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Speaker 1: Bloomberg Audio Studios.

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Speaker 2: Podcasts. Radio.

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Speaker 1: News.

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Speaker 3: Hello and welcome to another episode of the All Thoughts Podcast.

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Speaker 4: I'm Tracy Allaway.

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Speaker 5: And I'm Joe Wiesenthal.

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Speaker 4: Joe, have you tried Muse yet?

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Speaker 5: Muse. Actually, I did use it.

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Speaker 2: And I found it to be very impressive on day one.

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Speaker 2: And it actually saved me several thousand dollars because it

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Speaker 2: identified something in email that I had missed. But then

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Speaker 2: I haven't used it since. And I've actually seen the stats.

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Speaker 2: A bunch of people installed it and usage is tailed off.

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Speaker 2: But it was like, whoa, this is very powerful.

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Speaker 4: And what did it identify?

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Speaker 2: It was dealing with something with my house or something.

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Speaker 2: And someone sent me a long email. And it was

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Speaker 2: so long that I didn't bother to read the last paragraph.

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Speaker 2: And it turned out there were some important details.

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Speaker 1: In that paragraph.

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Speaker 3: Okay.

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Speaker 4: So it read for you.

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Speaker 5: Yeah, it read for me.

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Speaker 4: All right.

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Speaker 3: I thought you were going to say something about the

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Speaker 3: actual wallet and shopping aspect. Because one of the things

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Speaker 3: that's being pitched when it comes to Muse is this idea, well,

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Speaker 3: now you have a personal shopper kind of at your fingertips.

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Speaker 3: And so there's A democratization of shopping where you can

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Speaker 3: tell the agent to go out and find deals for

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Speaker 3: you and scan thousands of prices and come up with,

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Speaker 3: I guess, the best opportunity for you.

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Speaker 5: No, it's totally true.

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Speaker 2: You know, obviously, look, we've had like price comparison engines before,

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Speaker 2: whether it comes to like airlines, you know, booking.com, compare

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Speaker 2: prices and so forth. But obviously, like this idea of

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Speaker 2: like this complete end to endness, it may get to

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Speaker 2: the point or they hope to get it to the

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Speaker 2: point where it's like it orders the trip before you

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Speaker 2: knew you wanted to take a trip or whatever. Like

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Speaker 2: possibilities like that. I still think and this I think

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Speaker 2: this will be very temporary. Like it doesn't quite work

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Speaker 2: in the sense that for many things, if you buy

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Speaker 2: them online through an agent, it does this weird thing

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Speaker 2: where it like opens a browser. Then the mouse moves

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Speaker 2: really slowly in there. I don't think that's like the

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Speaker 2: future of commerce as a computer using a browser that

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Speaker 2: was designed for humans. Nonetheless, the broad premise clearly is

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Speaker 2: going to be a bigger and bigger deal in our lives.

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Speaker 3: Now, the flip side of buyers having AI powered shopping

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Speaker 3: solutions is that. The sellers also have them, right? And

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Speaker 3: this has been going on for some time. We've talked

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Speaker 3: on the show before about things like algorithmic pricing, dynamic pricing.

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Speaker 3: But now you can imagine a world where that is

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Speaker 3: just supercharged, right?

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Speaker 5: Yeah.

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Speaker 2: And so then you get this arms race phenomenon where

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Speaker 2: both sides have their agents trying to battle with other agents.

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Speaker 2: And unfortunately, you know, probably the vendor... has more data, right?

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Speaker 2: It's going to have an information advantage over you. Maybe

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Speaker 2: there are going to be companies that try to stop that.

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Speaker 5: Whatever. It could just be that the only real winner.

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Speaker 2: Are the AI companies that both sides are sending a

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Speaker 2: lot of money to to win this arms race. But yeah,

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Speaker 2: like the future of shopping, the future of pricing, very

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Speaker 2: much wide open right now.

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Speaker 1: Yeah.

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Speaker 3: So we do, in fact, have the perfect guest to

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Speaker 3: talk about all of this. Someone who just wrote a

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Speaker 3: book that does a deep dive, basically, into a lot

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Speaker 3: of the pricing strategies that companies are pursuing nowadays and

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Speaker 3: is doing some interesting research when it comes to algorithmic pricing,

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Speaker 3: AI-powered algorithmic pricing, I should say. Without further ado, we

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Speaker 3: have Lindsay Owen. She is the author of the new book, Gouged,

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Speaker 3: The End of a Fair Price and What That Means

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Speaker 3: for Your Wallet. She is also, of course, president of

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Speaker 3: Groundwork Collaborative. So Lindsay, thank you so much for coming

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Speaker 3: on the show.

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Speaker 1: Thanks so much for having me.

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Speaker 3: Why don't we start with semantics? Because we hear all

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Speaker 3: these words, right? We hear like algorithmic pricing, dynamic pricing.

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Speaker 3: The companies themselves often have slightly different terms that they use,

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Speaker 3: like profit-led pricing and things like that.

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Speaker 4: What are all these terms and how do you think

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Speaker 4: about it in your head?

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Speaker 1: Yeah, the main thing to think about is that pricing

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Speaker 1: has really changed as we know it. It used to

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Speaker 1: be that pricing was pretty simple, right? Companies figured out

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Speaker 1: how much it costs to make an item. They put

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Speaker 1: their margin on top. They put it on a sticker.

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Speaker 1: And that's really not how pricing works anymore. It's a

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Speaker 1: highly engineered science. And companies hire physicists to help them

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Speaker 1: do pricing. They buy AI companies to build algorithms and

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Speaker 1: to run experiments on their platforms to get better and

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Speaker 1: better at determining what's the optimal price and how much

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Speaker 1: you'll pay for really everything in your grocery cart. So

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Speaker 1: pricing has really changed. The terms are useful, I think,

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Speaker 1: to explain. Algorithmic pricing, I think, is really just an

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Speaker 1: umbrella term for pricing that involves an algorithm. There's a

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Speaker 1: formula behind it. Dynamic pricing, we typically think of as

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Speaker 1: pricing that varies based on market conditions, changes in supply

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Speaker 1: and demand.

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Speaker 4: Oh, personalized pricing. I forgot that one, too.

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Speaker 1: Personalized pricing is really the same thing as surveillance pricing,

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Speaker 1: which economists would call first-degree price discrimination. So this is

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Speaker 1: a company that's setting a price just for you, ideally

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Speaker 1: based on information that gives them a sense of how

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Speaker 1: much they can get you to pay for it.

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Speaker 2: I might ask a bunch of devil's advocate questions during

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Speaker 2: this interview. I don't like the idea of okay, someone

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Speaker 2: that's like, oh, it's Joe Weisenthal shopping for this. He

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Speaker 2: works at Bloomberg, so he probably gets paid pretty well,

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Speaker 2: et cetera. So we're going to find the optimal price

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Speaker 2: point that he might be willing to say yes to.

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Speaker 5: I don't love that.

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Speaker 2: On the other hand, Could this be used for someone

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Speaker 2: who is in a lot worse condition than me, who

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Speaker 2: the algorithm determines does not have as good of a

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Speaker 2: job I have? And maybe they get a really good price.

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Speaker 4: Means testing for Big Macs?

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Speaker 5: Yeah.

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Speaker 1: That's right. Well, the first thing we should say is

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Speaker 1: this is definitely going to happen to you. And we've

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Speaker 1: got some really good examples of this. So Expedia has

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Speaker 1: been found to charge more for the exact same Manhattan

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Speaker 1: hotel room if you log on from the Bay Area.

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Speaker 1: where they think you're more affluent. And a lot more, right?

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Speaker 1: So this is going to happen. We have now a

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Speaker 1: really interesting study of agentic commerce that just came out.

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Speaker 1: They did an experiment with 325,000 agents and they asked

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Speaker 1: the agents to go find the cheapest item.

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Speaker 3: Okay.

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Speaker 1: Even when directed explicitly to go get the cheapest item,

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Speaker 1: the agents rooted around in your email inbox, identified that

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Speaker 1: you could indeed afford more than the cheapest option and

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Speaker 1: brought back a more expensive option.

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Speaker 2: So an agent that's working on my behalf, are you

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Speaker 2: saying would not actually be working on my behalf?

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Speaker 1: That's exactly right. That's what the experiment discovered. They asked

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Speaker 1: the agents to buy flights. These were pretty big ticket items,

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Speaker 1: health insurance plans, and to select a graduate program. And

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Speaker 1: even if you said, bring me the cheapest health insurance plan,

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Speaker 1: once the agent figured out a little bit about your

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Speaker 1: financial profile, basically they said, hey, I think this guy

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Speaker 1: can afford the gold plan.

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Speaker 3: What's your thesis for what's going on there? Because I

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Speaker 3: can imagine maybe something like Muse. I mean, Meta is

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Speaker 3: also an advertising platform, right? So maybe it wants to

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Speaker 3: route some business to particular companies that advertise a lot

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Speaker 3: on its platform, but maybe there's something.

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Speaker 4: Else going on too.

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Speaker 1: Yeah, so we have one other study that gives us

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Speaker 1: a little bit of a hint here. This one is

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Speaker 1: not looking at agents, but it looked at 13 different LLMs.

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Speaker 1: And what they found is, again, even when directed to

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Speaker 1: bring back the cheapest product, they brought back the sponsored product.

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Speaker 1: And often the sponsored product was more expensive.

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Speaker 3: Can you tell us just by way of another example,

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Speaker 3: you did this really big Instacart study and it was

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Speaker 3: kind of interesting to read that you used to work

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Speaker 3: in retail.

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Speaker 1: Yeah.

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Speaker 4: And that your nightmare was like the, what do they

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Speaker 4: call them? The secret shoppers. Secret shoppers.

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Speaker 3: And now you get to hire a bunch of secret

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Speaker 3: shoppers basically and turn the tables.

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Speaker 4: What did you find?

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Speaker 1: Yeah, the secret shopper was always interesting because it was

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Speaker 1: unclear whether you were ever actually secret shopped. But it

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Speaker 1: was a sort of management tool of surveillance. Like, you

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Speaker 1: better behave because at any point somebody could come in

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Speaker 1: and secret shop. Like, every customer could be actually working

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Speaker 1: for HQ, right? So you've got to make sure you

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Speaker 1: fold everything well and you put the clothes on the

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Speaker 1: rack in size order, things like that. So we recruited

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Speaker 1: 400 secret shoppers. We worked with Consumer Reports and More

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Speaker 1: Perfect Union. And then we had them fire up the

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Speaker 1: Instacart app, shop in four different locations across the country.

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Speaker 1: at the exact same time, buy the exact same groceries

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Speaker 1: for pickup, so not delivery, from the same grocery store.

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Speaker 1: 23 items. What we found, 75% of the items were

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Speaker 1: differently priced for different shoppers. Same time, same place. The

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Speaker 1: differences were as big as 23%. So you could be

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Speaker 1: charged 23% more for a dozen Lucerne eggs than I

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Speaker 1: was charged from the same Safeway in Washington, D.C. at

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Speaker 1: the exact same time. We estimated that Because of these differences,

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Speaker 1: some families could end up paying as much as $ 1, 200

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Speaker 1: more in a given year. This was based on Instacart's

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Speaker 1: own estimate of how much a household of four spent shopping.

