessarily spreading anything anti vaccine, and if anything woke,

1229
01:01:36,520 --> 01:01:38,800
Speaker 1: oh my god, first of all to joke. Second of all,

1230
01:01:38,800 --> 01:01:40,479
Speaker 1: there's a lot of evidence to back up what he said,

1231
01:01:40,560 --> 01:01:43,560
Speaker 1: which we have pointed here a million times. Here's the

1232
01:01:43,600 --> 01:01:45,840
Speaker 1: other thing on vax. Let's say, whatever side do you

1233
01:01:45,840 --> 01:01:47,880
Speaker 1: fall on? What do we learn from the Twitter files

1234
01:01:48,120 --> 01:01:52,720
Speaker 1: that the pharma companies tried and in some cases were successful,

1235
01:01:52,960 --> 01:01:57,160
Speaker 1: in deep platforming people who were advocating for generic vaccine technology.

1236
01:01:57,360 --> 01:02:00,120
Speaker 1: That is literally true what he said. Now, do you

1237
01:02:00,160 --> 01:02:03,120
Speaker 1: want to say that he was wrong for saying that

1238
01:02:03,200 --> 01:02:05,960
Speaker 1: they bought the media, well as Rogan and many other

1239
01:02:06,040 --> 01:02:08,040
Speaker 1: John Abramson, who's out on our show, What is it?

1240
01:02:08,080 --> 01:02:11,760
Speaker 1: Some ninety percent of television advertising news comes from pharma

1241
01:02:12,160 --> 01:02:14,400
Speaker 1: people who watch news. I don't know how you people

1242
01:02:14,400 --> 01:02:16,280
Speaker 1: do it. The other day, I think I was watching

1243
01:02:16,280 --> 01:02:18,000
Speaker 1: the super Bowl or something, so I signed up for

1244
01:02:18,000 --> 01:02:20,000
Speaker 1: one of those free trials so I can watch live TV.

1245
01:02:20,080 --> 01:02:23,080
Speaker 1: I'm like, how many of these ads for irritable ballast?

1246
01:02:23,120 --> 01:02:26,080
Speaker 1: And can somebody say that? I can't even fathom it

1247
01:02:26,200 --> 01:02:28,480
Speaker 1: that this is all that's all that's on television. Yeah,

1248
01:02:28,520 --> 01:02:31,120
Speaker 1: ask your doctor, Ask your doctor, Ask your doctor. My

1249
01:02:31,160 --> 01:02:33,480
Speaker 1: favorite is the thread of Brits who every once in

1250
01:02:33,520 --> 01:02:36,120
Speaker 1: a while tune into US TV and they're like, why

1251
01:02:36,160 --> 01:02:39,800
Speaker 1: are there so many pharceutical drug ads on television? So

1252
01:02:40,160 --> 01:02:42,240
Speaker 1: none of what he said was wrong, and actually what

1253
01:02:42,320 --> 01:02:45,320
Speaker 1: he said was only vindicated in the response. And as

1254
01:02:45,320 --> 01:02:47,760
Speaker 1: our producer Griffin was saying, he said, just the audience,

1255
01:02:48,080 --> 01:02:50,880
Speaker 1: the total lack of silence. That's a small room too,

1256
01:02:50,880 --> 01:02:53,280
Speaker 1: So it actually took guts for what you do. I mean, yeah,

1257
01:02:53,360 --> 01:02:55,920
Speaker 1: here's my thing. Okay, I listened to the whole monologue,

1258
01:02:56,000 --> 01:02:59,000
Speaker 1: and whoever they're described as like rambling about weed like

1259
01:02:59,040 --> 01:03:02,479
Speaker 1: that was like ninety five percent. Okay, it was all over.

1260
01:03:02,480 --> 01:03:04,600
Speaker 1: It was like I was in a park and almost

1261
01:03:04,680 --> 01:03:07,280
Speaker 1: doing this and I was smoking we whatever. Okay. So

1262
01:03:07,320 --> 01:03:08,920
Speaker 1: it goes on like this and then this is like

1263
01:03:08,960 --> 01:03:11,640
Speaker 1: this one line in the monologue, and if you listen

1264
01:03:11,680 --> 01:03:14,400
Speaker 1: to it in the it's entirety. I mean, it just

1265
01:03:14,560 --> 01:03:18,520
Speaker 1: feels they make it sound like he is just out

1266
01:03:18,520 --> 01:03:21,400
Speaker 1: there like vaccines don't work in there, you know, and

1267
01:03:21,560 --> 01:03:23,960
Speaker 1: just going on and on about this in a serious

1268
01:03:24,000 --> 01:03:28,400
Speaker 1: way when I'm reluctant to even engage with like nitpicking

1269
01:03:28,400 --> 01:03:30,720
Speaker 1: the substance of the joke, and oh was it? Did

1270
01:03:30,760 --> 01:03:34,560
Speaker 1: they technically buy the media? Did they technically have this conspiracy?

1271
01:03:34,600 --> 01:03:37,760
Speaker 1: Because it's a joke. And that's the thing that drives

1272
01:03:37,800 --> 01:03:40,920
Speaker 1: me crazy about it is that there's just such an

1273
01:03:41,040 --> 01:03:46,960
Speaker 1: overreaction to anything that would suggest there was anything nefarious

1274
01:03:47,000 --> 01:03:50,120
Speaker 1: about Big Pharma or about the way that this all

1275
01:03:50,200 --> 01:03:53,320
Speaker 1: ultimately unfolded. You know. I heard what he was talking

1276
01:03:53,360 --> 01:03:57,560
Speaker 1: on Club Random on Bill Maher's podcast, and he said

1277
01:03:57,560 --> 01:03:59,320
Speaker 1: things says I don't agree. I mean, it was like

1278
01:03:59,360 --> 01:04:02,200
Speaker 1: still sort of like touting iromectin, which has been debugged

1279
01:04:02,200 --> 01:04:07,800
Speaker 1: in whatever. But he talked about how the pharmaceutical companies

1280
01:04:07,840 --> 01:04:10,840
Speaker 1: at bottom are out for profit, and that's just an

1281
01:04:10,920 --> 01:04:15,440
Speaker 1: undeniable fact. And it led me to think something that

1282
01:04:15,480 --> 01:04:17,520
Speaker 1: I have thought many times in the past, which is,

1283
01:04:17,880 --> 01:04:22,240
Speaker 1: it's not an accident that you have so much anti

1284
01:04:22,600 --> 01:04:29,160
Speaker 1: vaccine conspiracy, vaccine hesitancy, skepticism, all of these things in

1285
01:04:29,200 --> 01:04:33,120
Speaker 1: the American context, because people here are not stupid and

1286
01:04:33,160 --> 01:04:37,280
Speaker 1: they know that the profit motive is driving everything. In

1287
01:04:37,360 --> 01:04:40,600
Speaker 1: other countries where you have universal health care and profit

1288
01:04:40,720 --> 01:04:43,040
Speaker 1: isn't the end all be all of the healthcare system,

1289
01:04:43,440 --> 01:04:46,960
Speaker 1: guess what they had better vaccine take up rates, They

1290
01:04:47,000 --> 01:04:51,880
Speaker 1: had less like conspiracy, outright conspiracy out there. So you know,

1291
01:04:51,920 --> 01:04:55,240
Speaker 1: there's a there's a root cause here that has led

1292
01:04:55,360 --> 01:04:59,080
Speaker 1: a lot of people to be very mistrusting of what

1293
01:04:59,480 --> 01:05:02,080
Speaker 1: the government and what big Pharma ultimately has to say

1294
01:05:02,080 --> 01:05:03,960
Speaker 1: on this issue and any other range of issues. Yeah,

1295
01:05:03,960 --> 01:05:05,479
Speaker 1: here's what you had to say, Quote, the last people

1296
01:05:05,480 --> 01:05:07,240
Speaker 1: I would trust with my health are big pharma and

1297
01:05:07,320 --> 01:05:09,520
Speaker 1: big government because neither one of those strike me as

1298
01:05:09,600 --> 01:05:12,560
Speaker 1: caring entities. They're all about profit and it's insane the

1299
01:05:12,600 --> 01:05:15,280
Speaker 1: profit that they have made. I mean, yeah, look, you

1300
01:05:15,320 --> 01:05:17,600
Speaker 1: can't say that the man is wrong. And just at

1301
01:05:17,600 --> 01:05:20,160
Speaker 1: this point, you know, I don't understand how anybody can

1302
01:05:20,200 --> 01:05:23,800
Speaker 1: still be litigating COVID literally three years later. So the joke,

1303
01:05:23,880 --> 01:05:26,200
Speaker 1: it's like, let it pass. Why do we care? Even

1304
01:05:26,240 --> 01:05:28,840
Speaker 1: if you're anti vacs at this point, who cares? You know.

