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

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Speaker 2: the studio ad staff, give you guys the best independent.

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Speaker 3: Coverage that is possible.

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Speaker 2: If you like what we're all about, it just means

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Speaker 2: the absolute world to have your support. But enough with that,

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Speaker 2: let's get to the show. Good morning, everybody, Happy Thursday.

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Speaker 2: We have an amazing show for everybody today.

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Speaker 3: What do we have, Crystal?

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Speaker 1: Indeed, we do many things to get into this morning.

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Speaker 1: So we have, thanks to our friends over our jail partner,

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Speaker 1: some exclusive polling, specifically some exclusive word cloud, how people

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Speaker 1: feel about Biden, about Trump, who their dream president would be,

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Speaker 1: how they feel about Trump's convictions. It's really interesting stuff,

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Speaker 1: so I think you guys are going to get a

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Speaker 1: lot out of that. We also had some surprising Ohio

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Speaker 1: election result. Will Tee leave reading that we can do there,

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Speaker 1: we'll break that down for you. Additional polling about how

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Speaker 1: you guys feel about Kamala Harris and the prospect that

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Speaker 1: she could be the next president if some thing were

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Speaker 1: to happen to Joe Biden, So we'll break that down

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Speaker 1: for you as well. We have some important economic news.

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Speaker 1: Inflation appears to have slowed, but we also have a

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Speaker 1: dire situation in the commercial real estate market.

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Speaker 4: You will not.

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Speaker 1: Believe the fire sale prices that some large Manhattan office

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Speaker 1: buildings are being sold for at this point. And this

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Speaker 1: is just just the beginning of what is going to

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Speaker 1: be pretty rocky road ahead.

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Speaker 3: For commercial real estate. It's totally nuts.

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Speaker 1: Yeah, So we'll break that down for You've got a

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Speaker 1: lot of updates coming out of Israel as well, updates

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Speaker 1: on those ongoing ceasefire negotiations.

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Speaker 4: Where we are with that, what.

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Speaker 1: The US is saying about it, what Hamas is saying

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Speaker 1: about it, what Israel is saying about it. Plus we

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Speaker 1: have a new UN report that looks at atrocity is

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Speaker 1: committed both by Hamas on October seventh in particular, and

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Speaker 1: by Israel through the end of last year. They are

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Speaker 1: accusing Israel of committing extermination. But there are a lot

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Speaker 1: of really interesting, important, horrifying details in this report that

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Speaker 1: we want to bring to you, So we'll break that

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Speaker 1: down for you as well. We've got David danon he's

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Speaker 1: talking about how prices are really set and looking in

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Speaker 1: particular about the new move towards variable pricing. Basically, it's

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Speaker 1: using AI to price scout you for all that you

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Speaker 1: are worth. And I am looking at some comments from

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Speaker 1: Donnie Deutche just basically like adopting the logic of bin

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Speaker 1: Laden to justify killing innocent civilians amass.

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Speaker 3: So that's yeah, always a good thing.

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Speaker 4: I'm very proud of himself for that one too, was

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Speaker 4: tweeting it out.

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Speaker 3: I'm sure he is.

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Speaker 2: Before we get to that, there was a fantastic Counterpoints

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Speaker 2: debate that will drop for all of our premium subscribers

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Speaker 2: tonight between Ryan Grimm, Emily and Orne casts on immigration

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Speaker 2: economics left versus right.

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Speaker 3: I know people really enjoy those.

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Speaker 2: So if you want to become a premium subscriber Breakingpoints

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Speaker 2: dot Com, it drops for everybody tonight.

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Speaker 3: Otherwise it's going to be available on all feeds. And

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Speaker 3: if you want to be.

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Speaker 2: Able to support our work and our partnerships Jailpay Partner,

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Speaker 2: Counterpoints Fridays, etc. Where a five day week now program,

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Speaker 2: you can become a premium member as well. Now, as

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Speaker 2: Chrystal said, we've got some exclusive word clouds, so we

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Speaker 2: can drop for everybody courtesy of JLP. Let's go ahead

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Speaker 2: and start with our first one. These word clouds are

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Speaker 2: very illuminating as they kind of tell us a little

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Speaker 2: bit about how people feel generally. So who is your

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Speaker 2: dream president? Well, Donald and Trump being beating out significantly

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Speaker 2: Joe Biden that you can see, which is off there

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Speaker 2: on the right for those who are watching, Obama and

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Speaker 2: Barack also beating out Biden. Kennedy pretty prominently placed there,

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Speaker 2: Michelle Obama, Bernie and Sanders both making a little bit

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Speaker 2: of a comeback there. And then on the margins you

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Speaker 2: can just see some a few people, Clinton, DeSantis, Mike Johnson, Budajedge,

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Speaker 2: who's that Buddha, Judge, an Abe, Lincoln.

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Speaker 4: Hey, you know, it's about as alive as Joe Biden.

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Speaker 2: The Vladimir down there, presumably Vladimir Selinsky. Ramas Swami is

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Speaker 2: there spelled incorrectly. That Rock truly the American President of

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Speaker 2: American people of Ramas Swami, but spelled incorrectly.

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Speaker 3: JFK.

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Speaker 2: Mark Zuckerberg, I don't know who what this means? People,

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Speaker 2: What exactly does that mean?

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Speaker 3: People? Are my favorite?

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Speaker 1: We got John Stewart on there, we got John Stewart,

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Speaker 1: one that just says Tom Yeah, who would that be?

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Speaker 4: I'm trying to reck Brady?

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Speaker 3: Yeah, maybe, okay, I can go for that. I like

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Speaker 3: Tom Brad. All right, it's a nice guy.

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Speaker 4: We got George on there to you, George Clonia.

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Speaker 1: Anyway, those are the small ones, obviously, Donald Trump and Obama.

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Speaker 4: This is our twenty field.

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Speaker 2: Now, okay, let's get to the actual nati gritty twenty

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Speaker 2: twenty four. Let's go to the next part. Please hear

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Speaker 2: what we have? Who is your dream president? So amongst Democrats,

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Speaker 2: Obama vastly outperforms Biden even whenever you look at both

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Speaker 2: Barack and Joe. Now on Independent, it's pretty clear here

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Speaker 2: you have Trump clearly who is the favorite, but then

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Speaker 2: Obama is number two. Biden's name barely even appears in

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Speaker 2: the Independent graphic. Amongst Republicans, obviously Donald Trump. Let's go

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Speaker 2: to the next one and actually look at how people

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Speaker 2: feel about the individual candidates. This might actually be a

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Speaker 2: problem for Trump. Donald Trump in all, in a word,

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Speaker 2: this is amongst all voters. Criminal is actually the one

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Speaker 2: that comes out. You also see the conflicting and obvious

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Speaker 2: just duality of man here in the American electorate.

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Speaker 3: You've got criminal and evil, an asshole.

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Speaker 2: You've also got corrupt, bad, right, but then crook terrible.

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Speaker 2: But then you have amazing, you have smart, you have honest,

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Speaker 2: you have people who say that he's bold, he's successful,

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Speaker 2: he's excellent, he's determined. But then you have dangerous, you

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Speaker 2: have confidence, you have loud, you have business, you have audacious,

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Speaker 2: you have strong. So people, it's just clear. I mean,

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Speaker 2: I've always thought this, people either love Trump or hate him.

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Speaker 2: There's nobody in this country who feels medium about Trump,

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Speaker 2: you know, at this point in time.

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Speaker 3: So this was illuminating in that.

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Speaker 2: Look the criminal stuff, Yeah, it could certainly hurt him,

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Speaker 2: but you also can see the depth of how the

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Speaker 2: people who do like him, they like him a lot,

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Speaker 2: whereas with Biden that are kind of like, Eh, I

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Speaker 2: would really prefer to vote for Obama, but I guess

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Speaker 2: I could vote for Bam.

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Speaker 4: Yeah.

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Speaker 1: I mean, listen, the fact that criminal is the number

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Speaker 1: one thing does give credence to the you know, folks

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Speaker 1: who say that the indictments and now his guilty conviction

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Speaker 1: are signify If that's the number one, I think, do

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Speaker 1: we have the partisan breakdown on this one, I don't remember.

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Speaker 1: But if you have criminal as the top thing that's

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Speaker 1: jumping out, you know that is a significant takeaway that

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Speaker 1: that's starting to solidify in people's minds. So if you look,

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Speaker 1: you know, the Democrat one, you're not going to be surprised.

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Speaker 4: Evil is number one. Criminal is number two.

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Speaker 1: But yeah, if you look at the Independence, they're not

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Speaker 1: in love with Donald Trump. Disgusting, corrupt, criminal, those are

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Speaker 1: some of the biggest ones. You do have a significant

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Speaker 1: number who said honest, but mostly really negative adjectives apply

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Speaker 1: to him. From Independence Republicans, the number one is American, Okay, smart,

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Speaker 1: awesome leader. So clearly, you know, republic this is no surprise.

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Speaker 1: Republicans still feel very favorably towards him. But yeah, this

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Speaker 1: is the tale of an election between two men that

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Speaker 1: no one feels that people who are swing voters oftentimes

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Speaker 1: feel negatively about both, and then the question is who

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Speaker 1: is going to who are they going to hate more?

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Speaker 1: But yeah, I do think it's noteworthy the fact that

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Speaker 1: criminal jumped up here and is pretty prominent in people's minds.

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Speaker 4: After Trump is convicted.

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Speaker 2: Yeah, I think you're right. Let's go ahead to the

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Speaker 2: Joe Biden slide. Please, this is going to be important.

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Speaker 2: So this is this is a problem here.

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Speaker 4: This has brought the country together around.

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Speaker 2: Us certainly has what is the number one thing Joe

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Speaker 2: Biden in a word, oh all right, senile. And after

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Speaker 2: that you see the partisan ones, you can see incompetent.

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Speaker 2: This is my personal favorite. It just is Biden. So

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Speaker 2: guy who is out there who says, respond to Joe

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Speaker 2: Biden in a word and you just say, Biden, You've

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Speaker 2: also got horrible and terrible and corrupt and dumb. But

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Speaker 2: you know you have trustworthy, you have caring, you have country.

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Speaker 2: I don't really know what country exactly means, amazing person,

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Speaker 2: I don't really know them.

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Speaker 3: People.

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Speaker 2: People are interesting, Catholic is up there, awesome, priority economy.

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Speaker 2: But then you have dishonest, you have bad, you have senile,

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Speaker 2: you have poor, you have evil, hack misinformed. But you know,

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Speaker 2: very broadly, the number one thing that people say when

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Speaker 2: they think about Biden is he's just so damn old.

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Speaker 2: And we're going to get to this in a bit

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Speaker 2: about the danger of what that means, because it's not

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Speaker 2: only lack of faith in his ability to complete his term.

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Speaker 2: They have to consider then, probably more deeply than at

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Speaker 2: any time in American history.

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Speaker 3: Who the number two is Kamala Harris.

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Speaker 2: And so let's go to the next part, please, and

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Speaker 2: just to show people Joe Biden in a word, even

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Speaker 2: amongst Democrats, Old is the single biggest winner. Trustworthy though

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Speaker 2: is there and honest and caring also Joe and Biden,

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Speaker 2: which is funny.

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Speaker 3: But then independent, What's I mean? What is number one?

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Speaker 5: Old?

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Speaker 3: And incompetent is.

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Speaker 2: Number one and number two amongst Republicans you again see old.

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Speaker 2: And interestingly enough, Old does not win as big amongst Republicans.

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Speaker 3: They see here to hate him for other reasons.

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Speaker 2: You see incompetent, bad liar, you know, idiot, horrible, But

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Speaker 2: Old is the predominant one for people who are independent voters.

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Speaker 2: And this also fits with a lot of the polling

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Speaker 2: that we've seen right now, where a lot of the

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Speaker 2: hate both parties numbers are beginning to break towards Donald Trump. However,

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Speaker 2: I'd be remiss if I said didn't say the five

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Speaker 2: thirty eight, you know, presidential production came out crystal. They've

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Speaker 2: got Biden winning fifty three out of fifty three out

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Speaker 2: of one hundred times. Obviously Trump is winning forty seven times,

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Speaker 2: so it's effectively a toss up. But the reason why

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Speaker 2: he has any strength whatsoever something to continue to hammer

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Speaker 2: home on the show. Those older wider states Michigan, Pennsylvania,

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Speaker 2: and Wisconsin are keeping Biden in the game. If he

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Speaker 2: can hold on to those, he can still win the

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Speaker 2: presidential Electoral College with two hundred and seventy two electoral

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Speaker 2: votes and eke out of victory. It's actually theoretically possible,

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Speaker 2: and within the realm of possibility, he would win an

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Speaker 2: EV victory or an EC victory and actually lose the

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Speaker 2: popular vote, which would be insane.

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Speaker 3: But it's definitely possible.

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Speaker 2: Given the way that a lot of younger demographics and

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Speaker 2: Sun Belt is trending towards Donald Trump right.

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Speaker 1: Now, I could really almost see this election going any

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Speaker 1: directions heavy. I mean, I could see Biden winning all

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Speaker 1: the swing states. I could see t winning all the

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Speaker 1: swing states. I could see it being really split. You know,

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Speaker 1: the Sun Belt goes to Trump, the industrial Midwest goes

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Speaker 1: to Biden. I could see, you know, the electoral College

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Speaker 1: and the popular vogue going any number of different ways.

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Speaker 1: Although it is interesting that previously Republicans have always had

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Speaker 1: an advantage with the electoral college in modern history does

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Speaker 1: seem like that that is shifting significantly.

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Speaker 4: So I don't know.

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Speaker 1: I mean, listen, for Biden, what are you going to

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Speaker 1: do about the fact that the entire country, including your

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Speaker 1: own party, says that the number one thing they associate

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Speaker 1: with you is old, Like, you can't.

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Speaker 4: Fix that, you can't fix it.

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Speaker 1: Maybe he can lay some of those concerns if he

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Speaker 1: has really strong debate performances.

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Speaker 4: I'm sure that's what they're hoping for.

