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Speaker 1: Hello. Welcome to Fast Politics. I'm Molly Johnfest, and we

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Speaker 1: have a great show for you today. Later in the show,

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Speaker 1: we'll talk to Lindsay Owens about her new book, Gouge,

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Speaker 1: The End of a Fair Price, and what that means

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Speaker 1: for your wallet. But first, we have The Lincoln Project's

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Speaker 1: own Rick Wilson.

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Speaker 2: Rick?

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Speaker 1: On Trump celebrating making a fortune. Rick Wilson.

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Speaker 3: Good morning, Molly.

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Speaker 1: We got Jesse Cannon here with a special clip.

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Speaker 2: Because somebody would say the fake news, he's bragging about

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Speaker 2: making money. Well, people are suffering at the, you know, because,

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Speaker 2: you know, you're still paying lower than Biden and Obama.

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Speaker 2: And they don't have nuclear weapons. But they're going to

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Speaker 2: be coming. No, I could just see the fake headlines.

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Speaker 2: President Trump is talking about all the money they're making

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Speaker 2: while people are suffering at the pump. That'll be the headline.

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Speaker 2: But I said, I don't give a damn.

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Speaker 3: I'm going to say it.

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Speaker 2: We're making a freaking fortune. Our country is getting stronger

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Speaker 2: and stronger all the time.

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Speaker 1: Anyway, the point is, Rick Wilson, Donald Trump wants us

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Speaker 1: to know that gas companies are making a lot of money.

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Speaker 3: I am so glad to hear our struggling mom and

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Speaker 3: pop oil companies are making a lot of money. Right.

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Speaker 1: Chevron, Exxon. You could sense that they were making a

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Speaker 1: lot of money if you went to the pumps and

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Speaker 1: you spent a couple hundred dollars trying to fill your

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Speaker 1: car up with gas.

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Speaker 3: I mean, look, this is classic Donald Trump, not reading

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Speaker 3: the room in the slightest. This is, you know, people

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Speaker 3: are absolutely freaked out about gas and diesel prices in

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Speaker 3: this country right now. And I've reached the point where

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Speaker 3: I believe the conspiracy theory that he wants to lose now,

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Speaker 3: that he wants to lose. Because this is like, I mean,

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Speaker 3: at some point, he's going to come out in the

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Speaker 3: next week and go, I'm going to favor a national

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Speaker 3: bill to eat puppies and kittens for every meal or

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Speaker 3: whatever it's going to be. But he's really not trying

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Speaker 3: to win. He's trying hard not to win at this point.

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Speaker 1: This is an amazing, amazing... stretch right now and i

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Speaker 1: think part of it is just because he's so disinhibited

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Speaker 1: because he's 80 and he's exhausted and he's crisscrossing the country.

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Speaker 3: And he's making these like weird noises well he.

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Speaker 1: Looks exhausted i mean when you see these photos and

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Speaker 1: look we are all exhausted i'm 48 and i'm as

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Speaker 1: tired as i've ever been.

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Speaker 3: I'm 62 i work 100 hours a week i'm beat

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Speaker 3: down like a damn dog And I get it. And

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Speaker 3: I'm not even traveling every single day.

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Speaker 1: I knew you when you were 52.

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Speaker 3: I wish.

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Speaker 1: I knew you then. And now we're old.

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Speaker 3: You did know me then.

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Speaker 1: Yeah.

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Speaker 3: No, go on.

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Speaker 1: I mean, we just all lost a decade of our

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Speaker 1: life to this guy. And what I think is important

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Speaker 1: about this moment is like every poll, usually before an election,

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Speaker 1: we see polls tightening. That's not what we're seeing here.

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Speaker 3: We are not seeing the polls tightening right now. This

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Speaker 3: is a very different circumstance. And look, there are a

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Speaker 3: few state polls that have settled into a bracket and

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Speaker 3: they don't seem to really be moving up or down

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Speaker 3: very much right now. I mean, we've got Democrats leading

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Speaker 3: by three, four, five points in a bunch of places. Right.

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Speaker 3: And those seem to have sort of stabilized. I think

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Speaker 3: what happened is the gigantic tidal wave of money that

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Speaker 3: they spent.

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Speaker 2: Yeah.

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Speaker 3: Froze the field in some places, but it didn't bring

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Speaker 3: the Republicans back up. No. I think we're going to look, Molly,

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Speaker 3: I think we're going to look back at Texas and

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Speaker 3: see that between the Senate committee, uh, spending about $ 150

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Speaker 3: million and Elon Musk and other outside groups spending about another,

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Speaker 3: you might as well have taken that money out of

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Speaker 3: the bank, put it in a bag and pour diesel

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Speaker 3: fuel on it and set it on fire for all

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Speaker 3: the good it's doing for Ken Paxton in Texas.

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Speaker 1: They should have spent it at Chanel for shoes for me, man.

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Speaker 3: You know what?

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Speaker 1: Because it would have gotten more use.

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Speaker 3: This is an application of large sums of money that

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Speaker 3: I believe many Americans could get behind. Yes.

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Speaker 1: I think we should start by talking about Texas because

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Speaker 1: there are these early voting numbers. We must say the

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Speaker 1: prayer of early voting, which is, dear God, protect me

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Speaker 1: from early voting numbers and the hopium they produce. But

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Speaker 1: that said, the early voting out of Texas is low

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Speaker 1: frequency Hispanic voters.

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Speaker 3: Right. Right. And there is not a single piece of

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Speaker 3: polling information about Texas Hispanics, whether it's in congressional polling,

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Speaker 3: Senate or governor's race polling, that shows they are doing

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Speaker 3: anything but coming out to knock the fuck out of

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Speaker 3: the Republicans.

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Speaker 1: Right.

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Speaker 3: And they are young. They are young. They've broken away

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Speaker 3: from the GOP. Look, they're not raving progressive Democrats, but

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Speaker 3: they are fucking furious with the economy, with immigration. with

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Speaker 3: the broken promises. And they are going to take it

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Speaker 3: out on the GOP. It is going to be a whooping.

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Speaker 2: Yeah.

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Speaker 1: These are voters you get, you rent, you don't keep them, right?

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Speaker 1: They're not ideologically very lefty. They're voting on affordability. And

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Speaker 1: also the statistic I saw, one in four Latino voters

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Speaker 1: knows someone who has been either harassed or taken by ICE.

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Speaker 2: Right.

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Speaker 3: And look, they also... Many Hispanics in Texas, particularly in

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Speaker 3: those vital Rio Grande Valley counties, where we've got two

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Speaker 3: of the big congressional fights in Texas happening, they are,

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Speaker 3: as my former colleague Mike Madrid says, these people are,

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Speaker 3: they are, they're the people that are working people. They're

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Speaker 3: the ones going to the construction site to put, and

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Speaker 3: they got to put gas in that truck.

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Speaker 2: Right.

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Speaker 3: And then they can't get people on the job because

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Speaker 3: they've all been scared away from work or deported. Yeah.

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Speaker 3: And it is a perfect storm of both the economy

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Speaker 3: and immigration in Texas. And look, you've got 63% of

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Speaker 3: Texans say that there should be a system where people

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Speaker 3: can come from foreign countries and work in the state

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Speaker 3: of Texas. Right. The Republicans, they got on this crack

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Speaker 3: a decade ago that immigration would turn up their base.

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Speaker 3: And mostly it was just bullshit and talk. But for

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Speaker 3: the last two years, Stephen Miller and his goon squad

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Speaker 3: got their way. And all of a sudden, a lot

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Speaker 3: of these people who are functionally very conservative, functionally Republican,

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Speaker 3: they're like, Jesus, come on, man. You're killing my business.

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Speaker 3: You're killing my farm. You're killing my company. And they're

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Speaker 3: not happy about it.

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Speaker 1: I still believe the worst thing that Donald Trump has

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Speaker 1: done so far, the thing that has hurt him the most,

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Speaker 1: that has given him the least upside— except maybe his ego,

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Speaker 1: has been the tariffs. Because that's for no one, right?

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Speaker 1: We're making things more expensive. We have no idea why

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Speaker 1: you want to farm, but now your soybeans are worthless.

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Speaker 1: We need to write you a big check.

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Speaker 3: There is, when we look back on the history of

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Speaker 3: this administration, we will determine that in the very, very,

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Speaker 3: very beginning, when Peter Navarro and And these other dipshits

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Speaker 3: around Trump said, oh, the tariffs are great. Yes, sir,

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Speaker 3: that you're an economic genius. We're going to look back

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Speaker 3: on that. And that will be seen as the inflection

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Speaker 3: point that killed the MAGA movement in a major way.

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Speaker 3: That killed all the casual MAGA. Right. The sort of

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Speaker 3: reflexively Republican, yeah, I'm on the libs, man. The cultists

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Speaker 3: will always be there, 15%, 20%, whatever it is. But

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Speaker 3: the casual mongers were killed by the tariffs. It hurt

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Speaker 3: their businesses. It hurt their families. It's hurt their ability

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Speaker 3: to provide health care and food for their kids. It's

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Speaker 3: just that it was insane.

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Speaker 1: There are three things, I think, if we look back

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Speaker 1: at the history of Donald Trump just cooking his coalition. Tariffs,

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Speaker 1: Epstein files, and the war in the Middle East.

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Speaker 3: Yep. It's the trifecta of stupidity, which I'm going to

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Speaker 3: write a piece called that.

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Speaker 1: Coming up with ideas left and right. But I do

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Speaker 1: think that Texas will be very interesting. We still have another,

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Speaker 1: you know, whatever, nine days, right? Nine days?

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Speaker 3: Yep. Nine days as of today.

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Speaker 1: Nine days. So we'll see what happens in Texas. And

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Speaker 1: then we're going to see, we have the administration going

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Speaker 1: after Alaska. Todd Blanche today on the Sunday show is

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Speaker 1: trying to defend being told by Donald Trump to get

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Speaker 1: involved in the Alaska Senate race. Discuss.

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Speaker 3: Jeff's kiss. Genius. My favorite part of this whole thing,

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Speaker 3: aside from the flagrant illegality of it, the unconstitutional nature

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Speaker 3: of it, the raw political stupidity of it. My favorite

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Speaker 3: part of it was Stephen Miller was like, this is

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Speaker 3: the most outrageous election story in American history. And I'm like, bitch,

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Speaker 3: y'all did January 6th.

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Speaker 1: The best part though, is like, it says incumbent Dan

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Speaker 1: Sullivan or Democrat.

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Speaker 3: It says Republican Dan Sullivan incumbent.

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Speaker 4: Yeah.

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Speaker 3: Look, This is also a game I would be, if

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Speaker 3: Republicans want to play this game, they better be very

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Speaker 3: fucking careful. Because I have seen them do this trick

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Speaker 3: before in other races where they get a lookalike or

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Speaker 3: a soundalike name. Oh, yeah. And this is not something

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Speaker 3: that's new.