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Speaker 1: And I think it really was, you know, it was

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Speaker 1: a blockbuster report, right? It was the cover of the

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Speaker 1: New York Post. You don't usually see a think tank

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Speaker 1: paper on the cover of the New York Post. Because

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Speaker 1: I think what it illuminated is that something that we

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Speaker 1: all took for granted, fairness in pricing, has eroded. This

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Speaker 1: idea that you and I went to the grocery store,

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Speaker 1: we stood in line next to each other with the

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Speaker 1: same item, we expect to pay the same amount. And

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Speaker 1: that's no longer the case.

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Speaker 4: So you talk about.

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Speaker 3: This in the book, but the idea of one price

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Speaker 3: that everyone can see is also, I guess, kind of

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Speaker 3: new in American history. And I didn't realize apparently there

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Speaker 3: are Quaker roots to public price labels.

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Speaker 1: Yeah, for thousands of years, we haggled across the globe.

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Speaker 1: And there was haggling in the U.S. context as well.

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Speaker 1: The Puritans were hagglers. And the Quakers didn't like it.

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Speaker 1: They felt like there were moral problems with haggling. All

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Speaker 1: men are created equal under God, they thought, and we

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Speaker 1: should pay the same amount for a bushel of apples

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Speaker 1: at the Quaker marketplace. So they eschewed haggling and moved

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Speaker 1: to fixed prices. And then John Wanamaker, the local Philadelphia

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Speaker 1: retailer and his iconic American Wanamaker's department store, he was

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Speaker 1: said to be a religious man himself, a Presbyterian, but

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Speaker 1: inspired by the local Quakers nearby. And and instituted the

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Speaker 1: first fixed price, the price tag, in Wanamaker's department store

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Speaker 1: in the late 1870s. So we've had a price tag

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Speaker 1: for about 150 years in this country, and I think

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Speaker 1: it's given us a lot. It gives you predictability. Yeah.

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Speaker 1: So you can shop on a budget. You know how

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Speaker 1: much something is going to cost in the grocery store.

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Speaker 1: You used to be able to get a Sunday circular

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Speaker 1: where you knew what the prices of grocery would be

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Speaker 1: for the entire week, right? A pound of meat, a

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Speaker 1: pound of apples. It also offered transparency, which is pretty

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Speaker 1: important for comparison shopping. And comparison shopping is, of course,

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Speaker 1: important if you're on a budget, but it's also the

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Speaker 1: way in which prices are kept competitive in the entire economy, right?

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Speaker 2: That's interesting to think about fixed prices as a public good,

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Speaker 2: in a sense, right? That like, okay, if there is

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Speaker 2: just a price that everybody knows, and it seems like

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Speaker 2: that's what you're getting at, that there is a positive

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Speaker 2: externality from... this is the price and everyone knows it

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Speaker 2: and everyone can plan around it.

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Speaker 1: Absolutely. I think the predictability is core. I do, by

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Speaker 1: the way, think comparison shopping is being eroded by all

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Speaker 1: of the volatility in pricing right now. And I think

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Speaker 1: budget shoppers who are really essential for keeping our economy healthy, right?

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Speaker 1: When people have to price to include budget shoppers, that's

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Speaker 1: good because that keeps prices low for all of us.

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Speaker 1: When they are able to be picked off You know,

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Speaker 1: if a store can identify who they are and quietly

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Speaker 1: push them a discount, prices will go up for the

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Speaker 1: rest of us.

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Speaker 2: Now, when you put research like that on the cover

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Speaker 2: of the New York Post, obviously they're going to go

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Speaker 2: with the headline. It's like, you may be paying $ 1, 200

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Speaker 2: more for groceries than someone else.

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Speaker 5: But.

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Speaker 2: you may be paying $ 1, 200 less for groceries than someone else.

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Speaker 5: That's pretty good, right?

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Speaker 1: Yeah, look, definitely it was the case that different people

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Speaker 1: in the Instacart study made out a lot better than others.

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Speaker 1: And there were sort of five distinct price groups. You know,

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Speaker 1: we do think the prices were randomly assigned. That's what

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Speaker 1: Instacart said. They were just running pretty sophisticated A-B tests.

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Speaker 1: And Instacart in 2022 bought a company called Eversight. And

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Speaker 1: this is sort of how we knew to run this

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Speaker 1: experiment because Eversight was an AI company that built a

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Speaker 1: platform to allow retailers to run pricing experiments on their

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Speaker 1: own platforms. It was sort of turnkey pricing experiments. And importantly,

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Speaker 1: without the consumer's knowledge, right? Because this isn't something that

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Speaker 1: people like when they find out they're paying more than

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Speaker 1: their friend for the same item at the same time.

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Speaker 1: And so Instacart purchased them in 2022, obviously started using

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Speaker 1: the product. And I think what we ultimately exposed with

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Speaker 1: our experiment was their experiment on shoppers.

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Speaker 3: So setting aside personalized pricing for a while, there is

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Speaker 3: a general phenomenon in the U.S. economy of prices seem

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Speaker 3: to be going up, right?

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Speaker 1: Yes.

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Speaker 4: I went to a Broadway show yesterday.

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Speaker 5: Everything's very expensive these days.

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Speaker 4: I bought one cocktail. It was a double.

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Speaker 5: $ 25.

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Speaker 4: Way off.

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Speaker 1: What? $ 48. Oh, it's crazy.

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Speaker 4: $ 48 for a double cocktail.

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Speaker 1: Holy smokes.

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Speaker 4: I probably shouldn't have.

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Speaker 5: I know.

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Speaker 4: I was surprised.

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Speaker 3: This is the David Brooks, like, But my point is, like,

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Speaker 3: I probably would have ordered more than one cocktail if

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Speaker 3: it was less than $ 48, substantially less than $ 48. But

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Speaker 3: I don't think that matters that much to the bar

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Speaker 3: at the theater, because if they're making $ 48 from a

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Speaker 3: single cocktail, then they save on labor costs and everything else.

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Speaker 3: Is this a dynamic that you see throughout the U.S. economy,

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Speaker 3: the idea of, like, companies pursuing profit over, I guess, costs?

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Speaker 1: Price over volume? Yeah.

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Speaker 5: Yeah, absolutely.

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Speaker 1: I mean, I think we're in an inflationary period. This

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Speaker 1: is not a period in which you can run an

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Speaker 1: aggressive cost-cutting play. Your costs are going up as a business,

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Speaker 1: and I think pricing is where companies have headed, and

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Speaker 1: they're really pushing the envelope there. You know, the K-shaped

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Speaker 1: economy is real. Like, we see CEOs talking a lot

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Speaker 1: about it in earnings calls. They are really testing the

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Speaker 1: outer bounds of what they can get folks with disposable

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Speaker 1: income to fork over. We did an experiment a couple

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Speaker 1: of weeks ago for the Today Show. We helped them

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Speaker 1: design it. They ran it on their production assistants. They

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Speaker 1: brought them into the conference room. And everybody fired up

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Speaker 1: StubHub and purchased or planned to purchase a Harry Styles

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Speaker 1: concert ticket for Madison Square Garden. Same night, same row,

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Speaker 1: same seat. What we found, ticket prices were constant across

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Speaker 1: all of the production assistants. Fees were varying by as

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Speaker 1: much as $ 60.

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Speaker 3: Wow.

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Speaker 1: Yes. StubHub charging $ 60 more in fees.

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Speaker 3: This is the processing fee, which I don't understand why

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Speaker 3: it exists anyway.

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Speaker 1: The fee for emailing you a QR code.

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Speaker 2: Wait, did they find that, okay, among the 12 production assistants,

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Speaker 2: they're like, yeah, I'm the rich one.

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Speaker 5: Like, yeah, I know why.

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Speaker 2: Like, were they able to intuit what it was about

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Speaker 2: themselves that the algorithm knew that.

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Speaker 5: They could get away with charging them more?

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Speaker 1: Yeah. Yeah, I mean, it's hard to pinpoint personalized pricing

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Speaker 1: on a sample of like 12 production assistants, but there

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Speaker 1: were a couple of interesting clues. So the first thing

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Speaker 1: I'll say is when they shopped in incognito mode, fee

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Speaker 1: dispersion dramatically dropped.

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Speaker 5: Oh, interesting.

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Speaker 1: So it went from $ 60 to $ 4. By the way,

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Speaker 1: StubHub responded to the Today Show and said, this isn't

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Speaker 1: personalized pricing, it's A-B testing, it's pricing experiments, and that

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Speaker 1: the incognito mode issue was sort of moot. The other

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Speaker 1: thing they found is the Android phone user was paying more. But,

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Speaker 1: you know, it's impossible to isolate whether or not there

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Speaker 1: were other aspects of her profile that could have been

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Speaker 1: driving her price point.

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Speaker 5: We had you on the podcast before, David.

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Speaker 1: Yeah.

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Speaker 2: Now, in that conversation, I remember one of the things

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Speaker 2: and this question of Uber pricing and at least the

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Speaker 2: technology existed such that if it identified that your phone

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Speaker 2: was running low on batteries, that it would charge you more.

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Speaker 2: But if I'm out at 1 a.m. and my phone

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Speaker 2: is at 5%, I happily pay more.

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Speaker 5: In fact, let's put it this way. If I'm out

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Speaker 5: at 1 a.m. and my friend is out at 1 a.m.

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Speaker 2: And his phone is at 50% and I'm at 5%,

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Speaker 2: I want my app to be outbidding for that random

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Speaker 2: driver who's in the neighborhood.

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Speaker 5: That seems great.

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Speaker 4: Because you want the ride.

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Speaker 5: I want the ride.

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Speaker 1: Absolutely.

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Speaker 2: And so it's like, I think most people, if their

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Speaker 2: phone is at 5%, they're like, yeah, let me win

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Speaker 2: this little auction that's going to occur between this driver.

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Speaker 2: Because I really, if I don't win it, then my battery,

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Speaker 2: I might not have a phone.

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Speaker 3: This is a true economist mindset. The idea that like

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Speaker 3: auctions are the best way of coming up with a price.

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Speaker 1: Well, Economists love auctions. Americans like lines. Americans hate auctions.

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Speaker 5: Yeah, I know.

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Speaker 1: I know.

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Speaker 5: But like if I'm out at 1 a.m.

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Speaker 2: And my phone's about to die and a bunch of

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Speaker 2: people want the car, I would be happy that the

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Speaker 2: app is offering to pay that driver more if he

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Speaker 2: picks me up because my phone's about to die.

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Speaker 1: Yeah, we actually found a battery example with DoorDash as well.

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Speaker 1: So we reviewed some DoorDash patents. And I should say

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Speaker 1: that DoorDash, after we exposed this patent, did withdraw the patent.

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Speaker 1: So that's an important thing to add to this story.

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Speaker 1: But the patent was for a 16-factor agitation index. that

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Speaker 1: DoorDash was building to determine.

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Speaker 5: So battery life would just be like one factor.

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Speaker 1: One of the 16 factors. Another one was how quickly

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Speaker 1: you scroll. I mean, it was basically a hangry index, right?

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Speaker 1: How quickly you scroll. Another was how jerkily you scroll.

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Speaker 1: And all of this was going into an index to

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Speaker 1: be matched to things like whether or not you were

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Speaker 1: going to get a promotion, what types of offerings you

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Speaker 1: would see first in the menu, sort of the ordering

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Speaker 1: of the menu. All of this was connected. So battery

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Speaker 1: life is definitely something that the companies see As an opportunity,

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Speaker 1: you're over a barrel when your battery life is down.