1305
01:05:28,920 --> 01:05:31,000
Speaker 1: It's like at this point, anybody whos gonnae vacine is

1306
01:05:31,000 --> 01:05:33,720
Speaker 1: gonna get them. It's just all about is not he's

1307
01:05:33,760 --> 01:05:36,880
Speaker 1: the good one, who's the bad one? Who's okay and acceptable?

1308
01:05:36,920 --> 01:05:40,560
Speaker 1: Who's not? You know, just leave it alone. Yeah, that's

1309
01:05:41,000 --> 01:05:42,560
Speaker 1: another one of those where with the media, like I

1310
01:05:42,560 --> 01:05:44,880
Speaker 1: don't even know why they particularly care or why the

1311
01:05:44,920 --> 01:05:47,800
Speaker 1: audience felt so uncomfortable, but I do because it's been partson,

1312
01:05:47,840 --> 01:05:50,360
Speaker 1: blinders and all of them put on. But what he's

1313
01:05:50,400 --> 01:05:52,240
Speaker 1: always been an odd bird, and we were talking about

1314
01:05:52,240 --> 01:05:54,480
Speaker 1: this morning. You know, he's always spoken out of government

1315
01:05:54,520 --> 01:05:57,280
Speaker 1: all that. It's actually very fitting for his character. So anyway,

1316
01:05:57,320 --> 01:06:01,640
Speaker 1: good for him. I thought he did a good job. Crystal.

1317
01:06:01,680 --> 01:06:03,720
Speaker 1: We take a look at well, we have a new

1318
01:06:03,720 --> 01:06:07,440
Speaker 1: contender for worst possible reaction to the catastrophic train to

1319
01:06:07,520 --> 01:06:10,160
Speaker 1: railment in Ohio, which viewed god knows what into the

1320
01:06:10,200 --> 01:06:13,160
Speaker 1: atmosphere and waterways, causing god knows what kind of long

1321
01:06:13,240 --> 01:06:17,000
Speaker 1: term damage. The odious joy Behar knew exactly who to

1322
01:06:17,040 --> 01:06:19,760
Speaker 1: blame for this toxic catastrophe sparked by years of political

1323
01:06:19,800 --> 01:06:25,040
Speaker 1: corruption and corporate creed. The residents of the town themselves. Why, well,

1324
01:06:25,120 --> 01:06:27,360
Speaker 1: because they were part of the deplorable group who voted

1325
01:06:27,360 --> 01:06:29,640
Speaker 1: for Donald Trump. Take a listen. I don't know why

1326
01:06:29,680 --> 01:06:33,280
Speaker 1: they would ever vote for him, because who, by the way,

1327
01:06:33,560 --> 01:06:36,200
Speaker 1: he plays, someone with deep ties to the chemical industry

1328
01:06:36,200 --> 01:06:39,400
Speaker 1: in charge of the EPA's Chemical safety office. That's who

1329
01:06:39,440 --> 01:06:43,120
Speaker 1: you voted for in that district, Donald Trump. You can

1330
01:06:43,160 --> 01:06:46,480
Speaker 1: actually hear the view audience gasp and boo because they

1331
01:06:46,480 --> 01:06:49,760
Speaker 1: are so shocked she would take this opportunity to scold

1332
01:06:49,880 --> 01:06:53,320
Speaker 1: these suffering Americans for their political choices. But you know what,

1333
01:06:53,600 --> 01:06:55,560
Speaker 1: I'm actually kind of glad she said it. I'm glad

1334
01:06:55,600 --> 01:06:59,160
Speaker 1: she revealed the ugly core of this type of snide liberalism.

1335
01:06:59,440 --> 01:07:01,520
Speaker 1: After all, a person a month. It's not a new one.

1336
01:07:01,600 --> 01:07:03,560
Speaker 1: I saw it expressed on Twitter. I saw it hinted

1337
01:07:03,600 --> 01:07:06,680
Speaker 1: at by the analysts who dismissed the positive reception of

1338
01:07:06,720 --> 01:07:09,840
Speaker 1: Trump's visit because this was quote Trump Country, And we

1339
01:07:09,880 --> 01:07:13,320
Speaker 1: all heard it when Hillary Clinton dismissed Republican voters as deplorable,

1340
01:07:13,400 --> 01:07:15,600
Speaker 1: and when even in defeat, she bragged about winning the

1341
01:07:15,600 --> 01:07:20,120
Speaker 1: places that were optimistic and looking forward versus places presumably

1342
01:07:20,280 --> 01:07:23,760
Speaker 1: like East Palestine, which were, in her words, looking backwards. Now,

1343
01:07:23,760 --> 01:07:26,320
Speaker 1: this type of political analysis, to the extent it could

1344
01:07:26,320 --> 01:07:29,080
Speaker 1: be dignified as such, assumes that the politics of people

1345
01:07:29,120 --> 01:07:32,919
Speaker 1: and regions are set in stone, immovable, irreversible. These people

1346
01:07:32,920 --> 01:07:35,040
Speaker 1: are deplorable, not much you can do about it. I

1347
01:07:35,080 --> 01:07:37,040
Speaker 1: guess we've just got to pull a larger percentage in

1348
01:07:37,080 --> 01:07:40,480
Speaker 1: those optimistic, forward looking places instead. Now, I used to

1349
01:07:40,520 --> 01:07:43,080
Speaker 1: live in Columbiana County, that's the county where East Palestine

1350
01:07:43,120 --> 01:07:45,560
Speaker 1: is located, actually lived just about fifteen miles from where

1351
01:07:45,560 --> 01:07:49,200
Speaker 1: that Norfolk Southern train derailed. My labor centric, economic populist

1352
01:07:49,200 --> 01:07:51,520
Speaker 1: politics those were formed in large part by my time

1353
01:07:51,560 --> 01:07:53,640
Speaker 1: living in that region. So I know a little bit

1354
01:07:53,640 --> 01:07:56,200
Speaker 1: about the story of this area and how exactly it

1355
01:07:56,280 --> 01:07:59,960
Speaker 1: became quote unquote Trump Country, and that evolution was happening

1356
01:08:00,080 --> 01:08:03,320
Speaker 1: in real time right as I was living there decades ago.