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Speaker 1: And it's not something Democrats came up with either. Right.

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Speaker 3: Democrats didn't invent this. And here's the other part of this.

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Speaker 3: Todd Blanch is inside the 90-day window where the DOJ is.

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Speaker 3: does not deal with state elections.

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Speaker 1: What 90-day window?

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Speaker 3: I know that's one of those norms and traditions in Washington,

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Speaker 3: but it is also going to be one of the

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Speaker 3: 100 reasons that when Mark Elias walks into a courtroom,

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Speaker 3: they're going to go, get the fuck out of here, Blanche.

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Speaker 1: Yeah, yeah. But Todd trying to defend it with Dana

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Speaker 1: Bash today.

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Speaker 3: I saw that clip. That did not go well.

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Speaker 1: Well, he's so puffed up, you know, because he's been

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Speaker 1: a defense lawyer for so long. And he says, well, Dana,

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Speaker 1: you're not being fair. Say the whole thing. Don't take

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Speaker 1: Donald Trump out of context. Take him literally, but not spiritually. Physically,

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Speaker 1: but not appropriately.

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Speaker 3: I mean— Metaphorically, but not hypothetically. Whatever. Listen, Blanche, once again,

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Speaker 3: improving the theory, they're not sending their best. Blanche is

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Speaker 3: better behind the curtain trying to play the games with

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Speaker 3: the Epstein files and all that stuff than he is

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Speaker 3: trying to articulate the administration's inarticulable policies. That's right. This

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Speaker 3: guy is, it's one more preview. And I have been

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Speaker 3: telling a lot of Democrats this week. Yes, they're going

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Speaker 3: to play screw around on election day and after. They're

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Speaker 3: going to play these stupid games, but they're also stupid people.

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Speaker 1: Yeah, thank God.

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Speaker 3: And in a lot of these cases, the degree to

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Speaker 3: which they will get traction is going to be directly

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Speaker 3: correlated with how much of a whoop ass we deliver

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Speaker 3: on election day.

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Speaker 4: Right.

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Speaker 3: If it is a monstrous 1994 style blowout. Or 2006

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Speaker 3: style blowout. Or 1980.

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Speaker 1: Will you talk about 1980?

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Speaker 3: Well, I was in junior high school. I was too. Yes,

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Speaker 3: I know. Because you're fresh and young and delicate. Um,

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Speaker 3: 1980 was another one of these absolute wipeouts. 1974, an

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Speaker 3: absolute wipeout. Why? Because corruption and inflation are shitty fucking

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Speaker 3: hands to be dealt in an election year. They're even stupider.

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Speaker 1: When you've created them.

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Speaker 3: If you made them yourself.

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Speaker 2: Yeah.

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Speaker 1: Yeah.

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Speaker 2: Okay.

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Speaker 3: If you did it yourself.

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Speaker 1: Yeah.

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Speaker 3: And, and, you know, fair or not, um, 1994, um,

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Speaker 3: came to be seen as a referendum on corruption. We

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Speaker 3: don't even think, like, the story is so stupid now

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Speaker 3: in our retrospective, like, world on fire view. Let's say

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Speaker 3: a little more. The Democrats got blown out because they

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Speaker 3: were able to bounce checks at the House bank and

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Speaker 3: get away with it. And use postage from the House

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Speaker 3: post office for free. That was the scandal of 94!

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Speaker 1: And the Democrats were fucking seized! Yeah.

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Speaker 3: It was astounding. 19 or 2006 inflation, more high gas prices.

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Speaker 3: And I, I would remember, I would go around to

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Speaker 3: my Republican candidates that year. I had like 35 races.

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Speaker 3: I'd go around the country and be like, I'm getting

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Speaker 3: my ass kicked on gas prices and nothing we did.

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Speaker 1: Right.

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Speaker 3: Right. Right.

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Speaker 1: Because people that really I mean, it's so funny because

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Speaker 1: it's like we're so high minded and we're like, they're

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Speaker 1: not going to like the the kleptocracy. They're not going

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Speaker 1: to like. But what they don't like is that a

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Speaker 1: year ago it cost forty five dollars to fill your

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Speaker 1: tank up with gas and now cost eighty five dollars.

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Speaker 3: I mean, yeah. And, you know, it's it's interesting because

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Speaker 3: they can spin in their minds a lot of things

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Speaker 3: about Trump. Oh, he wasn't really, really with Epstein. And oh,

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Speaker 3: the war with Iran, we got to keep them from

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Speaker 3: getting a nuke. But they can't spend that gas price

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Speaker 3: when they see it. They can't spend the grocery bill

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Speaker 3: when they're like three bags of groceries for 150 bucks.

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Speaker 3: They can't spend those things in their minds because they're real.

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Speaker 3: They're tangible. They're right in their face. They can't listen.

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Speaker 3: Fox News, you go on Fox News, they're like, the

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Speaker 3: economy is so great for everybody. It's amazing.

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Speaker 1: Right.

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Speaker 3: And yet, and yet these people feel like AI is

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Speaker 3: taking their jobs.

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Speaker 1: Right.

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Speaker 3: Um, billionaires and, and Trump's family are getting rich as shit,

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Speaker 3: which he's bragging about it. Yeah. Getting so rich. Um,

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Speaker 3: And they see the other symbolic things, the ballroom. It's like, hey,

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Speaker 3: I know you're waiting in line at the food pantry

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Speaker 3: this month, but have you seen the new gold leaf

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Speaker 3: I put in the Oval Office?

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Speaker 1: Or the helicopter pad that I moved because I didn't

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Speaker 1: want to get my feet wet.

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Speaker 3: Right.

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Speaker 1: I mean, that's a great example of something. If you're

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Speaker 1: a voter and you're like... I mean, so I do

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Speaker 1: think when we look at this election, there are a

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Speaker 1: number of unforced errors. Talk us through. I want you

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Speaker 1: to talk. We're like next week will be the last

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Speaker 1: podcast before the midterms that you and I do. But

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Speaker 1: I want you to tell me what could happen this

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Speaker 1: last run up to the week that like barring some

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Speaker 1: kind of really like game changing situation. What do you

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Speaker 1: think if we stay where we are? What are you watching?

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Speaker 3: My biggest fear right now is Democrats will start getting complacent.

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Speaker 1: Okay.

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Speaker 3: And they will say, okay, we're going to win this.

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Speaker 3: It's fine. It's good. And guys, the cliche of all

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Speaker 3: cliches about every vote counting really, really matters. I'm also,

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Speaker 3: as we get into this last part of the early

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Speaker 3: voting window next week, where it's going to shut down

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Speaker 3: a lot of places, you know, midweek, late week, next week,

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Speaker 3: people will say, I'll wait till election day. Please go

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Speaker 3: vote early, folks. Those are my big fears that we

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Speaker 3: stop the pressure and we don't get the vote out.

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Speaker 3: The stopping the pressure, the other temptation Democrats will have

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Speaker 3: this week, and I love you guys right now. I

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Speaker 3: know your consultants are like, we should end the campaign

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Speaker 3: on a high note and talk about our plan to

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Speaker 3: rebuild Highway 14. No, stay on the economy and the corruption.

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Speaker 3: It's the thing that's working. Beat the drum until election

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Speaker 3: night and every poll is closed. Stay on these people.

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Speaker 3: Trump is going to do something crazy this week. I

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Speaker 3: don't know what. He's going to try to reset. He's

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Speaker 3: going to try to have an externality. And I also

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Speaker 3: predict this week we're going to get like a big alert.

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Speaker 3: It's going to be all over Fox. They're going to say,

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Speaker 3: Iranian terrorists allied with trans Antifa are here to attack you.

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Speaker 1: What about a caravan?

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Speaker 3: You know, the problem with the caravan right now, Is

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Speaker 3: the caravan still in like Honduras? And it seems like

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Speaker 3: a thousand miles away. It's like a little ephemeral. And

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Speaker 3: also if you've.

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Speaker 1: Sealed the border, right? Your whole thing is you've sealed

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Speaker 1: the border.

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Speaker 3: So what's the caravan going to do? Are they going

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Speaker 3: to get across your laser alligator moat or whatever the

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Speaker 3: fuck they claim is out there?

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Speaker 1: This week, the thing that really did sink Trump world

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Speaker 1: though this week was that Vivek Ramaswamy ad. Talk us

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Speaker 1: just for two seconds.

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Speaker 3: Listen, I am not known to pull a lot of

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Speaker 3: punches when it comes to negative ads, but this ad

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Speaker 3: was a shit pile from the top to the bottom.

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Speaker 3: They found a mentally deranged homeless woman. In her 80s.

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Speaker 1: In her 80s.

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Speaker 4: Who lives in her car with cats.

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Speaker 3: Who lives in her car with cats. This is a

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Speaker 3: woman who stayed with a man who not only raped

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Speaker 3: Amy Atkinson. But also abused her and her brother. Yeah.

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Speaker 3: And who had also apparently sexually abused another child from

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Speaker 3: another marriage.

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Speaker 1: His biological daughter, I think.

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Speaker 3: Right. Amy Acton rightly cut this woman out of her

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Speaker 3: life 40 years ago. And Vivek Ramaswamy's people, and I

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Speaker 3: want to say these names so you hear them. Yeah.

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Speaker 3: Jay Chabria, Ben Yoho, and Rex Alsace, and Andrew Sarabian.

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Speaker 3: These guys who ran... and who are his big senior counselors,

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Speaker 3: senior advisors, and media consultants, they decided to make this ad,

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Speaker 3: leak it without a campaign disclaimer on it to sort

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Speaker 3: of get a road test of it.

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Speaker 1: And then to double down.

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Speaker 3: It has blown up in their faces like no ad

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Speaker 3: I've ever seen in my life. Like no ad, I've

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Speaker 3: never seen an ad do more damage to a candidate

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Speaker 3: than this ad has done to Vivek Ramaswamy. Not only

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Speaker 3: has it absolutely destroyed reversed any trend in this race

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Speaker 3: where he could pick back up. But Acton is now,

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Speaker 3: she has the wind that her sails, and she and

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Speaker 3: David Pepper are going to be governor and lieutenant governor

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Speaker 3: of that state because of this ad. Ohio, folks, was

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Speaker 3: the reddest of red states, okay, in the Rust Belt.

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Speaker 3: It was the rock solid Republican super state. It is

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Speaker 3: now going to have a Democratic governor who Because Vivek

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Speaker 3: Ramaswamy and his campaign team of Ben Yoho and Andrew

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Speaker 3: Sarabian and Rex Alsass and Jay Chabria are fucking morons.

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Speaker 1: Yeah.

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Speaker 3: They are mouth-breathing, drool-bucket fucking morons. And they did this.

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Speaker 3: And it also, by the way, has put radiation all

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Speaker 3: over J.D. Vance and all over Donald Trump.

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Speaker 5: Because, you know, there's nothing that makes you look more

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Speaker 5: like an asshole than defending an ad about a woman

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Speaker 5: who was raped as a child.