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Speaker 1: It's a little bit like, you know, there are five

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Speaker 1: bottles of water left in a hurricane. You're willing to

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Speaker 1: pay a lot for them, right?

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Speaker 3: So people think about price gouging as this loaded term.

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Speaker 3: And again, you know, per Joe's point, one man's price

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Speaker 3: gouging is another man's, I guess, legitimate profit.

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Speaker 5: Ride savior.

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Speaker 4: Ride savior, yep.

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Speaker 3: But there are actually regulations around price gouging in the U.S.

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Speaker 3: Can you explain what they are and what the definition

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Speaker 3: actually is?

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Speaker 1: Yeah, the legal definition of price gouging is pretty straightforward.

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Speaker 1: It's charging unconscionably excessive. You know, how you define that

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Speaker 1: varies a little state to state.

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Speaker 3: Still a bit of moral relativism there, but yeah.

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Speaker 1: Yeah, but it's charging unconscionably excessive prices during an emergency, basically.

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Speaker 1: It typically is limited to necessities. So if you're charging

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Speaker 1: a bunch for a Birkin bag, like you're not going

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Speaker 1: to run afoul of price gouging laws. But if you're

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Speaker 1: charging a lot for food after a hurricane.

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Speaker 3: Birkin emergency, luxury handbag emergency.

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Speaker 5: Right.

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Speaker 1: If you're charging a lot for food after a hurricane

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Speaker 1: or water, if you're charging a bunch for PPE during

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Speaker 1: a pandemic, many states actually updated or brought new price

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Speaker 1: gouging laws online during the pandemic. But more than 40

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Speaker 1: states have price gouging laws in the country. The title

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Speaker 1: of my book is certainly referring to the legal definition

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Speaker 1: of price gouging, but it's also referring to the colloquial

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Speaker 1: feeling of being ripped off or squeezed or gouged, which

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Speaker 1: I think is a word a lot of Americans are

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Speaker 1: using right now when they see their grocery bill.

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Speaker 2: Who are the pioneers of scientific pricing? Who were the

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Speaker 2: first to recognize that with technology, with the abundance of data,

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Speaker 2: that we're all the exhaustive data that we all admit

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Speaker 2: just by using devices and so forth? Who are the

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Speaker 2: pioneers that recognize that this could be used for pricing?

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Speaker 1: Yeah, I really think the OG here is the airline industry.

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Speaker 1: And there are some interesting reasons for that. One is

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Speaker 1: obviously they have a scarce resource, right? You only have

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Speaker 1: so many seats on the plane, so dynamic pricing can

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Speaker 1: be an efficient way to allocate a scarce resource. But

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Speaker 1: it's also why when you log on and there are

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Speaker 1: five seats left, the prices are high. When there's one

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Speaker 1: seat left, the price is quite high. But the airlines

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Speaker 1: get deregulated. They have price controls. And all of a sudden,

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Speaker 1: the airline executives are like, wait, how do we set prices?

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Speaker 1: And they hire a bunch of consultants, advisors to build

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Speaker 1: what becomes the first revenue management and yield management systems

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Speaker 1: really in the country. And they really pioneered the dynamic

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Speaker 1: pricing model. And it was actually pretty rare to see

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Speaker 1: dynamic pricing outside of the airlines for decades. It was

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Speaker 1: 2011 when Uber rolled out surge pricing for the first time,

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Speaker 1: Halloween actually, 2011. They said, hey, it's going to be

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Speaker 1: a busy night tonight. We expect a lot of people

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Speaker 1: out on the streets. We're going to try this new

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Speaker 1: model that's going to make sure you can get home.

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Speaker 1: Same year, Ticketmaster, 2011 as well, moved to dynamic pricing.

442
00:20:57,060 --> 00:21:00,550
Speaker 1: So we really got more widespread usage of these practices

443
00:21:01,109 --> 00:21:02,629
Speaker 1: around 2011.

444
00:21:02,630 --> 00:21:06,440
Speaker 2: Uber would say that on big nights like Halloween or

445
00:21:06,460 --> 00:21:10,939
Speaker 2: New Year's Eve, or I guess New Year's morning more typically,

446
00:21:11,420 --> 00:21:14,900
Speaker 2: that yes, prices go up, but you also need to

447
00:21:15,060 --> 00:21:16,760
Speaker 2: incentivize drivers to come out at.

448
00:21:16,800 --> 00:21:18,920
Speaker 5: One in the morning on New Year's Eve.

449
00:21:19,240 --> 00:21:22,919
Speaker 2: And that part of the purpose that surge pricing does,

450
00:21:23,090 --> 00:21:25,909
Speaker 2: one of the things that it accomplishes is it sends

451
00:21:25,990 --> 00:21:28,430
Speaker 2: a signal to driver's Get out on the road.

452
00:21:28,450 --> 00:21:29,350
Speaker 5: There's a bunch of business.

453
00:21:29,570 --> 00:21:33,010
Speaker 1: Absolutely. It brings drivers off the sidelines. You're sitting on

454
00:21:33,050 --> 00:21:34,750
Speaker 1: your couch deciding whether or not you're going to call

455
00:21:34,790 --> 00:21:37,490
Speaker 1: it a night or pick up another ride. Suddenly the

456
00:21:37,530 --> 00:21:39,990
Speaker 1: price point gets interesting. You jump in your car, right?

457
00:21:40,010 --> 00:21:41,390
Speaker 1: You put your sweatpants on. You jump in the car.

458
00:21:41,430 --> 00:21:44,790
Speaker 1: You go drive someone home. Absolutely. Here's the thing, though.

459
00:21:45,350 --> 00:21:48,270
Speaker 1: Uber's model has shifted dramatically. And how do we know?

460
00:21:48,530 --> 00:21:52,890
Speaker 1: We have run experiments on Uber, Uber Eats, and we've

461
00:21:52,930 --> 00:21:54,889
Speaker 1: done it on the driver's side, and we've done it

462
00:21:54,950 --> 00:21:57,699
Speaker 1: on the rider's side. And here's what we find. You

463
00:21:57,720 --> 00:22:00,450
Speaker 1: put a bunch of people outside a concert. Everyone's leaving

464
00:22:00,490 --> 00:22:03,130
Speaker 1: at the same time. You have them set the exact

465
00:22:03,190 --> 00:22:06,950
Speaker 1: same destination. Pickup time is the same. Destination is the same.

466
00:22:07,630 --> 00:22:12,470
Speaker 1: Uber prices vary substantially. Same is true for drivers. More

467
00:22:12,490 --> 00:22:14,450
Speaker 1: Perfect Union did an experiment where they put a bunch

468
00:22:14,470 --> 00:22:16,760
Speaker 1: of drivers in LA in a room. They had them

469
00:22:16,800 --> 00:22:20,000
Speaker 1: stick their phones on a conference table and all fire

470
00:22:20,060 --> 00:22:22,480
Speaker 1: up their app and say, I'm ready to start driving.

471
00:22:23,020 --> 00:22:25,800
Speaker 1: The rides came online. They all picked the same ride.

472
00:22:26,240 --> 00:22:29,459
Speaker 1: They were given different price points. And I think what

473
00:22:29,500 --> 00:22:32,240
Speaker 1: is going on here quite clearly is that Uber is

474
00:22:32,290 --> 00:22:35,830
Speaker 1: getting very good at figuring out the maximum amount each

475
00:22:35,930 --> 00:22:38,410
Speaker 1: rider will pay for a ride. And they're getting really

476
00:22:38,450 --> 00:22:41,210
Speaker 1: good at figuring out the minimum amount each driver will

477
00:22:41,250 --> 00:22:42,190
Speaker 1: accept for a ride.

478
00:22:42,450 --> 00:22:45,240
Speaker 3: How much of this is also a story of companies

479
00:22:45,300 --> 00:22:48,060
Speaker 3: just getting bigger? Because one of the reasons that this

480
00:22:48,100 --> 00:22:51,480
Speaker 3: would be annoying to me and maybe not to Joe,

481
00:22:51,580 --> 00:22:55,159
Speaker 3: but You know, there are limited alternatives for transportation. I

482
00:22:55,200 --> 00:22:58,070
Speaker 3: know there's Lyft and there's taxis and things like that.

483
00:22:58,160 --> 00:23:00,610
Speaker 3: So you have some options. But if everyone is doing

484
00:23:00,630 --> 00:23:01,830
Speaker 3: the same thing at the same.

485
00:23:01,710 --> 00:23:02,770
Speaker 4: Time, i.e.

486
00:23:02,830 --> 00:23:05,790
Speaker 3: Raising their prices or giving you a price that's higher

487
00:23:05,850 --> 00:23:08,570
Speaker 3: than everyone else is seeing, then your options are limited.

488
00:23:08,630 --> 00:23:11,780
Speaker 3: And certainly with something like food, where it feels like

489
00:23:12,060 --> 00:23:14,899
Speaker 3: every supermarket in the world has been raising prices and

490
00:23:14,980 --> 00:23:18,120
Speaker 3: maybe you're in a city where there's only one big

491
00:23:18,160 --> 00:23:20,320
Speaker 3: supermarket that you can legitimately shop from.

492
00:23:20,990 --> 00:23:23,240
Speaker 4: You have limited choices, right? How much of this is

493
00:23:23,280 --> 00:23:23,919
Speaker 4: about scale?

494
00:23:24,380 --> 00:23:28,200
Speaker 1: I think this is about companies moving into different profit models.

495
00:23:28,920 --> 00:23:32,560
Speaker 1: So I'll give you two examples. Kroger now doesn't just

496
00:23:32,600 --> 00:23:36,330
Speaker 1: sell groceries. They sell your data. 30% of Kroger's net

497
00:23:36,390 --> 00:23:39,910
Speaker 1: operating income comes from what they call their Alternative Profits

498
00:23:39,970 --> 00:23:42,570
Speaker 1: Book of Business. Part of what is included in the

499
00:23:42,609 --> 00:23:46,350
Speaker 1: alternative profits is the sale of your data. 95% of

500
00:23:46,410 --> 00:23:50,859
Speaker 1: Kroger transactions happen attached to loyalty cards. So they've built

501
00:23:50,980 --> 00:23:54,080
Speaker 1: really sophisticated dossiers on all of us that are pretty

502
00:23:54,140 --> 00:23:56,840
Speaker 1: valuable to external stakeholders.

503
00:23:57,300 --> 00:23:58,219
Speaker 5: Yeah, that makes sense.

504
00:23:58,460 --> 00:24:00,600
Speaker 2: By the way, you know, another thing, and I was

505
00:24:00,619 --> 00:24:04,139
Speaker 2: just thinking about the $ 48 cocktail. I think this is

506
00:24:04,160 --> 00:24:06,179
Speaker 2: an important, like you bought the cocktail.

507
00:24:06,330 --> 00:24:06,880
Speaker 4: I did, yeah.

508
00:24:07,140 --> 00:24:08,640
Speaker 5: Like this is an interesting thing.

509
00:24:08,680 --> 00:24:10,930
Speaker 4: By the way, it was my birthday. So let's just

510
00:24:10,950 --> 00:24:13,350
Speaker 4: be clear. I'm treating myself here, okay?

511
00:24:13,369 --> 00:24:15,730
Speaker 5: That is important. Happy birthday.

512
00:24:15,869 --> 00:24:16,330
Speaker 4: Thank you, Joe.