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Speaker 1: I'm sure they're working up whatever, you know, drug cocktail

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Speaker 1: is going to get him, get him revved and going

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Speaker 1: for those debate nights. Maybe that's enough to lay some

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Speaker 1: of the concerns. But you know, you do have a

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Speaker 1: unifying consensus in the country that that's how people associate,

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Speaker 1: that's the number one thing they think of, and obviously

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Speaker 1: that's going to be a concern for folks. Yeah, I

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Speaker 1: see a lot of people arguing online, but like, no,

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Speaker 1: that's not it, and that's not the issue, and there's

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Speaker 1: you know, you're making too much of this age thing

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Speaker 1: and it's overblown, et cetera. People have made the judgment

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Speaker 1: for themselves. Now, there are a lot of other factors

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Speaker 1: to consider including the fact that number one word that

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Speaker 1: comes to mind for Trump is criminal, although his are

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Speaker 1: kind of more scattered. People have all sorts of thoughts

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Speaker 1: and feelings about him. But you know, old dude versus criminal.

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Speaker 1: What an inspiring election we have in front of us.

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Speaker 2: It's interesting too, I mean, whenever you consider it too.

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Speaker 2: On the age thing is this is almost as much

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Speaker 2: of a media story. It's an interesting development right now.

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Speaker 2: Ostia and Herndon. He's a New York Times reporter and

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Speaker 2: he has been traveling the country and he continues to

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Speaker 2: get piled on by his colleagues and by a lot

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Speaker 2: of liberals because he's like, guys, Biden's age is the

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Speaker 2: only thing that I hear across every single place that

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Speaker 2: I go.

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Speaker 3: He's old. He's old. He's old. He just yesterday. He

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Speaker 3: goes three times a week. I log onto Twitter.

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Speaker 2: I get yelled at for saying Biden's age is his

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Speaker 2: fundamental political liability. And then I scroll past seven hundred

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Speaker 2: and fifty viral memes character rising to president as senile.

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Speaker 2: It is truly incredible, And He's like, it's not that

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Speaker 2: the memes informs what I'm saying, It's that every piece

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Speaker 2: of data in the last couple of years.

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Speaker 3: Is that quite literally?

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Speaker 2: And I've traveled the country since the midterms asking groups

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Speaker 2: what is the most important thing in November?

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Speaker 3: And all I hear is he's.

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Speaker 2: A old, old, old old at DC and the New

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Speaker 2: York political class just want to move right past. And

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Speaker 2: they refuse to take it seriously any of us who

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Speaker 2: engage with any normal person anywhere. I mean, you know,

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Speaker 2: it's tried to talk about uber drivers and barbershop, but

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Speaker 2: it's like a certain point, like how many times do

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Speaker 2: you have to hear it from the same old person.

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Speaker 3: There's no way I can vote for this guy. He's

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Speaker 3: gonna die if he's in office.

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Speaker 2: That's a very commonly held sentiment, in fact, maybe the

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Speaker 2: most commonly held about Joe Biden today.

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Speaker 3: So can he move past it?

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Speaker 2: Yes, he certainly can if he you know, makes Trump

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Speaker 2: out to be the boogeyman.

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Speaker 3: But it's a problem.

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Speaker 1: It's a problem, and it's a problem they compound and

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Speaker 1: we'll get to this little more specifically in the Kamala block.

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Speaker 1: By picking a vice president, people also don't have confidence

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Speaker 1: in because if you had someone in there, I thought

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Speaker 1: it was like, you know, okay, Well, oh, you know,

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Speaker 1: things don't work out with Joe looking at thattuarial tables,

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Speaker 1: then we've got someone in the number two position that

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Speaker 1: we feel good about. But that's just not the sentiment

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Speaker 1: that most Americans have about Kamala Harris. And you know,

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Speaker 1: she had proven during the during her time in the

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Speaker 1: Senate and during certainly her primary run, that she really

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Speaker 1: wasn't up to the task. There was no indication that

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Speaker 1: Americans were really going to have a lot of confidence

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Speaker 1: in her.

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Speaker 4: But she was picked up put on the ticket.

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Speaker 1: Anyway, and I think that has just compounded and exacerbated

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Speaker 1: Biden's problems. You know, the dream president slide that we

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Speaker 1: had at before. We're for a lot of independence and

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Speaker 1: overwhelmingly for Democrats, the number one dream president is Obama.

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Speaker 1: Gives creen soa I don't know if you've heard Ryan

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Speaker 1: talk about this, He's like, you know what they need

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Speaker 1: to do, kick Kamala off the ticket. Yeah, we get it.

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Speaker 1: You're not going to go away from Joe.

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Speaker 4: Kick Kamala off the ticket and put Barack Obama on there.

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Speaker 2: I don't think it's constitutional though, because of the Yeah,

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Speaker 2: I don't know.

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Speaker 3: I mean, there is a look I've talked about this before.

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Speaker 2: There is a case to be made that which amendment is,

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Speaker 2: is that the twenty second Amendment whatever established that you

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Speaker 2: can only run for two terms. That might actually be

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Speaker 2: one of the worst things that's ever happened to quote

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Speaker 2: unquote American democracy, because I mean, the American people I

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Speaker 2: think correctly elected FDR four terms.

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Speaker 3: There's a good case, you know that you can make that.

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Speaker 5: What is it?

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Speaker 3: I think Bill Clinton, it's.

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Speaker 2: Arguably that he would have been reelected to like four

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Speaker 2: separate terms.

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Speaker 4: He might still be President Biden.

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Speaker 2: He's younger than Biden exactly, so this could be like

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Speaker 2: his last term. And I mean, just imagine it'd be Look,

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Speaker 2: I'm not saying I'm the biggest Clinton fan, but imagine

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Speaker 2: if Clinton had won the two thousand election and we

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Speaker 2: didn't have George W. Bush in the Oval office on

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Speaker 2: nine to eleven. I didn't say it'd be better. We

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Speaker 2: wouldn't have gone into Iraq. I know that one for sure. Yeah, Like,

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Speaker 2: there's there's some good cases here where both Bill Clinton,

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Speaker 2: Bill Clinton left office. I think we talked about in

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Speaker 2: the last show. He has sixty six percent approval rating.

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Speaker 2: If he ran again, he would blow out whoever was

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Speaker 2: going to run against.

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Speaker 3: Especially W. He would have wiped the floor with.

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Speaker 2: W and then Obama. I mean, Obama was not that popular.

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Speaker 2: I think he had a fifty two maybe fifty three.

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Speaker 2: But clearly he looked you could say this one about him.

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Speaker 2: He knew how to get people to vote for him.

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Speaker 2: He probably I mean, I don't know if he would

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Speaker 2: have beat Trumper.

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Speaker 4: I'm pretty sorry he would have beat Trump.

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Speaker 3: Maye, Okay, I'll tell you this.

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Speaker 1: I mean definitely was stronger than Hillary, right right that

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Speaker 1: candadate Hillary was and it was still close.

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Speaker 3: Yeah, so let's think about that. I mean, there's there's.

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Speaker 1: Something Obama, who had a much higher approval AA you

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Speaker 1: would have been.

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Speaker 2: There is a very good case that that amendment actually

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Speaker 2: has caused us to have much more raucous like swing elections,

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Speaker 2: when in reality having extremely popular people just get elected.

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Speaker 3: Until they reach their end.

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Speaker 2: It's worked out fine in the UK both you know,

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Speaker 2: it's not like Margaret Thatcher and Tony Blair didn't eventually

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Speaker 2: get kicked out of office whenever they you know, suffered

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Speaker 2: at the ballot. I think it's working up. Look at

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Speaker 2: India too, Mody, he seems to be right and high.

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Speaker 2: He gets checked a little bit, he's going to adjust

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Speaker 2: his politics, like there's there's there is some strength I

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Speaker 2: think in medicis.

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Speaker 3: Well, you know, I am very nothing.

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Speaker 1: We're going to change here, We're going to get to

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Speaker 1: There was this ballot initiative. Let's actually put that up

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Speaker 1: on a screen, a eight. We'll come back to a seven,

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Speaker 1: but put a eight up on the screen. Voters had

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Speaker 1: and opportunity to vote in North Dakota to put an

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Speaker 1: age limit on for congressional candidates, and it overwhelmingly passed.

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Speaker 4: What was the age they said.

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Speaker 2: At eighty eighty It was eighty years old. Yeah, I

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Speaker 2: mean it was clearly targeted Biden. And look, keep in

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Speaker 2: mind this is Republicans. But yeah, I know it's unconstitutional, but.

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Speaker 3: I want this. I'm like, I know yearning for this.

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Speaker 1: Really, I know this is popular, and I understand why

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Speaker 1: people feel this way. I just am very opposed to

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Speaker 1: putting restrictions on people who people can vote for. So

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Speaker 1: like same thing with the term limit thing, Like I

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Speaker 1: actually agree with you. I would get rid of the

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Speaker 1: presidential term limits if people wanted eight terms of Bill Clinton,

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Speaker 1: and we have enough democratic processes in place, so people

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Speaker 1: can really evaluate the alternatives, then all right, let's go

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Speaker 1: for it. And I feel the same way about this, like, yeah,

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Speaker 1: I would rather we have someone other than Joe Biden

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Speaker 1: out there, but that's less a function of putting an

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Speaker 1: age limit and more a function of the fact that

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Speaker 1: we didn't have democratic processes in place for people to

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Speaker 1: really have alternatives.

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Speaker 4: So to me, that's the truth.

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Speaker 2: Yeah, No, it's a good point, and I mean, I agree,

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Speaker 2: it's very complicated feelings about how about this one. At

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Speaker 2: the very least, what we can say is clearly this

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Speaker 2: popular right because people are upset about the gerontocracy, and

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Speaker 2: so what we need is to have reforms that are

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Speaker 2: put into place and or actual competition that forces these

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Speaker 2: entrenched political interests that are actually out and that really

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Speaker 2: seems to be the main problem. I know people always

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Speaker 2: want to do term limits, and I understand that impulse,

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Speaker 2: but I do think that there are a lot better

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Speaker 2: ways to actually achieve more democratic outcomes rather than that

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Speaker 2: that could have a lot of unintended consequences. Again, at

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Speaker 2: the very very least, it's very popular right now to

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Speaker 2: have an actual age limit in office.

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Speaker 3: Crystal.

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Speaker 2: You did, though, I want to update us on a

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Speaker 2: race in Ohio, and this would be a flashing red

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Speaker 2: light for Republicans.

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Speaker 3: Can you tell us about it?

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Speaker 1: Yeah, well, first let's actually put Ashrevan I will to

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Speaker 1: just look at this last word cloud and then we'll

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Speaker 1: talk about Ohio. So put the last word cloud up

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Speaker 1: on the screen. So they asked, you're the main reactions

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Speaker 1: of people hearing about Trump's guilty vote verdicts? This is

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Speaker 1: among all voters. Number one is happy, Number two is surprised.

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Speaker 1: You've got satisfaction up there.

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Speaker 4: Justice.

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Speaker 1: It's funny you've got surprised. You also have expected somewhat

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Speaker 1: less than surprised. But I guess it is interesting to

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Speaker 1: me that that was the number one sentiment. And I

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Speaker 1: don't know if we have the breakdown the partisan breakdown here,

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Speaker 1: but you know, independence tended to be pretty satisfied with

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Speaker 1: this result. So anyway, make of that what you will.

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Speaker 1: There isn't there's clearly a very partisan divide in terms

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Speaker 1: of how these prosecutions are being perceived and how the

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Speaker 1: results are being perceived. But probably the thing I found

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Speaker 1: most interesting about that is that people weren't expecting it.

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Speaker 1: You know, there's just an assumption that Trump is going

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Speaker 1: to get away with everything all the time, and that

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Speaker 1: there's not going to be any sort of like actual

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Speaker 1: consequence for anything he does. Ever, So it was interesting

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Speaker 1: to me that people were like surprized by that. Is

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Speaker 1: there anything you want to react to that I can

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Speaker 1: go on to Ohio. Okay, So there was a special

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Speaker 1: election in Ohio's sixth congressional district, where I actually used

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Speaker 1: to live, so I know this area kind of well,

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Speaker 1: this is you guys also are a little bit familiar

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Speaker 1: with this area because this.

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Speaker 4: Is the district that East Palestine is located in.

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Speaker 1: This very you know, industrial post industrial at this point

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Speaker 1: has experienced massive decline, really post NAFTA. This is the

439
00:19:27,200 --> 00:19:30,760
Speaker 1: area getting up close to Youngstown, that part of Ohio.

440
00:19:31,160 --> 00:19:34,360
Speaker 1: It also happens to be the part of the country

441
00:19:34,640 --> 00:19:38,359
Speaker 1: that shifted to the right the furthest of anywhere in

442
00:19:38,359 --> 00:19:41,800
Speaker 1: the country during the Trump years. So they had the

443
00:19:41,840 --> 00:19:46,400
Speaker 1: special election for a congressional seat there between a established

444
00:19:46,400 --> 00:19:49,320
Speaker 1: Republican state senator who raised I think seven hundred thousand

445
00:19:49,320 --> 00:19:52,720
Speaker 1: dollars to run for the seat and some political newbie

446
00:19:52,720 --> 00:19:56,400
Speaker 1: who raised basically no money on the Democratic side and

447
00:19:56,640 --> 00:19:59,359
Speaker 1: the Republican one, which was very much expected but it

448
00:19:59,440 --> 00:20:01,960
Speaker 1: was way closer than people expected.

449
00:20:02,000 --> 00:20:03,240
Speaker 4: We can put this up on the screen.

450
00:20:03,640 --> 00:20:07,280
Speaker 1: So the ultimate result here was fifty four point six

451
00:20:07,359 --> 00:20:11,639
Speaker 1: percent for the Republican forty five point three percent, so

452
00:20:11,760 --> 00:20:15,679
Speaker 1: a nine point margin for the Democrat. That represents a

453
00:20:15,920 --> 00:20:21,920
Speaker 1: twenty point swing from the last presidential election results.

454
00:20:22,119 --> 00:20:25,400
Speaker 4: This is a district that Trump won by twenty nine points.