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Speaker 3: And J.D. Vance, his presidential race, I promise you that

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Speaker 3: ad will be around his neck. Some of his Republican

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Speaker 3: rivals in the primary will be like, I love Donald

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Speaker 3: Trump too. But when J.D. Vance defended an ad about rape,

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Speaker 3: it was too much. I mean, it is, I guarantee you,

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Speaker 3: the guys around Marco Rubio were like, oh! J.D., keep talking, J.D.,

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Speaker 3: keep talking!

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Speaker 1: Yeah, J.D. Vance defending that ad. Rick Wilson!

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Speaker 3: Chef's kiss.

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Speaker 6: All right, it's time for the headlines.

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Speaker 3: Guess who's back in the news?

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Speaker 6: Your friend Hunter Biden, because the DOJ is accusing ex-FBI

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Speaker 6: officials of lying.

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Speaker 1: What if Jared Kushner... phone is the real Hunter Biden's laptop?

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Speaker 3: I like this. I like this.

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Speaker 1: Yeah. So the Justice Department alleges that two ex-FBI officials

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Speaker 1: lied to Congress about the Bureau's investigation into Hunter Biden.

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Speaker 1: According to a complaint unveiled on Friday, Republicans in Congress

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Speaker 1: have continued to question the government's handling of the probes

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Speaker 1: into the former president's son, who ultimately was convicted of

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Speaker 1: federal gun charges and pleaded guilty to tax offenses. Here's

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Speaker 1: what's going to happen here. Here's a big fucking problem

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Speaker 1: for Republicans. You're going to go after Hunter Biden, whose

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Speaker 1: father is now president, who's never going to be president again,

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Speaker 1: and who's a painter. And you're going to ignore the

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Speaker 1: glaring possibility that Don Jr., whose wedding was paid for

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Speaker 1: by a Russian oligarch... and who sits on the board

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Speaker 1: of multiple companies that Donald Trump's administration does business with,

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Speaker 1: it's going to be a much bigger fucking problem. So

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Speaker 1: I implore Republicans to... At least take a moment to

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Speaker 1: think about how this is going to go.

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Speaker 3: Yeah, that's a great way of looking at it.

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Speaker 1: I mean, who even cares? I mean, Hunter Biden, whatever.

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Speaker 1: Who cares?

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Speaker 6: Yet again, one of the, you know, we talk about

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Speaker 6: their incompetence a lot. One of the other things that

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Speaker 6: is truly amazing about this administration is their inability to

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Speaker 6: ever keep the eye on the ball of where, like,

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Speaker 6: if I was trying to be a psychotic fascist like

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Speaker 6: they are, I would at least have my eyes set

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Speaker 6: on the prize.

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Speaker 1: I would try to be a little more focused.

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Speaker 3: Yes.

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Speaker 4: That's my hot take.

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Speaker 2: Yeah.

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Speaker 6: Well, speaking of that, Molly, you know, it's my favorite

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Speaker 6: summer festival every year.

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Speaker 3: Pardon Palooza.

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Speaker 6: And that's what apparently the Trump administration is looking at

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Speaker 6: for their junior staffers because they've been doing all sorts

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Speaker 6: of fuckery.

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Speaker 1: OK, so here's the thing. Donald Trump is floating this

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Speaker 1: because he wants people in administration to do things that

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Speaker 1: they could get arrested for.

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Speaker 4: Period, paragraph.

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Speaker 1: Like Donald Trump is very good at putting things into

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Speaker 1: the media that he wants people to know. And that's

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Speaker 1: what this is. If you are a member of his

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Speaker 1: administration and you are a junior person and you're about

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Speaker 1: to do something illegal, I would pause because I do

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Speaker 1: not think Donald Trump is going to make good on

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Speaker 1: this promise on his way out the door. And I

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Speaker 1: really think like this is one of these things where

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Speaker 1: he is, you know, saying, well, I'm going to give

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Speaker 1: this one a pardon. I'm going to pardon that one.

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Speaker 1: And he says, well, sleepy Joe Biden gave pardons to everyone,

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Speaker 1: mostly to people he thought Donald Trump would target, in

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Speaker 1: which Donald Trump did, in fact, target them. I do

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Speaker 1: really think like this is a Hail Mary to try

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Speaker 1: to get people to keep going, doing the stuff that

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Speaker 1: Donald Trump wants them to do. I think this is

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Speaker 1: going to be a real like this, a real sign

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Speaker 1: that there's a lot of unhappiness in the Trump administration.

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Speaker 3: Right.

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Speaker 1: That people are worried about going to jail because you

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Speaker 1: don't float a story like this unless you're worried about

440
00:22:59,750 --> 00:23:01,810
Speaker 1: your people not doing what you want them to do.

441
00:23:02,330 --> 00:23:04,710
Speaker 1: And like this is like with all this is like

442
00:23:04,770 --> 00:23:06,670
Speaker 1: the same thing as all these stories that have started

443
00:23:06,710 --> 00:23:11,730
Speaker 1: coming out. about Trump world, like, you know, the Alina Haba,

444
00:23:12,030 --> 00:23:16,070
Speaker 1: you know, bribery for pardon story or that, you know,

445
00:23:16,270 --> 00:23:19,590
Speaker 1: all these little stories are coming out because the people

446
00:23:19,630 --> 00:23:22,560
Speaker 1: in this administration are starting to get nervous. And Donald

447
00:23:22,580 --> 00:23:26,600
Speaker 1: Trump is evaluating mass pardons for junior staffers because he

448
00:23:26,660 --> 00:23:30,500
Speaker 1: sees that people are getting nervous. And so somebody leaked

449
00:23:30,540 --> 00:23:33,939
Speaker 1: this story to the Washington Sun. By the way, the

450
00:23:33,960 --> 00:23:35,830
Speaker 1: Washington Sun is a lot of people who used to

451
00:23:35,869 --> 00:23:38,909
Speaker 1: be at the Washington Post, right, who have moved on.

452
00:23:38,970 --> 00:23:44,190
Speaker 1: So they're very sourced in D.C. and they are getting ready.

453
00:23:44,630 --> 00:23:46,090
Speaker 4: They just want D.C.

454
00:23:46,130 --> 00:23:49,190
Speaker 1: People to see this and be willing to do pretty

455
00:23:49,250 --> 00:23:53,580
Speaker 1: incomprehensible stuff. If I were a junior staffer in the

456
00:23:53,619 --> 00:23:56,220
Speaker 1: Trump administration, which I would never be because I'm not

457
00:23:56,240 --> 00:24:00,030
Speaker 1: an idiot. I would not do anything illegal and I

458
00:24:00,070 --> 00:24:02,470
Speaker 1: would save all the receipts because they're going to come

459
00:24:03,109 --> 00:24:05,340
Speaker 1: after you and you're going to have to spend millions

460
00:24:05,359 --> 00:24:08,980
Speaker 1: of dollars in lawyer fees. And you're much better off

461
00:24:09,119 --> 00:24:11,760
Speaker 1: if you have every receipt and every time you didn't

462
00:24:11,800 --> 00:24:13,459
Speaker 1: do something illegal, they asked you to.

463
00:24:13,960 --> 00:24:17,480
Speaker 6: Yep, that is correct. And also hope you didn't do

464
00:24:17,520 --> 00:24:19,480
Speaker 6: it in any states where they could prosecute you.

465
00:24:19,800 --> 00:24:21,880
Speaker 1: Yeah. Oh, no, this is going to be like it's

466
00:24:21,940 --> 00:24:25,000
Speaker 1: going to be hearing after hearing and trial after trial.

467
00:24:25,260 --> 00:24:25,460
Speaker 3: Yep.

468
00:24:25,760 --> 00:24:30,980
Speaker 6: So federal judges have ruled that detained immigrants don't have

469
00:24:31,060 --> 00:24:32,119
Speaker 6: constitutional rights.

470
00:24:32,560 --> 00:24:35,820
Speaker 1: So this is this Fifth Circuit of Appeals rule that

471
00:24:35,980 --> 00:24:39,810
Speaker 1: ICE detainees that they are not entitled to receive a

472
00:24:39,850 --> 00:24:44,030
Speaker 1: bond hearing. There is no world in which this does

473
00:24:44,150 --> 00:24:48,600
Speaker 1: not keep going up the system. It's a decision that

474
00:24:48,630 --> 00:24:51,940
Speaker 1: blocks the efforts by federal judges who have been ordering

475
00:24:51,960 --> 00:24:56,260
Speaker 1: the release of immigrants. So this is like. Courts fighting

476
00:24:56,280 --> 00:25:01,050
Speaker 1: with each other. And it's also I mean, obviously, it's

477
00:25:01,210 --> 00:25:06,090
Speaker 1: morally reprehensible, but I think it keeps going up the courts.

478
00:25:06,330 --> 00:25:09,520
Speaker 1: And I think that what you're going to see here

479
00:25:09,780 --> 00:25:12,580
Speaker 1: is that a lot of stuff is administration is doing

480
00:25:12,600 --> 00:25:18,119
Speaker 1: when it comes to ICE is illegal. illegal, unconstitutional, probably

481
00:25:18,200 --> 00:25:23,280
Speaker 1: has a lot more longer tail than we think. And

482
00:25:23,520 --> 00:25:26,850
Speaker 1: it's just problematic in any which way. I don't think

483
00:25:27,160 --> 00:25:30,490
Speaker 1: that any of this stays. I think it all keeps

484
00:25:30,550 --> 00:25:31,970
Speaker 1: going up to higher courts.

485
00:25:32,350 --> 00:25:33,429
Speaker 3: Yeah.

486
00:25:33,710 --> 00:25:37,330
Speaker 6: So this is an interesting note in the fight against

487
00:25:37,369 --> 00:25:40,530
Speaker 6: data centers. A couple of California cities have pulled the

488
00:25:40,609 --> 00:25:44,070
Speaker 6: plug on them after state regulations don't scale up fast enough.

489
00:25:44,380 --> 00:25:47,000
Speaker 1: Part of this is that people don't want like half

490
00:25:47,060 --> 00:25:51,010
Speaker 1: a data center, right? Like if you start building a

491
00:25:51,050 --> 00:25:53,490
Speaker 1: data center, I mean, I don't think anyone wants a

492
00:25:53,510 --> 00:25:56,010
Speaker 1: data center. And I think that ultimately this is where

493
00:25:56,030 --> 00:25:58,450
Speaker 1: we're going to get to. I mean, the data center situation,

494
00:25:58,470 --> 00:26:01,110
Speaker 1: there are Democrats who agreed to do data centers who

495
00:26:01,150 --> 00:26:04,520
Speaker 1: now realize they were wrong. There are Republicans who are

496
00:26:04,660 --> 00:26:08,220
Speaker 1: so on the take that they don't care. But mostly

497
00:26:08,400 --> 00:26:11,080
Speaker 1: if you have voters voting, they're not going to want them.