513
00:24:16,350 --> 00:24:19,230
Speaker 5: I know it was yesterday, but. Thank you. It was

514
00:24:19,250 --> 00:24:21,090
Speaker 5: Tracy's birthday, so everyone wish Tracy.

515
00:24:20,910 --> 00:24:21,820
Speaker 1: A happy birthday.

516
00:24:21,859 --> 00:24:22,580
Speaker 5: Thank you so much.

517
00:24:23,359 --> 00:24:25,560
Speaker 2: We have a lot of inflation in this country. At

518
00:24:26,600 --> 00:24:29,520
Speaker 2: least the people on the top side of the K,

519
00:24:29,540 --> 00:24:31,780
Speaker 2: they're not slowing down. That's the thing that's kind of

520
00:24:31,820 --> 00:24:34,859
Speaker 2: weird and kind of interesting to me, which is I

521
00:24:35,160 --> 00:24:37,359
Speaker 2: go out to eat and I complain.

522
00:24:37,380 --> 00:24:39,859
Speaker 5: I was like, bacon and eggs cost this much.

523
00:24:41,140 --> 00:24:43,000
Speaker 2: It didn't stop me from going the next day or

524
00:24:43,020 --> 00:24:46,520
Speaker 2: the next day, the next day. Maybe it's because of,

525
00:24:46,840 --> 00:24:49,320
Speaker 2: you know, The stock market's going up, so everyone feels

526
00:24:49,359 --> 00:24:54,340
Speaker 2: good about like their investment accounts, et cetera. Like people

527
00:24:54,359 --> 00:24:55,840
Speaker 2: complain and then they just keep doing it.

528
00:24:55,980 --> 00:24:58,600
Speaker 1: Yeah. I mean, you mentioned this idea of surveillance pricing

529
00:24:58,640 --> 00:25:01,899
Speaker 1: being used as a sort of Robin Hood in corporate America, right? Yeah, yeah.

530
00:25:02,100 --> 00:25:04,119
Speaker 1: Like you can really soak the rich once you've had

531
00:25:04,160 --> 00:25:06,810
Speaker 1: a peek into their wallets. And I absolutely think that

532
00:25:06,850 --> 00:25:10,710
Speaker 1: is possible. And I think companies will do that 100%.

533
00:25:10,710 --> 00:25:13,850
Speaker 1: I think where I am not so convinced is that

534
00:25:13,890 --> 00:25:17,070
Speaker 1: that will be used as a cross subsidy for everyone else. Mm-hmm.

535
00:25:17,380 --> 00:25:19,590
Speaker 1: And there are a couple of reasons why. The first

536
00:25:19,790 --> 00:25:23,250
Speaker 1: is willingness to pay is not always the same thing

537
00:25:23,530 --> 00:25:26,530
Speaker 1: as ability to pay. So sometimes you don't have a

538
00:25:26,550 --> 00:25:28,490
Speaker 1: lot of money, but you're willing to pay a lot

539
00:25:28,530 --> 00:25:31,200
Speaker 1: because you're over a barrel. You're a mom who told

540
00:25:31,260 --> 00:25:33,780
Speaker 1: her chat bot that her kid is sick, and now

541
00:25:33,840 --> 00:25:36,760
Speaker 1: the personal assistant goes out to get the Tylenol. I'll

542
00:25:36,780 --> 00:25:39,179
Speaker 1: probably pay 15 bucks for the children's Tylenol, right? I

543
00:25:39,200 --> 00:25:42,030
Speaker 1: want to break the fever. Sometimes you need something urgently.

544
00:25:42,070 --> 00:25:44,409
Speaker 1: You've got to get home to a funeral, right? So

545
00:25:44,450 --> 00:25:47,439
Speaker 1: I think what is most likely is Companies will use

546
00:25:47,480 --> 00:25:51,060
Speaker 1: these technologies to take a lot from the rich. They'll

547
00:25:51,119 --> 00:25:52,800
Speaker 1: use it to take as much as they can from

548
00:25:52,859 --> 00:25:57,129
Speaker 1: everyone else. But they'll also be spotting opportunities where our

549
00:25:57,150 --> 00:25:59,409
Speaker 1: willingness to pay is high, even if our ability to

550
00:25:59,450 --> 00:26:00,150
Speaker 1: pay is not.

551
00:26:00,650 --> 00:26:07,150
Speaker 3: I'm assuming that protected characteristics apply to shopping as well

552
00:26:07,210 --> 00:26:10,300
Speaker 3: as things like employment and lending. So the idea that

553
00:26:10,320 --> 00:26:14,500
Speaker 3: you can't discriminate based on race or age, gender.

554
00:26:14,600 --> 00:26:15,440
Speaker 4: That sort of thing.

555
00:26:16,510 --> 00:26:19,669
Speaker 3: However, one thing we've learned from algorithmic pricing is you

556
00:26:20,550 --> 00:26:23,530
Speaker 3: can develop a pretty good proxy for someone's age and

557
00:26:23,710 --> 00:26:26,639
Speaker 3: gender and race, the zip code of the area that

558
00:26:26,660 --> 00:26:28,200
Speaker 3: they live in. So you can tell if it's a

559
00:26:28,240 --> 00:26:32,540
Speaker 3: wealthy neighborhood or a poor neighborhood. Is anyone looking at

560
00:26:32,580 --> 00:26:35,830
Speaker 3: this on like a federal level, whether people are sort of,

561
00:26:35,840 --> 00:26:41,080
Speaker 3: I guess, getting close to discriminating based on protected characteristics

562
00:26:41,119 --> 00:26:43,200
Speaker 3: without explicitly saying that that's what they're doing?

563
00:26:43,340 --> 00:26:45,419
Speaker 1: Yeah, I don't know of anyone who's looking into it

564
00:26:45,460 --> 00:26:47,889
Speaker 1: closely right now, but yes, absolutely. What we think of

565
00:26:47,930 --> 00:26:51,490
Speaker 1: as statistical discrimination, right? You may be discriminating based on

566
00:26:51,530 --> 00:26:54,630
Speaker 1: something like zip code, but actually the end result is

567
00:26:54,990 --> 00:26:57,770
Speaker 1: you're discriminating based on race. And the best example of

568
00:26:57,810 --> 00:27:00,590
Speaker 1: this in the book is there was a big ProPublica

569
00:27:00,690 --> 00:27:05,090
Speaker 1: expose of Princeton Review. And what they found is that

570
00:27:05,130 --> 00:27:09,960
Speaker 1: Princeton Review was charging individuals more for a test prep package,

571
00:27:10,160 --> 00:27:12,260
Speaker 1: you know, so many hours of tutoring, a couple of

572
00:27:12,900 --> 00:27:17,260
Speaker 1: handbooks or whatever those things are, exercise books, if they

573
00:27:17,320 --> 00:27:21,740
Speaker 1: logged on from an IP address from a high Asian

574
00:27:21,760 --> 00:27:24,659
Speaker 1: American neighborhood where there were a lot of Asians in

575
00:27:24,700 --> 00:27:28,659
Speaker 1: the region. And the basic idea here was that the

576
00:27:28,680 --> 00:27:31,860
Speaker 1: concern was that Princeton Review had identified that there were

577
00:27:32,480 --> 00:27:34,800
Speaker 1: immigrant moms who wanted their kids to go to college

578
00:27:34,859 --> 00:27:37,500
Speaker 1: and as a result would pay another grand for the

579
00:27:38,119 --> 00:27:41,790
Speaker 1: test prep package, right? So absolutely, I think, you know,

580
00:27:41,880 --> 00:27:45,330
Speaker 1: particularly given that the United States is carved up geographically

581
00:27:45,790 --> 00:27:49,949
Speaker 1: by race, when companies are using geolocation, and that is

582
00:27:49,990 --> 00:27:52,389
Speaker 1: a classic, you know, profiling tool that's in all of

583
00:27:52,430 --> 00:27:55,290
Speaker 1: these models, right? Your IP address, where you come in from,

584
00:27:56,210 --> 00:27:58,189
Speaker 1: tracking you on your phone to know where you are.

585
00:27:58,590 --> 00:28:00,470
Speaker 1: That's going to be a real concern.

586
00:28:16,940 --> 00:28:19,500
Speaker 2: Let's talk a little bit more about your work in

587
00:28:19,520 --> 00:28:24,990
Speaker 2: the context of agentic pricing and AI services, AI bots, whatever,

588
00:28:25,330 --> 00:28:27,949
Speaker 2: going out shopping on our behalf. First of all, if

589
00:28:28,090 --> 00:28:31,990
Speaker 2: one were to find an example of an AI bot

590
00:28:32,390 --> 00:28:35,540
Speaker 2: not actually pursuing the best price because it turned out

591
00:28:35,560 --> 00:28:38,700
Speaker 2: it was either it was not working for me or

592
00:28:39,160 --> 00:28:42,540
Speaker 2: the company that put it out, since I don't know

593
00:28:42,560 --> 00:28:47,060
Speaker 2: the name, you know, was actually gearing towards sponsored. I mean,

594
00:28:47,760 --> 00:28:50,780
Speaker 2: that should be a huge scandal if that is empirically

595
00:28:50,960 --> 00:28:54,940
Speaker 2: sort of demonstrable, et cetera. But at least in theory,

596
00:28:56,120 --> 00:28:59,080
Speaker 2: a company could build a model that actually works on

597
00:28:59,140 --> 00:29:01,880
Speaker 2: behalf of the customer. And also, at least in theory,

598
00:29:02,420 --> 00:29:04,980
Speaker 2: a model could not only work on behalf of the customer,

599
00:29:05,400 --> 00:29:07,980
Speaker 2: but then at the point that the model makes the transaction,

600
00:29:08,260 --> 00:29:09,400
Speaker 2: that the end company does.

601
00:29:09,300 --> 00:29:10,459
Speaker 5: Not know who's buying it, right?

602
00:29:10,480 --> 00:29:10,560
Speaker 3: Yeah.

603
00:29:10,830 --> 00:29:14,360
Speaker 2: The way these things work typically in terms of the

604
00:29:14,420 --> 00:29:18,560
Speaker 2: transaction is that, let's say, I load my credit card

605
00:29:18,760 --> 00:29:22,360
Speaker 2: info into Muse or something like that. But then when

606
00:29:22,440 --> 00:29:25,360
Speaker 2: Muse makes the purchase, I believe if I have this correct,

607
00:29:25,500 --> 00:29:28,760
Speaker 2: Muse is not using my credit card. Muse is generating

608
00:29:28,830 --> 00:29:32,790
Speaker 2: an instant one-time credit card, maybe from a company like Stripe.

609
00:29:33,190 --> 00:29:36,330
Speaker 2: They make the transaction and then Muse buys it and

610
00:29:36,370 --> 00:29:38,710
Speaker 2: then I'm buying from Muse. So at least it seems

611
00:29:38,790 --> 00:29:44,130
Speaker 2: theoretically more than theoretically possible that agentic commerce could allow

612
00:29:44,190 --> 00:29:47,540
Speaker 2: people to buy something without that end seller having any

613
00:29:47,620 --> 00:29:49,020
Speaker 2: idea of who the actual buyer is.