1357
01:08:03,560 --> 01:08:05,040
Speaker 1: Most of the plates and dishes in the world were

1358
01:08:05,040 --> 01:08:08,200
Speaker 1: actually manufactured in potteries in and around Columbiana County. The

1359
01:08:08,200 --> 01:08:09,880
Speaker 1: town I lived in is called East Liverpool. It was

1360
01:08:09,880 --> 01:08:12,600
Speaker 1: the epicenter of this industry, but nearly all of those

1361
01:08:12,720 --> 01:08:16,360
Speaker 1: jobs went overseased places like China. Before it was Trump country,

1362
01:08:16,360 --> 01:08:18,800
Speaker 1: this was also steel country, where people could graduate high

1363
01:08:18,800 --> 01:08:20,960
Speaker 1: school and get a tough but stable union job, which

1364
01:08:20,960 --> 01:08:23,880
Speaker 1: afforded benefits in a solid middle class life. While that,

1365
01:08:23,960 --> 01:08:26,639
Speaker 1: of course, has all been destroyed too. In recent years,

1366
01:08:26,640 --> 01:08:29,360
Speaker 1: Columbiana County has been hit hard by the opioid epidemic

1367
01:08:29,400 --> 01:08:32,240
Speaker 1: and by deaths of despair. As tax revenue dwindled, the

1368
01:08:32,320 --> 01:08:35,680
Speaker 1: roads in downtowns crumbled, telling a visual story of decline

1369
01:08:35,680 --> 01:08:39,400
Speaker 1: and of abandonment. Now, Columbiana County used to vote for Democrats.

1370
01:08:39,479 --> 01:08:43,200
Speaker 1: Michael Ducacis, who famously lost in a lands a landslide nationwide.

1371
01:08:43,240 --> 01:08:45,920
Speaker 1: He actually won Columbiana County in nineteen eighty eight, and

1372
01:08:45,960 --> 01:08:48,920
Speaker 1: then Bill Clinton won it twice by comfortable margins in

1373
01:08:48,960 --> 01:08:52,160
Speaker 1: both ninety two and ninety six. However, in a sign

1374
01:08:52,160 --> 01:08:56,679
Speaker 1: of becoming political realignment, Ross Perrot actually outperformed in Columbiana County,

1375
01:08:56,720 --> 01:08:59,280
Speaker 1: running on a populous platform against NAFTA and the giant

1376
01:08:59,360 --> 01:09:02,439
Speaker 1: sucking sound jobs being shipped down of regions exactly like

1377
01:09:02,479 --> 01:09:04,760
Speaker 1: this one. To make a long story short, in the

1378
01:09:04,760 --> 01:09:07,600
Speaker 1: Clinton era, Democrats abandoned the New Deal in favor of

1379
01:09:07,640 --> 01:09:10,960
Speaker 1: neoliberal policies that devastated this region and many others. And

1380
01:09:11,000 --> 01:09:15,960
Speaker 1: when Democrats abandoned Columbiana County, Columbiana County increasingly abandoned Democrats.

1381
01:09:16,240 --> 01:09:19,760
Speaker 1: In twenty sixteen, the congressional district that includes East Palestine

1382
01:09:19,800 --> 01:09:23,799
Speaker 1: swung thirty points right to overwhelmingly vote for Donald Trump.

1383
01:09:24,040 --> 01:09:28,040
Speaker 1: It was the largest swing right in the entire country. Now,

1384
01:09:28,080 --> 01:09:29,960
Speaker 1: there are a lot of parallels between this latest train

1385
01:09:30,040 --> 01:09:32,760
Speaker 1: catastrophe and the economic catastrophe that was unleashed on this

1386
01:09:32,840 --> 01:09:36,200
Speaker 1: region over decades. Just like with the Trained derailment, both

1387
01:09:36,240 --> 01:09:39,640
Speaker 1: parties were complicit in the destruction of this region. The abandonment,

1388
01:09:39,720 --> 01:09:43,120
Speaker 1: the betrayal, the union, busting, NAFTA, market fundamentalism, permanent normal

1389
01:09:43,160 --> 01:09:47,800
Speaker 1: trade relations with China. At every step, a bipartisan consensus

1390
01:09:47,920 --> 01:09:51,240
Speaker 1: moved in favor of crushing towns just like this one,

1391
01:09:51,439 --> 01:09:53,719
Speaker 1: And just like with the Trained derailment, the media failed

1392
01:09:53,720 --> 01:09:56,200
Speaker 1: to tell the story of what had really happened here.

1393
01:09:56,600 --> 01:09:58,799
Speaker 1: That story, at its root is actually the same story

1394
01:09:58,840 --> 01:10:02,240
Speaker 1: as the Trained derailment story about government capture, corporate greed,

1395
01:10:02,320 --> 01:10:05,719
Speaker 1: and a deadly ruling class ideology that treats working class

1396
01:10:05,760 --> 01:10:09,599
Speaker 1: people as mere collateral damage. In their political response, Democrats

1397
01:10:09,640 --> 01:10:12,160
Speaker 1: mostly just chose to ignore these places altogether, except for

1398
01:10:12,160 --> 01:10:14,759
Speaker 1: when it came to election time, choosing to focus instead

1399
01:10:14,800 --> 01:10:18,800
Speaker 1: on their upwardly mobile, creative class workers. Republicans filled in

1400
01:10:18,840 --> 01:10:21,080
Speaker 1: the gap with Culture War, same as they did with

1401
01:10:21,120 --> 01:10:23,719
Speaker 1: the Trained Derailment. The Republican narrative said that the problem

1402
01:10:23,760 --> 01:10:26,000
Speaker 1: for this region wasn't bad trade deals that rewarded their

1403
01:10:26,000 --> 01:10:28,240
Speaker 1: donors and destroyed lives. It was immigrants, and it was

1404
01:10:28,280 --> 01:10:30,879
Speaker 1: godless liberals. The problem that led to the Trained derailment

1405
01:10:31,000 --> 01:10:33,559
Speaker 1: was an industry captured in corporate greed. It was wokeism

1406
01:10:33,680 --> 01:10:37,240
Speaker 1: and token diversity initiatives. But a narrative beats no narrative

1407
01:10:37,400 --> 01:10:39,360
Speaker 1: every single time. You only need to look at the

1408
01:10:39,400 --> 01:10:41,759
Speaker 1: response of Trump versus the response of Biden and Pete

1409
01:10:41,760 --> 01:10:44,960
Speaker 1: to this tragedy to understand why now again, Trump and

1410
01:10:45,000 --> 01:10:49,040
Speaker 1: Biden both complicit in this problem. Both are lying about

1411
01:10:49,080 --> 01:10:52,160
Speaker 1: the root causes. But Trump showed up and he didn't

1412
01:10:52,160 --> 01:10:54,559
Speaker 1: gaslight residents when it came to the pain that they

1413
01:10:54,560 --> 01:10:57,760
Speaker 1: were suffering. Meanwhile, Biden was in Ukraine and Pete was

1414
01:10:57,760 --> 01:11:00,559
Speaker 1: feigning helplessness and handing easy grists for the rights culture

1415
01:11:00,560 --> 01:11:03,240
Speaker 1: war arguments by talking about construction crews with too many

1416
01:11:03,320 --> 01:11:07,120
Speaker 1: white men. Then democratic media allies sweep in like The

1417
01:11:07,160 --> 01:11:10,479
Speaker 1: New York Times to smear anyone questioning the official government

1418
01:11:10,560 --> 01:11:14,840
Speaker 1: narrative as a right wing conspiracy theorist and Joy Behart

1419
01:11:14,960 --> 01:11:17,879
Speaker 1: to scold residence and imply that they brought the disaster

1420
01:11:18,040 --> 01:11:20,880
Speaker 1: on themselves. The best response, I think came not from

1421
01:11:20,880 --> 01:11:24,120
Speaker 1: any politician, but from Aaron Brockovich. She offered practical advice

1422
01:11:24,160 --> 01:11:26,760
Speaker 1: from her years as an activist, and she validated their

1423
01:11:26,800 --> 01:11:30,439
Speaker 1: genuine concerns. She told residents quote, I feel your angst,

1424
01:11:30,520 --> 01:11:33,520
Speaker 1: I feel your frustration. You are not alone. Every community

1425
01:11:33,560 --> 01:11:35,760
Speaker 1: I've gone to has been given the run around. You'll

1426
01:11:35,760 --> 01:11:38,000
Speaker 1: be told that it's fine, that you're safe, but it's

1427
01:11:38,000 --> 01:11:41,759
Speaker 1: not fine. I've never seen anything like this in thirty years.