498
00:26:11,730 --> 00:26:14,480
Speaker 1: So this is a big problem. I mean, it's actually

499
00:26:14,560 --> 00:26:18,480
Speaker 1: really interesting because like I understand data centers, they're bad

500
00:26:18,540 --> 00:26:21,300
Speaker 1: for the environment. They're bad for the economy. They make

501
00:26:21,359 --> 00:26:25,020
Speaker 1: life more expensive. They're loud and they don't do anything.

502
00:26:25,040 --> 00:26:29,139
Speaker 1: They don't create jobs. The problem is eventually people aren't

503
00:26:29,160 --> 00:26:31,720
Speaker 1: going to be happy when they realize they can't like

504
00:26:31,960 --> 00:26:35,720
Speaker 1: roam or they can't do stuff on their phone. So, like,

505
00:26:36,190 --> 00:26:39,149
Speaker 1: Republicans have actually and tech companies have actually done themselves

506
00:26:39,190 --> 00:26:41,330
Speaker 1: a huge disservice. Like, they came in there and they

507
00:26:41,350 --> 00:26:43,150
Speaker 1: were like, AI is going to take your jobs. Also,

508
00:26:43,230 --> 00:26:45,510
Speaker 1: we want a data center. Also, we need a tax

509
00:26:45,570 --> 00:26:49,310
Speaker 1: incentive to build it. But now, like, there's no reason

510
00:26:49,410 --> 00:26:51,270
Speaker 1: if you're a voter to ever support this.

511
00:26:51,609 --> 00:26:52,189
Speaker 3: Mm-hmm.

512
00:26:52,210 --> 00:26:52,360
Speaker 2: Yep.

513
00:26:52,580 --> 00:26:55,860
Speaker 3: And I still keep thinking about that.

514
00:26:56,040 --> 00:26:58,520
Speaker 6: Like, so much of the time we've spent doing this podcast,

515
00:26:58,540 --> 00:27:01,700
Speaker 6: you're told, like, oh, well, you know, politics moves slow.

516
00:27:01,760 --> 00:27:02,179
Speaker 3: It's like that.

517
00:27:02,590 --> 00:27:05,170
Speaker 6: Obama thing, like, you know, it's a big giant boat

518
00:27:05,190 --> 00:27:06,139
Speaker 6: and you can tilt.

519
00:27:05,890 --> 00:27:06,600
Speaker 3: It a little left or right.

520
00:27:06,859 --> 00:27:08,980
Speaker 6: And then you see an issue like this where public

521
00:27:09,040 --> 00:27:11,740
Speaker 6: opinion changed so fast as everyone saw the realities of

522
00:27:11,760 --> 00:27:13,940
Speaker 6: this and went, no way do I want anything to

523
00:27:13,960 --> 00:27:14,400
Speaker 6: do with this.

524
00:27:14,760 --> 00:27:18,639
Speaker 1: I mean, I would also add, like, I think that

525
00:27:19,859 --> 00:27:23,320
Speaker 1: that Governor Gavin Newsom has been wildly disappointing in a

526
00:27:23,400 --> 00:27:26,150
Speaker 1: million different ways on his way out the door, like

527
00:27:26,190 --> 00:27:30,430
Speaker 1: from not endorsing to trying to legislate against billionaire taxes

528
00:27:30,450 --> 00:27:33,950
Speaker 1: and Like, all that stuff, if he thinks that's not

529
00:27:33,990 --> 00:27:36,010
Speaker 1: going to come back to bite him in the ass

530
00:27:36,150 --> 00:27:40,250
Speaker 1: in 2028, then when he is an area podcaster trying

531
00:27:40,270 --> 00:27:43,230
Speaker 1: to run for president, then he is, I think he's

532
00:27:43,390 --> 00:27:44,380
Speaker 1: really confused.

533
00:27:44,540 --> 00:27:46,220
Speaker 6: Yeah, it's about as good a call as it was

534
00:27:46,240 --> 00:27:47,600
Speaker 6: to podcast with Charlie Kirk.

535
00:27:48,859 --> 00:27:55,020
Speaker 1: Lindsay Owens is the president of Groundwork Collaborative. And the

536
00:27:55,300 --> 00:27:59,419
Speaker 1: author of Gouged, The End of a Fair Price and

537
00:27:59,520 --> 00:28:01,210
Speaker 1: What That Means for Your Wallet.

538
00:28:01,770 --> 00:28:04,790
Speaker 4: Welcome, welcome, Lindsay. Thanks so much for having me. It's

539
00:28:04,810 --> 00:28:05,710
Speaker 4: really nice to be back.

540
00:28:06,020 --> 00:28:10,140
Speaker 1: Your book came out. two weeks ago and you immediately

541
00:28:10,180 --> 00:28:12,720
Speaker 1: broke news and then you got pushback from all the

542
00:28:12,760 --> 00:28:15,400
Speaker 1: right people. I want you to give us the sort

543
00:28:15,440 --> 00:28:20,530
Speaker 1: of 10,000 foot pitch on what Gouged is about and

544
00:28:20,650 --> 00:28:24,510
Speaker 1: then tell us a little bit about how these corporations

545
00:28:24,810 --> 00:28:26,930
Speaker 1: immediately showed you were correct.

546
00:28:27,190 --> 00:28:31,330
Speaker 4: Yeah, great. Look, Gouged is a book about how pricing

547
00:28:31,369 --> 00:28:33,669
Speaker 4: has changed in this country. Pricing used to be simple.

548
00:28:34,109 --> 00:28:36,490
Speaker 4: Companies figure out how much it costs to make an item.

549
00:28:36,520 --> 00:28:38,820
Speaker 4: If you're going to sell lemonade, how much did the

550
00:28:38,880 --> 00:28:41,219
Speaker 4: lemons cost? How much did the sugar cost? How much

551
00:28:41,240 --> 00:28:43,340
Speaker 4: did the paper cups cost? How much did you want

552
00:28:43,360 --> 00:28:45,800
Speaker 4: to get paid for your labor? They added all that up.

553
00:28:46,160 --> 00:28:48,459
Speaker 4: They put a profit margin on top. You know, we

554
00:28:48,500 --> 00:28:50,700
Speaker 4: expect folks to make a bit of profit in their

555
00:28:50,740 --> 00:28:53,370
Speaker 4: business enterprises. And then they stuck a sticker on it.

556
00:28:53,780 --> 00:28:55,790
Speaker 4: And that's how pricing used to work in this country.

557
00:28:56,380 --> 00:28:59,530
Speaker 4: And that's not true anymore. Today, pricing is a highly

558
00:28:59,590 --> 00:29:03,910
Speaker 4: engineered science. And the new corporate credo is less about

559
00:29:04,130 --> 00:29:06,300
Speaker 4: how much it costs to make something. That's really just

560
00:29:06,340 --> 00:29:10,459
Speaker 4: the floor. Now companies are looking to charge you the

561
00:29:10,680 --> 00:29:14,220
Speaker 4: absolute maximum that you'll pay in any given situation. They're

562
00:29:14,240 --> 00:29:17,080
Speaker 4: going to charge you more for an umbrella on a

563
00:29:17,120 --> 00:29:19,480
Speaker 4: rainy day. They're going to charge you more for sunscreen

564
00:29:19,580 --> 00:29:23,060
Speaker 4: in Caesar's Palace on a hot day. But they're also

565
00:29:23,120 --> 00:29:26,690
Speaker 4: going to look into your wallet and figure out. how

566
00:29:26,750 --> 00:29:29,070
Speaker 4: much they can get you to fork over. And they've

567
00:29:29,090 --> 00:29:33,120
Speaker 4: gotten better and better at using your data, tracking your

568
00:29:33,160 --> 00:29:37,810
Speaker 4: purchase history, taking a look at your demographic information, figuring

569
00:29:37,850 --> 00:29:40,780
Speaker 4: out if you're logging on from a high income code,

570
00:29:41,540 --> 00:29:45,700
Speaker 4: and really combining all of that information in their pricing determinations.

571
00:29:46,030 --> 00:29:48,870
Speaker 4: So everything has changed in the last 20 years.

572
00:29:49,490 --> 00:29:53,070
Speaker 1: So explain to us what happened with Walmart.

573
00:29:53,110 --> 00:29:57,490
Speaker 4: So look, Walmart is not actually a huge percentage of Gouged,

574
00:29:57,540 --> 00:30:01,680
Speaker 4: but it has become an outsized percentage of the conversation

575
00:30:01,740 --> 00:30:06,540
Speaker 4: around Gouged because they weighed in. So a few days

576
00:30:06,620 --> 00:30:10,170
Speaker 4: before my book came out, The CEO wrote an open

577
00:30:10,190 --> 00:30:13,820
Speaker 4: letter about Walmart's pricing practices that felt a little bit

578
00:30:13,840 --> 00:30:18,260
Speaker 4: like a subtweet of me, but I didn't want to

579
00:30:18,280 --> 00:30:21,020
Speaker 4: make it all about me. It wasn't addressed to me. But,

580
00:30:21,060 --> 00:30:22,800
Speaker 4: you know, it echoed a lot of my language and

581
00:30:22,860 --> 00:30:25,900
Speaker 4: said things like, you know, we will price products, not people.

582
00:30:26,020 --> 00:30:28,520
Speaker 4: We're not going to use these tools. And then a

583
00:30:28,900 --> 00:30:31,900
Speaker 4: little less than a week after my book came out,

584
00:30:32,040 --> 00:30:34,780
Speaker 4: they really escalated. And this time they did write a

585
00:30:34,800 --> 00:30:37,140
Speaker 4: letter to me and they posted it on their website

586
00:30:37,220 --> 00:30:41,080
Speaker 4: and tweeted it out from their accounts, you know, accusing

587
00:30:41,140 --> 00:30:47,979
Speaker 4: me of, mischaracterizing their pricing practices and basically saying, yeah, yeah, yeah,

588
00:30:48,070 --> 00:30:51,210
Speaker 4: she talked about our patents, she talked about our earnings calls,

589
00:30:51,750 --> 00:30:54,570
Speaker 4: but don't worry, just because we built all these technologies,

590
00:30:54,630 --> 00:30:57,530
Speaker 4: just because we patented all these technologies, A, we're not

591
00:30:57,610 --> 00:31:00,090
Speaker 4: using them, and B, we would never use them.

592
00:31:00,550 --> 00:31:01,390
Speaker 1: Are they using them?

593
00:31:01,890 --> 00:31:04,830
Speaker 4: Well, it's a little hard to know, right? You know,

594
00:31:05,050 --> 00:31:09,190
Speaker 4: we don't have full information about Walmart's practices, pricing practices.

595
00:31:09,290 --> 00:31:11,770
Speaker 4: You know, you and I don't have access to their algorithms.

596
00:31:12,260 --> 00:31:15,060
Speaker 4: We don't know how their electronic shelf labels make determinations.