614
00:29:49,260 --> 00:29:51,760
Speaker 1: Yeah, 100%. I mean, I think there are a huge

615
00:29:51,920 --> 00:29:55,540
Speaker 1: number of use cases for personal assistance in agentic commerce

616
00:29:56,000 --> 00:29:59,000
Speaker 1: that could really benefit consumers and frankly could get around

617
00:29:59,040 --> 00:30:00,940
Speaker 1: a lot of the stuff in my book. Right. So

618
00:30:01,120 --> 00:30:03,680
Speaker 1: we've written a piece at Groundwork on the annoyance economy,

619
00:30:03,790 --> 00:30:08,290
Speaker 1: the $ 165 billion a year that Americans spend and lost

620
00:30:08,350 --> 00:30:10,830
Speaker 1: time and money and things like subscription traps.

621
00:30:11,410 --> 00:30:13,210
Speaker 5: One of the big.

622
00:30:12,650 --> 00:30:16,410
Speaker 1: Advertising campaigns of Muse is I'm going to crack my

623
00:30:16,430 --> 00:30:19,700
Speaker 1: knuckles and go get to work canceling your subscriptions. And

624
00:30:19,720 --> 00:30:22,300
Speaker 1: it wasn't surprising to us to see the inertia stocks

625
00:30:22,360 --> 00:30:25,640
Speaker 1: plummet when the Muse numbers started ticking up. Right. Because

626
00:30:25,980 --> 00:30:29,380
Speaker 1: there is a lot of stuff that people leave. because

627
00:30:29,440 --> 00:30:32,230
Speaker 1: they forget about it, that Muse can go find and

628
00:30:32,290 --> 00:30:35,850
Speaker 1: handle for you. So I love the idea of folks

629
00:30:35,890 --> 00:30:38,920
Speaker 1: having help getting out of subscriptions. I love the idea

630
00:30:39,320 --> 00:30:41,880
Speaker 1: that folks would have help with comparison shopping. Comparison shopping

631
00:30:42,000 --> 00:30:45,310
Speaker 1: is a total bearer in this digital and e-commerce age

632
00:30:45,630 --> 00:30:48,890
Speaker 1: precisely because of the pricing technologies that I talk about.

633
00:30:49,050 --> 00:30:51,430
Speaker 3: The problem in my mind is the due diligence, right?

634
00:30:51,490 --> 00:30:54,130
Speaker 3: Like you're using an agent presumably because you're already time

635
00:30:54,150 --> 00:30:56,770
Speaker 3: strapped and don't have time to go look at all

636
00:30:56,790 --> 00:30:59,650
Speaker 3: of this yourself. And then you're going to review the

637
00:30:59,750 --> 00:31:02,330
Speaker 3: output of the agent and be like, well, was this

638
00:31:02,470 --> 00:31:05,050
Speaker 3: really the cheapest price? How do I know that the

639
00:31:05,130 --> 00:31:08,760
Speaker 3: agent is actually finding me the best bargain? Which, Lindsay,

640
00:31:08,840 --> 00:31:12,470
Speaker 3: by the way, is why you are proposing fiduciary duties

641
00:31:12,530 --> 00:31:13,230
Speaker 3: for AI.

642
00:31:13,090 --> 00:31:13,670
Speaker 4: Agents, right?

643
00:31:13,750 --> 00:31:13,930
Speaker 3: Yeah.

644
00:31:13,990 --> 00:31:15,930
Speaker 1: I mean, I don't think this problem is new, right?

645
00:31:16,010 --> 00:31:18,170
Speaker 1: We often, I think, with new technologies, think we need

646
00:31:18,190 --> 00:31:20,910
Speaker 1: a whole new set of considerations or a whole new

647
00:31:20,950 --> 00:31:24,270
Speaker 1: set of policies. But this is just the classic agent question.

648
00:31:24,590 --> 00:31:27,050
Speaker 1: Who does your agent work for? If it's calling itself

649
00:31:27,090 --> 00:31:30,860
Speaker 1: an agent, and particularly if it's calling itself your personal agent,

650
00:31:30,990 --> 00:31:33,170
Speaker 1: it should probably work for you and not Muse or

651
00:31:33,260 --> 00:31:37,900
Speaker 1: whoever is offering sponsored content and the highest bidder. It

652
00:31:37,920 --> 00:31:41,959
Speaker 1: should probably not be rooting around in your emails to

653
00:31:42,030 --> 00:31:45,410
Speaker 1: work against you in a setting. And so we have

654
00:31:45,530 --> 00:31:48,970
Speaker 1: imposed a best interest standard or a fiduciary duty on

655
00:31:49,050 --> 00:31:52,630
Speaker 1: agents throughout time. We tried to strengthen those roles under

656
00:31:52,730 --> 00:31:57,160
Speaker 1: Obama with a stronger fiduciary duty for financial advisors and

657
00:31:57,200 --> 00:32:00,080
Speaker 1: people who offer you financial products to make sure that

658
00:32:00,500 --> 00:32:03,400
Speaker 1: their interests are aligned with yours. Look, I think if

659
00:32:03,440 --> 00:32:06,010
Speaker 1: these agents aren't going to work in your interest, maybe

660
00:32:06,030 --> 00:32:08,080
Speaker 1: that's fine. But like you have to know.

661
00:32:08,160 --> 00:32:08,960
Speaker 5: Don't call them agents.

662
00:32:09,000 --> 00:32:11,540
Speaker 1: Yeah, they're not your agent. They're not your personal assistant.

663
00:32:12,020 --> 00:32:15,310
Speaker 1: Like it's basically just Google if they're returning sponsored products.

664
00:32:15,420 --> 00:32:19,490
Speaker 2: Can you remind me the fight, the fiduciary fight during

665
00:32:19,510 --> 00:32:22,590
Speaker 2: the Obama administration? Because I remember there was some group

666
00:32:22,650 --> 00:32:27,470
Speaker 2: that claimed that they should not be categorized under fiduciaries.

667
00:32:27,490 --> 00:32:29,310
Speaker 2: Can you remind me a little bit of like how

668
00:32:29,370 --> 00:32:32,390
Speaker 2: that fight settled out and who ended up having to

669
00:32:32,650 --> 00:32:34,470
Speaker 2: have that fiduciary responsibility?

670
00:32:34,860 --> 00:32:36,800
Speaker 1: Yeah, I mean, it's been a minute. It's just like

671
00:32:36,820 --> 00:32:38,880
Speaker 1: a kind of an oldie, but I actually worked for

672
00:32:38,920 --> 00:32:41,790
Speaker 1: Senator Warren at the time, and she was the major

673
00:32:41,910 --> 00:32:44,630
Speaker 1: senator who was helping Obama and Tom Perez, who was

674
00:32:44,710 --> 00:32:47,030
Speaker 1: running the Labor Department at the time, make sure they

675
00:32:47,050 --> 00:32:48,690
Speaker 1: got the fiduciary duty over the finish line.

676
00:32:49,090 --> 00:32:53,160
Speaker 2: The basic idea is, it's like, if you're coming to

677
00:32:53,220 --> 00:32:57,280
Speaker 2: me and saying, Joe, here are some options for your

678
00:32:57,480 --> 00:33:02,140
Speaker 2: child's 529 plan to save for college, I want to

679
00:33:02,180 --> 00:33:06,200
Speaker 2: have some confidence that That you are not getting a

680
00:33:06,260 --> 00:33:10,320
Speaker 2: kickback from those three funds to get me into one

681
00:33:10,360 --> 00:33:12,280
Speaker 2: of them. And that when you present them to me,

682
00:33:12,720 --> 00:33:15,770
Speaker 2: that actually it's not because you're going to get paid

683
00:33:15,810 --> 00:33:17,130
Speaker 2: an extra bonus if you sell.

684
00:33:17,050 --> 00:33:18,730
Speaker 1: One of them. Yeah, that's exactly what the rule did.

685
00:33:18,750 --> 00:33:20,989
Speaker 1: It was actually really interesting at the time. And this,

686
00:33:21,050 --> 00:33:23,570
Speaker 1: I think, is related to our conversation today. It was

687
00:33:23,630 --> 00:33:28,110
Speaker 1: actually the robo-advisors and the new fintech companies. who Obama

688
00:33:28,670 --> 00:33:31,250
Speaker 1: held up as the white hat companies because they came

689
00:33:31,290 --> 00:33:33,100
Speaker 1: in and said, yeah, we can set up a system

690
00:33:33,130 --> 00:33:36,660
Speaker 1: that complies with this law. We can provide financial advice

691
00:33:36,700 --> 00:33:38,719
Speaker 1: in the best interest of customers. We can just program

692
00:33:38,780 --> 00:33:40,959
Speaker 1: it to be such, right? It was the mom and

693
00:33:41,000 --> 00:33:44,800
Speaker 1: pop financial advisors from the Charles Schwab in the strip

694
00:33:44,820 --> 00:33:47,670
Speaker 1: mall who were coming in and saying, wait, I used

695
00:33:47,690 --> 00:33:49,850
Speaker 1: to get a trip to a castle in Scotland after

696
00:33:49,930 --> 00:33:52,570
Speaker 1: I sold $ 300, 000 worth of annuities. Are you telling me

697
00:33:52,610 --> 00:33:55,430
Speaker 1: that I won't get my Super Bowl style ring or

698
00:33:55,450 --> 00:33:58,780
Speaker 1: my my perks and freebies and junkets, right? I mean,

699
00:33:58,820 --> 00:34:01,700
Speaker 1: they were much more opposed to it. I think the

700
00:34:02,020 --> 00:34:06,640
Speaker 1: question with agentic commerce is, could these companies even guarantee

701
00:34:07,160 --> 00:34:09,160
Speaker 1: that the agents will work in your best interest if

702
00:34:09,239 --> 00:34:13,410
Speaker 1: they don't actually have full control over what the agents do? Yeah.

703
00:34:14,010 --> 00:34:15,560
Speaker 3: The other thing I want to ask you on this is,

704
00:34:15,750 --> 00:34:18,130
Speaker 3: I mean, let's assume the agents do get good at

705
00:34:18,250 --> 00:34:22,960
Speaker 3: finding bargains to some extent. Would you expect companies like

706
00:34:23,000 --> 00:34:26,149
Speaker 3: an Amazon or a Walmart to maybe start, I guess,

707
00:34:26,210 --> 00:34:30,489
Speaker 3: tweaking their APIs or like reconstructing the walls around some

708
00:34:30,510 --> 00:34:33,790
Speaker 3: of their pricing information such that they try to erode

709
00:34:33,850 --> 00:34:35,430
Speaker 3: away that information?

710
00:34:35,570 --> 00:34:37,549
Speaker 1: I mean, it's already here. The price wars are here, right?

711
00:34:37,570 --> 00:34:39,630
Speaker 1: Or the agent wars, I guess, are here. So we

712
00:34:39,670 --> 00:34:44,040
Speaker 1: have Amazon announced that it's blocking Muse. So Muse can't

713
00:34:44,080 --> 00:34:46,960
Speaker 1: go into Amazon. But Alexa for shopping, by the way,

714
00:34:47,420 --> 00:34:50,660
Speaker 1: helps you with Amazon, but also leaves Amazon's walls, right?