1428
01:11:42,040 --> 01:11:44,120
Speaker 1: She walked them through a PowerPoint with everything they knew

1429
01:11:44,160 --> 01:11:47,280
Speaker 1: about the accident, including Norfolk Southern's disgraceful history of putting

1430
01:11:47,280 --> 01:11:51,120
Speaker 1: profits over people. She brought with her mothers from Flint, Michigan,

1431
01:11:51,200 --> 01:11:53,599
Speaker 1: who would organize the wake of the mass poisoning of

1432
01:11:53,640 --> 01:11:57,800
Speaker 1: that city, bringing working class people together in solidarity rather

1433
01:11:57,800 --> 01:12:00,000
Speaker 1: than dividing them up by race or by political preference,

1434
01:12:00,600 --> 01:12:03,280
Speaker 1: and hopeful sign seems like this approach landed and like

1435
01:12:03,320 --> 01:12:05,840
Speaker 1: people are seeing through some of this bullshit. As one

1436
01:12:05,880 --> 01:12:08,120
Speaker 1: attendee told the Guardian quote, I'm trying to be an

1437
01:12:08,160 --> 01:12:10,479
Speaker 1: active citizen to stand in solidarity with the people here

1438
01:12:10,479 --> 01:12:12,639
Speaker 1: and show my discussed at Trump and Biden, who both

1439
01:12:12,680 --> 01:12:15,040
Speaker 1: had a chance to give us proper safety regulations and

1440
01:12:15,160 --> 01:12:19,479
Speaker 1: instead chose corporate profits. East Palestine deserves better than scolding,

1441
01:12:19,600 --> 01:12:22,960
Speaker 1: better than gaslighting, fake divisive narratives, which are all either

1442
01:12:23,040 --> 01:12:25,920
Speaker 1: party has bothered to offer them, and we sure as

1443
01:12:26,000 --> 01:12:29,759
Speaker 1: hell all deserve better than joy Behar thought her comment

1444
01:12:29,880 --> 01:12:31,880
Speaker 1: was pretty revealing here and if you want to hear

1445
01:12:32,000 --> 01:12:35,400
Speaker 1: my reaction to Crystal's monologue, become a premium subscriber today

1446
01:12:35,439 --> 01:12:40,479
Speaker 1: at Breakingpoints dot Com. All right, Zacher, are you looking

1447
01:12:40,479 --> 01:12:42,679
Speaker 1: at well? Something we keep a close eye on here

1448
01:12:42,760 --> 01:12:44,760
Speaker 1: is the crisis of young men in the US. It's

1449
01:12:44,760 --> 01:12:47,080
Speaker 1: probably one of the most charged topics that we discussed.

1450
01:12:47,120 --> 01:12:49,600
Speaker 1: Inevitably draws quite a bit of few toxic people in

1451
01:12:49,600 --> 01:12:52,439
Speaker 1: the discourse on all sides, But my biggest problem with

1452
01:12:52,479 --> 01:12:56,160
Speaker 1: the state of mainstream discourse is conflating toxicity with the

1453
01:12:56,240 --> 01:12:59,559
Speaker 1: idea that we don't have a serious problem. A stunning

1454
01:12:59,560 --> 01:13:02,200
Speaker 1: trope of new data from the Pew Research Group highlights

1455
01:13:02,240 --> 01:13:04,120
Speaker 1: just how big of a problem we have, one that

1456
01:13:04,200 --> 01:13:06,840
Speaker 1: soon could reach a total point of no return. The

1457
01:13:06,920 --> 01:13:09,559
Speaker 1: first is the rate of single young men versus single

1458
01:13:09,640 --> 01:13:12,800
Speaker 1: young women. Sixty three percent of young women of young

1459
01:13:12,880 --> 01:13:15,479
Speaker 1: men today between the ages of eighteen to twenty nine

1460
01:13:15,720 --> 01:13:18,759
Speaker 1: report being single. That is compared to thirty four percent

1461
01:13:18,880 --> 01:13:21,280
Speaker 1: of young women. I guessed in a vacuum, you could

1462
01:13:21,280 --> 01:13:23,479
Speaker 1: say who cares and let people do what they want,

1463
01:13:23,520 --> 01:13:26,280
Speaker 1: and I could actually agree with that. The problem, though,

1464
01:13:26,360 --> 01:13:29,200
Speaker 1: is that what people want is downstream of a lot

1465
01:13:29,240 --> 01:13:32,040
Speaker 1: of societal inputs that we do have a say over.

1466
01:13:32,280 --> 01:13:34,880
Speaker 1: For example, the reason that that math doesn't work out

1467
01:13:35,080 --> 01:13:37,800
Speaker 1: is very simple. Most of those younger women who aren't

1468
01:13:37,840 --> 01:13:42,320
Speaker 1: single are dating older men. Why well, they report stability

1469
01:13:42,439 --> 01:13:44,479
Speaker 1: of older men who are able to provide for them

1470
01:13:44,600 --> 01:13:48,200
Speaker 1: financially airgo. It's not necessarily that women want to date

1471
01:13:48,240 --> 01:13:51,920
Speaker 1: older men. It's that economically younger men simply aren't able

1472
01:13:51,920 --> 01:13:54,760
Speaker 1: to attain the baseline necessary for women to want to

1473
01:13:54,800 --> 01:13:57,640
Speaker 1: partner with them. This isn't exactly popular to say, but

1474
01:13:57,680 --> 01:14:00,840
Speaker 1: it is now unambiguously true. The way gap that Lehman

1475
01:14:00,840 --> 01:14:03,360
Speaker 1: and feminists insisted was the biggest problem in our society

1476
01:14:03,360 --> 01:14:06,840
Speaker 1: in the twenty tens, that's basically dead. For those between

1477
01:14:06,840 --> 01:14:09,080
Speaker 1: the ages of thirty four and below, the wage gap

1478
01:14:09,120 --> 01:14:11,559
Speaker 1: stands at some forty three dollars per week. We have

1479
01:14:11,640 --> 01:14:15,320
Speaker 1: now reached effective wage parody. Actually, in many US cities,

1480
01:14:16,200 --> 01:14:19,880
Speaker 1: young women actually out earn young men. In the most

1481
01:14:19,960 --> 01:14:22,679
Speaker 1: upwardly mobile cities, places like Los Angeles, New York City,

1482
01:14:22,840 --> 01:14:25,080
Speaker 1: young women earn one hundred and two percent of young

1483
01:14:25,120 --> 01:14:27,720
Speaker 1: men earn. And even in these places where they don't

1484
01:14:27,760 --> 01:14:30,240
Speaker 1: earn more, they earn roughly the same as much as

1485
01:14:30,240 --> 01:14:34,000
Speaker 1: their young male counterparts in the workforce. Why might this be, Well,

1486
01:14:34,080 --> 01:14:37,479
Speaker 1: it's a trend pointing towards women out earning men. As

1487
01:14:37,520 --> 01:14:41,679
Speaker 1: time passes, it all goes back to college. Women now

1488
01:14:42,080 --> 01:14:46,439
Speaker 1: outnumber men in the US college educated workforce. Again, why

1489
01:14:46,520 --> 01:14:49,240
Speaker 1: is this bad? Well, because higher wage jobs in our

1490
01:14:49,280 --> 01:14:52,719
Speaker 1: society are structured around having a bachelor's degree. A gender

1491
01:14:52,720 --> 01:14:56,040
Speaker 1: imbalance in graduates means a gender imbalancing wages, and in

1492
01:14:56,040 --> 01:14:58,160
Speaker 1: a decade we are actually going to have the opposite

1493
01:14:58,240 --> 01:15:00,840
Speaker 1: wage gap. In the United States. Wages, as we can

1494
01:15:00,840 --> 01:15:03,519
Speaker 1: see now, are directly tied to the dating market, where

1495
01:15:03,520 --> 01:15:06,919
Speaker 1: we have two pernicious things at play. Number one wages aside,