597
00:31:15,600 --> 00:31:21,810
Speaker 4: We don't know how their chatbot is programmed, right? But

598
00:31:21,980 --> 00:31:26,930
Speaker 4: I think the veritable arsenal of patents that they have

599
00:31:27,530 --> 00:31:30,470
Speaker 4: applied for and been granted for a whole host of

600
00:31:30,530 --> 00:31:34,250
Speaker 4: technologies to do things like change the price of mayonnaise

601
00:31:34,290 --> 00:31:38,450
Speaker 4: after we put tuna in our cart, or they have

602
00:31:38,490 --> 00:31:42,300
Speaker 4: this one patent for an appeasement engine. where they use

603
00:31:42,360 --> 00:31:47,560
Speaker 4: demographic information and information about your quote persona to determine

604
00:31:47,600 --> 00:31:50,680
Speaker 4: whether or not they need to give you a refund

605
00:31:50,720 --> 00:31:53,400
Speaker 4: or a discount when they screw up. We jokingly call

606
00:31:53,440 --> 00:31:57,480
Speaker 4: it the Karen patent because they're trying to figure out like,

607
00:31:57,560 --> 00:32:00,330
Speaker 4: can we get this person to go away and tell

608
00:32:00,350 --> 00:32:02,230
Speaker 4: them to pound sand? Or are we going to have

609
00:32:02,270 --> 00:32:05,200
Speaker 4: to apologize for the fact that they're product came a

610
00:32:05,220 --> 00:32:07,400
Speaker 4: week late and give them a coupon or a discount

611
00:32:07,440 --> 00:32:08,140
Speaker 4: or something like that.

612
00:32:08,240 --> 00:32:08,420
Speaker 3: Yeah.

613
00:32:08,880 --> 00:32:11,720
Speaker 4: So look, I mean, it is a little confusing that

614
00:32:11,740 --> 00:32:14,700
Speaker 4: they're patenting all these technologies, building all these technologies, but

615
00:32:14,920 --> 00:32:17,870
Speaker 4: not planning to use any of them and, you know,

616
00:32:17,880 --> 00:32:19,310
Speaker 4: saying they're not planning to use any of them and

617
00:32:19,350 --> 00:32:21,110
Speaker 4: saying even they would never use any of them. And

618
00:32:21,150 --> 00:32:22,790
Speaker 4: I don't know why you're building them and I don't

619
00:32:22,810 --> 00:32:27,110
Speaker 4: know why you're patenting them. So we've said, look, withdraw

620
00:32:27,130 --> 00:32:29,250
Speaker 4: your pending patents. That's easy to do. You can just

621
00:32:29,290 --> 00:32:32,730
Speaker 4: tell the patent office, never mind. They haven't agreed to

622
00:32:32,770 --> 00:32:36,250
Speaker 4: do that. We've said relinquish your existing patents, which is

623
00:32:36,290 --> 00:32:39,490
Speaker 4: something you can do. They haven't agreed to do that.

624
00:32:39,910 --> 00:32:42,940
Speaker 4: They have said that for the tuna mayo patent, the

625
00:32:43,040 --> 00:32:44,640
Speaker 4: one where you have tuna in your cart and then

626
00:32:44,680 --> 00:32:47,840
Speaker 4: the price of mayo goes up, they won't renew it.

627
00:32:48,120 --> 00:32:50,640
Speaker 4: But it doesn't expire until 2039. So it's not much

628
00:32:50,660 --> 00:32:53,760
Speaker 4: of a promise. It's an alarming story.

629
00:32:58,030 --> 00:33:01,660
Speaker 1: So all of this stuff, it is happening not just

630
00:33:01,760 --> 00:33:06,600
Speaker 1: at Walmart. It is happening everywhere. And give us more examples.

631
00:33:07,000 --> 00:33:07,220
Speaker 3: Yeah.

632
00:33:07,280 --> 00:33:09,479
Speaker 4: So in the book, I talk a lot about surveillance pricing.

633
00:33:09,520 --> 00:33:12,770
Speaker 4: This is the practice of sort of spying on you

634
00:33:12,950 --> 00:33:16,450
Speaker 4: to get better at overcharging you. I give three examples

635
00:33:16,510 --> 00:33:20,010
Speaker 4: that I think folks are really familiar with. So Expedia,

636
00:33:21,050 --> 00:33:24,040
Speaker 4: they have been found to be charging individuals more for

637
00:33:24,080 --> 00:33:26,350
Speaker 4: the exact same hotel room. So let's say you and

638
00:33:26,390 --> 00:33:28,430
Speaker 4: I were logging on to Expedia. We picked the same

639
00:33:28,490 --> 00:33:32,040
Speaker 4: hotel room in New York to stay at tonight. If

640
00:33:32,140 --> 00:33:35,020
Speaker 4: you were logging on from an IP address in a

641
00:33:35,080 --> 00:33:38,360
Speaker 4: wealthier area, you would be charged more for the exact

642
00:33:38,380 --> 00:33:42,440
Speaker 4: same hotel room than me. So that's a classic example

643
00:33:42,480 --> 00:33:46,810
Speaker 4: of surveillance pricing where they're using information about your geography

644
00:33:46,950 --> 00:33:52,010
Speaker 4: to ascertain information about your income or estimate information about

645
00:33:52,030 --> 00:33:55,340
Speaker 4: your income. and then use it to vary pricing for

646
00:33:55,360 --> 00:33:58,940
Speaker 4: the same item at the same time. Another example that

647
00:34:00,060 --> 00:34:03,960
Speaker 4: I've talked about is Princeton Review. They were charging families

648
00:34:04,010 --> 00:34:08,670
Speaker 4: more for test preparation services if they logged on from

649
00:34:08,750 --> 00:34:12,790
Speaker 4: geographies that had a higher percentage of Asian American families.

650
00:34:13,350 --> 00:34:17,060
Speaker 4: And that one is just really heartbreaking because the basic

651
00:34:17,100 --> 00:34:20,939
Speaker 4: assessment was, look, these immigrant families really want their kids

652
00:34:20,960 --> 00:34:24,000
Speaker 4: to go to college, they will spend a lot to

653
00:34:24,440 --> 00:34:26,600
Speaker 4: get their kids ready for college. And so we're going

654
00:34:26,620 --> 00:34:31,000
Speaker 4: to charge them more. Another example is Target. If you

655
00:34:31,120 --> 00:34:35,759
Speaker 4: use their app, a journalist uncovered that the prices were

656
00:34:35,800 --> 00:34:37,920
Speaker 4: going up in the app if you got to the

657
00:34:37,960 --> 00:34:42,100
Speaker 4: parking lot. So here using location tracking on your phone

658
00:34:42,520 --> 00:34:46,160
Speaker 4: to basically say, hey, if this person has taken the

659
00:34:46,200 --> 00:34:48,540
Speaker 4: trouble to drive to the store and get to the

660
00:34:48,560 --> 00:34:51,690
Speaker 4: parking lot, They're probably going to make the purchase. We

661
00:34:51,710 --> 00:34:53,390
Speaker 4: can go for a higher price point.

662
00:34:53,969 --> 00:34:54,469
Speaker 1: Weird.

663
00:34:54,850 --> 00:34:57,469
Speaker 4: Yeah, so many people are doing in-app shopping now and

664
00:34:57,530 --> 00:34:58,250
Speaker 4: store pickup.

665
00:34:58,550 --> 00:35:00,270
Speaker 1: But wouldn't it be cheaper in the store if you

666
00:35:00,290 --> 00:35:01,010
Speaker 1: went in the store?

667
00:35:01,290 --> 00:35:04,410
Speaker 4: Potentially if you walked in, but you probably didn't notice, right?

668
00:35:04,750 --> 00:35:07,549
Speaker 4: This was a journalist who did a pretty sophisticated investigation

669
00:35:07,590 --> 00:35:11,230
Speaker 4: and uncovered this. But most shoppers, you know, aren't tracking

670
00:35:11,270 --> 00:35:14,790
Speaker 4: this that closely, right? I mean, companies spend a lot

671
00:35:14,830 --> 00:35:19,090
Speaker 4: of time obscuring this, you know, obscuring these pricing practices.

672
00:35:19,719 --> 00:35:23,359
Speaker 4: And this is why Walmart, you know, was so upset. Like,

673
00:35:23,400 --> 00:35:27,680
Speaker 4: when customers find out about these patents, when they find

674
00:35:27,739 --> 00:35:31,460
Speaker 4: out about these pricing practices, they're really mad and they

675
00:35:31,520 --> 00:35:33,870
Speaker 4: vote with their wallets. They go elsewhere, right? They're going

676
00:35:33,890 --> 00:35:37,150
Speaker 4: to go shop at Aldi instead of Walmart for low-priced groceries.

677
00:35:37,890 --> 00:35:40,950
Speaker 1: Are there places that are bright spots, like places where

678
00:35:40,989 --> 00:35:44,090
Speaker 1: this is not happening or where they've chosen not to

679
00:35:44,130 --> 00:35:46,339
Speaker 1: do it so that we can all go shop there?

680
00:35:46,610 --> 00:35:46,880
Speaker 4: Yeah.

681
00:35:47,040 --> 00:35:47,219
Speaker 2: I mean.

682
00:35:47,750 --> 00:35:49,570
Speaker 4: I think Costco is a good example. It's not a

683
00:35:49,630 --> 00:35:53,540
Speaker 4: perfect example because they operate under a different model. The

684
00:35:53,600 --> 00:35:56,380
Speaker 4: membership subscription dues are a big part of their revenue.

685
00:35:56,400 --> 00:35:59,320
Speaker 4: You know, it's also a little hard to do comparison

686
00:35:59,340 --> 00:36:02,900
Speaker 4: shopping when you're at Costco because the sizes are different

687
00:36:03,219 --> 00:36:05,439
Speaker 4: than you get in most other stores and you can't

688
00:36:05,460 --> 00:36:09,360
Speaker 4: always compare unit prices. But Costco uses a cost plus model.

689
00:36:09,739 --> 00:36:13,509
Speaker 4: They cap the margin on top of how much an

690
00:36:13,530 --> 00:36:17,140
Speaker 4: item costs at like 14%. I mean, it's not thin,

691
00:36:17,480 --> 00:36:20,390
Speaker 4: it's not a thin margin, but it is capped. And

692
00:36:20,430 --> 00:36:24,290
Speaker 4: what that means is they are not basing their prices

693
00:36:24,310 --> 00:36:28,250
Speaker 4: on your willingness to pay. You are not getting, you know,

694
00:36:28,330 --> 00:36:30,530
Speaker 4: you and I are not getting different discounts or different

695
00:36:30,550 --> 00:36:33,690
Speaker 4: pricing at a store like Costco. The other example I

696
00:36:33,730 --> 00:36:40,890
Speaker 4: love is the Savannah Bananas, the baseball variety show, because

697
00:36:40,930 --> 00:36:43,549
Speaker 4: I do think, you know, sports and concerts have gotten

698
00:36:43,630 --> 00:36:46,650
Speaker 4: so expensive in this country and it's increasingly hard to

699
00:36:46,690 --> 00:36:48,750
Speaker 4: like work all week and actually enjoy a night out

700
00:36:48,770 --> 00:36:51,450
Speaker 4: at a concert or a sporting event. And that's because

701
00:36:51,530 --> 00:36:54,720
Speaker 4: companies have changed how they price concerts and sporting events.