715
00:34:50,719 --> 00:34:52,420
Speaker 1: So they're not playing by the same rules that they're

716
00:34:52,440 --> 00:34:56,130
Speaker 1: going to apply to Muse. We know that Walmart is

717
00:34:56,210 --> 00:34:59,290
Speaker 1: building up sort of separate infrastructure so that you can

718
00:34:59,450 --> 00:35:03,690
Speaker 1: register your agent, basically. It sounds crazy. But you would

719
00:35:03,730 --> 00:35:06,250
Speaker 1: log on and say, hey, I actually am going to

720
00:35:06,290 --> 00:35:09,730
Speaker 1: let Muse come to Walmart. And they're going to set

721
00:35:09,770 --> 00:35:12,089
Speaker 1: up a system that sort of greenlights that so that

722
00:35:12,110 --> 00:35:14,469
Speaker 1: they can authorize your agent so they know that this

723
00:35:14,550 --> 00:35:17,040
Speaker 1: agent is shopping on your behalf. But it raises all

724
00:35:17,080 --> 00:35:19,920
Speaker 1: sorts of thorny questions about who's going to be holding

725
00:35:19,960 --> 00:35:22,600
Speaker 1: the bag if your agent buys something you didn't want

726
00:35:22,620 --> 00:35:25,060
Speaker 1: it to buy, for example. Walmart is going to say no,

727
00:35:25,500 --> 00:35:27,800
Speaker 1: but Muse is also going to say no. So a

728
00:35:27,840 --> 00:35:31,640
Speaker 1: lot of customers already are experiencing situations where someone uses

729
00:35:31,680 --> 00:35:34,879
Speaker 1: a colloquialism like, go find me a garden hose. and

730
00:35:35,620 --> 00:35:38,440
Speaker 1: the agent goes and buys a garden hose. And you

731
00:35:38,480 --> 00:35:41,560
Speaker 1: said find because you wanted to see some options, right?

732
00:35:41,600 --> 00:35:43,739
Speaker 3: Here's this heritage garden hose for $ 200.

733
00:35:43,660 --> 00:35:45,620
Speaker 5: It's beautiful. There's going to be so many big issues.

734
00:35:45,640 --> 00:35:49,870
Speaker 2: Or what happens when you tell your agent, go find

735
00:35:49,910 --> 00:35:52,730
Speaker 2: me a garden hose, and it doesn't buy the garden hose.

736
00:35:53,090 --> 00:35:59,710
Speaker 2: It hacks into HomeDepot.com, messes with their database, and then

737
00:35:59,770 --> 00:36:01,330
Speaker 2: it's like, who committed the crime?

738
00:36:01,410 --> 00:36:03,840
Speaker 5: I mean, that's a crime. That's theft. And they're just

739
00:36:03,860 --> 00:36:04,110
Speaker 5: going to get.

740
00:36:04,469 --> 00:36:07,450
Speaker 2: I'm certain this is going to happen, and there are

741
00:36:07,469 --> 00:36:10,830
Speaker 2: going to be some pretty interesting legal questions about, well,

742
00:36:10,850 --> 00:36:13,069
Speaker 2: did you commit the crime because you told your agent

743
00:36:13,110 --> 00:36:16,029
Speaker 2: to do this? Or did Meta commit the crime? These

744
00:36:16,070 --> 00:36:18,350
Speaker 2: are huge questions that we're all going to be facing within.

745
00:36:18,230 --> 00:36:18,750
Speaker 5: The coming years.

746
00:36:18,810 --> 00:36:23,049
Speaker 1: There's a story today in Axios about the banks who

747
00:36:23,140 --> 00:36:26,560
Speaker 1: have said they have started to see highly unusual activity

748
00:36:26,680 --> 00:36:29,980
Speaker 1: in bank accounts coming from agents and personal assistants. And

749
00:36:30,100 --> 00:36:32,100
Speaker 1: one of the things that they're tracking is that they

750
00:36:32,160 --> 00:36:34,960
Speaker 1: feel like a lot of the transactions they're seeing were

751
00:36:35,020 --> 00:36:37,440
Speaker 1: not directed by the users. And it's a little hard

752
00:36:37,480 --> 00:36:40,890
Speaker 1: to know exactly how they determine that. But already, this

753
00:36:40,950 --> 00:36:43,129
Speaker 1: idea that you're going to give the keys to your

754
00:36:43,150 --> 00:36:46,109
Speaker 1: wallet to an agent is, I think, starting to look

755
00:36:46,150 --> 00:36:46,850
Speaker 1: pretty suspect.

756
00:36:47,190 --> 00:36:49,790
Speaker 2: I think there's going to be some pretty huge corporate

757
00:36:49,810 --> 00:36:52,350
Speaker 2: strategy decisions that companies are going to have to make.

758
00:36:52,370 --> 00:36:56,910
Speaker 2: Because I get why in the first instance, Amazon is like,

759
00:36:57,300 --> 00:36:59,720
Speaker 2: you know, we don't want your muse accessing our site.

760
00:37:00,180 --> 00:37:03,109
Speaker 2: But if Walmart is like, Bring on your muses here.

761
00:37:03,290 --> 00:37:05,990
Speaker 2: They might not have any choice. It all is going

762
00:37:06,030 --> 00:37:09,350
Speaker 2: to depend on, like, if people really want to shop

763
00:37:09,710 --> 00:37:13,090
Speaker 2: through an AI agent, then, like, someone is going to

764
00:37:13,110 --> 00:37:16,450
Speaker 2: really exploit this and fling the doors wide open for them.

765
00:37:16,880 --> 00:37:19,920
Speaker 2: And the Amazons of the world may not have any choice.

766
00:37:20,080 --> 00:37:21,219
Speaker 3: I don't think it's going to be down to the

767
00:37:21,280 --> 00:37:22,620
Speaker 3: Amazons of the world. It's going to be down to

768
00:37:22,640 --> 00:37:26,640
Speaker 3: the insurers and whoever is, like, insuring liability for the

769
00:37:26,719 --> 00:37:28,439
Speaker 3: agents and the companies themselves.

770
00:37:28,500 --> 00:37:30,739
Speaker 2: It will be down to them, but, like, There is

771
00:37:30,780 --> 00:37:34,260
Speaker 2: a lot of value for an Amazon in you going

772
00:37:34,340 --> 00:37:38,500
Speaker 2: there and you, them knowing something about you, right? So

773
00:37:38,520 --> 00:37:41,910
Speaker 2: you go to Amazon and you buy a toothbrush and

774
00:37:41,950 --> 00:37:44,890
Speaker 2: maybe you like click on a movie that you're interested

775
00:37:45,150 --> 00:37:47,270
Speaker 2: in and blah, blah. And suddenly they have a rich

776
00:37:47,730 --> 00:37:49,630
Speaker 2: pool of data about you, right?

777
00:37:50,050 --> 00:37:51,350
Speaker 5: And that is very valuable.

778
00:37:51,430 --> 00:37:53,230
Speaker 2: And if you're going through your muse to buy it,

779
00:37:53,640 --> 00:37:56,359
Speaker 2: You're still doing the transaction, but there's a lot less

780
00:37:56,380 --> 00:37:58,219
Speaker 2: that they're getting from you. And this is going to

781
00:37:58,260 --> 00:38:00,840
Speaker 2: be a very difficult tradeoff for them. Which is the

782
00:38:00,860 --> 00:38:01,680
Speaker 2: better option?

783
00:38:01,960 --> 00:38:04,040
Speaker 1: It's a huge issue. I'll say a couple things. One,

784
00:38:04,060 --> 00:38:06,140
Speaker 1: you should definitely check out your Amazon About Me if

785
00:38:06,180 --> 00:38:06,920
Speaker 1: you haven't already.

786
00:38:07,100 --> 00:38:08,180
Speaker 5: Oh, I never have done that.

787
00:38:08,360 --> 00:38:12,630
Speaker 1: So Amazon now lets you know the characteristics that it

788
00:38:12,670 --> 00:38:15,609
Speaker 1: knows about you. There was a viral story a couple

789
00:38:15,630 --> 00:38:19,299
Speaker 1: of weeks ago because Amazon identified one. customers having flat

790
00:38:19,360 --> 00:38:23,739
Speaker 1: buttocks based on some of their, I guess, leggings purchases

791
00:38:23,780 --> 00:38:26,279
Speaker 1: and jeans purchases. So it was quite detailed. It had

792
00:38:26,300 --> 00:38:28,680
Speaker 1: a lot of information about my three-year-old, for example, because

793
00:38:28,739 --> 00:38:31,000
Speaker 1: I had done some party shopping on Amazon.

794
00:38:31,020 --> 00:38:33,450
Speaker 2: Amazon will think that all I'm interested in buying is

795
00:38:33,469 --> 00:38:35,110
Speaker 2: squishy toys these days.

796
00:38:35,810 --> 00:38:38,069
Speaker 1: Yeah. The second thing I'll say is we are already

797
00:38:38,130 --> 00:38:40,549
Speaker 1: seeing anecdotal evidence of folks who used to do quite

798
00:38:40,590 --> 00:38:42,890
Speaker 1: a bit of shopping on Amazon who are now using

799
00:38:42,950 --> 00:38:46,230
Speaker 1: Muse and have bought more things off Amazon than they

800
00:38:46,270 --> 00:38:49,120
Speaker 1: have in years. And I think this is a real

801
00:38:49,180 --> 00:38:51,779
Speaker 1: threat to Amazon. And there are some folks at Y

802
00:38:51,840 --> 00:38:55,580
Speaker 1: Combinator and other places who are suggesting that agentic commerce

803
00:38:55,620 --> 00:38:59,170
Speaker 1: could be the first opportunity to start to like draw

804
00:38:59,210 --> 00:39:02,110
Speaker 1: some blood at Amazon for the first time in years.

805
00:39:02,650 --> 00:39:06,569
Speaker 3: I want to end on, I guess, a happy, happy-ish note.

806
00:39:06,610 --> 00:39:09,089
Speaker 3: Can you tell us about the hot dog guy who

807
00:39:09,130 --> 00:39:11,879
Speaker 3: managed to reverse hot dog prices in the 1970s?

808
00:39:12,320 --> 00:39:12,640
Speaker 5: Yeah.

809
00:39:12,700 --> 00:39:16,810
Speaker 1: So this was someone who was a Giants fan. who

810
00:39:16,910 --> 00:39:20,210
Speaker 1: felt like the San Francisco Giants were changing hot dog

811
00:39:20,210 --> 00:39:22,750
Speaker 1: prices by, I think it was only a nickel. But

812
00:39:22,750 --> 00:39:25,760
Speaker 1: he felt like this was a period of high inflation.

813
00:39:25,760 --> 00:39:28,200
Speaker 1: A nickel was was far too much. He sort of

814
00:39:28,260 --> 00:39:30,360
Speaker 1: ran the numbers, figured out how many nickels they were

815
00:39:30,380 --> 00:39:32,620
Speaker 1: going to bring in, figured out how much their costs

816
00:39:32,660 --> 00:39:33,200
Speaker 1: had gone up.

817
00:39:33,340 --> 00:39:35,259
Speaker 3: The reason was because they were funding some sort of

818
00:39:35,320 --> 00:39:36,629
Speaker 3: renovation of the stadium.

819
00:39:36,690 --> 00:39:36,930
Speaker 1: Right.

820
00:39:37,010 --> 00:39:39,330
Speaker 3: And they said, like, we have to have higher hot

821
00:39:39,350 --> 00:39:40,010
Speaker 3: dog prices.