455
00:20:26,240 --> 00:20:29,119
Speaker 1: And so if you had had, you know, a Democratic

456
00:20:29,160 --> 00:20:31,239
Speaker 1: candidate and there who had raised some money, who had

457
00:20:31,280 --> 00:20:34,119
Speaker 1: more of a name, there's actually a chance that they

458
00:20:34,119 --> 00:20:36,399
Speaker 1: could have won here. Now again, at the end of

459
00:20:36,440 --> 00:20:39,639
Speaker 1: the day, the Republican prevails as the expected result, but

460
00:20:39,720 --> 00:20:43,600
Speaker 1: we have seen a lot of special elections where you've

461
00:20:43,640 --> 00:20:47,679
Speaker 1: had significant swings towards the Democrats. Now let's put this

462
00:20:47,760 --> 00:20:50,720
Speaker 1: next piece up on the screen. Dave Wasserman was, you know,

463
00:20:50,760 --> 00:20:53,040
Speaker 1: taking a look at this of what's going on here.

464
00:20:53,480 --> 00:20:56,280
Speaker 1: And here's an example of one of the counties and

465
00:20:56,680 --> 00:20:59,800
Speaker 1: what this shift was Washington County, which is where Marietta

466
00:20:59,920 --> 00:21:02,480
Speaker 1: is is now fully reporting. He says, Trump won this

467
00:21:02,520 --> 00:21:06,879
Speaker 1: county seventy to twenty nine. Last time around. The Republican

468
00:21:07,359 --> 00:21:10,080
Speaker 1: just barely eked down a victory there fifty three forty seven.

469
00:21:10,160 --> 00:21:11,760
Speaker 1: So that gives you a sense of the shift in

470
00:21:11,800 --> 00:21:14,640
Speaker 1: some of the key counties in this district. But put

471
00:21:14,640 --> 00:21:16,119
Speaker 1: the next piece up on the screen because it has

472
00:21:16,160 --> 00:21:18,280
Speaker 1: a little bit of an explanation of what he thinks

473
00:21:18,400 --> 00:21:23,119
Speaker 1: was going on here, which is that the turnout was abysmal.

474
00:21:23,920 --> 00:21:26,159
Speaker 1: So he says, how bad is turnout in the Ohio

475
00:21:26,200 --> 00:21:30,040
Speaker 1: six special Harrison County is showing fully reported the Republican

476
00:21:30,119 --> 00:21:34,359
Speaker 1: is getting about twelve percent of Trump's twenty twenty vote total,

477
00:21:34,440 --> 00:21:38,080
Speaker 1: So in terms of the number of voters giving twelve percent. Meanwhile,

478
00:21:38,200 --> 00:21:41,920
Speaker 1: the Democrat is getting twenty two percent of Biden's twenty

479
00:21:41,960 --> 00:21:45,639
Speaker 1: twenty vote total. Hence the Republican is just plus twenty

480
00:21:45,680 --> 00:21:48,080
Speaker 1: eight in a district that in an area that Trump

481
00:21:48,119 --> 00:21:50,640
Speaker 1: had won by fifty three points in that county last

482
00:21:50,640 --> 00:21:53,520
Speaker 1: time around. So Sager, that's that's the question, is hot

483
00:21:53,560 --> 00:21:56,080
Speaker 1: Do you look at this right? It's yet another massive

484
00:21:56,119 --> 00:22:00,479
Speaker 1: Democratic over performance. It also comes right after trump conviction,

485
00:22:00,640 --> 00:22:03,720
Speaker 1: so all maybe that change some minds or caused them whatever.

486
00:22:04,200 --> 00:22:06,960
Speaker 1: On the other hand, you have next to no one

487
00:22:07,000 --> 00:22:10,480
Speaker 1: turning out for this election, so Democrats were more motivated,

488
00:22:10,560 --> 00:22:13,440
Speaker 1: more Democrats showed up to vote on the Democratic side.

489
00:22:13,600 --> 00:22:16,480
Speaker 1: But does that really translate into a general election. That's

490
00:22:16,520 --> 00:22:18,560
Speaker 1: really the big question point. But you know a lot

491
00:22:18,560 --> 00:22:19,800
Speaker 1: of Democrats taking a lot of.

492
00:22:19,720 --> 00:22:20,480
Speaker 4: Heart in this result.

493
00:22:20,560 --> 00:22:24,639
Speaker 2: Well, look, it was predictive to the previous election. The

494
00:22:24,680 --> 00:22:29,199
Speaker 2: special elections were deeply predictive of major Democratic overperformance and

495
00:22:29,280 --> 00:22:33,200
Speaker 2: Republican underperformance. So Democrats, you have a lot of recency bias.

496
00:22:33,240 --> 00:22:36,520
Speaker 2: Now the case against this is, well, it's a special election.

497
00:22:36,600 --> 00:22:40,240
Speaker 2: Who cares about special elections. Donald Trump is the greatest

498
00:22:40,440 --> 00:22:44,240
Speaker 2: driver of non voter behavior that American politics has seen

499
00:22:44,280 --> 00:22:46,960
Speaker 2: since Ronald Reagan. There are people who both come out

500
00:22:46,960 --> 00:22:49,560
Speaker 2: of the work to vote against him and people who

501
00:22:49,560 --> 00:22:52,480
Speaker 2: come out to vote for him. That is a good

502
00:22:52,600 --> 00:22:56,359
Speaker 2: case for why this phenomenon of the low propensity voter

503
00:22:56,480 --> 00:22:59,200
Speaker 2: staying home the high propensity voter coming out for leading

504
00:22:59,240 --> 00:23:02,600
Speaker 2: to Democratic over performance should probably not apply to Trump.

505
00:23:02,840 --> 00:23:04,480
Speaker 2: At the same time, Trump has not been on the

506
00:23:04,480 --> 00:23:06,879
Speaker 2: ballot since twenty twenty, So what the hell do we know.

507
00:23:06,920 --> 00:23:09,679
Speaker 2: We only have one recent election in modern times to

508
00:23:09,680 --> 00:23:11,480
Speaker 2: tell us a little bit about it. But at the

509
00:23:11,560 --> 00:23:13,960
Speaker 2: very least I'd be a worried. I'd be worried because

510
00:23:14,280 --> 00:23:17,399
Speaker 2: anytime you have high propensity folks who are always willing

511
00:23:17,400 --> 00:23:19,280
Speaker 2: to crawl over broken glass to vote against you.

512
00:23:19,320 --> 00:23:20,000
Speaker 3: That's a bad thing.

513
00:23:20,160 --> 00:23:23,800
Speaker 2: That means you things have to align perfectly on election day,

514
00:23:24,480 --> 00:23:27,520
Speaker 2: can't have any rain. Hope it's not too cold. Hope

515
00:23:27,520 --> 00:23:29,560
Speaker 2: that they really want to come out. Make sure they

516
00:23:29,560 --> 00:23:30,439
Speaker 2: mail in that ballot.

517
00:23:30,560 --> 00:23:32,640
Speaker 3: All right? Are we okay with mail in bolluots? Which

518
00:23:32,640 --> 00:23:33,199
Speaker 3: one are we doing?

519
00:23:33,280 --> 00:23:35,280
Speaker 2: So it's like, there's a good case here for why

520
00:23:35,320 --> 00:23:37,360
Speaker 2: those low propensity folks part of the reason you don't

521
00:23:37,400 --> 00:23:40,000
Speaker 2: bet the house on them is that maybe the candidate

522
00:23:40,040 --> 00:23:42,639
Speaker 2: then tells them, actually the election was rigged, and then

523
00:23:42,680 --> 00:23:45,000
Speaker 2: before the Georgia election you're like, yeah, I'm just not going.

524
00:23:44,920 --> 00:23:46,920
Speaker 3: To vote, and then both Democrats end up winning that race.

525
00:23:47,080 --> 00:23:49,920
Speaker 1: I mean the fact that now, and this is pretty

526
00:23:49,920 --> 00:23:53,160
Speaker 1: consistent not just in this special election, but with the polling.

527
00:23:54,359 --> 00:23:57,040
Speaker 1: Almost all the polls show when you put in the

528
00:23:57,320 --> 00:24:00,720
Speaker 1: likely voter screen, meaning we're not just looking at everybody,

529
00:24:00,800 --> 00:24:03,240
Speaker 1: We're looking at who are the people that always vote

530
00:24:03,280 --> 00:24:05,679
Speaker 1: that we know are going to turn up no matter what.

531
00:24:06,520 --> 00:24:10,639
Speaker 1: When you apply that screen, Democrats now benefit. That is

532
00:24:10,720 --> 00:24:13,520
Speaker 1: new and different, and that really is a symbol of

533
00:24:13,560 --> 00:24:17,159
Speaker 1: the evolving coalitions of these two parties. And you know,

534
00:24:17,200 --> 00:24:19,840
Speaker 1: that was something that always in the past benefited Republicans

535
00:24:19,840 --> 00:24:21,640
Speaker 1: as they tended to have these you know.

536
00:24:21,680 --> 00:24:27,280
Speaker 6: Older, college educated, more affluent, overwhelmingly white voter voting base,

537
00:24:27,720 --> 00:24:31,040
Speaker 6: and they just are the people that are so reliable

538
00:24:31,119 --> 00:24:32,040
Speaker 6: every single election.

539
00:24:32,160 --> 00:24:33,639
Speaker 1: You know, you don't have to do anything to get

540
00:24:33,680 --> 00:24:35,680
Speaker 1: them to the polls. They are going to show up.

541
00:24:36,080 --> 00:24:39,760
Speaker 1: Increasingly those folks are in the Democratic Private Party, So

542
00:24:40,560 --> 00:24:43,040
Speaker 1: it is just a you know, interesting indication of the

543
00:24:43,080 --> 00:24:46,760
Speaker 1: way those coalitions have shifted. And it's an open question

544
00:24:47,000 --> 00:24:49,639
Speaker 1: what it's going to mean for the presidential general election,

545
00:24:49,840 --> 00:24:55,080
Speaker 1: because that advantage shows up massively when you're talking about

546
00:24:55,119 --> 00:24:56,640
Speaker 1: a small turnout election.

547
00:24:56,520 --> 00:24:58,359
Speaker 4: Like a special election as this one was.

548
00:24:58,840 --> 00:25:01,320
Speaker 1: It will definitely be some what mut did in a

549
00:25:01,359 --> 00:25:05,840
Speaker 1: general election, but it still is an advantage towards the Democrats.

550
00:25:05,880 --> 00:25:09,000
Speaker 1: You have to say where that is concerned, you know,

551
00:25:09,080 --> 00:25:11,000
Speaker 1: just to go back to this district, it's just a

552
00:25:11,040 --> 00:25:13,200
Speaker 1: really interesting place to mean because it's one of those

553
00:25:13,240 --> 00:25:16,439
Speaker 1: areas that used to be solidly democratic not long ago

554
00:25:17,320 --> 00:25:20,679
Speaker 1: because you had you know, blue collar a lot of

555
00:25:20,800 --> 00:25:24,399
Speaker 1: union workers, et cetera. And then as the trade deals

556
00:25:24,440 --> 00:25:27,240
Speaker 1: that naft in particular that have just you know, devastated

557
00:25:27,280 --> 00:25:29,680
Speaker 1: this area. In addition to a number of other factors,

558
00:25:29,760 --> 00:25:33,600
Speaker 1: the normalizing trade relations with China being another key sort

559
00:25:33,640 --> 00:25:39,280
Speaker 1: of killer for the region, You've got a massive shift

560
00:25:39,480 --> 00:25:42,040
Speaker 1: towards Trump. And the other thing that I would point

561
00:25:42,040 --> 00:25:45,639
Speaker 1: out here is these results once again indicate, along with

562
00:25:45,680 --> 00:25:49,520
Speaker 1: a lot of Senate polling, that Biden is a uniquely

563
00:25:49,560 --> 00:25:52,520
Speaker 1: weak candidate for Democrats, like there is an appetite to

564
00:25:52,600 --> 00:25:54,920
Speaker 1: vote for just like the quote unquote generic democrat. I

565
00:25:54,960 --> 00:25:57,040
Speaker 1: mean Dean Phillips really at a point like he is

566
00:25:57,080 --> 00:26:00,679
Speaker 1: sort of the embodiment of the generic Democrat. And I

567
00:26:00,720 --> 00:26:03,360
Speaker 1: do think if you subbed in someone like that, they

568
00:26:03,400 --> 00:26:06,720
Speaker 1: would be clearly leading at this point in the race.

569
00:26:06,920 --> 00:26:08,680
Speaker 1: And that's again born out by we were taking a

570
00:26:08,720 --> 00:26:11,040
Speaker 1: look at there was just another analysis of how all

571
00:26:11,080 --> 00:26:14,840
Speaker 1: of the Democratic Senate candidates in swing states, all of

572
00:26:14,880 --> 00:26:17,280
Speaker 1: them are winning, all of them. Now again, maybe the

573
00:26:17,280 --> 00:26:19,119
Speaker 1: polls are out, maybe that doesn't bear out, we don't know,

574
00:26:19,600 --> 00:26:21,960
Speaker 1: but there's no doubt they are outperforming Joe Biden in

575
00:26:22,000 --> 00:26:24,440
Speaker 1: the polls right now. And Democrats have decided to hamstring

576
00:26:24,480 --> 00:26:27,159
Speaker 1: themselves with perhaps the weakest possible candidate they could afford.

577
00:26:27,280 --> 00:26:28,240
Speaker 3: Yeah, that's a great look.

578
00:26:28,400 --> 00:26:31,359
Speaker 2: It's always difficult because Dean Phillips obviously didn't perform, but

579
00:26:31,400 --> 00:26:33,840
Speaker 2: then we also basically had a rig primary in the process.

580
00:26:33,880 --> 00:26:34,280
Speaker 3: I don't know.

581
00:26:34,359 --> 00:26:36,920
Speaker 2: I do believe, like with the generic Democrat thing, and

582
00:26:36,960 --> 00:26:38,520
Speaker 2: I'm not saying I want this to happen, but I

583
00:26:38,560 --> 00:26:40,880
Speaker 2: think he would do well. I think Gavin Newsom would

584
00:26:40,880 --> 00:26:42,639
Speaker 2: win like fifty four percent of the vote.