702
00:36:54,800 --> 00:36:59,100
Speaker 4: Ticketmaster in 2011 introduced dynamic pricing and the result was predictable.

703
00:36:59,180 --> 00:37:02,910
Speaker 4: Prices are higher for tickets. FIFA over the summer for

704
00:37:02,930 --> 00:37:06,850
Speaker 4: the first time in FIFA's history used dynamic pricing in

705
00:37:06,870 --> 00:37:10,970
Speaker 4: the U.S. and the results were predictable, higher prices for tickets.

706
00:37:12,010 --> 00:37:15,350
Speaker 4: The Savannah Bananas don't price tickets dynamically. And even though

707
00:37:15,390 --> 00:37:18,890
Speaker 4: they actually have huge demand for their seats and have

708
00:37:19,030 --> 00:37:23,839
Speaker 4: sold out like, you know, Super Bowl venues, they use

709
00:37:23,880 --> 00:37:26,360
Speaker 4: a lottery system. So everyone has a chance to get

710
00:37:26,400 --> 00:37:28,980
Speaker 4: a ticket. And once you get in, you can select

711
00:37:29,020 --> 00:37:31,080
Speaker 4: a ticket at face value. They're not going to vary

712
00:37:31,120 --> 00:37:33,140
Speaker 4: the ticket price based on how many seats are left

713
00:37:33,200 --> 00:37:35,340
Speaker 4: in the stadium. So those are my favorite examples.

714
00:37:36,540 --> 00:37:44,000
Speaker 1: Airline tickets are particularly egregious and they do not make

715
00:37:44,340 --> 00:37:48,049
Speaker 1: like any, as far as I can tell, any sense.

716
00:37:48,090 --> 00:37:51,150
Speaker 1: Like a great example is like you're flying to LA

717
00:37:51,930 --> 00:37:58,370
Speaker 1: and at the 6 a.m. flight, the price is, you know, whatever, $ 400.

718
00:37:57,410 --> 00:38:01,750
Speaker 1: At the 7 a.m., it's $ 900. At the 10 a.m.,

719
00:38:01,810 --> 00:38:04,930
Speaker 1: it's $ 200. I mean, it just seems to make no sense.

720
00:38:04,989 --> 00:38:06,089
Speaker 1: Can you talk us through that?

721
00:38:06,500 --> 00:38:06,739
Speaker 3: Yeah.

722
00:38:07,239 --> 00:38:09,920
Speaker 4: I mean, look, airline prices are a problem in this

723
00:38:09,960 --> 00:38:12,240
Speaker 4: country for a couple of reasons. This is a little wonky,

724
00:38:12,300 --> 00:38:15,980
Speaker 4: but the first reason is really important. It's because airlines

725
00:38:16,020 --> 00:38:18,799
Speaker 4: are regulated only at the federal level. So in other

726
00:38:18,880 --> 00:38:22,960
Speaker 4: areas of the economy, like an entrepreneurial state governor or

727
00:38:23,060 --> 00:38:25,109
Speaker 4: state attorney general can say, you know what, I really

728
00:38:25,120 --> 00:38:27,150
Speaker 4: don't like what these companies are doing. I'm going to

729
00:38:27,160 --> 00:38:29,230
Speaker 4: make the rules stricter in New York. I'm going to

730
00:38:29,250 --> 00:38:33,069
Speaker 4: make the rules stricter in California. And those changes have

731
00:38:33,190 --> 00:38:37,000
Speaker 4: real benefits for consumers because sometimes other states follow suit.

732
00:38:37,140 --> 00:38:39,820
Speaker 4: Sometimes a company says, hey, like, I don't want to comply.

733
00:38:40,160 --> 00:38:41,620
Speaker 4: You know, I don't want to have to price one

734
00:38:41,640 --> 00:38:43,569
Speaker 4: way in California and one way in Colorado. So I'm

735
00:38:43,590 --> 00:38:46,650
Speaker 4: just going to change my policy to the California policy.

736
00:38:47,730 --> 00:38:51,069
Speaker 4: That can't happen in the airlines because state law is

737
00:38:51,170 --> 00:38:55,350
Speaker 4: preempted at the federal level for the airlines. So Mayor Pete,

738
00:38:55,530 --> 00:38:58,359
Speaker 4: when he became the secretary of transportation, did a lot

739
00:38:58,380 --> 00:39:02,260
Speaker 4: of really cool stuff to try to make airline pricing

740
00:39:02,300 --> 00:39:06,669
Speaker 4: better for consumers and And unfortunately, Duffy has basically rolled

741
00:39:06,710 --> 00:39:08,380
Speaker 4: it all back or killed it in the cradle.

742
00:39:08,390 --> 00:39:08,819
Speaker 3: I'm shocked.

743
00:39:09,200 --> 00:39:10,780
Speaker 4: I'm shocked.

744
00:39:10,820 --> 00:39:13,640
Speaker 1: The road rules guy is not a fan of saving

745
00:39:13,660 --> 00:39:15,000
Speaker 1: us money. Yes, go on. Yeah.

746
00:39:15,120 --> 00:39:17,400
Speaker 4: I mean, so it used to be that you were

747
00:39:17,420 --> 00:39:19,680
Speaker 4: going to have to, you know, get a hotel room

748
00:39:19,719 --> 00:39:22,560
Speaker 4: night and a meal ticket if your plane got canceled

749
00:39:22,580 --> 00:39:25,750
Speaker 4: and you were compensated. And that happened in Europe. Yeah.

750
00:39:26,150 --> 00:39:26,410
Speaker 1: Yeah.

751
00:39:26,910 --> 00:39:30,989
Speaker 4: And Duffy just rolled that back, right? So that's the

752
00:39:31,010 --> 00:39:33,190
Speaker 4: first thing to say about the airlines. It's really hard

753
00:39:33,270 --> 00:39:35,840
Speaker 4: to get wins for consumers. You have to do it

754
00:39:35,860 --> 00:39:38,960
Speaker 4: at the federal level. State laws are preempted and we

755
00:39:38,980 --> 00:39:42,560
Speaker 4: were making some progress and not anymore. The second thing

756
00:39:42,580 --> 00:39:44,759
Speaker 4: to say about the airlines is You know, when the

757
00:39:44,820 --> 00:39:47,360
Speaker 4: airlines were deregulated in the 70s, you know, there used

758
00:39:47,380 --> 00:39:50,600
Speaker 4: to be price controls. So airfares were controlled by the government.

759
00:39:51,239 --> 00:39:54,710
Speaker 4: And when the airlines were deregulated, the airline companies had

760
00:39:54,730 --> 00:39:56,649
Speaker 4: to figure out how to do pricing on their own.

761
00:39:56,930 --> 00:40:00,529
Speaker 4: And they actually instituted the first dynamic pricing models. The

762
00:40:00,570 --> 00:40:02,730
Speaker 4: first place in the U.S. where we got dynamic pricing

763
00:40:02,750 --> 00:40:05,330
Speaker 4: was the airlines. And most of us are used to that.

764
00:40:05,750 --> 00:40:07,410
Speaker 4: We know if there's five seats left on a plane,

765
00:40:07,430 --> 00:40:09,049
Speaker 4: we're going to pay more. If there's one seat left

766
00:40:09,090 --> 00:40:10,350
Speaker 4: on a plane, we're going to pay a lot more,

767
00:40:10,430 --> 00:40:13,529
Speaker 4: especially if we're buying a last minute ticket. But they

768
00:40:13,590 --> 00:40:17,660
Speaker 4: have really been the inventors and exporters of the worst

769
00:40:17,719 --> 00:40:20,780
Speaker 4: pricing practices in the economy. Dynamic pricing started in the airlines.

770
00:40:21,200 --> 00:40:23,799
Speaker 4: Unbundling started in the airlines. This is the idea that

771
00:40:23,840 --> 00:40:26,640
Speaker 4: like the ticket price no longer includes things like the

772
00:40:26,680 --> 00:40:33,359
Speaker 4: luggage or seat selection, right? Like they started... unbundling and

773
00:40:33,450 --> 00:40:35,509
Speaker 4: the add-on fees, which now we see in a whole

774
00:40:35,550 --> 00:40:38,049
Speaker 4: host of other areas of the economy. So they're the

775
00:40:38,070 --> 00:40:42,669
Speaker 4: worst offenders and their revenue management systems are increasingly very

776
00:40:42,730 --> 00:40:47,589
Speaker 4: sophisticated and pricing changes very frequently, which is why, you know,

777
00:40:47,660 --> 00:40:50,280
Speaker 4: when you log on in the morning, it's one price.

778
00:40:50,300 --> 00:40:52,060
Speaker 4: When you log on two hours later, if you get,

779
00:40:52,239 --> 00:40:53,799
Speaker 4: you know, you get caught up, you check your email

780
00:40:53,820 --> 00:40:55,779
Speaker 4: and forget you were booking your flight and then you

781
00:40:55,800 --> 00:40:58,180
Speaker 4: finally get back around to it. Now the price is different.

782
00:40:58,780 --> 00:41:00,990
Speaker 4: It's also why if you and your spouse sit down

783
00:41:01,030 --> 00:41:03,509
Speaker 4: and book flights, you know, at the same time on

784
00:41:03,530 --> 00:41:08,080
Speaker 4: two different computers, you often see two different prices. So look,

785
00:41:08,120 --> 00:41:10,200
Speaker 4: the airline companies are working as hard as they can

786
00:41:10,280 --> 00:41:12,180
Speaker 4: to figure out the maximum they can charge for every

787
00:41:12,219 --> 00:41:14,169
Speaker 4: seat on every plane. That's the short version.

788
00:41:15,320 --> 00:41:19,460
Speaker 1: But they don't necessarily make like do they mean does

789
00:41:19,480 --> 00:41:21,080
Speaker 1: this translate to profits?

790
00:41:21,340 --> 00:41:24,420
Speaker 4: Yeah, it's interesting. The airlines are like pretty low margin companies.

791
00:41:25,280 --> 00:41:26,870
Speaker 4: And that's why they do a lot of other things

792
00:41:26,910 --> 00:41:28,010
Speaker 4: like sell credit cards.

793
00:41:28,130 --> 00:41:28,470
Speaker 6: Right.

794
00:41:28,570 --> 00:41:33,130
Speaker 4: And, you know, make money on revolving consumer debt. But

795
00:41:33,450 --> 00:41:35,990
Speaker 4: part of the reason that they use these dynamic pricing

796
00:41:36,030 --> 00:41:39,259
Speaker 4: models is to try to get the maximum for each seat.