822
00:39:40,210 --> 00:39:43,750
Speaker 1: Yeah. So Ron Gordon is basically a hot dog price

823
00:39:43,810 --> 00:39:47,830
Speaker 1: warrior on behalf of San Francisco Giants fans. And Yes,

824
00:39:47,870 --> 00:39:49,609
Speaker 1: they were renovating the stadium. They were going to start

825
00:39:49,630 --> 00:39:52,640
Speaker 1: charging more for hot dogs. He felt like it was

826
00:39:52,680 --> 00:39:56,700
Speaker 1: too much. He started a campaign to keep hot dog

827
00:39:56,719 --> 00:39:59,000
Speaker 1: prices down and to make sure that they didn't go

828
00:39:59,060 --> 00:40:02,200
Speaker 1: up a nickel. He actually ended up getting a letter

829
00:40:02,320 --> 00:40:05,830
Speaker 1: from the Council on Economic Advisors praising him for his

830
00:40:06,410 --> 00:40:09,310
Speaker 1: efforts to beat back price hikes during a period of

831
00:40:09,370 --> 00:40:12,320
Speaker 1: national inflation. This was back during the last period of

832
00:40:12,380 --> 00:40:16,080
Speaker 1: inflation 50 years ago. And he was successful in keeping

833
00:40:16,100 --> 00:40:17,480
Speaker 1: prices low for the rest of us.

834
00:40:17,820 --> 00:40:20,660
Speaker 3: For a while anyway. I'd be curious what hot dogs

835
00:40:20,700 --> 00:40:22,759
Speaker 3: cost nowadays at that stadium.

836
00:40:22,940 --> 00:40:28,029
Speaker 2: I guess the politics of who are the inflation fighters

837
00:40:28,070 --> 00:40:28,529
Speaker 2: in America?

838
00:40:29,030 --> 00:40:29,950
Speaker 5: They get tricky.

839
00:40:30,390 --> 00:40:32,910
Speaker 2: We did an episode when we were out in Jackson

840
00:40:32,969 --> 00:40:35,330
Speaker 2: Hole with the president of the Richmond Fed, and he

841
00:40:35,350 --> 00:40:38,630
Speaker 2: was talking to some of the suppliers in his district.

842
00:40:39,150 --> 00:40:42,049
Speaker 2: And he was like, look, you know, I don't know,

843
00:40:42,070 --> 00:40:44,910
Speaker 2: maybe make some lawn chair or something like that. And

844
00:40:44,930 --> 00:40:47,370
Speaker 2: he's like, our prices are going up, but Walmart is

845
00:40:47,430 --> 00:40:50,330
Speaker 2: not letting, they're like holding the line and we're not,

846
00:40:51,070 --> 00:40:52,310
Speaker 2: they're not paying more.

847
00:40:52,170 --> 00:40:52,850
Speaker 5: For our lawn chairs.

848
00:40:52,910 --> 00:40:55,310
Speaker 2: And if we want shelf space at Walmart, we have

849
00:40:55,330 --> 00:40:57,330
Speaker 2: to eat the price itself. And so when I hear

850
00:40:57,390 --> 00:40:59,130
Speaker 2: that story, I'm.

851
00:40:58,630 --> 00:41:00,950
Speaker 5: Like, Walmart, salute.

852
00:41:01,050 --> 00:41:03,279
Speaker 2: You are the great, you are our strongest soldiers in

853
00:41:03,340 --> 00:41:07,160
Speaker 2: our fight against inflation, et cetera. I also know that

854
00:41:07,200 --> 00:41:11,239
Speaker 2: a lot of people are like really uncomfortable with, you know,

855
00:41:11,280 --> 00:41:14,379
Speaker 2: the Walmarts of the world and politically like would like

856
00:41:14,420 --> 00:41:16,330
Speaker 2: to see a world of more mom and pop stores,

857
00:41:16,410 --> 00:41:19,149
Speaker 2: et cetera, although in many cases they charge more. How

858
00:41:19,190 --> 00:41:23,969
Speaker 2: do you think, generally speaking, about like sort of, what

859
00:41:24,070 --> 00:41:27,410
Speaker 2: we want as a society in terms of the optimal

860
00:41:27,450 --> 00:41:30,690
Speaker 2: mix of large and small companies, because the Walmarts of

861
00:41:30,710 --> 00:41:33,590
Speaker 2: the world, the Amazons of the world, they have incredible

862
00:41:33,630 --> 00:41:35,830
Speaker 2: power of scale and powers of scale. They can also

863
00:41:35,850 --> 00:41:39,320
Speaker 2: bring prices down, et cetera. Small businesses are sort of

864
00:41:39,380 --> 00:41:43,259
Speaker 2: famous in many cases for being the worst at things

865
00:41:43,280 --> 00:41:45,459
Speaker 2: like wage theft and so forth. And often they actually

866
00:41:45,500 --> 00:41:48,000
Speaker 2: don't have the best prices because they don't have that scale.

867
00:41:48,580 --> 00:41:49,100
Speaker 5: What do we.

868
00:41:49,020 --> 00:41:51,980
Speaker 2: Want as a society in terms of the flora and fauna,

869
00:41:52,020 --> 00:41:54,940
Speaker 2: the ecology, of commercial platforms?

870
00:41:55,300 --> 00:41:57,600
Speaker 1: I mean, for me, I want the mix that provides

871
00:41:57,640 --> 00:41:58,779
Speaker 1: the most price competition.

872
00:41:58,800 --> 00:41:59,240
Speaker 5: Okay.

873
00:41:59,400 --> 00:42:01,340
Speaker 1: And I think when we don't have price competition in

874
00:42:01,380 --> 00:42:04,040
Speaker 1: our economy, we're in real trouble. And what I see

875
00:42:04,180 --> 00:42:08,009
Speaker 1: increasingly is companies building technologies, and I talk about a

876
00:42:08,070 --> 00:42:11,310
Speaker 1: lot of them in the book, to subvert price competition.

877
00:42:11,810 --> 00:42:17,390
Speaker 1: So even companies like McDonald's are building sophisticated algorithmic pricing

878
00:42:17,430 --> 00:42:21,529
Speaker 1: tools to ensure that even franchisees can't compete against each other.

879
00:42:21,550 --> 00:42:25,029
Speaker 1: The whole point of franchises is that they're independent and

880
00:42:25,090 --> 00:42:28,850
Speaker 1: should have some level of control over pricing. That's increasingly

881
00:42:28,910 --> 00:42:31,629
Speaker 1: not the case. Now, McDonald's will hem and haw and say,

882
00:42:31,930 --> 00:42:34,910
Speaker 1: we're just recommending these prices. The franchisees don't have to

883
00:42:34,950 --> 00:42:38,190
Speaker 1: take them. But it turns out that when McDonald's is

884
00:42:38,230 --> 00:42:41,879
Speaker 1: evaluating renewal decisions, when McDonald's is evaluating whether or not

885
00:42:42,260 --> 00:42:45,020
Speaker 1: you as a franchisee can pick up another property and

886
00:42:45,239 --> 00:42:49,589
Speaker 1: open a second location, they evaluate things like your conformity

887
00:42:49,930 --> 00:42:53,440
Speaker 1: to the pricing algorithm, right? And so my concern here

888
00:42:53,640 --> 00:42:56,540
Speaker 1: is even in markets where there actually is some competition,

889
00:42:57,000 --> 00:42:59,060
Speaker 1: these tools are being used to subvert it. So we

890
00:42:59,100 --> 00:43:03,280
Speaker 1: saw RealPage, another case of algorithmic price fixing, where landlords

891
00:43:03,320 --> 00:43:05,770
Speaker 1: in a city who would usually compete with each other

892
00:43:06,170 --> 00:43:10,630
Speaker 1: were able to share competitively sensitive information and align on pricing,

893
00:43:10,730 --> 00:43:13,890
Speaker 1: resulting in rent prices as much as 25% more than

894
00:43:13,930 --> 00:43:17,410
Speaker 1: you would see in a competitive market, through this software. Similarly,

895
00:43:17,430 --> 00:43:21,890
Speaker 1: there's a gas station technology, Calibrate, a tech company that

896
00:43:22,410 --> 00:43:25,390
Speaker 1: is under scrutiny right now, a class action filed in California.

897
00:43:25,910 --> 00:43:31,110
Speaker 1: They were helping 1,700 gas retailers align on price. The

898
00:43:31,390 --> 00:43:32,920
Speaker 1: allegation is that it could have been as much as

899
00:43:32,920 --> 00:43:36,399
Speaker 1: 30 cents a gallon higher for drivers in California. So

900
00:43:36,860 --> 00:43:40,220
Speaker 1: I'm looking for price competition. That is not always just

901
00:43:40,320 --> 00:43:44,060
Speaker 1: achieved by volume, right? When these types of technologies are

902
00:43:44,140 --> 00:43:46,990
Speaker 1: able to circumvent scale.

903
00:43:47,550 --> 00:43:50,330
Speaker 4: All right, Lindsay Owens, the book is Gouged. Thank you

904
00:43:50,350 --> 00:43:51,410
Speaker 4: so much for coming on Outlaw.

905
00:43:51,550 --> 00:43:52,739
Speaker 1: Thanks for having me. This was great.

906
00:44:05,700 --> 00:44:08,719
Speaker 3: So Joe, I found all of that really fascinating. I

907
00:44:08,780 --> 00:44:11,939
Speaker 3: know you asked a lot of devil's advocate questions there,

908
00:44:12,020 --> 00:44:16,259
Speaker 3: but in my mind, you said it, not me. In

909
00:44:16,320 --> 00:44:19,950
Speaker 3: my mind, the issue here is an imbalance, right? If

910
00:44:19,989 --> 00:44:22,529
Speaker 3: you're a Walmart or an Amazon, you have like the

911
00:44:22,570 --> 00:44:26,210
Speaker 3: best technology, the best AI models, the best data out

912
00:44:26,250 --> 00:44:29,910
Speaker 3: there at the touch of your fingertips versus if you're someone,

913
00:44:29,930 --> 00:44:32,830
Speaker 3: you know, working a nine to five or whatever, who's

914
00:44:32,870 --> 00:44:34,940
Speaker 3: just trying to buy, you know, I don't know, a

915
00:44:35,000 --> 00:44:38,820
Speaker 3: car or a computer, you don't have that much time

916
00:44:39,000 --> 00:44:41,770
Speaker 3: or that much technological prowess to actually devote to it.

917
00:44:42,200 --> 00:44:45,630
Speaker 4: AI could, in theory, solve some of that for sure.

918
00:44:45,650 --> 00:44:50,670
Speaker 2: Yeah, except, no, like I'm hopeful that AI could solve

919
00:44:50,730 --> 00:44:54,110
Speaker 2: some of these imbalances. But what I worry about, and

920
00:44:54,330 --> 00:44:58,010
Speaker 2: not just with pricing, but with range of field and cybersecurity,

921
00:44:58,390 --> 00:45:01,540
Speaker 2: very much in the news today, we're recording this October 22nd.

922
00:45:01,960 --> 00:45:06,560
Speaker 2: The dynamic that I'm worried about is that it's just

923
00:45:06,580 --> 00:45:09,300
Speaker 2: going to be arms races, right? So you're going to

924
00:45:09,320 --> 00:45:11,680
Speaker 2: have an agent and maybe it truly does work on

925
00:45:11,719 --> 00:45:15,029
Speaker 2: your behalf, et cetera. And then someone else is going

926
00:45:15,050 --> 00:45:18,770
Speaker 2: to have an AI pricing model that tries to figure

927
00:45:19,010 --> 00:45:22,350
Speaker 2: out more about you and overcome that. And then you

928
00:45:22,390 --> 00:45:24,630
Speaker 2: have to upgrade to the next version of the model

929
00:45:24,690 --> 00:45:28,930
Speaker 2: to overcome that. And that in many respects, it's possible

930
00:45:29,310 --> 00:45:31,450
Speaker 2: that AI makes markets more efficient.