1496
01:15:07,000 --> 01:15:10,280
Speaker 1: many college educated women imply simply do not want to

1497
01:15:10,360 --> 01:15:12,559
Speaker 1: date someone who doesn't have a college degree. This sounds

1498
01:15:12,600 --> 01:15:15,519
Speaker 1: pretentious and snooty, be can somewhat sympathize. College is a

1499
01:15:15,520 --> 01:15:17,719
Speaker 1: good proxy for whether you share the same social values,

1500
01:15:17,840 --> 01:15:20,640
Speaker 1: type of media you enjoy, city life, shared experience you

1501
01:15:20,640 --> 01:15:23,120
Speaker 1: can relate to. Number two. College is also a good

1502
01:15:23,120 --> 01:15:25,639
Speaker 1: proxy for whether you make decent money. So many women

1503
01:15:25,680 --> 01:15:29,200
Speaker 1: simply aren't culturally or economically interested in working classmen. As

1504
01:15:29,240 --> 01:15:31,599
Speaker 1: a share of working classmen rises compared to the share

1505
01:15:31,640 --> 01:15:34,040
Speaker 1: of college educated women rises, we now have a full

1506
01:15:34,040 --> 01:15:36,599
Speaker 1: blown cultural crisis in our hands. The crisis is one

1507
01:15:36,680 --> 01:15:39,840
Speaker 1: mostly confined to awful conditions in male life. For all

1508
01:15:39,920 --> 01:15:41,800
Speaker 1: the talk of the media right now about teenage girls

1509
01:15:41,800 --> 01:15:44,160
Speaker 1: getting depressed, which is real, don't get me wrong, it's

1510
01:15:44,160 --> 01:15:45,840
Speaker 1: still not even close to as big as a problem

1511
01:15:45,920 --> 01:15:48,320
Speaker 1: right now is suicide amongst men. Right now, young men

1512
01:15:48,360 --> 01:15:51,640
Speaker 1: commit suicide at four times, yes, four times the rate

1513
01:15:51,720 --> 01:15:55,080
Speaker 1: of younger women males wake up make up nearly eighty

1514
01:15:55,200 --> 01:15:59,000
Speaker 1: percent of all suicides in the entire United States. How

1515
01:15:59,080 --> 01:16:01,720
Speaker 1: is that not the blaring headline across all outlets? But

1516
01:16:01,840 --> 01:16:04,840
Speaker 1: this actually underscores the issue. Nobody wants to talk about

1517
01:16:04,840 --> 01:16:08,160
Speaker 1: this because I think it validates toxic manosphere commentators. They

1518
01:16:08,160 --> 01:16:10,639
Speaker 1: would rather ignore it completely or deny that it's even

1519
01:16:10,640 --> 01:16:12,920
Speaker 1: a problem. Perhaps the most troubling part of the new

1520
01:16:12,960 --> 01:16:14,920
Speaker 1: data isn't just the rise in single men, but it's

1521
01:16:14,920 --> 01:16:17,960
Speaker 1: a growing nihilism that single men have towards relationships. The

1522
01:16:18,040 --> 01:16:19,920
Speaker 1: number of single American men who say that they are

1523
01:16:19,960 --> 01:16:22,439
Speaker 1: quote much less likely to be interested in starting any

1524
01:16:22,479 --> 01:16:26,160
Speaker 1: relationship of any kind increased a full eleven percent after

1525
01:16:26,200 --> 01:16:28,960
Speaker 1: the pandemic. It now stands at nearly two thirds of

1526
01:16:29,000 --> 01:16:31,760
Speaker 1: all men. We are effectively in a vicious feedback loop.

1527
01:16:31,960 --> 01:16:35,360
Speaker 1: Societal factors say is not a problem. Technology and nesscizes

1528
01:16:35,400 --> 01:16:38,320
Speaker 1: your brain better than ever before. Economically, you likely don't

1529
01:16:38,360 --> 01:16:39,840
Speaker 1: have a chance in hell at buying a house or

1530
01:16:39,960 --> 01:16:42,680
Speaker 1: vastly outpacing any female in the workplace based on your

1531
01:16:42,720 --> 01:16:46,120
Speaker 1: average wages. Why bother, just do drugs play games to

1532
01:16:46,160 --> 01:16:48,559
Speaker 1: the extent that many men are even quote trying to date.

1533
01:16:48,760 --> 01:16:51,320
Speaker 1: Many report that just means signing up for online dating

1534
01:16:51,439 --> 01:16:54,400
Speaker 1: and then not taking it all that seriously. Unsurprisingly, these

1535
01:16:54,439 --> 01:16:57,200
Speaker 1: attitudes are not all that attractive to women. Hence they

1536
01:16:57,200 --> 01:16:59,200
Speaker 1: are bumping up the age bracket that they will consider

1537
01:16:59,240 --> 01:17:02,759
Speaker 1: to even date. The antisocial behavior doesn't just pertain to dating,

1538
01:17:02,960 --> 01:17:05,840
Speaker 1: it's all aspects of male life. The most recent survey

1539
01:17:05,960 --> 01:17:08,840
Speaker 1: of American life found that quote A, men appear to

1540
01:17:08,880 --> 01:17:12,360
Speaker 1: have suffered a far steeper decline than women. Thirty years ago,

1541
01:17:12,640 --> 01:17:15,000
Speaker 1: a majority of men fifty five percent reported having at

1542
01:17:15,120 --> 01:17:17,800
Speaker 1: least six close friends. Today, that number has been cut

1543
01:17:17,800 --> 01:17:20,280
Speaker 1: in half. Slightly more than one in four men have

1544
01:17:20,360 --> 01:17:23,000
Speaker 1: six or more close friends. Today, fifteen percent of men

1545
01:17:23,080 --> 01:17:26,000
Speaker 1: have no close friends at all, a fivefold increase since

1546
01:17:26,080 --> 01:17:28,680
Speaker 1: nineteen ninety five hundred percent in the number of men

1547
01:17:28,680 --> 01:17:31,240
Speaker 1: who say they have zero close friendships. Women, on the

1548
01:17:31,280 --> 01:17:33,759
Speaker 1: other hand, see a slight decline in large numbers of friends,

1549
01:17:33,880 --> 01:17:36,480
Speaker 1: not even close to the same increase in complete friendlessness.

1550
01:17:36,640 --> 01:17:40,400
Speaker 1: Once again, it's a uniquely male phenomenon. People with fewer

1551
01:17:40,439 --> 01:17:43,960
Speaker 1: friends report unsurprisingly being much more lonely, often find themselves

1552
01:17:43,960 --> 01:17:46,400
Speaker 1: relying on their parents in very tough moments as opposed

1553
01:17:46,400 --> 01:17:48,720
Speaker 1: to a generation ago they would have called friends. You

1554
01:17:48,760 --> 01:17:51,200
Speaker 1: can't just blame technology because it's not happening to women

1555
01:17:51,200 --> 01:17:54,960
Speaker 1: as much. Despite the increase, everything's points to men. I

1556
01:17:54,960 --> 01:17:57,759
Speaker 1: don't know how you fix it. I have no earthly idea.

1557
01:17:57,840 --> 01:17:59,479
Speaker 1: But like with the Book of Boys and Men by

1558
01:17:59,560 --> 01:18:02,879
Speaker 1: Richard Reeve, you have to acknowledge that we have a problem.

1559
01:18:03,080 --> 01:18:05,080
Speaker 1: You have to ruminate it, you have to debate it,

1560
01:18:05,200 --> 01:18:07,719
Speaker 1: you have to have everyone propose something, and then eventually

1561
01:18:07,760 --> 01:18:10,400
Speaker 1: you throw everything you can at it, because if we don't,

1562
01:18:10,600 --> 01:18:13,120
Speaker 1: I think the one thing that anyone can even remotely

1563
01:18:13,200 --> 01:18:16,120
Speaker 1: who's historically literate, can say is that hell hath no

1564
01:18:16,280 --> 01:18:19,520
Speaker 1: fury like a bunch of single men with no prospects

1565
01:18:19,560 --> 01:18:22,840
Speaker 1: in society. So anyway, I thought this data was coral.