797
00:41:40,360 --> 00:41:43,819
Speaker 4: And the goal is to charge consumers like business travelers

798
00:41:43,920 --> 00:41:47,100
Speaker 4: who are less price sensitive because, you know, I've been there.

799
00:41:47,239 --> 00:41:49,220
Speaker 4: You're putting it on the company card. You're not worrying

800
00:41:49,260 --> 00:41:52,180
Speaker 4: about the price as much to charge them more, right?

801
00:41:52,280 --> 00:41:55,020
Speaker 4: To eke out as much as they can for every flight.

802
00:41:56,000 --> 00:41:59,920
Speaker 4: And in the book, I talk about Delta's partnership with Fetcher.

803
00:41:59,940 --> 00:42:04,200
Speaker 4: It was an AI company that really supercharged Delta's ability

804
00:42:04,300 --> 00:42:08,870
Speaker 4: to charge more for different routes without worrying about losing

805
00:42:09,790 --> 00:42:14,879
Speaker 4: customers to the competition. And Fetcher has a white paper

806
00:42:15,840 --> 00:42:19,660
Speaker 4: that explains their AI pricing model. And it has two phases.

807
00:42:19,780 --> 00:42:23,339
Speaker 4: Phase one is the exploration phase where they're learning about

808
00:42:23,380 --> 00:42:26,260
Speaker 4: your competitors, learning about your customers, learning about your roots

809
00:42:26,300 --> 00:42:29,320
Speaker 4: and their price points. And phase two, according to Fetcher's

810
00:42:29,460 --> 00:42:33,480
Speaker 4: own white paper, is the exploitation phase. where they use

811
00:42:33,520 --> 00:42:37,560
Speaker 4: all the information they've learned about customers and competitors to

812
00:42:37,580 --> 00:42:39,340
Speaker 4: start dialing up pricing aggressively.

813
00:42:39,760 --> 00:42:43,120
Speaker 1: Wow, that's so nuts. Do we see this? Are there

814
00:42:43,200 --> 00:42:46,419
Speaker 1: any places where you were like shocked at how where

815
00:42:46,480 --> 00:42:50,340
Speaker 1: they came in, you know, like hospitals or art museums

816
00:42:50,460 --> 00:42:51,600
Speaker 1: or someplace where.

817
00:42:51,480 --> 00:42:54,940
Speaker 4: You were like, whoa? Yeah, so I do think dynamic

818
00:42:54,960 --> 00:42:58,560
Speaker 4: pricing is creeping into a whole host of areas of

819
00:42:58,580 --> 00:43:01,219
Speaker 4: the economy where you wouldn't expect it. There was a

820
00:43:01,260 --> 00:43:05,120
Speaker 4: great article published from an NBC reporter a year ago

821
00:43:05,140 --> 00:43:09,360
Speaker 4: that interviewed a whole host of museums and zoos and

822
00:43:09,460 --> 00:43:14,420
Speaker 4: found that they were increasingly moving to dynamic pricing. They

823
00:43:14,440 --> 00:43:18,120
Speaker 4: had often already implemented variable pricing and, you know, some

824
00:43:18,140 --> 00:43:21,620
Speaker 4: variable pricing makes sense, right? You're a botanical garden. You're

825
00:43:21,640 --> 00:43:23,930
Speaker 4: going to charge more in the spring than in the winter.

826
00:43:24,350 --> 00:43:26,589
Speaker 4: Like there's less stuff to see in a botanical garden

827
00:43:26,610 --> 00:43:28,790
Speaker 4: in the winter. You still want people to come. You

828
00:43:28,830 --> 00:43:31,029
Speaker 4: lower your price point to try to bring folks in

829
00:43:31,090 --> 00:43:34,029
Speaker 4: to the I don't know, the greenhouse where you can

830
00:43:34,050 --> 00:43:36,029
Speaker 4: see a few things in the winter, right? Or the

831
00:43:36,050 --> 00:43:41,270
Speaker 4: bonsai collection that's inside, right? But increasingly, even museums and

832
00:43:41,310 --> 00:43:45,319
Speaker 4: zoos are moving to more sophisticated dynamic pricing to be

833
00:43:45,360 --> 00:43:47,560
Speaker 4: able to do things like raise the price point at

834
00:43:47,600 --> 00:43:49,520
Speaker 4: a zoo on a nice day or raise the price

835
00:43:49,540 --> 00:43:52,700
Speaker 4: point at a children's museum on a rainy day. And,

836
00:43:53,160 --> 00:43:56,850
Speaker 4: you know, look, maybe that's okay. But I think for

837
00:43:56,930 --> 00:44:03,290
Speaker 4: most families, predictability is important, right? And pricing volatility undermines predictability.

838
00:44:03,530 --> 00:44:05,990
Speaker 4: And predictability is important if you're on a budget, knowing

839
00:44:06,050 --> 00:44:07,850
Speaker 4: how much you're going to have to shell out for

840
00:44:07,870 --> 00:44:10,190
Speaker 4: a vacation, knowing how much you're going to have to

841
00:44:10,230 --> 00:44:13,270
Speaker 4: shell out for a week of groceries. That's getting harder

842
00:44:13,310 --> 00:44:17,530
Speaker 4: and harder as dynamic pricing takes hold in the economy.

843
00:44:17,810 --> 00:44:20,250
Speaker 4: The other examples I think in the book that are

844
00:44:20,330 --> 00:44:24,870
Speaker 4: so surprising are the ways in which new algorithms are

845
00:44:24,950 --> 00:44:30,450
Speaker 4: allowing competitors to coordinate, even in industries that aren't very concentrated.

846
00:44:30,850 --> 00:44:32,489
Speaker 4: So it used to be that if you wanted to

847
00:44:32,530 --> 00:44:35,589
Speaker 4: run like a price fixing play, it would really only

848
00:44:35,630 --> 00:44:38,190
Speaker 4: be possible in an arena where there were three or

849
00:44:38,230 --> 00:44:40,870
Speaker 4: four players because you've got to pull everyone together, right?

850
00:44:40,890 --> 00:44:44,169
Speaker 4: You've got to get in your smoky backfilled room. You've

851
00:44:44,360 --> 00:44:47,960
Speaker 4: got your smoky room. You've got to hatch your plan.

852
00:44:48,000 --> 00:44:51,660
Speaker 4: You've got to lock arms and maybe do a blood

853
00:44:51,780 --> 00:44:53,920
Speaker 4: oath or whatever and make sure there are going to

854
00:44:53,940 --> 00:44:56,239
Speaker 4: be no defectors because if someone defects, they're going to

855
00:44:56,300 --> 00:45:00,640
Speaker 4: undercut you and then your scheme is gone. But now

856
00:45:01,300 --> 00:45:07,110
Speaker 4: there are so many algorithms that allow competitors across large

857
00:45:07,170 --> 00:45:12,489
Speaker 4: swaths of the economy to come together, share competitively sensitive information,

858
00:45:12,510 --> 00:45:16,870
Speaker 4: and and align on pricing, which eliminates some of the

859
00:45:16,910 --> 00:45:21,520
Speaker 4: coordination costs of price fixing. And where we see it now,

860
00:45:21,920 --> 00:45:24,600
Speaker 4: there are two really good examples. The first is RealPage,

861
00:45:24,620 --> 00:45:28,160
Speaker 4: a real estate tech company that the Biden administration went

862
00:45:28,260 --> 00:45:31,420
Speaker 4: after for price fixing that built an app that was

863
00:45:31,480 --> 00:45:35,009
Speaker 4: allowing landlords to coalesce around higher rent prices. And it

864
00:45:35,050 --> 00:45:37,690
Speaker 4: was resulting in rent prices that were like $ 120 a

865
00:45:37,730 --> 00:45:41,710
Speaker 4: month more expensive in cities like Denver. The Trump administration,

866
00:45:41,750 --> 00:45:44,710
Speaker 4: of course, immediately settled with RealPage for a slap on

867
00:45:44,730 --> 00:45:48,529
Speaker 4: the wrist. The second is a company called Calibrate. There's

868
00:45:48,550 --> 00:45:52,430
Speaker 4: now a class action lawsuit in California against Calibrate. This

869
00:45:52,469 --> 00:45:54,700
Speaker 4: was similar to RealPage. It was an app that was

870
00:45:54,739 --> 00:45:59,200
Speaker 4: letting gas station retailers across the state coalesce around high prices.

871
00:45:59,600 --> 00:46:02,620
Speaker 4: The class action alleges that it was 30 cents a

872
00:46:02,640 --> 00:46:04,940
Speaker 4: gallon more than it would have been in a competitive market.

873
00:46:05,120 --> 00:46:07,040
Speaker 4: I mean, you think about how high gas prices are

874
00:46:07,080 --> 00:46:09,660
Speaker 4: because of Trump's war in Iran. you add on a

875
00:46:09,800 --> 00:46:13,100
Speaker 4: price-fixing tax of 30 cents a gallon, I mean, that's

876
00:46:13,140 --> 00:46:16,500
Speaker 4: pretty brutal. But the ways in which, you know, a

877
00:46:16,980 --> 00:46:19,200
Speaker 4: big point in my book is, look, these impulses aren't new.

878
00:46:19,580 --> 00:46:22,220
Speaker 4: There were always companies that were out to gouge or

879
00:46:22,260 --> 00:46:24,460
Speaker 4: make an extra buck or nickel and dime you or

880
00:46:24,540 --> 00:46:28,239
Speaker 4: squeeze you. What's new is really the technologies that these

881
00:46:28,300 --> 00:46:32,210
Speaker 4: companies have at their disposal now. The means to execute

882
00:46:32,330 --> 00:46:36,500
Speaker 4: this has grown more sophisticated and It's easier to hide

883
00:46:36,540 --> 00:46:39,380
Speaker 4: it from us. We can't always tell what's happening. We

884
00:46:39,420 --> 00:46:42,370
Speaker 4: think it's the war in Iran that's resulting in high prices. Well,

885
00:46:42,430 --> 00:46:46,490
Speaker 4: that's true. And also it's companies picking our pockets.

886
00:46:46,670 --> 00:46:50,750
Speaker 1: So I want you to tell us what this is

887
00:46:51,070 --> 00:46:54,830
Speaker 1: unpopular and we could see administrate. Obviously, this is like

888
00:46:54,870 --> 00:46:58,160
Speaker 1: this the understatement of the year, but it's unpopular and

889
00:46:58,180 --> 00:47:01,820
Speaker 1: you could see a Democrat vote. House, the Democratic House

890
00:47:01,940 --> 00:47:05,960
Speaker 1: and Senate, and maybe when there's a, you know, trying.

891
00:47:06,040 --> 00:47:09,760
Speaker 1: And since Trump is so erratic, maybe trying to pass

892
00:47:09,820 --> 00:47:10,480
Speaker 1: something here.

893
00:47:10,820 --> 00:47:12,760
Speaker 4: What would be if you could do?