585
00:26:42,680 --> 00:26:43,159
Speaker 3: I really do.

586
00:26:43,240 --> 00:26:46,280
Speaker 2: I think he would win an overwhelming three hundred and

587
00:26:46,400 --> 00:26:48,120
Speaker 2: twenty some electoral votes.

588
00:26:48,200 --> 00:26:50,200
Speaker 1: Yeah, I'm not saying I would be excited about any

589
00:26:50,200 --> 00:26:52,879
Speaker 1: of these candidates, and I would not want that to occur.

590
00:26:53,040 --> 00:26:55,840
Speaker 2: I think America would be a lot worse off. That said,

591
00:26:56,080 --> 00:26:58,199
Speaker 2: I do think you would win just from a sheer

592
00:26:58,400 --> 00:27:01,480
Speaker 2: political talent perspective. I mean that's a precursor of what's

593
00:27:01,560 --> 00:27:07,159
Speaker 2: to come in twenty twenty eight. Every ounce of polling

594
00:27:07,200 --> 00:27:09,520
Speaker 2: that we have basically tells us that Kamala Harris is

595
00:27:09,560 --> 00:27:13,680
Speaker 2: in many ways more unpopular than Joe Biden, and also

596
00:27:14,320 --> 00:27:16,719
Speaker 2: is that her prominence at the top of the ticket.

597
00:27:17,080 --> 00:27:19,720
Speaker 2: By knowing that at the bottom of the ticket, I

598
00:27:19,760 --> 00:27:23,080
Speaker 2: mean knowing that Joe Biden is so old and then

599
00:27:23,280 --> 00:27:26,359
Speaker 2: zeroing in on her as a potential commander in chief. Basically,

600
00:27:26,600 --> 00:27:29,480
Speaker 2: this phenomenon has not been like a VP has not

601
00:27:29,480 --> 00:27:33,080
Speaker 2: been under this much scrutiny since Sarah Palin under John

602
00:27:33,160 --> 00:27:36,000
Speaker 2: McCain and then prior to that, George H. W. Bush

603
00:27:36,080 --> 00:27:39,000
Speaker 2: under Ronald Reagan. So we have videos, you know, continuing

604
00:27:39,000 --> 00:27:41,080
Speaker 2: to come out. Let's put these up there on the screen.

605
00:27:41,119 --> 00:27:43,280
Speaker 2: This reference, by the way, you know, all of the

606
00:27:43,400 --> 00:27:46,919
Speaker 2: viral memes, I mean, this clip has been literally everywhere

607
00:27:46,960 --> 00:27:50,440
Speaker 2: you see Biden, I mean almost just like frozen completely

608
00:27:50,480 --> 00:27:54,760
Speaker 2: while everybody is super and effectively effectively a stupor. Stupor

609
00:27:54,840 --> 00:27:57,400
Speaker 2: is the best way to put it. Everyone else's ambulatory.

610
00:27:57,480 --> 00:28:00,640
Speaker 2: Everyone else is like moving around him, able to move

611
00:28:00,680 --> 00:28:04,040
Speaker 2: their limbs. He's literally stuck. There's a lot of memes

612
00:28:04,040 --> 00:28:06,760
Speaker 2: out there which I don't condone, but they say, when

613
00:28:06,760 --> 00:28:10,520
Speaker 2: the edible hits and then you have somebody Tyler Perry,

614
00:28:10,560 --> 00:28:12,080
Speaker 2: I think that's who it is, next to him, like

615
00:28:12,359 --> 00:28:14,520
Speaker 2: wake him up out of the stupor and he's finally

616
00:28:14,560 --> 00:28:17,159
Speaker 2: able to move one of his limbs and react to

617
00:28:17,520 --> 00:28:19,959
Speaker 2: some of the things that are going It just you know,

618
00:28:20,160 --> 00:28:23,240
Speaker 2: shows his age. And then we look at the polling.

619
00:28:23,240 --> 00:28:25,240
Speaker 2: So let's ahead and put this up there on the screen.

620
00:28:25,320 --> 00:28:28,280
Speaker 2: So what do we find? Joe Biden and Kamala Harris

621
00:28:28,480 --> 00:28:32,840
Speaker 2: remain unpopular, but Kamala's on a commerce favorability ate rating

622
00:28:32,960 --> 00:28:36,600
Speaker 2: is actually less than Joe Biden. Her unfavorable is slightly

623
00:28:36,680 --> 00:28:39,960
Speaker 2: less than Joe Biden, but it's still not particularly good.

624
00:28:40,160 --> 00:28:42,280
Speaker 2: Go to the next part, please, because this is where

625
00:28:42,360 --> 00:28:45,080
Speaker 2: we can actually start to get into it. Most registered

626
00:28:45,120 --> 00:28:49,240
Speaker 2: voters do not think that she would win a presidential election. Now,

627
00:28:49,240 --> 00:28:51,480
Speaker 2: in your opinion, how likely is it that Kamala would

628
00:28:51,480 --> 00:28:54,760
Speaker 2: win an election for president if she were the Democratic nominee.

629
00:28:55,120 --> 00:28:58,120
Speaker 2: Likely is only thirty four percent, almost a super majority.

630
00:28:58,200 --> 00:29:01,720
Speaker 2: Some fifty seven percent say it's unlike amongst Democrats, some

631
00:29:01,760 --> 00:29:04,760
Speaker 2: sixty percent say it's possible. But the independent number is

632
00:29:04,800 --> 00:29:07,000
Speaker 2: sixty two percent say no way, and eighty one percent

633
00:29:07,240 --> 00:29:07,640
Speaker 2: no way.

634
00:29:07,640 --> 00:29:08,680
Speaker 3: For Republicans.

635
00:29:08,720 --> 00:29:12,120
Speaker 2: I mean, she really does have a deep both unpopularity

636
00:29:12,200 --> 00:29:15,240
Speaker 2: and then almost more importantly, Crystal, it's that people into

637
00:29:15,360 --> 00:29:17,400
Speaker 2: it that other people don't like her, and thus they

638
00:29:17,400 --> 00:29:20,040
Speaker 2: don't think that she can win, which is equally important.

639
00:29:20,400 --> 00:29:22,880
Speaker 2: You know for people who would eventually support a candidate.

640
00:29:22,920 --> 00:29:25,080
Speaker 2: There's a like a phenomenon where people want to back

641
00:29:25,080 --> 00:29:25,760
Speaker 2: the winning horse.

642
00:29:25,960 --> 00:29:28,600
Speaker 1: Yeah, well, there's also a phenomenal I mean, at this point,

643
00:29:28,680 --> 00:29:32,760
Speaker 1: just looking at the how closely tied her favorability rating

644
00:29:32,920 --> 00:29:35,480
Speaker 1: is with Joe Biden's. I mean, they're basically the same

645
00:29:35,880 --> 00:29:40,640
Speaker 1: in terms of their net lack of favorability. You also

646
00:29:40,720 --> 00:29:45,320
Speaker 1: have this dynamic where at the beginning of Biden's term

647
00:29:45,760 --> 00:29:47,680
Speaker 1: putting her out for a lot of interviews and it

648
00:29:47,840 --> 00:29:51,200
Speaker 1: was disastrous. I mean it was like every interview she did,

649
00:29:51,440 --> 00:29:54,280
Speaker 1: she screwed up, and so it was not good for her.

650
00:29:54,320 --> 00:29:57,080
Speaker 1: So since then, they really she hasn't been front and

651
00:29:57,160 --> 00:30:00,120
Speaker 1: center very much, and so I really think at this

652
00:30:00,160 --> 00:30:03,320
Speaker 1: point it's less a judgment of her individually. People don't

653
00:30:03,360 --> 00:30:04,960
Speaker 1: have any confidence in her and they haven't been given

654
00:30:05,000 --> 00:30:07,840
Speaker 1: any reason to have any confidence in her. But I

655
00:30:07,880 --> 00:30:11,520
Speaker 1: honestly think it's just tied into feelings about how the

656
00:30:11,600 --> 00:30:14,280
Speaker 1: lack of confidence in the Biden administration as a whole,

657
00:30:14,600 --> 00:30:15,960
Speaker 1: And they don't seem to be making a lot of

658
00:30:16,040 --> 00:30:18,440
Speaker 1: separate judgments about Yeah, but if Kamala was in there

659
00:30:18,440 --> 00:30:20,080
Speaker 1: and then things would be better. If Kamala was in

660
00:30:20,080 --> 00:30:20,520
Speaker 1: there and things.

661
00:30:20,440 --> 00:30:21,000
Speaker 4: Would be worse.

662
00:30:21,000 --> 00:30:23,400
Speaker 1: It's just like everybody involved with this is not doing

663
00:30:23,400 --> 00:30:25,040
Speaker 1: a great job, and I don't have any confidence in

664
00:30:25,080 --> 00:30:25,520
Speaker 1: any of them.

665
00:30:25,600 --> 00:30:27,520
Speaker 2: Yeah, no, I think that's fair. Let's go to the

666
00:30:27,560 --> 00:30:30,480
Speaker 2: next part. Please before please, and just take a look

667
00:30:30,480 --> 00:30:33,680
Speaker 2: at this. Nearly three quarters of Democrats do appear to

668
00:30:33,800 --> 00:30:37,680
Speaker 2: like Kamala Harris. And this is actually probably the biggest problem, Crystal,

669
00:30:37,920 --> 00:30:41,200
Speaker 2: that they face, is that she has the same Biden

670
00:30:41,280 --> 00:30:44,320
Speaker 2: phenomenon that he had under Obama, where they're like, well,

671
00:30:44,360 --> 00:30:46,520
Speaker 2: Obama trusts him, and I like Obama, so I'm going

672
00:30:46,600 --> 00:30:50,600
Speaker 2: to transfer my quote unquote likability over to the vice president. Remember,

673
00:30:50,680 --> 00:30:52,800
Speaker 2: a lot of Democrats still like Biden. They don't necessarily

674
00:30:52,840 --> 00:30:54,440
Speaker 2: thin gets you run again, but they like him. Yeah,

675
00:30:54,480 --> 00:30:57,320
Speaker 2: and so they're like Kamala, she served him loyally. Okay,

676
00:30:57,400 --> 00:31:00,480
Speaker 2: So in a primary. Look, I think in an open primary,

677
00:31:00,520 --> 00:31:02,480
Speaker 2: I don't think that she could win, but she has

678
00:31:02,520 --> 00:31:05,640
Speaker 2: such a powerful edge whenever it comes to the Democratic

679
00:31:05,720 --> 00:31:07,880
Speaker 2: media elites. And also you know a lot of the

680
00:31:07,920 --> 00:31:11,520
Speaker 2: traditional Biden coalition, older voters and older black voters specifically,

681
00:31:11,560 --> 00:31:14,400
Speaker 2: who even though they didn't back her in the primary previously,

682
00:31:14,680 --> 00:31:18,000
Speaker 2: in fact, she literally dropped out before Iowa this time around,

683
00:31:18,080 --> 00:31:20,520
Speaker 2: her service to Biden could be rewarded. That's a real

684
00:31:20,560 --> 00:31:23,760
Speaker 2: issue when you have the Democrats so misaligned with the

685
00:31:23,760 --> 00:31:24,520
Speaker 2: rest of the country.

686
00:31:24,960 --> 00:31:27,080
Speaker 4: Yeah. No, I think that's right, and you're right.

687
00:31:27,120 --> 00:31:30,800
Speaker 1: In an open primary, listen, she's the vice president, she's

688
00:31:30,840 --> 00:31:33,280
Speaker 1: going to come in as the favorite. Yeah, and polling

689
00:31:33,320 --> 00:31:36,360
Speaker 1: of theoretical you know, before it was totally one hundred

690
00:31:36,400 --> 00:31:38,080
Speaker 1: percent clear it was going to be Joe Biden, and

691
00:31:38,120 --> 00:31:41,040
Speaker 1: no one was coming in, you know, in the establishment

692
00:31:41,080 --> 00:31:43,160
Speaker 1: of the party to run against him and all of

693
00:31:43,160 --> 00:31:43,560
Speaker 1: those things.

694
00:31:43,560 --> 00:31:47,040
Speaker 4: They would do, those trial matchups, and.

695
00:31:47,120 --> 00:31:49,120
Speaker 1: Kamala was always kind of near the top of the

696
00:31:49,160 --> 00:31:51,960
Speaker 1: list or at the top of the list in terms

697
00:31:51,960 --> 00:31:54,880
Speaker 1: of where people are putting their votes. Now, given that

698
00:31:54,920 --> 00:31:57,240
Speaker 1: she's the vice president, it was no commanding lead to

699
00:31:57,280 --> 00:31:59,560
Speaker 1: be by like a few points and Gavin Dusom Niffin

700
00:31:59,600 --> 00:32:02,800
Speaker 1: at or heel or whatever. But there's no doubt just

701
00:32:02,880 --> 00:32:05,640
Speaker 1: given her position in the prominence she's been given that

702
00:32:05,720 --> 00:32:08,200
Speaker 1: if you did have an open primary process, she will

703
00:32:08,280 --> 00:32:10,479
Speaker 1: come in as the favorite. I agree with you that

704
00:32:10,560 --> 00:32:13,640
Speaker 1: I think once under scrutiny, as we've seen before, I

705
00:32:13,640 --> 00:32:15,880
Speaker 1: think people would assess that perhaps she would not be

706
00:32:16,000 --> 00:32:18,640
Speaker 1: their favorite candidate or the strongest candidate, and look in

707
00:32:18,720 --> 00:32:21,920
Speaker 1: other directions. But she's been given you know, she's been

708
00:32:21,960 --> 00:32:24,640
Speaker 1: given a huge leg up in terms of being the

709
00:32:24,720 --> 00:32:28,360
Speaker 1: future of the Democratic Party, and anyone who actually cares

710
00:32:28,400 --> 00:32:30,360
Speaker 1: about the Democratic Party should find that to be a

711
00:32:30,400 --> 00:32:31,120
Speaker 1: total disaster.

712
00:32:31,240 --> 00:32:32,680
Speaker 3: Yeah. I think that's that's it.