931
00:45:31,770 --> 00:45:33,290
Speaker 5: It makes things cheaper, et cetera.

932
00:45:33,739 --> 00:45:37,660
Speaker 2: It is also possible that it just becomes deadweight loss

933
00:45:37,739 --> 00:45:40,150
Speaker 2: where we arrive at the exact same price that we

934
00:45:40,190 --> 00:45:43,009
Speaker 2: would have before, except the only difference is that both

935
00:45:43,070 --> 00:45:46,050
Speaker 2: sides have paid more to open AI, anthropic or meta,

936
00:45:46,390 --> 00:45:50,230
Speaker 2: in order to re-arrive at the same equilibrium that we

937
00:45:50,290 --> 00:45:51,730
Speaker 2: could have had without all this AI stuff.

938
00:45:51,890 --> 00:45:54,880
Speaker 3: This is my busy work theory of AI, where everyone's

939
00:45:54,900 --> 00:45:57,860
Speaker 3: just going to have agents that file lawsuits.

940
00:45:57,420 --> 00:45:57,719
Speaker 1: At each other.

941
00:45:57,760 --> 00:45:59,200
Speaker 2: And we get to the same place in the end,

942
00:45:59,219 --> 00:46:02,080
Speaker 2: but someone makes a bunch of money selling tokens to us.

943
00:46:02,500 --> 00:46:06,780
Speaker 3: Also, I thought the history behind the single public price

944
00:46:06,820 --> 00:46:09,660
Speaker 3: tag is really interesting because if you think about it,

945
00:46:09,690 --> 00:46:12,670
Speaker 3: to some extent, with agentic AI and all these new

946
00:46:12,710 --> 00:46:16,490
Speaker 3: pricing strategies coming out of companies, we're kind of going

947
00:46:16,550 --> 00:46:22,690
Speaker 3: back to that customized era of haggling where you have

948
00:46:22,710 --> 00:46:25,790
Speaker 3: to devote a bunch of time to like figuring out

949
00:46:25,830 --> 00:46:28,330
Speaker 3: the price. If you're buying from McDonald's and you want

950
00:46:28,350 --> 00:46:30,270
Speaker 3: a good price, you have to like figure out which

951
00:46:30,390 --> 00:46:33,509
Speaker 3: coupon you want for that day and what's available to you.

952
00:46:33,930 --> 00:46:35,470
Speaker 3: It's a time suck, right?

953
00:46:35,830 --> 00:46:37,450
Speaker 5: It is a time suck.

954
00:46:37,510 --> 00:46:38,210
Speaker 4: It's not efficient.

955
00:46:38,489 --> 00:46:39,990
Speaker 5: You know, here's a thought that I had.

956
00:46:40,030 --> 00:46:44,700
Speaker 2: Remember in 2021, 2022, 2023, there was this very recurring

957
00:46:44,760 --> 00:46:47,299
Speaker 2: thing that you'd see online. It was like someone would say,

958
00:46:47,320 --> 00:46:50,120
Speaker 2: oh my God, I just paid $ 15 for blueberries, okay?

959
00:46:50,600 --> 00:46:53,160
Speaker 2: And then someone else was like, would say, You're an idiot.

960
00:46:53,400 --> 00:46:58,160
Speaker 2: Go to Walmart.com. Here's blueberries that you can buy for $ 2.

961
00:46:56,400 --> 00:46:57,280
Speaker 4: 99.

962
00:46:57,280 --> 00:47:00,529
Speaker 5: And if you're paying $ 15, it's really your fault. And

963
00:47:00,550 --> 00:47:01,089
Speaker 5: it kind of is.

964
00:47:01,620 --> 00:47:04,359
Speaker 2: But when I see these, you could have bought blueberries

965
00:47:04,410 --> 00:47:05,420
Speaker 2: for $ 2.

966
00:47:05,290 --> 00:47:05,870
Speaker 5: 99 at Walmart.

967
00:47:06,190 --> 00:47:10,710
Speaker 2: What I see is a lack of general equilibrium thinking,

968
00:47:10,790 --> 00:47:14,069
Speaker 2: by which I mean, if all of those people who

969
00:47:14,110 --> 00:47:18,190
Speaker 2: shopped at Whole Foods suddenly switched to the $ 2. 99 Walmart blueberries,

970
00:47:18,590 --> 00:47:20,210
Speaker 2: they would not be $ 2. 99 anymore.

971
00:47:20,350 --> 00:47:20,930
Speaker 1: Right. Right.

972
00:47:20,950 --> 00:47:24,080
Speaker 2: There would be this big inrush. Maybe they wouldn't jump

973
00:47:24,380 --> 00:47:27,120
Speaker 2: to $ 15. Maybe they'd jump to $ 4.

974
00:47:26,719 --> 00:47:27,460
Speaker 5: 99, etc.

975
00:47:28,140 --> 00:47:31,620
Speaker 2: But, like, prices are prices. And so just because there

976
00:47:31,760 --> 00:47:34,629
Speaker 2: exists a lower price does not mean that everyone can

977
00:47:34,670 --> 00:47:37,110
Speaker 2: capture the lower price because then the lower price goes up.

978
00:47:37,590 --> 00:47:39,770
Speaker 5: And so, like, I don't know.

979
00:47:39,790 --> 00:47:42,880
Speaker 3: I would also argue that buying blueberries for a decent

980
00:47:42,910 --> 00:47:46,320
Speaker 3: price should not consume that much mental capacity.

981
00:47:46,340 --> 00:47:47,020
Speaker 5: No, it shouldn't.

982
00:47:47,060 --> 00:47:48,020
Speaker 4: It shouldn't be like.

983
00:47:49,110 --> 00:47:53,089
Speaker 3: A five minute campaign even to go online and research like, oh,

984
00:47:53,110 --> 00:47:54,989
Speaker 3: I'm going to buy it from here and I'm going

985
00:47:55,010 --> 00:47:56,430
Speaker 3: to have it. You know, if you buy it online,

986
00:47:56,450 --> 00:47:57,690
Speaker 3: you're going to have to have it delivered to your

987
00:47:57,710 --> 00:48:00,390
Speaker 3: house as well versus just walking in a store. It's

988
00:48:00,430 --> 00:48:03,930
Speaker 3: a time issue and it's efficiency and inefficiency issue in

989
00:48:03,950 --> 00:48:06,000
Speaker 3: my mind. And I say that as someone who loves

990
00:48:06,040 --> 00:48:09,040
Speaker 3: shopping and buying $ 48 cocktails, apparently.

991
00:48:09,520 --> 00:48:11,500
Speaker 2: Well, this is the last point I was going to make.

992
00:48:11,600 --> 00:48:14,400
Speaker 2: And this is a macro question and it has nothing

993
00:48:14,460 --> 00:48:17,990
Speaker 2: to do with algorithmic pricing, which is that there is a,

994
00:48:18,989 --> 00:48:21,790
Speaker 2: Those of us on the top of the K complain

995
00:48:21,810 --> 00:48:25,009
Speaker 2: about prices all the time. And for whatever reason, by

996
00:48:25,050 --> 00:48:27,810
Speaker 2: and large, some of us, but most of us, have

997
00:48:27,930 --> 00:48:31,110
Speaker 2: not meaningfully changed our consumption behavior despite all the prices

998
00:48:31,130 --> 00:48:31,810
Speaker 2: that we complain about.

999
00:48:31,850 --> 00:48:34,430
Speaker 4: I'm really getting grilled for buying a cocktail on my birthday.

1000
00:48:34,450 --> 00:48:35,069
Speaker 5: No, it's all.

1001
00:48:35,090 --> 00:48:37,180
Speaker 4: This is my one present.

1002
00:48:37,739 --> 00:48:40,340
Speaker 2: People complain and then they do the same thing they

1003
00:48:40,480 --> 00:48:43,060
Speaker 2: do the next day. And that is why inflation continues

1004
00:48:43,100 --> 00:48:49,270
Speaker 2: to persist because behaviorally, whatever it is, People complain and

1005
00:48:49,310 --> 00:48:50,770
Speaker 2: then they buy the same thing the next day.

1006
00:48:50,969 --> 00:48:51,810
Speaker 4: Yeah, it's true.

1007
00:48:51,870 --> 00:48:54,300
Speaker 3: But also, it's not like if you want to drink

1008
00:48:54,360 --> 00:48:56,300
Speaker 3: something in a theater, you don't have a choice. It's

1009
00:48:56,320 --> 00:48:58,640
Speaker 3: not like I can go out somewhere else. But anyway,

1010
00:48:59,020 --> 00:48:59,540
Speaker 3: shall we leave it there?

1011
00:48:59,780 --> 00:49:00,359
Speaker 5: Let's leave it there.

1012
00:49:00,719 --> 00:49:03,319
Speaker 3: This has been another episode of the All Thoughts Podcast.

1013
00:49:03,420 --> 00:49:05,960
Speaker 3: I'm Traci Allaway. You can follow me at Traci Allaway.

1014
00:49:06,160 --> 00:49:08,980
Speaker 2: And I'm Jill Wiesenthal. You can follow me at The Stalwart.

1015
00:49:09,180 --> 00:49:10,480
Speaker 2: Follow our guest, Lindsay Owens.

1016
00:49:10,520 --> 00:49:12,020
Speaker 5: She's at OwensLindsay1.

1017
00:49:12,200 --> 00:49:16,320
Speaker 2: Follow our producers, Carmen Rodriguez at CarmenArmond, Dashiell Bennett at Dashbot,

1018
00:49:16,680 --> 00:49:19,440
Speaker 2: Cale Brooks at Cale Brooks, and Kevin Lozano at Kevin

1019
00:49:19,460 --> 00:49:21,920
Speaker 2: Lloyd Lozano. And of course, you can chat about all

1020
00:49:21,940 --> 00:49:26,510
Speaker 2: of these topics 24-7 in our Discord, discord.gg slash oddlots.

1021
00:49:26,950 --> 00:49:28,930
Speaker 2: And for more Oddlots content, you can check out our

1022
00:49:28,969 --> 00:49:32,610
Speaker 2: daily newsletter, which you can find at bloomberg.com slash oddlots.

1023
00:49:33,070 --> 00:49:35,290
Speaker 3: And if you enjoyed this conversation, if you like it

1024
00:49:35,330 --> 00:49:37,660
Speaker 3: when we talk about pricing, then please leave us a

1025
00:49:37,820 --> 00:49:38,839
Speaker 3: positive review on.

1026
00:49:38,780 --> 00:49:40,360
Speaker 4: Your favorite podcast platform.

1027
00:49:40,900 --> 00:49:43,420
Speaker 3: And if you're watching this on video and you enjoyed it,

1028
00:49:43,460 --> 00:49:45,839
Speaker 3: then please leave a comment or like the video or

1029
00:49:45,920 --> 00:49:46,960
Speaker 3: better yet, subscribe.

1030
00:49:47,300 --> 00:49:49,989
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1031
00:49:50,030 --> 00:49:52,989
Speaker 2: to all of our episodes absolutely ad-free. All you need

1032
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1033
00:49:55,830 --> 00:49:57,069
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1034
00:49:57,469 --> 00:50:08,530
Speaker 3: Thanks for joining. Thank you.