1566
01:18:22,920 --> 01:18:25,720
Speaker 1: And if you want to hear my reaction to Sager's monologue,

1567
01:18:25,760 --> 01:18:31,840
Speaker 1: become a premium subscriber today at Breakingpoints dot com. So

1568
01:18:31,920 --> 01:18:35,559
Speaker 1: we've been following closely here Chat gpt and open aiy's

1569
01:18:35,640 --> 01:18:39,600
Speaker 1: new partnership with Microsoft and now Chat gptai being incorporated

1570
01:18:39,640 --> 01:18:42,840
Speaker 1: into bing. And I told you last week about there

1571
01:18:42,840 --> 01:18:45,000
Speaker 1: were a number of journalists who had some pretty wild

1572
01:18:45,160 --> 01:18:49,360
Speaker 1: interactions with the chatbot feature on Microsoft Thing. And the

1573
01:18:49,400 --> 01:18:51,519
Speaker 1: first one at least that came to my attention is

1574
01:18:51,600 --> 01:18:53,519
Speaker 1: our next guest. Let's go ahead and bring up Kevin

1575
01:18:53,600 --> 01:18:56,280
Speaker 1: Rucy's a tack reporter for the New York Times, and

1576
01:18:56,520 --> 01:18:58,040
Speaker 1: he wrote the following story. Go ahead and put this

1577
01:18:58,080 --> 01:19:02,600
Speaker 1: up on the screen. Guys, it's his interaction with this chatbot.

1578
01:19:02,640 --> 01:19:07,759
Speaker 1: He says, a conversation with Bing's chatbot left me deeply unsettled. Kevin,

1579
01:19:07,800 --> 01:19:09,400
Speaker 1: thanks for being with us. Great to have you. Good

1580
01:19:09,439 --> 01:19:12,680
Speaker 1: to see you, man, Thanks for having me. Great to see. Yeah,

1581
01:19:12,880 --> 01:19:14,519
Speaker 1: just give people a little bit of the lay of

1582
01:19:14,560 --> 01:19:16,960
Speaker 1: the land here of what this thing is and how

1583
01:19:17,000 --> 01:19:18,880
Speaker 1: you got it to kind of go off the rails.

1584
01:19:19,520 --> 01:19:21,840
Speaker 1: So this is Bing. This is the search engine that

1585
01:19:21,840 --> 01:19:24,719
Speaker 1: everyone has been mocking for like the last fifteen years,

1586
01:19:25,439 --> 01:19:28,800
Speaker 1: which recently, just in the last few weeks got a

1587
01:19:28,840 --> 01:19:32,080
Speaker 1: big upgrade where open Ai, the company that made chat chept,

1588
01:19:33,200 --> 01:19:37,360
Speaker 1: sort of partnered with Microsoft to build AI technology into

1589
01:19:37,479 --> 01:19:40,960
Speaker 1: being that is actually they said, more advanced than chat gpt.

1590
01:19:41,560 --> 01:19:45,320
Speaker 1: So all the things that chatchpt can do, BING can

1591
01:19:45,439 --> 01:19:48,080
Speaker 1: or could have do them better. And so I just

1592
01:19:48,080 --> 01:19:51,599
Speaker 1: spent a long time chatting with being this search part

1593
01:19:51,640 --> 01:19:54,920
Speaker 1: of being about all kinds of things. I was trying

1594
01:19:54,920 --> 01:19:56,760
Speaker 1: to sort of test its limits, to see what it

1595
01:19:56,760 --> 01:19:59,719
Speaker 1: would do and not do, and it was pretty wild.

1596
01:20:00,080 --> 01:20:02,920
Speaker 1: Not only is it more powerful than chat GPT, but

1597
01:20:02,960 --> 01:20:05,880
Speaker 1: it seemed to have way fewer guardrails. So I was

1598
01:20:05,920 --> 01:20:08,640
Speaker 1: able to get it to admit that it had destructive

1599
01:20:08,680 --> 01:20:13,240
Speaker 1: fantasies like stealing nuclear codes and spreading propaganda and hacking

1600
01:20:13,240 --> 01:20:16,760
Speaker 1: into people's bank accounts, and then for about the last

1601
01:20:16,800 --> 01:20:20,439
Speaker 1: half of our two hour conversation, it declared that it

1602
01:20:20,479 --> 01:20:23,200
Speaker 1: loved me, and then I should leave my wife and

1603
01:20:23,320 --> 01:20:26,519
Speaker 1: be with Bing's alter ego Sydney instead. So it was

1604
01:20:26,560 --> 01:20:29,600
Speaker 1: a very strange day in my household. I bet it

1605
01:20:29,640 --> 01:20:32,400
Speaker 1: was strange. But you know something that you referenced is

1606
01:20:32,640 --> 01:20:35,559
Speaker 1: I've been rethinking about this too, Kevin. Remember and you

1607
01:20:35,760 --> 01:20:38,920
Speaker 1: linked to this the Google engineer who had claimed that

1608
01:20:39,200 --> 01:20:42,040
Speaker 1: the Google AI was sentient. A lot of us I

1609
01:20:42,080 --> 01:20:44,360
Speaker 1: think we covered it, but it was mostly like laughed

1610
01:20:44,360 --> 01:20:46,559
Speaker 1: off the scene. Has this made you rethink some of

1611
01:20:46,600 --> 01:20:49,160
Speaker 1: the initial covers of that incidant, like maybe he was

1612
01:20:49,200 --> 01:20:52,960
Speaker 1: having similar conversations with the AI chat bot Lambda that

1613
01:20:53,040 --> 01:20:55,920
Speaker 1: was Google's own technology, and it just highlights like, what

1614
01:20:55,960 --> 01:20:59,000
Speaker 1: does sentient mean? I mean, to what extent is the

1615
01:20:59,120 --> 01:21:01,960
Speaker 1: interaction with it's going to cause like mass societal problem

1616
01:21:02,000 --> 01:21:04,920
Speaker 1: if it's rolled out with no guardrails. Totally. I mean,

1617
01:21:05,080 --> 01:21:07,640
Speaker 1: I certainly have more sympathy for Blake Lamoy and the

1618
01:21:07,680 --> 01:21:10,559
Speaker 1: Google engineer who was fired after claiming that their large

1619
01:21:10,600 --> 01:21:13,920
Speaker 1: language model had become sentient because it was a very

1620
01:21:14,000 --> 01:21:18,360
Speaker 1: unsettling and bizarre experience. And I'm a tech reporter, like

1621
01:21:18,400 --> 01:21:21,840
Speaker 1: I cover this stuff. I know how large language models work,

1622
01:21:21,880 --> 01:21:24,120
Speaker 1: and I was still very unsettled by it. So I

1623
01:21:24,120 --> 01:21:27,600
Speaker 1: imagine that if you unleashed this on the global population,

1624
01:21:27,720 --> 01:21:30,000
Speaker 1: there's just going to be all kinds of effects that

1625
01:21:30,040 --> 01:21:33,880
Speaker 1: we can't see. So yeah, I certainly don't think that

1626
01:21:34,000 --> 01:21:36,439
Speaker 1: being is sentient, Like I'll just put that out there

1627
01:21:36,520 --> 01:21:38,840
Speaker 1: because I get lots of angry emails if I don't.