894
00:47:13,500 --> 00:47:16,359
Speaker 1: Obviously, there's probably a plethora of things you would need

895
00:47:16,390 --> 00:47:20,089
Speaker 1: to do. But just give me a top line. Like

896
00:47:20,190 --> 00:47:23,830
Speaker 1: if you're a Democratic elected trying to legislate, what would

897
00:47:23,870 --> 00:47:24,609
Speaker 1: the top line be?

898
00:47:25,010 --> 00:47:26,870
Speaker 4: Yeah. I mean, the first thing I'll say is I

899
00:47:26,890 --> 00:47:30,410
Speaker 4: think investigations are really critical here. Walmart says they're not

900
00:47:30,489 --> 00:47:32,650
Speaker 4: using this stuff. They have an arsenal of patents for

901
00:47:32,690 --> 00:47:36,510
Speaker 4: this stuff. Congress should subpoena Walmart in the new Congress.

902
00:47:36,550 --> 00:47:39,830
Speaker 4: The Democrats should subpoena Walmart. They should bring the CEO

903
00:47:39,950 --> 00:47:42,530
Speaker 4: in to answer questions under oath in front of Congress

904
00:47:42,840 --> 00:47:45,160
Speaker 4: so that Americans can learn for themselves what's going on.

905
00:47:45,540 --> 00:47:49,500
Speaker 4: So I think oversight, authority here, investigations by Congress in

906
00:47:49,540 --> 00:47:52,680
Speaker 4: January are critically important. I think the American people want

907
00:47:52,700 --> 00:47:56,000
Speaker 4: to see Congress standing up to corporate America. They want

908
00:47:56,020 --> 00:47:59,250
Speaker 4: to understand more how these guys are getting away with it.

909
00:47:59,350 --> 00:48:01,850
Speaker 4: They think Democrats in Congress are bought and paid for

910
00:48:02,290 --> 00:48:04,650
Speaker 4: by these companies. This is a good way to show

911
00:48:04,730 --> 00:48:07,629
Speaker 4: that you're willing to stand up to them. So that's

912
00:48:07,670 --> 00:48:12,170
Speaker 4: my number one, like first step recommendation. My second recommendation

913
00:48:12,370 --> 00:48:15,790
Speaker 4: is as part of the book and additionally, my think

914
00:48:15,810 --> 00:48:18,390
Speaker 4: tank has released something we call a shopper's bill of rights.

915
00:48:18,830 --> 00:48:22,069
Speaker 4: It is 12 basic rights and guarantees that we think

916
00:48:22,090 --> 00:48:25,930
Speaker 4: shoppers deserve in the modern era, in the era of e-commerce,

917
00:48:26,070 --> 00:48:29,180
Speaker 4: in the era of high tech, scan-and-go technology and smart

918
00:48:29,219 --> 00:48:33,320
Speaker 4: cards in the grocery store in the era increasingly of

919
00:48:33,340 --> 00:48:38,080
Speaker 4: AI-assisted shopping, personal shopping assistance, all of this. JFK had

920
00:48:38,120 --> 00:48:41,680
Speaker 4: a consumer bill of rates, and we got a lot

921
00:48:41,760 --> 00:48:44,350
Speaker 4: as a result of that initiative, including the Consumer Product

922
00:48:44,370 --> 00:48:48,310
Speaker 4: Safety Commission, which makes sure that toasters don't blow our

923
00:48:48,350 --> 00:48:51,550
Speaker 4: houses up, right? And the government can issue a recall

924
00:48:51,590 --> 00:48:53,710
Speaker 4: when a product is unsafe. I think it's time to

925
00:48:53,750 --> 00:48:56,570
Speaker 4: get some of those basic guarantees and The guarantees in

926
00:48:56,590 --> 00:48:58,989
Speaker 4: the Shopper's Bill of Rights include things like a ban

927
00:48:59,030 --> 00:49:01,290
Speaker 4: on surveillance pricing so that we all have access to

928
00:49:01,570 --> 00:49:04,489
Speaker 4: one fair price and we aren't charged different amounts for

929
00:49:04,510 --> 00:49:07,140
Speaker 4: the same items. But they also include the right to

930
00:49:07,180 --> 00:49:10,779
Speaker 4: a refund without battling a chatbot for three hours. You know,

931
00:49:10,800 --> 00:49:13,640
Speaker 4: these companies will auto-populate your credit card when it's time

932
00:49:13,660 --> 00:49:15,140
Speaker 4: to take your money. But when it's time to give

933
00:49:15,160 --> 00:49:17,870
Speaker 4: you a refund, it's three hours with a chatbot and

934
00:49:17,890 --> 00:49:21,390
Speaker 4: a gift card, right, or a store credit. So I think,

935
00:49:21,410 --> 00:49:26,239
Speaker 4: you know, basic, basic guarantees and protections like that, you know,

936
00:49:26,280 --> 00:49:28,380
Speaker 4: haven't been available to most of us for some time.

937
00:49:28,430 --> 00:49:32,140
Speaker 4: I think it's time to restore those protections and enhance

938
00:49:32,180 --> 00:49:35,020
Speaker 4: the protections to make sure that shoppers are protected in

939
00:49:35,040 --> 00:49:36,660
Speaker 4: the age of agentic commerce as well.

940
00:49:36,739 --> 00:49:36,879
Speaker 3: So.

941
00:49:37,760 --> 00:49:40,940
Speaker 4: Shopper's Bill of Rights. We have 12 different rights, but

942
00:49:40,960 --> 00:49:41,920
Speaker 4: that's what we're hoping for.

943
00:49:42,340 --> 00:49:44,570
Speaker 1: Lindsay Owens, thank you. Thank you.

944
00:49:44,890 --> 00:49:45,790
Speaker 4: Thanks so much for having me.

945
00:49:45,810 --> 00:49:50,509
Speaker 6: And now your moment of fuckery.

946
00:49:50,730 --> 00:49:54,509
Speaker 1: Rick Wilson. Hey, Molly. What's your moment of fuckery?

947
00:49:54,950 --> 00:49:58,880
Speaker 3: My moment is good trouble fuckery this week. It is

948
00:49:58,920 --> 00:50:03,580
Speaker 3: the MAGA flip out, the MAGA insane overreaction to the

949
00:50:03,620 --> 00:50:06,940
Speaker 3: fact that both Barack Obama and Bill Clinton over the

950
00:50:06,980 --> 00:50:10,069
Speaker 3: last couple of days have been trolling Trump about the

951
00:50:10,130 --> 00:50:14,960
Speaker 3: White House. Obama said it looks like Saddam.

952
00:50:14,620 --> 00:50:19,790
Speaker 1: Decorated a cheesecake factory. It's funny, too, because they're always so,

953
00:50:20,650 --> 00:50:23,819
Speaker 1: you know, this crew is so mean. But then when

954
00:50:23,840 --> 00:50:26,960
Speaker 1: you say anything mean to them, they're so sensitive.

955
00:50:27,500 --> 00:50:30,239
Speaker 3: Scott Jennings is like, Barack Obama has never been to

956
00:50:30,280 --> 00:50:31,260
Speaker 3: a Cheesecake Factory.

957
00:50:31,320 --> 00:50:34,100
Speaker 5: He's a wealthy, wealthy, wealthy billionaire.

958
00:50:34,140 --> 00:50:36,859
Speaker 3: He's too rich to know about Cheesecake Factory. I mean,

959
00:50:37,200 --> 00:50:40,759
Speaker 3: Scott Jennings losing his shit about it was like the

960
00:50:40,800 --> 00:50:41,740
Speaker 3: missile has hit home.

961
00:50:42,110 --> 00:50:42,930
Speaker 1: Yeah, it's really true.

962
00:50:42,969 --> 00:50:45,450
Speaker 3: And other MAGA influencers are just like, how dare he

963
00:50:45,469 --> 00:50:47,890
Speaker 3: speak of the president's elegant decorating style?

964
00:50:47,910 --> 00:50:51,450
Speaker 1: That's right. The man who made fun of disabled people,

965
00:50:51,469 --> 00:50:55,820
Speaker 1: who said that E. Jean Carroll was not attractive enough

966
00:50:55,860 --> 00:51:00,500
Speaker 1: to rape. That guy, how dare you say he doesn't

967
00:51:00,540 --> 00:51:01,420
Speaker 1: decorate well?

968
00:51:01,719 --> 00:51:06,060
Speaker 3: Right. The guy who calls people pedophiles and transgender and

969
00:51:06,160 --> 00:51:09,859
Speaker 3: freaks and- Gay. And every other name in the book, they're-

970
00:51:12,210 --> 00:51:16,009
Speaker 3: the snowflake crew around him, particularly on the matter of

971
00:51:16,070 --> 00:51:21,160
Speaker 3: Trump's incredibly vulgar decorating style. And I will say, I

972
00:51:21,180 --> 00:51:23,920
Speaker 3: was the first with the Saddam Liberace jokes back in

973
00:51:23,960 --> 00:51:25,580
Speaker 3: my first book. Thank you very much.

974
00:51:25,880 --> 00:51:26,239
Speaker 1: You're welcome.

975
00:51:26,260 --> 00:51:29,080
Speaker 3: However, I'm glad it has gone mainstream. That is some

976
00:51:29,160 --> 00:51:29,860
Speaker 3: good fuckery.

977
00:51:30,180 --> 00:51:32,640
Speaker 1: That's right. And he really does decorate. I mean, the

978
00:51:32,680 --> 00:51:37,430
Speaker 1: Saddam comparisons really do, they really are apt when it

979
00:51:37,450 --> 00:51:38,049
Speaker 1: comes to his style.

980
00:51:38,070 --> 00:51:39,270
Speaker 3: Yeah, guys, go back and take a look at pictures

981
00:51:39,290 --> 00:51:43,549
Speaker 3: of the Saddam palaces before the war. There's an inspo there.

982
00:51:43,989 --> 00:51:45,310
Speaker 3: It's a mood board for Trump.

983
00:51:45,350 --> 00:51:49,610
Speaker 1: That's right. Donald Trump's Pinterest board is all just Saddam

984
00:51:49,650 --> 00:51:52,330
Speaker 1: Hussein photos. Rick Wilson.

985
00:51:52,350 --> 00:51:53,930
Speaker 3: All right. See you next week.

986
00:51:53,950 --> 00:51:59,150
Speaker 1: Next week. That's it for this episode of Fast Politics.

987
00:51:59,610 --> 00:52:05,510
Speaker 1: Tune in every Monday, Wednesday, Thursday, and Saturday to hear

988
00:52:05,630 --> 00:52:09,890
Speaker 1: the best minds in politics make sense of all this chaos.

989
00:52:10,360 --> 00:52:13,080
Speaker 1: If you enjoy this podcast, please send it to a

990
00:52:13,160 --> 00:52:17,160
Speaker 1: friend and keep the conversation going. Thanks for listening.