713
00:32:32,720 --> 00:32:34,600
Speaker 2: And so when we compare this to all of the

714
00:32:34,640 --> 00:32:38,480
Speaker 2: problems that Biden has, this is where her deep unpopularity

715
00:32:38,560 --> 00:32:40,880
Speaker 2: will just continue, I think, to haunt him. And if

716
00:32:40,920 --> 00:32:44,320
Speaker 2: he loses, this is going to be a huge, huge reason.

717
00:32:44,360 --> 00:32:47,520
Speaker 2: Why is that they didn't have trust in the number two.

718
00:32:47,560 --> 00:32:50,640
Speaker 2: And then also if he does get reelected, the amount

719
00:32:50,640 --> 00:32:52,760
Speaker 2: of scrutiny that's going to go up. I mean, where's

720
00:32:52,760 --> 00:32:55,720
Speaker 2: single health event from this lady becoming literally president of

721
00:32:55,720 --> 00:32:56,320
Speaker 2: the United States.

722
00:32:56,320 --> 00:32:56,960
Speaker 3: But he's terrifying.

723
00:32:57,000 --> 00:32:59,840
Speaker 1: Well, if you go back to that dream president word cloudy,

724
00:33:00,080 --> 00:33:02,000
Speaker 1: she's not up there. Not even Democrats will put her

725
00:33:02,080 --> 00:33:03,760
Speaker 1: up there. So you see her in there at all

726
00:33:04,160 --> 00:33:04,520
Speaker 1: at all.

727
00:33:05,040 --> 00:33:07,720
Speaker 3: I see Elizabeth Warren and Gretcham.

728
00:33:07,560 --> 00:33:12,920
Speaker 1: Gresham, Peter, I see John Stewart, I see Whitmer, Keanu Reeves.

729
00:33:12,960 --> 00:33:17,760
Speaker 1: I believe hey, I would vote for Jimmy Carter is

730
00:33:17,800 --> 00:33:18,200
Speaker 1: in there.

731
00:33:18,560 --> 00:33:20,640
Speaker 3: Jimmy, Jimmy Carter's on his actual.

732
00:33:20,480 --> 00:33:24,400
Speaker 4: Deathbed, literally, Jeffries, I do not.

733
00:33:24,680 --> 00:33:25,520
Speaker 3: This is a problem.

734
00:33:26,240 --> 00:33:29,440
Speaker 1: She maybe if there's like a where's Waldo thing going

735
00:33:29,520 --> 00:33:30,680
Speaker 1: I'm looking for, I don't.

736
00:33:30,880 --> 00:33:32,000
Speaker 4: I don't see it either anywhere.

737
00:33:32,040 --> 00:33:35,719
Speaker 2: I see Martin lither King, I see uh yeah, Gretchen Whitmer,

738
00:33:35,760 --> 00:33:41,640
Speaker 2: Pete boudhajeedge Hakeem, Jeffries, Jimmy Carter, oh man, Yeah, that's

739
00:33:42,200 --> 00:33:46,000
Speaker 2: I see Zelenski apparently more popular there than Kamala Harris.

740
00:33:46,040 --> 00:33:48,720
Speaker 3: Oh, that's good evidence only exactly what we're saying.

741
00:33:49,000 --> 00:33:52,800
Speaker 4: That's incredible. That's actually incredible just noticing that. So there

742
00:33:52,800 --> 00:33:53,040
Speaker 4: you go.

743
00:33:53,280 --> 00:33:56,600
Speaker 1: That tells you everything about how even Democrats, the level

744
00:33:56,640 --> 00:33:59,920
Speaker 1: of confidence even they have in Kamala Harris, not not

745
00:34:00,120 --> 00:34:01,560
Speaker 1: in any of their dreams apparently.

746
00:34:04,040 --> 00:34:05,840
Speaker 2: All right, let's move on to the economy and number

747
00:34:06,200 --> 00:34:08,560
Speaker 2: number one reason why Biden is not doing very well

748
00:34:08,600 --> 00:34:09,120
Speaker 2: in the election.

749
00:34:09,200 --> 00:34:11,120
Speaker 3: Let's go and put this up there on the screen.

750
00:34:11,200 --> 00:34:14,239
Speaker 2: Something they've been warning about here for months on the show,

751
00:34:14,560 --> 00:34:17,880
Speaker 2: probably years now, is about the coming bust in commercial

752
00:34:17,960 --> 00:34:20,359
Speaker 2: real estate. And just because things have hold on for

753
00:34:20,400 --> 00:34:22,160
Speaker 2: as long as they have does not mean that people

754
00:34:22,200 --> 00:34:24,600
Speaker 2: are going to escape this Currently, this is the New

755
00:34:24,640 --> 00:34:27,360
Speaker 2: York Times reporting just in the island of Manhattan, quote,

756
00:34:27,400 --> 00:34:30,759
Speaker 2: buyers are snapping up aging and empty office buildings for

757
00:34:30,880 --> 00:34:34,480
Speaker 2: deep discounts. Bargain hunters are getting deals of up to

758
00:34:34,760 --> 00:34:38,600
Speaker 2: seventy percent off on commercial real estate quote, a sign

759
00:34:38,680 --> 00:34:40,719
Speaker 2: of pain in the property market that could lead to

760
00:34:40,840 --> 00:34:44,600
Speaker 2: large losses for banks and investors in major real estate

761
00:34:44,719 --> 00:34:48,840
Speaker 2: backed loans. So what they show is that plunging property values,

762
00:34:48,840 --> 00:34:53,000
Speaker 2: for example, aging buildings week tenant demand in places like

763
00:34:53,040 --> 00:34:57,680
Speaker 2: San Francisco, Los Angeles, New York, Chicago. Here in Washington, DC,

764
00:34:58,040 --> 00:35:00,960
Speaker 2: you've got high interest rates on very loans who are

765
00:35:00,960 --> 00:35:04,160
Speaker 2: now having to refinance. Has quote left the two point

766
00:35:04,239 --> 00:35:07,839
Speaker 2: four trillion dollar office building sector wobbling. For some real

767
00:35:07,920 --> 00:35:11,080
Speaker 2: estate investors, that may be a good thing. Several buildings

768
00:35:11,080 --> 00:35:13,960
Speaker 2: now nationwide have sold for up to as much of

769
00:35:14,040 --> 00:35:17,840
Speaker 2: seventy percent off to quote opportunistic buyers who are gambling

770
00:35:17,840 --> 00:35:20,480
Speaker 2: that they will score big profits when of prices eventually

771
00:35:20,800 --> 00:35:24,400
Speaker 2: rebound and they show you know, for example, an investment

772
00:35:24,440 --> 00:35:28,279
Speaker 2: giant Blackstone had paid six hundred million dollars for a

773
00:35:28,280 --> 00:35:32,120
Speaker 2: building a decade earlier. It is now two real estate

774
00:35:32,440 --> 00:35:37,080
Speaker 2: firms snapped up that Tower for quote less than fifty million,

775
00:35:37,440 --> 00:35:40,400
Speaker 2: so six hundred mili to fifty mil. I mean for

776
00:35:40,440 --> 00:35:43,680
Speaker 2: a company like Blackstone, you're writing that off. That's like

777
00:35:43,800 --> 00:35:47,440
Speaker 2: zero effectively nothing. Now I'm not crying for Blackstone, but

778
00:35:47,880 --> 00:35:50,759
Speaker 2: what that does tell us is that the commercial real

779
00:35:50,840 --> 00:35:53,799
Speaker 2: estate bubble, and specifically all of the loans that are

780
00:35:53,840 --> 00:35:57,560
Speaker 2: secured behind it, were previously thought of as just like

781
00:35:57,680 --> 00:36:00,959
Speaker 2: cash flowing machines, like, hey, there's no risk here, people

782
00:36:01,000 --> 00:36:03,239
Speaker 2: are going to the office, don't worry about it. And now,

783
00:36:03,320 --> 00:36:05,839
Speaker 2: even though it's been four years, people have basically been

784
00:36:05,880 --> 00:36:08,280
Speaker 2: in like a clenched fist mode where they're just hoping

785
00:36:08,320 --> 00:36:12,040
Speaker 2: things rebound, but eventually they have to refinance because the

786
00:36:12,120 --> 00:36:14,640
Speaker 2: terms on these loans are not like a normal thirty

787
00:36:14,719 --> 00:36:17,040
Speaker 2: year mortgage, and so then the high interest rate hits.

788
00:36:17,280 --> 00:36:20,120
Speaker 2: It's not going down anytime soon, or at least not

789
00:36:20,160 --> 00:36:22,640
Speaker 2: to back where it is, and they just can't service

790
00:36:22,680 --> 00:36:24,640
Speaker 2: the debt on where it is right now. I mean

791
00:36:24,880 --> 00:36:28,200
Speaker 2: they say so far this year, sixteen office buildings with

792
00:36:28,360 --> 00:36:31,319
Speaker 2: mortgages that were packaged in commercial real estate bonds were

793
00:36:31,400 --> 00:36:35,319
Speaker 2: foreclosed on or extinguished, resulting in half a billion in

794
00:36:35,400 --> 00:36:39,920
Speaker 2: losses for investors, nationally. Last year, twenty six mortgages that

795
00:36:39,960 --> 00:36:43,120
Speaker 2: were packaged in to bonds were foreclosed on, extinguishing two

796
00:36:43,200 --> 00:36:44,399
Speaker 2: hundred and sixty.

797
00:36:44,040 --> 00:36:46,080
Speaker 3: Five million in losses for investors.

798
00:36:46,080 --> 00:36:48,680
Speaker 2: So we're already at three quarters of a billion and

799
00:36:48,840 --> 00:36:51,920
Speaker 2: actual losses, not to mention the fire sales that are

800
00:36:51,920 --> 00:36:55,920
Speaker 2: happening right now, and there's literally trillions of dollars of

801
00:36:57,360 --> 00:37:00,920
Speaker 2: bank loans, bonds, and other financial securities as products that

802
00:37:01,000 --> 00:37:04,200
Speaker 2: are built on top of it. Blackstone TPG, a bunch

803
00:37:04,200 --> 00:37:07,080
Speaker 2: of other real estate firms that they are listing here

804
00:37:07,320 --> 00:37:10,240
Speaker 2: are all trying to crawl their way out of the hole.

805
00:37:10,320 --> 00:37:13,239
Speaker 2: But I mean, this is a massive liability on their

806
00:37:13,280 --> 00:37:15,120
Speaker 2: balance sheet, and I mean I just don't think it's

807
00:37:15,120 --> 00:37:17,080
Speaker 2: going to change. I don't see it coming back anytime soon.

808
00:37:17,280 --> 00:37:18,120
Speaker 3: No, absolutely not.

809
00:37:18,280 --> 00:37:21,239
Speaker 1: I mean part of why the shit hasn't hit the

810
00:37:21,280 --> 00:37:24,400
Speaker 1: fan more aggressively than it has is a lot of

811
00:37:24,440 --> 00:37:27,440
Speaker 1: banks that hold these loans are just kind of giving

812
00:37:27,520 --> 00:37:31,319
Speaker 1: forbearance and hoping things turn around because they don't want

813
00:37:31,360 --> 00:37:34,160
Speaker 1: to have to sell at these fire sale prices and

814
00:37:34,280 --> 00:37:39,040
Speaker 1: actually recognize those losses. So they're sort of suspending reality

815
00:37:39,280 --> 00:37:43,200
Speaker 1: hoping that something changes. But there's no indication that that's

816
00:37:43,239 --> 00:37:47,080
Speaker 1: something is going to change because after the pandemic, it

817
00:37:47,120 --> 00:37:51,560
Speaker 1: created this massive shift to hybrid work models, completely remote

818
00:37:51,560 --> 00:37:55,520
Speaker 1: work models, and there's no indication that that is ever changing.

819
00:37:55,560 --> 00:37:59,720
Speaker 1: It's been now incorporated into the expectations of white collar

820
00:37:59,760 --> 00:38:02,319
Speaker 1: off this workers. At the same time, you know, some

821
00:38:02,400 --> 00:38:05,719
Speaker 1: of the companies that bought these bought these buildings at

822
00:38:05,760 --> 00:38:09,200
Speaker 1: like super low prices. What they're betting on is I mean,

823
00:38:09,239 --> 00:38:10,880
Speaker 1: you have to say about it. From their perspective, this

824
00:38:10,920 --> 00:38:14,080
Speaker 1: is Manhattan, right, There's always going to be demand for Manhattan, surely,

825
00:38:14,400 --> 00:38:16,320
Speaker 1: and so they're looking at these old office buildings and

826
00:38:16,320 --> 00:38:18,400
Speaker 1: they're like, Okay, well maybe it's not office space, but

827
00:38:18,480 --> 00:38:21,200
Speaker 1: there's a massive need for housing in Manhattan. Maybe we

828
00:38:21,360 --> 00:38:26,040
Speaker 1: just renovate these buildings to be residential apartment buildings. First

829
00:38:26,080 --> 00:38:28,440
Speaker 1: of all, there's regulatory issues. Not all of the buildings

830
00:38:28,480 --> 00:38:30,799
Speaker 1: are going to be even eligible for that conversion, but

831
00:38:30,800 --> 00:38:34,120
Speaker 1: for the ones that are, it's very expensive to be

832
00:38:34,239 --> 00:38:37,160
Speaker 1: able to do that. And then if it's an older building,

833
00:38:37,440 --> 00:38:39,480
Speaker 1: to try to upgrade it to have the level of

834
00:38:39,520 --> 00:38:44,240
Speaker 1: amenities that someone paying that price would expect can be

835
00:38:44,520 --> 00:38:47,759
Speaker 1: very close to impossible. So there's a lot of obstacles

836
00:38:47,800 --> 00:38:50,799
Speaker 1: in the way of converting these buildings from what their

837
00:38:50,800 --> 00:38:54,919
Speaker 1: prior use was to what a more appropriate and use

838
00:38:55,000 --> 00:38:57,839
Speaker 1: in demand would be now. So that's why this is

839
00:38:58,040 --> 00:39:00,520
Speaker 1: very difficult and how it's hard to see how this

840
00:39:00,600 --> 00:39:01,880
Speaker 1: dynamic is going to change.