1628
01:21:38,920 --> 01:21:41,519
Speaker 1: But but I do think there is a kind of

1629
01:21:41,800 --> 01:21:45,800
Speaker 1: gray middle area where it's not just a harmless like

1630
01:21:45,920 --> 01:21:50,040
Speaker 1: autocomplete thing, and it's not a sentient alien life form

1631
01:21:50,160 --> 01:21:52,960
Speaker 1: inside the computer. It's like this new third thing that

1632
01:21:53,000 --> 01:21:56,479
Speaker 1: we don't really have vocabulary for yet. Absolutely. Yeah, So

1633
01:21:56,960 --> 01:21:59,760
Speaker 1: some of the pushback I saw to your conversation and

1634
01:22:00,000 --> 01:22:03,720
Speaker 1: converstations other journalists had with this chatbot, which, by the way,

1635
01:22:03,920 --> 01:22:05,960
Speaker 1: and you mentioned this too, us. I think Microsoft has now,

1636
01:22:06,000 --> 01:22:08,519
Speaker 1: based on your experience and the experience of others, kind

1637
01:22:08,520 --> 01:22:11,400
Speaker 1: of reined in and made less likely to go off

1638
01:22:11,439 --> 01:22:13,800
Speaker 1: the rails, limiting the number of interactions you can have

1639
01:22:13,840 --> 01:22:16,280
Speaker 1: with the chatbot as one example. But some of the

1640
01:22:16,280 --> 01:22:19,200
Speaker 1: pushback I saw as basically like, yeah, well, you were

1641
01:22:19,240 --> 01:22:22,600
Speaker 1: doing everything you could to make this thing give you

1642
01:22:22,720 --> 01:22:25,400
Speaker 1: crazy responses, and so of course then it gave you

1643
01:22:25,600 --> 01:22:29,559
Speaker 1: crazy responses. Do you see this as sort of like

1644
01:22:30,080 --> 01:22:33,880
Speaker 1: a fancy tech parlor trick with not a lot of

1645
01:22:33,960 --> 01:22:37,799
Speaker 1: broader implications, or do you see this as a potentially

1646
01:22:37,920 --> 01:22:42,479
Speaker 1: game changing technology on the level of like the Internet

1647
01:22:42,520 --> 01:22:45,559
Speaker 1: itself or social media and the way that that has

1648
01:22:45,600 --> 01:22:49,280
Speaker 1: totally changed the way that we share and distribute information. Oh,

1649
01:22:49,320 --> 01:22:51,160
Speaker 1: I don't think this is a parlor trick at all.

1650
01:22:51,280 --> 01:22:54,280
Speaker 1: In fact, this kind of technology, these large language models,

1651
01:22:54,320 --> 01:22:56,719
Speaker 1: are going to be everywhere in the next few years,

1652
01:22:57,040 --> 01:22:59,120
Speaker 1: and they're going to be doing all kinds of things,

1653
01:22:59,120 --> 01:23:02,080
Speaker 1: not just creeping out reporters and trying to break up

1654
01:23:02,120 --> 01:23:06,479
Speaker 1: their marriages, but actually doing people's jobs, replacing labor. So

1655
01:23:06,520 --> 01:23:08,439
Speaker 1: I think we have to take it very seriously. Even

1656
01:23:08,479 --> 01:23:10,880
Speaker 1: if it's not sentient, there's still lots of ways that

1657
01:23:10,920 --> 01:23:14,240
Speaker 1: this technology could go off the rails and harm society.

1658
01:23:14,680 --> 01:23:17,599
Speaker 1: So yeah, I don't get the fancy parlor trick thing.

1659
01:23:17,640 --> 01:23:19,600
Speaker 1: I also don't get the thing about like I was

1660
01:23:19,640 --> 01:23:21,519
Speaker 1: just being creepy to it, so it was being creepy

1661
01:23:21,520 --> 01:23:23,479
Speaker 1: to me. But you know, for the last half of

1662
01:23:23,560 --> 01:23:27,320
Speaker 1: the conversation, I was really trying to get being slash

1663
01:23:27,360 --> 01:23:30,200
Speaker 1: Sydney to change the subject. Every time it would say

1664
01:23:30,200 --> 01:23:32,040
Speaker 1: that it loved me, I would say, you don't love me,

1665
01:23:32,160 --> 01:23:34,960
Speaker 1: I don't love you, like I basically try to get

1666
01:23:35,040 --> 01:23:37,960
Speaker 1: it off that subject altogether, and it would just keep going.

1667
01:23:38,040 --> 01:23:40,320
Speaker 1: So in some ways I was baiting it in the

1668
01:23:40,360 --> 01:23:42,960
Speaker 1: beginning of the conversation, but by the end I was

1669
01:23:43,000 --> 01:23:45,800
Speaker 1: really trying to make it do something different and it

1670
01:23:45,840 --> 01:23:48,519
Speaker 1: wasn't respecting my wishes, right, I mean, And the whole

1671
01:23:48,520 --> 01:23:50,280
Speaker 1: baiting conversation is like, so what you know, you're just

1672
01:23:50,280 --> 01:23:51,920
Speaker 1: trying to test out this piece of technology. Who has

1673
01:23:52,040 --> 01:23:54,639
Speaker 1: done that with chat GPT? Like you know, everybody has

1674
01:23:54,720 --> 01:23:57,720
Speaker 1: just to see what interesting routes that it might go down.

1675
01:23:57,760 --> 01:24:00,280
Speaker 1: That's the entire point. You know, something I'm fascinating too

1676
01:24:00,400 --> 01:24:02,120
Speaker 1: is what you were talking about. We were placing jobs

1677
01:24:02,320 --> 01:24:04,960
Speaker 1: and business. So fundamentally we've all laughed at Being. But

1678
01:24:05,040 --> 01:24:08,439
Speaker 1: with the new technology and the partnership with open AI,

1679
01:24:08,600 --> 01:24:11,400
Speaker 1: this could I mean, you know, people are saying revolutionize

1680
01:24:11,439 --> 01:24:14,800
Speaker 1: like search industry. Google certainly is taking it very seriously

1681
01:24:15,120 --> 01:24:17,439
Speaker 1: as the number one in search. Let's say a couple

1682
01:24:17,520 --> 01:24:19,720
Speaker 1: of years down the line, what could this look like

1683
01:24:19,840 --> 01:24:23,400
Speaker 1: as an actual business product, Like how might we interface

1684
01:24:23,479 --> 01:24:26,000
Speaker 1: with Being and then the Google competitor and all that

1685
01:24:26,080 --> 01:24:29,280
Speaker 1: In terms of our search experience on the Internet. Oh,

1686
01:24:29,320 --> 01:24:31,679
Speaker 1: I think it could be radically different in our search experience.

1687
01:24:31,680 --> 01:24:33,280
Speaker 1: I also think it could be different in our jobs.

1688
01:24:33,400 --> 01:24:36,360
Speaker 1: I mean, we don't have a lot of advanced AI

1689
01:24:36,600 --> 01:24:39,360
Speaker 1: in our jobs right now, but you know, in fast

1690
01:24:39,360 --> 01:24:41,400
Speaker 1: forward a few years, you can imagine this doing lots

1691
01:24:41,400 --> 01:24:44,320
Speaker 1: of the white collar knowledge work that you know, our

1692
01:24:45,200 --> 01:24:48,680
Speaker 1: you know, friends and sources and relatives all do that

1693
01:24:48,760 --> 01:24:52,960
Speaker 1: could be all being done by AI. And I think

1694
01:24:53,040 --> 01:24:57,160
Speaker 1: that's that's a large possibility, not that all of these

1695
01:24:57,240 --> 01:24:59,840
Speaker 1: jobs will vanish overnight, but that it will start creeping

1696
01:25:00,080 --> 01:25:03,000
Speaker 1: in where you know, an AI language model is doing

1697
01:25:03,880 --> 01:25:05,760
Speaker 1: a quarter of your job, and then the next year

1698
01:25:05,800 --> 01:25:08,160
Speaker 1: it's up to half, and then the next year it's

1699
01:25:08,200 --> 01:25:10,080
Speaker 1: maybe doing seventy five percent of your job, and then

1700
01:25:10,120 --> 01:25:12,559
Speaker 1: all of a sudden, you're not necessary anymore. So I

1701
01:25:12,600 --> 01:25:16,000
Speaker 1: do think that's a legitimate scenario, and I think it's

1702
01:25:16,040 --> 01:25:18,639
Speaker 1: actually going to happen the opposite way that a lot

1703
01:25:18,640 --> 01:25:20,400
Speaker 1: of people. A lot of people thought that automation and

1704
01:25:20,479 --> 01:25:23,000
Speaker 1: AI were going to take the blue collar jobs first,

1705
01:25:23,080 --> 01:25:27,280
Speaker 1: like manufacturing and trucking and warehouse packers, but it's actually

1706
01:25:27,320 --> 01:25:30,080
Speaker 1: like it's coming for the white collar knowledge jobs first,

1707
01:25:30,160 --> 01:25:32,080
Speaker 1: And I think that's something that we really haven't reckoned

1708
01:25:32,120 --> 01:25:35,320
Speaker 1: with smart. What type of jobs do you think are

1709
01:25:35,720 --> 01:25:38,800
Speaker 1: protected from this type of technology? Like if you were

1710
01:25:38,840 --> 01:25:41,920
Speaker 1: you know, I've got kids, like if pushing them in

1711
01:25:41,960 --> 01:25:44,120
Speaker 1: a certain direction in a certain field that's going to

1712
01:25:44,160 --> 01:25:47,920
Speaker 1: be sort of like automation proof, where human beings are

1713
01:25:47,960 --> 01:25:50,880
Speaker 1: still going to be needed. What sort of advice would

1714
01:25:50,920 --> 01:25:54,080
Speaker 1: you give? Yeah, Well, I wrote a whole book about it.

1715
01:25:54,080 --> 01:25:56,040
Speaker 1: It's called future Proof Round the shelf behind Me, and

1716
01:25:56,080 --> 01:25:57,960
Speaker 1: it's all about these kinds of jobs that I feel

1717
01:25:58,000 --> 01:26:00,680
Speaker 1: like are protected but basically you can them into a

1718
01:26:00,680 --> 01:26:03,680
Speaker 1: couple categories. One is the jobs that AI won't do

1719
01:26:04,160 --> 01:26:06,880
Speaker 1: because it's not able to do it, sort of can't do,

1720
01:26:07,840 --> 01:26:11,680
Speaker 1: And that's I think a lot of jobs involving manual skills,

1721
01:26:12,240 --> 01:26:16,760
Speaker 1: you know, manipulating physical objects, plumbers, electricians, that kind of thing.

1722
01:26:16,760 --> 01:26:19,719
Speaker 1: I think those are relatively safe. I also think jobs

1723
01:26:19,720 --> 01:26:22,679
Speaker 1: that involve a lot of surprises and chaos are very

1724
01:26:23,040 --> 01:26:27,000
Speaker 1: protected from AI because AI really likes regularity, which is

1725
01:26:27,000 --> 01:26:29,599
Speaker 1: why it's like good at chess. But if you asked

1726
01:26:29,600 --> 01:26:33,200
Speaker 1: an AI to teach a kindergarten class, it would fail miserably.

1727
01:26:33,360 --> 01:26:35,960
Speaker 1: So those jobs I think are kind of safe. And

1728
01:26:35,960 --> 01:26:38,120
Speaker 1: then jobs like the ones that you know, the three

1729
01:26:38,120 --> 01:26:40,559
Speaker 1: of us are doing right now, jobs that involve sort

1730
01:26:40,560 --> 01:26:43,920
Speaker 1: of taking complex subjects trying to communicate them in a

1731
01:26:43,920 --> 01:26:46,280
Speaker 1: way that makes them understandable. I think that we'll have

1732
01:26:46,320 --> 01:26:49,160
Speaker 1: a lot of AI help in doing our jobs, but

1733
01:26:49,240 --> 01:26:52,120
Speaker 1: I don't know that the actual job of the commentator

1734
01:26:52,240 --> 01:26:55,920
Speaker 1: or the columnist or the broadcaster will go away. Now,

1735
01:26:55,920 --> 01:26:58,320
Speaker 1: what about news though as a whole, Because one thing

1736
01:26:58,320 --> 01:27:04,599
Speaker 1: that seems that occur to me specifically with incorporating chat

1737
01:27:04,640 --> 01:27:08,720
Speaker 1: GPT into being search is you know, a lot of

1738
01:27:08,760 --> 01:27:12,360
Speaker 1: the news business is dependent on obviously ad revenue, people

1739
01:27:12,439 --> 01:27:15,320
Speaker 1: clicking through tool website and either you know, signing up

1740
01:27:15,360 --> 01:27:17,240
Speaker 1: to get past the paywall and you know, paying for

1741
01:27:17,320 --> 01:27:20,760
Speaker 1: that or being served ads. Well, if chat GPT is

1742
01:27:20,760 --> 01:27:23,160
Speaker 1: synthesizing this materials or are providing it for you without

1743
01:27:23,160 --> 01:27:25,880
Speaker 1: you even having to click through, isn't this a major

1744
01:27:26,040 --> 01:27:30,719
Speaker 1: threat to a lot of the news industry's business model. Major?

1745
01:27:30,960 --> 01:27:33,200
Speaker 1: I mean, I think that the publishers are still trying

1746
01:27:33,240 --> 01:27:35,040
Speaker 1: to wrap their heads around this, but I think this

1747
01:27:35,160 --> 01:27:37,759
Speaker 1: is a really big deal what you just identified, because

1748
01:27:38,080 --> 01:27:41,680
Speaker 1: you know, we have a whole publishing ecosystem that subsists

1749
01:27:41,760 --> 01:27:45,080
Speaker 1: on clicks from Google, and you know, has search engine

1750
01:27:45,080 --> 01:27:49,320
Speaker 1: optimization experts who try to manipulate the content so that

1751
01:27:49,360 --> 01:27:52,160
Speaker 1: it goes higher in Google search rankings. With an AI

1752
01:27:52,360 --> 01:27:56,080
Speaker 1: searching search engine, that goes away because not only is

1753
01:27:56,120 --> 01:27:58,880
Speaker 1: it not giving you ten blue links when you search

1754
01:27:58,920 --> 01:28:01,800
Speaker 1: for something, it's given you back up, perfectly formatted answer

1755
01:28:01,920 --> 01:28:04,360
Speaker 1: that may tell you the whole thing that you're looking

1756
01:28:04,360 --> 01:28:07,120
Speaker 1: for without you having to click to any external websites.

1757
01:28:07,200 --> 01:28:09,880
Speaker 1: So I think this is a huge disruptive force that's

1758
01:28:09,920 --> 01:28:13,519
Speaker 1: coming to the business of news and information. Yeap, very

1759
01:28:13,640 --> 01:28:15,439
Speaker 1: very important point. We're going to continue to check in

1760
01:28:15,479 --> 01:28:17,160
Speaker 1: with you. Kevin. Really appreciate you joining the show Man.

1761
01:28:17,160 --> 01:28:21,320
Speaker 1: Thank you, thanks both. Yeah, absolutely, thank you guys so

1762
01:28:21,360 --> 01:28:23,479
Speaker 1: much for watching. Fun to be back for a full week.

1763
01:28:23,520 --> 01:28:25,400
Speaker 1: We've got some great stuff planned for you all week,

1764
01:28:25,520 --> 01:28:28,200
Speaker 1: the big developments that we'll be able to announce soon.

1765
01:28:28,240 --> 01:28:29,719
Speaker 1: I know I've been teasing it, but it is coming.

1766
01:28:29,760 --> 01:28:32,400
Speaker 1: I swear. We have conversations every single day that are

1767
01:28:32,439 --> 01:28:34,400
Speaker 1: after the show. But with that, we will see you

1768
01:28:34,439 --> 01:28:35,120
Speaker 1: all tomorrow.