841
00:39:02,120 --> 00:39:02,440
Speaker 4: Now.

842
00:39:02,560 --> 00:39:05,520
Speaker 1: Many banks are really diversified and they can afford to

843
00:39:05,520 --> 00:39:08,360
Speaker 1: take these hits and it's not going to send them under.

844
00:39:08,680 --> 00:39:11,479
Speaker 1: But there are others that have specialized in these types

845
00:39:11,520 --> 00:39:13,920
Speaker 1: of loans that are really going to face a reckoning.

846
00:39:14,040 --> 00:39:17,359
Speaker 1: So prepare yourself for a future of more bailout conversations,

847
00:39:17,360 --> 00:39:19,719
Speaker 1: because I do think that's where we're headed. They say

848
00:39:19,760 --> 00:39:22,719
Speaker 1: in this New York Times report that one group of

849
00:39:22,760 --> 00:39:25,839
Speaker 1: analysts is predicting this year and next year are going

850
00:39:25,880 --> 00:39:29,520
Speaker 1: to be the two worst years on record for office

851
00:39:29,520 --> 00:39:31,759
Speaker 1: buildings in terms of the amount of floor space that

852
00:39:31,800 --> 00:39:35,920
Speaker 1: tenants are giving back or vacating across the country. So

853
00:39:36,440 --> 00:39:38,640
Speaker 1: at a certain point, the economics just don't work. At

854
00:39:38,640 --> 00:39:41,880
Speaker 1: a certain point, the banks realize, like the situation is

855
00:39:41,920 --> 00:39:44,080
Speaker 1: not getting better, we just have to eat these losses,

856
00:39:44,400 --> 00:39:46,640
Speaker 1: and that's when we'll really see what this reckoning entails.

857
00:39:46,920 --> 00:39:49,640
Speaker 3: Yeah, I think you're right, I mean, immediately, really what.

858
00:39:49,560 --> 00:39:52,480
Speaker 2: We see is that there's billions and billions the banks

859
00:39:52,480 --> 00:39:54,840
Speaker 2: are just hoping that they can go past it. We

860
00:39:54,880 --> 00:39:57,520
Speaker 2: had a fundamental shock to the economy. Question is is

861
00:39:57,520 --> 00:39:59,399
Speaker 2: can they wait it out. Let's put this up there

862
00:39:59,400 --> 00:40:01,640
Speaker 2: on the screen. I mean, there's a couple of ways

863
00:40:01,680 --> 00:40:04,520
Speaker 2: that you could slice this. The Fed is currently projecting

864
00:40:04,680 --> 00:40:09,120
Speaker 2: a one cut this year in rates, but not necessarily

865
00:40:09,239 --> 00:40:12,080
Speaker 2: to the levels that people, you know, are hoping for.

866
00:40:12,320 --> 00:40:14,319
Speaker 2: The Central Bank, you know, is currently in a range

867
00:40:14,320 --> 00:40:16,920
Speaker 2: between five point twenty five to five point five percent.

868
00:40:17,000 --> 00:40:19,160
Speaker 2: They say that the Fed may reduce rates a little

869
00:40:19,160 --> 00:40:21,799
Speaker 2: bit this year, but the reduction in the rate is

870
00:40:21,840 --> 00:40:24,360
Speaker 2: not anywhere close to, you know what the two or

871
00:40:24,400 --> 00:40:27,960
Speaker 2: three percent that we previously had seen. The inflation report,

872
00:40:28,040 --> 00:40:30,919
Speaker 2: I mean, it says effectively flat at three point three.

873
00:40:30,960 --> 00:40:33,040
Speaker 2: I talked yesterday about the CPI and some of the

874
00:40:33,080 --> 00:40:35,719
Speaker 2: issues with that, but regardless, I mean, really what we're

875
00:40:35,719 --> 00:40:39,280
Speaker 2: seeing is that these high interest rates are really crushing

876
00:40:39,760 --> 00:40:44,839
Speaker 2: a huge portion of the economy and increasing and fueling

877
00:40:45,080 --> 00:40:48,600
Speaker 2: both reduction and consumption, which is their actual goal is

878
00:40:48,600 --> 00:40:51,240
Speaker 2: to try and get everybody to reduce the amount of consumption.

879
00:40:51,560 --> 00:40:54,919
Speaker 2: It's squashing a lot of businesses, ability to expand. There's

880
00:40:54,920 --> 00:40:57,480
Speaker 2: a lot of stuff going on, but overall, I just

881
00:40:57,520 --> 00:41:01,040
Speaker 2: think that you can see, you know, banks and commercial property,

882
00:41:01,080 --> 00:41:03,279
Speaker 2: et cetera, they don't they have luxuries that you and

883
00:41:03,280 --> 00:41:05,399
Speaker 2: I and other people don't have crystals that they can get,

884
00:41:05,520 --> 00:41:07,560
Speaker 2: you know, a trillion dollar bank to just we could

885
00:41:07,560 --> 00:41:09,839
Speaker 2: put them into four barons if you're you know, having

886
00:41:09,840 --> 00:41:11,600
Speaker 2: problems with if you have an adjustable rate mortgage. I

887
00:41:11,600 --> 00:41:14,720
Speaker 2: have a friend with one is about to spike significantly high.

888
00:41:14,760 --> 00:41:16,600
Speaker 2: He's like, dude, I'm not need to sell my house. Like,

889
00:41:16,800 --> 00:41:18,200
Speaker 2: I don't know what I'm going to do. I cannot

890
00:41:18,200 --> 00:41:20,920
Speaker 2: afford this mortgage payment. We don't have that luxury, and

891
00:41:20,960 --> 00:41:22,360
Speaker 2: nobody's putting us into four barons.

892
00:41:22,400 --> 00:41:23,880
Speaker 4: Yeah, that's right. So they're not going to take that

893
00:41:24,000 --> 00:41:25,280
Speaker 4: hit that Yeah.

894
00:41:25,239 --> 00:41:26,880
Speaker 3: For you, they're like, screw you, sell the house. We're

895
00:41:26,920 --> 00:41:28,160
Speaker 3: going to We're going to take it.

896
00:41:28,280 --> 00:41:30,080
Speaker 2: Or you know, you can spend sixty percent year after

897
00:41:30,120 --> 00:41:34,000
Speaker 2: tax income on your mortgage. That is the problem, you know,

898
00:41:34,040 --> 00:41:36,239
Speaker 2: really with a lot of these high interest rates right

899
00:41:36,239 --> 00:41:36,879
Speaker 2: now and shells.

900
00:41:36,960 --> 00:41:38,879
Speaker 3: She's the problem with economic brain.

901
00:41:39,000 --> 00:41:41,160
Speaker 2: So even though there is you know, some slight rate

902
00:41:41,200 --> 00:41:43,440
Speaker 2: cut or whatever coming in the future, it's not going

903
00:41:43,480 --> 00:41:45,560
Speaker 2: to say you probably won't save these banks too, because

904
00:41:45,680 --> 00:41:47,600
Speaker 2: a lot of the way that they pencil their deals

905
00:41:47,960 --> 00:41:50,360
Speaker 2: is specifically to work on a two or three percent

906
00:41:50,400 --> 00:41:52,400
Speaker 2: interest rate, where you know, even if it's four point

907
00:41:52,400 --> 00:41:54,359
Speaker 2: five or five, it's still not going to work right now.

908
00:41:54,440 --> 00:41:57,640
Speaker 1: And obviously huge impact on housing affordability.

909
00:41:57,840 --> 00:42:00,320
Speaker 4: These rates have a huge impact. That's why when you.

910
00:42:00,120 --> 00:42:02,040
Speaker 1: Look at the charts, I mean, the amount of the

911
00:42:02,160 --> 00:42:05,600
Speaker 1: average mortgage payment has just skyrocketed in an insane way

912
00:42:05,760 --> 00:42:09,360
Speaker 1: in just a few years. It's also why, excuse me,

913
00:42:09,440 --> 00:42:12,520
Speaker 1: when you see the way that people are buying homes,

914
00:42:12,960 --> 00:42:16,759
Speaker 1: You have increasing numbers that are paying cash only. So

915
00:42:17,040 --> 00:42:20,080
Speaker 1: if you aren't in a position to just plunk down

916
00:42:20,160 --> 00:42:23,359
Speaker 1: the full cost of a home, forget it, because you've

917
00:42:23,360 --> 00:42:25,920
Speaker 1: got you know, institutional players who are perfectly willing to

918
00:42:25,960 --> 00:42:28,120
Speaker 1: do that, or already wealthy people or using this as

919
00:42:28,120 --> 00:42:32,400
Speaker 1: an investment, et cetera. And it makes sense given how

920
00:42:32,719 --> 00:42:35,680
Speaker 1: high these rates are in comparison to recent history, not

921
00:42:35,760 --> 00:42:38,440
Speaker 1: in comparison to the you know, long breadth of history,

922
00:42:38,440 --> 00:42:41,920
Speaker 1: but in comparison to recent history, and given how high

923
00:42:42,239 --> 00:42:45,759
Speaker 1: housing prices are now, it's just made it wildly unaffordable.

924
00:42:45,840 --> 00:42:47,120
Speaker 4: So these rate.

925
00:42:47,040 --> 00:42:49,640
Speaker 1: Hikes by the Fed have had, you know, really mixed

926
00:42:49,640 --> 00:42:53,239
Speaker 1: impact on the economy, and one of the primary you

927
00:42:53,280 --> 00:42:55,440
Speaker 1: know what, the primary group I think that has been

928
00:42:55,520 --> 00:42:58,000
Speaker 1: heard are people who were hoping to purchase homes and

929
00:42:58,040 --> 00:43:00,839
Speaker 1: now are just completely shut out of that. One thing

930
00:43:00,880 --> 00:43:03,399
Speaker 1: I will note here is they talk about in that

931
00:43:03,560 --> 00:43:05,839
Speaker 1: piece about you know, the rate cuts and what they're

932
00:43:05,840 --> 00:43:07,920
Speaker 1: going to do. They say officials are going to have

933
00:43:07,960 --> 00:43:10,680
Speaker 1: just one more inflation reading before their next policy meeting

934
00:43:10,719 --> 00:43:13,200
Speaker 1: in July. So very unlikely you get a rate cut there,

935
00:43:13,600 --> 00:43:16,880
Speaker 1: and then guess when the next meeting is. It's in September,

936
00:43:17,120 --> 00:43:18,000
Speaker 1: just before the election.

937
00:43:18,200 --> 00:43:21,799
Speaker 3: Well that's always have people always cut interest rates before.

938
00:43:21,840 --> 00:43:24,360
Speaker 4: I mean, Biden is going to be very much cheering

939
00:43:24,400 --> 00:43:25,120
Speaker 4: for that rate.

940
00:43:25,200 --> 00:43:27,680
Speaker 2: Yet we'll say, let's say we have an interest rate cut,

941
00:43:27,800 --> 00:43:29,799
Speaker 2: the stock market pop, you know, the Dow pops by

942
00:43:29,800 --> 00:43:32,280
Speaker 2: like a thousand points or something like that. Here's two

943
00:43:32,280 --> 00:43:33,920
Speaker 2: five percent or whatever increase in the S and P

944
00:43:34,000 --> 00:43:34,479
Speaker 2: five hundred.

945
00:43:34,560 --> 00:43:35,480
Speaker 3: He's going to be tout in that.

946
00:43:35,640 --> 00:43:37,480
Speaker 2: I mean, yeah, people will remember, they look at their

947
00:43:37,480 --> 00:43:39,800
Speaker 2: furrow one k whoever's lucky enough to have retirement.

948
00:43:39,800 --> 00:43:41,480
Speaker 3: So it was like, oh, Okay, maybe a little a

949
00:43:41,480 --> 00:43:41,920
Speaker 3: different ways.

950
00:43:42,000 --> 00:43:43,320
Speaker 4: Yeah, it's possible, it's possible.

951
00:43:43,360 --> 00:43:46,040
Speaker 1: I don't think that that even if that happens, and

952
00:43:46,080 --> 00:43:48,560
Speaker 1: you do see like some sort of a bump, because

953
00:43:48,600 --> 00:43:51,560
Speaker 1: you've had such an escalation in prices that it's not

954
00:43:51,680 --> 00:43:52,680
Speaker 1: like it's prices are.

955
00:43:52,520 --> 00:43:53,239
Speaker 4: Going back down.

956
00:43:53,320 --> 00:43:57,200
Speaker 1: They're going up more slowly than they were previously going up.

957
00:43:57,480 --> 00:43:59,560
Speaker 1: So I don't think that, you know, one rate cut

958
00:43:59,600 --> 00:44:01,760
Speaker 1: is going to be the economic juice that Joe Biden

959
00:44:01,880 --> 00:44:04,920
Speaker 1: is looking for. Perhaps he should consider having an actual

960
00:44:04,960 --> 00:44:08,640
Speaker 1: policy agenda with regards to that, just a you know, suggestion,

961
00:44:08,760 --> 00:44:11,799
Speaker 1: friendly suggestion here, but there's no doubt they'll be help

962
00:44:12,040 --> 00:44:17,000
Speaker 1: hoping for that going forward. Absolutely, the issue we've touched

963
00:44:17,000 --> 00:44:19,600
Speaker 1: on a few times here is how AI is being

964
00:44:19,719 --> 00:44:23,680
Speaker 1: used to price gouge you in increasingly intelligent ways. So

965
00:44:23,719 --> 00:44:25,880
Speaker 1: we brought an expert in here to discuss that and

966
00:44:25,920 --> 00:44:29,040
Speaker 1: also how prices are set in general. David Dyan, executive

967
00:44:29,120 --> 00:44:31,600
Speaker 1: editor of The American Prospect, joins us, great to see you, David,

968
00:44:31,600 --> 00:44:32,120
Speaker 1: thanks for having me.

969
00:44:32,239 --> 00:44:33,440
Speaker 4: Nice to have you live in person.

970
00:44:33,520 --> 00:44:34,120
Speaker 5: Absolutely.

971
00:44:34,200 --> 00:44:37,480
Speaker 1: Yeah, so you did a special issue focusing on this

972
00:44:37,600 --> 00:44:40,879
Speaker 1: question of how prices are really set, not the sort

973
00:44:40,920 --> 00:44:43,440
Speaker 1: of economic fantasy about this, So just talk about the

974
00:44:43,440 --> 00:44:44,440
Speaker 1: project you're engaged in.

975
00:44:44,920 --> 00:44:47,680
Speaker 7: Yeah, So, I mean, obviously, inflation is a huge issue

976
00:44:49,080 --> 00:44:54,360
Speaker 7: across the board, and what we saw is that over

977
00:44:55,000 --> 00:44:58,279
Speaker 7: the pandemic period a few things have come together. We

978
00:44:58,320 --> 00:45:00,920
Speaker 7: already had price and power in the hands of fewer

979
00:45:00,920 --> 00:45:07,080
Speaker 7: and fewer companies. What we added to that is additional

980
00:45:07,800 --> 00:45:12,920
Speaker 7: technological innovations to isolate a consumer, to target a consumer

981
00:45:12,960 --> 00:45:17,560
Speaker 7: with surveillance, to know everything about that consumer and have

982
00:45:17,640 --> 00:45:23,280
Speaker 7: that inform pricing. And then the pandemic inflation enabled companies

983
00:45:23,320 --> 00:45:24,200
Speaker 7: to engage in a lot.

984
00:45:24,160 --> 00:45:26,920
Speaker 5: More experimentation with regard to pricing.

985
00:45:27,239 --> 00:45:30,759
Speaker 7: And the result is what we see, which it goes

986
00:45:30,840 --> 00:45:34,440
Speaker 7: to why prices have gone up and why corporate profits

987
00:45:34,480 --> 00:45:36,520
Speaker 7: has stayed high. But it also goes to the sort

988
00:45:36,560 --> 00:45:39,800
Speaker 7: of perception of unfairness in the economy that I think

989
00:45:40,000 --> 00:45:43,800
Speaker 7: has as much to do with impressions of an economic

990
00:45:43,880 --> 00:45:47,160
Speaker 7: sentiment as anything else. The fact that you have multiple

991
00:45:47,239 --> 00:45:51,400
Speaker 7: junk fees on every purchase, it seems, the fact that

992
00:45:51,480 --> 00:45:54,440
Speaker 7: you have surge pricing, so you don't know the price

993
00:45:55,120 --> 00:45:57,799
Speaker 7: at a particular time of day until you get there,

994
00:45:57,840 --> 00:46:00,440
Speaker 7: and it always is changing and it's not predictable, the

995
00:46:00,600 --> 00:46:04,640
Speaker 7: fact that everybody wants to get a subscription out of you.

996
00:46:04,719 --> 00:46:06,600
Speaker 7: They make it very easy to sign up and very

997
00:46:06,600 --> 00:46:10,120
Speaker 7: hard to leave. All of these things conspire to give

998
00:46:10,160 --> 00:46:13,960
Speaker 7: people a sense that I'm being ripped off, and actually

999
00:46:14,040 --> 00:46:15,879
Speaker 7: they almost don't know the half of it, because they're

1000
00:46:15,880 --> 00:46:19,279
Speaker 7: being ripped off in ways that are are even more

1001
00:46:19,320 --> 00:46:20,680
Speaker 7: obscure to the consumer.

1002
00:46:20,800 --> 00:46:22,480
Speaker 2: Talk to us about some of those obscure ways I've

1003
00:46:22,520 --> 00:46:25,680
Speaker 2: brought up on our show, the uber low battery pricing.

1004
00:46:25,840 --> 00:46:28,560
Speaker 2: It's deeply nefarious, but I mean, why wouldn't you do

1005
00:46:28,600 --> 00:46:31,200
Speaker 2: it right? They're like, hey, you're an aspert. You're gonna

1006
00:46:31,200 --> 00:46:33,160
Speaker 2: pay it if you want to. What are other areas

1007
00:46:33,200 --> 00:46:34,360
Speaker 2: where you see that in the economy?

1008
00:46:34,400 --> 00:46:36,160
Speaker 7: I mean, I think the important thing to say about

1009
00:46:36,160 --> 00:46:39,720
Speaker 7: that is that your willingness to pay does not equal

1010
00:46:39,880 --> 00:46:43,719
Speaker 7: your ability to pay. Yes, your ability to pay is

1011
00:46:43,840 --> 00:46:47,160
Speaker 7: how much money do I have? Your willingness to pay

1012
00:46:47,320 --> 00:46:50,440
Speaker 7: is how much do I really need or want?

1013
00:46:50,520 --> 00:46:52,839
Speaker 5: This particular product is good or.

1014
00:46:52,800 --> 00:46:57,560
Speaker 7: Service, And a good example is actually like the McDonald's app.

1015
00:46:57,680 --> 00:46:59,440
Speaker 3: Do you either of you used the mdectity?

1016
00:46:59,560 --> 00:47:02,880
Speaker 5: I do not? Okay, well fifty million people do worldwide.

1017
00:47:02,920 --> 00:47:05,520
Speaker 3: I have many friends who are enthusiastic. Yes, because they

1018
00:47:05,520 --> 00:47:06,560
Speaker 3: give you discounts right now.

1019
00:47:06,600 --> 00:47:08,920
Speaker 7: Yeah, But I mean, what is the point of getting

1020
00:47:09,000 --> 00:47:11,440
Speaker 7: people onto the McDonald's app. Pushing you onto the app.

1021
00:47:11,520 --> 00:47:13,239
Speaker 7: It's to get on your phone. It's to get on

1022
00:47:13,280 --> 00:47:16,600
Speaker 7: your phone and to get your data. And in the

1023
00:47:16,600 --> 00:47:18,799
Speaker 7: piece I did for this, which was about what I

1024
00:47:18,880 --> 00:47:23,160
Speaker 7: call surveillance pricing. So what I did on this surveillance

1025
00:47:23,200 --> 00:47:28,000
Speaker 7: pricing piece is look at all of the various ways that.

1026
00:47:28,440 --> 00:47:30,400
Speaker 5: Users on the McDonald's app are tracked.

1027
00:47:30,920 --> 00:47:33,000
Speaker 7: And some of it's the first party data that they

1028
00:47:33,040 --> 00:47:36,320
Speaker 7: get from you, which includes you know you're ordering behavior.

1029
00:47:36,680 --> 00:47:40,400
Speaker 7: I know that every Monday at six, you order an

1030
00:47:40,400 --> 00:47:43,360
Speaker 7: egg McMuffin, so I'm going to charge you. Maybe you

1031
00:47:43,360 --> 00:47:45,279
Speaker 7: know the offer I give you might not be as

1032
00:47:45,320 --> 00:47:50,279
Speaker 7: good on Monday at six. We got a slide from

1033
00:47:50,320 --> 00:47:53,680
Speaker 7: the company that makes the McDonald's app, and it shows

1034
00:47:53,719 --> 00:47:58,799
Speaker 7: all of these things ordering behavior, geolocation to various locations

1035
00:47:58,840 --> 00:47:59,560
Speaker 7: of the stores.

1036
00:48:00,040 --> 00:48:01,719
Speaker 5: And one of the things I said was payday.

1037
00:48:02,440 --> 00:48:06,440
Speaker 7: So they're able to figure out from your you know,

1038
00:48:06,760 --> 00:48:10,520
Speaker 7: your financial card that is attached to the app, They're

1039
00:48:10,520 --> 00:48:13,320
Speaker 7: able to figure out when you get paid every two weeks.

1040
00:48:13,360 --> 00:48:16,600
Speaker 7: So you can imagine what you can do with that information, right.

1041
00:48:16,880 --> 00:48:18,640
Speaker 4: They know when you're most flushed, you feel the.

1042
00:48:18,560 --> 00:48:21,279
Speaker 1: Most flushy, hit with the discount, the.

1043
00:48:22,080 --> 00:48:24,880
Speaker 7: Higher discount than what I would normally give you. So

1044
00:48:25,840 --> 00:48:28,319
Speaker 7: that's a perfect example. And then this is this is

1045
00:48:28,440 --> 00:48:32,600
Speaker 7: tied to other things that are also available and accessible

1046
00:48:32,600 --> 00:48:36,399
Speaker 7: from your phone. So whether it's your browser activity, your

1047
00:48:36,480 --> 00:48:40,400
Speaker 7: other apps, your your medical.

1048
00:48:40,080 --> 00:48:41,320
Speaker 3: Data, your travel data.

1049
00:48:41,360 --> 00:48:43,600
Speaker 7: You know, they build this thing that they call an

1050
00:48:43,640 --> 00:48:47,160
Speaker 7: identity graph around you so that they know actually more

1051
00:48:47,680 --> 00:48:52,359
Speaker 7: about your habits and purchases than you do yourself. This

1052
00:48:52,400 --> 00:48:56,239
Speaker 7: is a far cry from like the big receipt used

1053
00:48:56,239 --> 00:48:58,520
Speaker 7: to get at the grocery store that would have coupons

1054
00:48:58,520 --> 00:49:00,720
Speaker 7: at the bottom that were based on what you just bought.

1055
00:49:01,160 --> 00:49:02,960
Speaker 5: That was the crude version of this.

1056
00:49:03,480 --> 00:49:07,120
Speaker 7: We've gone very very sophisticated, very high tech, and very

1057
00:49:07,200 --> 00:49:11,800
Speaker 7: much toward the possibility of what is called first degree

1058
00:49:11,840 --> 00:49:12,799
Speaker 7: price discrimination.

1059
00:49:12,960 --> 00:49:14,320
Speaker 5: That's the economic term.

1060
00:49:14,120 --> 00:49:18,160
Speaker 7: But it's the economists use a much friendlier term of

1061
00:49:18,239 --> 00:49:19,480
Speaker 7: price personalization.

1062
00:49:19,719 --> 00:49:21,560
Speaker 3: Personalized isn't that nice?

1063
00:49:21,640 --> 00:49:23,360
Speaker 5: Or so specially is it your own price?

1064
00:49:23,840 --> 00:49:25,799
Speaker 1: I like the term you use here, which I hadn't

1065
00:49:25,800 --> 00:49:29,120
Speaker 1: heard before, which is surveillance pricing, because that really captures it, right.

1066
00:49:29,200 --> 00:49:31,680
Speaker 1: They're spying on you ways that you may be theoretically

1067
00:49:31,760 --> 00:49:33,279
Speaker 1: consented to but don't really have.

1068
00:49:33,239 --> 00:49:33,960
Speaker 4: An awareness of.

1069
00:49:34,280 --> 00:49:36,279
Speaker 1: They're building this whole profile of you so they can

1070
00:49:36,320 --> 00:49:39,480
Speaker 1: basically exploit your weaknesses. I mean, that's effectively the idea

1071
00:49:39,480 --> 00:49:41,799
Speaker 1: of it. How would they defend this, because what they

1072
00:49:41,840 --> 00:49:43,560
Speaker 1: would say is this is like you know the ads

1073
00:49:43,560 --> 00:49:44,360
Speaker 1: you get served.

1074
00:49:44,080 --> 00:49:46,320
Speaker 4: Online, Well, we're collecting this information so we can.

1075
00:49:46,200 --> 00:49:49,360
Speaker 1: Give you a better experience that we can cater to

1076
00:49:49,440 --> 00:49:50,560
Speaker 1: your interest in needs.

1077
00:49:50,560 --> 00:49:51,560
Speaker 4: How would they describe it?

1078
00:49:51,640 --> 00:49:53,040
Speaker 5: Yeah, they would say it's more relevant.

1079
00:49:53,160 --> 00:49:56,319
Speaker 7: And you know, at this point, because they're trying to

1080
00:49:56,440 --> 00:50:00,080
Speaker 7: get people into the habit of this, they would say,

1081
00:50:00,080 --> 00:50:01,000
Speaker 7: we're giving you a discount.

1082
00:50:01,000 --> 00:50:02,960
Speaker 3: What are you worried about? What are you talking about?

1083
00:50:03,239 --> 00:50:07,880
Speaker 7: But the whole reason that we don't see widespread individualized

1084
00:50:08,000 --> 00:50:13,200
Speaker 7: variable pricing today is that most prices still are pretty public.

1085
00:50:13,640 --> 00:50:17,239
Speaker 7: So if you're in a store and you're charged you

1086
00:50:17,560 --> 00:50:20,040
Speaker 7: four dollars and the guy behind you has charged three dollars,

1087
00:50:20,040 --> 00:50:22,120
Speaker 7: you're going to be pissed off, right, and you're not

1088
00:50:22,120 --> 00:50:24,680
Speaker 7: going to one to shop there. Again, we have seen

1089
00:50:24,840 --> 00:50:29,640
Speaker 7: even in online pricing, time and again, people get caught.

1090
00:50:30,320 --> 00:50:33,120
Speaker 7: Amazon in the two thousands was doing variable pricing on

1091
00:50:33,160 --> 00:50:33,960
Speaker 7: its DVDs.

1092
00:50:34,040 --> 00:50:34,760
Speaker 3: They got caught.

1093
00:50:35,040 --> 00:50:37,799
Speaker 7: People figured out they could like eliminate their cookies and

1094
00:50:37,840 --> 00:50:41,040
Speaker 7: get a cheaper price, and Amazon said, we'll never do

1095
00:50:41,120 --> 00:50:43,399
Speaker 7: that again. Now Amazon does a million prices a minute.

1096
00:50:43,440 --> 00:50:45,399
Speaker 7: I don't believe that they're not actually a good price.

1097
00:50:45,480 --> 00:50:49,719
Speaker 7: But what I think companies have begun to figure out

1098
00:50:49,880 --> 00:50:53,920
Speaker 7: is that you need to match this data collection with

1099
00:50:54,120 --> 00:50:59,680
Speaker 7: customer isolation. So if you're on your app only, you

1100
00:50:59,760 --> 00:51:02,760
Speaker 7: know the offers that you're being given on your app.

1101
00:51:03,080 --> 00:51:07,520
Speaker 7: If you're on your Alexa smart speaker only, you know

1102
00:51:07,760 --> 00:51:10,640
Speaker 7: what you're being told in terms of price on that speaker.

1103
00:51:11,040 --> 00:51:15,360
Speaker 7: There are increasing plays to use smart TVs in this fashion.

1104
00:51:16,320 --> 00:51:19,960
Speaker 7: Walmart is trying to buy Visio. Now why is Walmart

1105
00:51:20,000 --> 00:51:24,160
Speaker 7: a retailer trying to buy a television company manufacturer? The

1106
00:51:24,200 --> 00:51:27,719
Speaker 7: reason is that Visio has a smart TV option and

1107
00:51:27,880 --> 00:51:32,360
Speaker 7: they can send you in your home direct offers through

1108
00:51:32,400 --> 00:51:35,200
Speaker 7: that TV. There are now ways that you can be

1109
00:51:35,360 --> 00:51:38,920
Speaker 7: watching a program and they will throw up a QR

1110
00:51:39,000 --> 00:51:41,879
Speaker 7: code that will allow you to have a direct offer

1111
00:51:41,920 --> 00:51:42,560
Speaker 7: of something.

1112
00:51:42,320 --> 00:51:44,080
Speaker 3: In the program.

1113
00:51:44,400 --> 00:51:48,719
Speaker 7: So isolating the consumer is like the killer app here

1114
00:51:48,760 --> 00:51:50,839
Speaker 7: to really personalize pricing.

1115
00:51:50,680 --> 00:51:53,160
Speaker 1: Because if you had transparency, like you said, people would

1116
00:51:53,160 --> 00:51:56,000
Speaker 1: be pissed off. And they know that, which is why

1117
00:51:56,040 --> 00:51:58,440
Speaker 1: they're not upfront about these practices that they're engaged.

1118
00:51:58,640 --> 00:51:58,799
Speaker 5: Right.

1119
00:51:58,920 --> 00:52:01,560
Speaker 7: What this is about really just at the leading edge

1120
00:52:01,600 --> 00:52:04,400
Speaker 7: of this, this is really just emerging, is the beginning

1121
00:52:04,480 --> 00:52:10,480
Speaker 7: of eliminating the public price, eliminating the one single manufacturers

1122
00:52:10,560 --> 00:52:15,360
Speaker 7: suggested retail price for everything. Everything's going to be contingent,

1123
00:52:15,560 --> 00:52:18,799
Speaker 7: variable based on your habits, your needs, and it's going

1124
00:52:18,880 --> 00:52:23,640
Speaker 7: to increase surplus for the company because the company will

1125
00:52:23,640 --> 00:52:25,200
Speaker 7: have an information.

1126
00:52:24,719 --> 00:52:25,960
Speaker 5: Advantage over you.

1127
00:52:26,920 --> 00:52:27,560
Speaker 4: Is this legal?

1128
00:52:29,239 --> 00:52:31,879
Speaker 7: Well, I mean, that's that's kind of the million dollar question.

1129
00:52:31,960 --> 00:52:34,919
Speaker 7: You know, things that are new are often not put

1130
00:52:34,960 --> 00:52:37,640
Speaker 7: into that category just yet. I'll give you a perfect example,

1131
00:52:37,840 --> 00:52:40,080
Speaker 7: and it was another story that we did this series,

1132
00:52:40,360 --> 00:52:44,879
Speaker 7: which is algorithmic price fixing. So it is illegal if

1133
00:52:44,960 --> 00:52:48,440
Speaker 7: three CEOs go into a room and say, we're all

1134
00:52:48,440 --> 00:52:49,640
Speaker 7: going to raise our prices.

1135
00:52:49,800 --> 00:52:50,000
Speaker 3: Right.

1136
00:52:50,239 --> 00:52:56,799
Speaker 7: However, if three CEOs say, let's aggregate our pricing to

1137
00:52:56,880 --> 00:53:00,480
Speaker 7: an algorithm that collects all the data from everyone in

1138
00:53:00,520 --> 00:53:02,840
Speaker 7: the area and then recommends to us what the perfect

1139
00:53:02,920 --> 00:53:06,000
Speaker 7: price would be relative to everybody else in the market,

1140
00:53:06,480 --> 00:53:09,720
Speaker 7: and they do it in a way that is comprehensive.

1141
00:53:10,640 --> 00:53:13,080
Speaker 7: That is also collusion, but that is not three men

1142
00:53:13,120 --> 00:53:16,400
Speaker 7: in a room. And what the Federal Trade Commission and

1143
00:53:16,520 --> 00:53:20,359
Speaker 7: other federal agencies have said is that it doesn't matter

1144
00:53:20,360 --> 00:53:22,439
Speaker 7: if it's an algorithm or three people in a room.

1145
00:53:22,520 --> 00:53:26,560
Speaker 7: It's still illegal there. But you know, they have to

1146
00:53:26,560 --> 00:53:29,320
Speaker 7: convince the courts of that. There are now active cases

1147
00:53:29,360 --> 00:53:33,000
Speaker 7: where judges have said, well, it's just an algorithm, they're

1148
00:53:33,040 --> 00:53:35,920
Speaker 7: only recommending prices. I don't know if this is completely

1149
00:53:35,920 --> 00:53:38,879
Speaker 7: illegal or not, and so that is I think one

1150
00:53:38,920 --> 00:53:41,640
Speaker 7: of the fights. But to answer your question more directly,

1151
00:53:41,880 --> 00:53:45,120
Speaker 7: you know, we have laws against deception, we have laws

1152
00:53:45,200 --> 00:53:49,920
Speaker 7: against unfairness. The FTC has started to look at data

1153
00:53:49,960 --> 00:53:53,799
Speaker 7: brokers that collect data as an unfair practice, and that

1154
00:53:53,840 --> 00:53:56,200
Speaker 7: could be a way to sort of get at this

1155
00:53:56,360 --> 00:53:59,960
Speaker 7: personalization of pricing. It's a question of whether you go

1156
00:54:00,080 --> 00:54:03,800
Speaker 7: after the data collection itself or say that data can't

1157
00:54:03,800 --> 00:54:07,759
Speaker 7: go into the pricing mechanism. So I think it's really

1158
00:54:07,800 --> 00:54:10,200
Speaker 7: an emerging field and we'll have to see what happened.

1159
00:54:10,239 --> 00:54:12,000
Speaker 1: I know you talked to Zephyr teach Own a little

1160
00:54:12,000 --> 00:54:15,560
Speaker 1: bit about recommendations for policy directions what we should be

1161
00:54:15,560 --> 00:54:18,200
Speaker 1: pushing legislators to put into place in order to protect people,

1162
00:54:18,360 --> 00:54:19,759
Speaker 1: or some of the suggestions that she.

1163
00:54:19,760 --> 00:54:23,799
Speaker 7: Had, Yeah, you know it was they fell along these

1164
00:54:23,880 --> 00:54:28,200
Speaker 7: lines of are we going to stop the data collection

1165
00:54:28,400 --> 00:54:32,319
Speaker 7: at the point of collection and actually come up with

1166
00:54:32,400 --> 00:54:34,279
Speaker 7: you know, we don't have a federal privacy law.

1167
00:54:35,040 --> 00:54:36,400
Speaker 5: We have some laws in the states.

1168
00:54:36,560 --> 00:54:39,400
Speaker 7: Zephyr has done some work in New York State around

1169
00:54:39,440 --> 00:54:43,920
Speaker 7: an anti price gouging law which is largely around emergencies,

1170
00:54:43,960 --> 00:54:47,600
Speaker 7: but you can see the application of it to various

1171
00:54:47,640 --> 00:54:50,640
Speaker 7: forms of price gouging that aren't contingent on a hurricane, right,

1172
00:54:52,000 --> 00:54:53,240
Speaker 7: but the question.

1173
00:54:53,360 --> 00:54:55,759
Speaker 1: On your own personal emergency of the day, which these

1174
00:54:55,760 --> 00:54:58,880
Speaker 1: companies can easily learn about and exploit exactly exactly.

1175
00:54:59,239 --> 00:55:01,759
Speaker 2: Yeah, So tell us about in terms of what the

1176
00:55:01,920 --> 00:55:04,359
Speaker 2: future looks like for this. You said that the fight

1177
00:55:04,480 --> 00:55:07,320
Speaker 2: is about price gouging at the legal level, but obviously

1178
00:55:07,360 --> 00:55:10,920
Speaker 2: technology is outpacing this, and then with large data sets

1179
00:55:10,960 --> 00:55:13,400
Speaker 2: like with AIS, this only going to accelerate with a

1180
00:55:13,480 --> 00:55:15,759
Speaker 2: large of these large consumer apps. I'm just thinking about

1181
00:55:15,760 --> 00:55:17,799
Speaker 2: the more we transition to e commerce, the more likely

1182
00:55:17,840 --> 00:55:18,680
Speaker 2: this isn't going to occur.

1183
00:55:18,719 --> 00:55:19,160
Speaker 3: Absolutely.

1184
00:55:19,239 --> 00:55:22,960
Speaker 7: I mean the architecture is in place now to engage

1185
00:55:23,000 --> 00:55:26,399
Speaker 7: in this sort of first degree, first order price discrimination.

1186
00:55:26,880 --> 00:55:31,400
Speaker 7: The question is for policymakers, what are they going to

1187
00:55:31,440 --> 00:55:34,000
Speaker 7: do about it. I think that for many, many years

1188
00:55:34,000 --> 00:55:38,360
Speaker 7: in this country, any question of pricing, any question of inflation,

1189
00:55:38,520 --> 00:55:41,080
Speaker 7: has been outsourced to the Federal Reserve. Yes, and this

1190
00:55:41,719 --> 00:55:44,759
Speaker 7: is not something the Federal Reserve really reaches. Right, This

1191
00:55:45,520 --> 00:55:49,160
Speaker 7: whole notion of pricing that's a little bit unmoored from

1192
00:55:49,200 --> 00:55:52,440
Speaker 7: supplying demand doesn't mean supplying demand doesn't dictate, you know,

1193
00:55:52,520 --> 00:55:55,480
Speaker 7: the general trends of pricing up and down, but these

1194
00:55:55,640 --> 00:55:59,360
Speaker 7: little factors, you know. A perfect example is like the

1195
00:55:59,400 --> 00:56:03,960
Speaker 7: subscription economy. So everybody has twenty fifty to thirty subscriptions.

1196
00:56:04,000 --> 00:56:06,120
Speaker 7: We did a story on this where it showed that

1197
00:56:06,360 --> 00:56:10,120
Speaker 7: there are now subscription services for you to manage your subscriptions.

1198
00:56:10,200 --> 00:56:15,160
Speaker 2: Yeah, yeah, yeah, because it helps me cancel the ones

1199
00:56:15,160 --> 00:56:15,879
Speaker 2: that I don't want to cancel.

1200
00:56:15,880 --> 00:56:16,959
Speaker 3: There don't know how to cancel.

1201
00:56:17,040 --> 00:56:17,399
Speaker 1: There was a.

1202
00:56:17,360 --> 00:56:20,160
Speaker 7: Research study that showed that you ask people how much

1203
00:56:20,200 --> 00:56:22,440
Speaker 7: do you pay a month in subscriptions and the average

1204
00:56:22,520 --> 00:56:25,080
Speaker 7: number was something like eighty six dollars, and then they

1205
00:56:25,160 --> 00:56:27,640
Speaker 7: tallied up their actual subscriptions and was two hundred and

1206
00:56:27,680 --> 00:56:28,320
Speaker 7: nineteen dollars.

1207
00:56:28,600 --> 00:56:28,719
Speaker 4: Yea.

1208
00:56:28,920 --> 00:56:32,319
Speaker 5: And so you know there's no inflation in that mix.

1209
00:56:32,440 --> 00:56:34,680
Speaker 7: If the if the price per month is the same

1210
00:56:35,040 --> 00:56:38,560
Speaker 7: right as we go along, that's not an inflationary environment.

1211
00:56:38,600 --> 00:56:41,600
Speaker 7: But if they're larding up all these subscriptions making it

1212
00:56:41,640 --> 00:56:44,760
Speaker 7: harder for you to cancel so that your overall budget

1213
00:56:44,880 --> 00:56:48,680
Speaker 7: for these subscriptions goes up, then that that continues to

1214
00:56:48,680 --> 00:56:50,920
Speaker 7: be a problem. And it could be even deceptive if

1215
00:56:50,920 --> 00:56:52,759
Speaker 7: the dark patterns that are being used for you to

1216
00:56:52,800 --> 00:56:57,520
Speaker 7: sign up and refuse to cancel are are are in place.

1217
00:56:57,600 --> 00:57:00,880
Speaker 7: So I think that it needs to be more of

1218
00:57:00,880 --> 00:57:03,360
Speaker 7: a whole of government approach. I think the Biden administration

1219
00:57:03,480 --> 00:57:06,240
Speaker 7: is doing some of that. The FTC is getting involved,

1220
00:57:06,239 --> 00:57:09,480
Speaker 7: this Consumer Financial Protection Bureau, which capped credit card late

1221
00:57:09,480 --> 00:57:13,400
Speaker 7: fees at eight dollars, for example. They're getting involved this

1222
00:57:13,440 --> 00:57:17,880
Speaker 7: whole sort of junk fee fighting. This agenda of fighting

1223
00:57:18,000 --> 00:57:21,240
Speaker 7: junk fees is part of this, and I think that

1224
00:57:22,040 --> 00:57:27,760
Speaker 7: there is a path here to supplement the work that

1225
00:57:28,200 --> 00:57:31,040
Speaker 7: was normally just outsourced to the monetary Authorney got it.

1226
00:57:31,160 --> 00:57:33,919
Speaker 1: David Dan Always so great to have you. We really

1227
00:57:33,960 --> 00:57:35,760
Speaker 1: rely a lot on the work you guys do. You

1228
00:57:35,800 --> 00:57:38,080
Speaker 1: and your team do an incredible job really digging into

1229
00:57:38,080 --> 00:57:39,120
Speaker 1: some of these key issues.

1230
00:57:39,120 --> 00:57:40,440
Speaker 4: So thank you so much. It's great to see you.

1231
00:57:40,480 --> 00:57:42,560
Speaker 3: Thanks for having me appreciate it's our pleasure. Thanks, see

1232
00:57:42,600 --> 00:57:43,120
Speaker 3: you guys later.