Ep. 268: “Your Privacy Is Being Taxed”

David: Do we want a world in which you have to pay a premium if you want to shop anonymously? This is about taxing people who don’t turn over their data and manipulating people who do.

Wizard of Oz: Pay no attention to that man behind the curtain.

Beau: We’ve been talking about the hidden mechanisms behind the prices you pay when you go shopping. Doesn’t matter where. What we found is the price you’re paying may not be the same as the price someone else has to pay, and that’s because in cyberspace, there are no price tags.

Wizard of Oz: Toto, I have a feeling we’re not in Kansas anymore.

Beau: Not Kansas. The Wild West. Since us normies got access to the World Wide Web, it has existed a few steps ahead of the law. Big Tech has always had an “ask for forgiveness, not permission” vibe.

The Jaynetts- Who Stole The Cookie From The Cookie Jar: Who stole the cookie from the cookie jar?

Beau: The old, “Who, me? Couldn’t be.” If Big Tech had hands and they did what they do now before the internet existed, they’d have handcuffs on them.

CBS News Clip: How are the companies responding to the FTC investigation?

CBS News Clip: Basically when you get a request like this from the FTC, all you can say is you’re going to comply with the investigation.

CBS News Clip: So this is obviously the very beginning, but what would…

Beau: But for those who think that’s probably not gonna change anything, show of hands… without handcuffs on them.

Mad Max Beyond Thunderdrome (1985) Clip: This is a stick-up. Anybody moves, and they’re dead meat.

Beau: So here’s my prediction: everyone is going to be talking about this by Black Friday of 2026, or maybe the cyber, you know, Giving Tuesday, or whatever it’s called. It’s gonna happen. I’m Beau Friedlander, and you’re listening to What The Hack, the podcast that asks, in a world where your data is everywhere, how do you stay safe online?

Beau: We’ve been getting under the hood of something called surveillance pricing lately, and so far all we’ve really figured out is that, one, it exists, and two, it’s complicated. The algorithms seem to have algorithms, and if there isn’t a ghost in the machine, it certainly feels like there’s spyware in my machines. But feelings aren’t facts. Spyware isn’t legal.

Target Instagram Clip: Have you guys seen those kids? But it was actually way worse than that. Sorry kids. These baskets. This kid is telling us that he noticed that there are now trackers on the baskets within Target. Not just on the baskets, they’re also on the carts. Back in ’23, they filed for this patent.

Beau: That’s not spyware. That’s spy equipment, and no one is forcing you to use that cart or basket at Target, and you can turn off your phone or delete the app or at least turn off Bluetooth. It seems like a lot to do just to walk through a store without being monitored, right? The fact is we’re all being measured, weighed, and financially harvested, and it’s a problem. We’ve talked to Chris the producer, a YouTuber, also known as Chris Parr, who built a fake identity—It’s not a fake identity. It was a real identity of someone who didn’t exist—and a new name, a new phone, a new credit card, and found out at least in an N of one super turbocharged anecdotal way, that the richest zip code in Minnesota got charged less for a box of White Castle sliders than a broke college student 20 miles away in Dinkytown.

Chris: Like it wasn’t super scientific. Literally these were sitting next to each other and it’s 11% cheaper than the price that I got on my phone.

Beau: Then last week we looked at Instacart, Uber, and Lyft. Hundreds of shoppers tested a few of these apps in real time and found different prices shopping for exactly the same thing.

Grace: And some items actually carried five different price points at the same time.

Beau: But before we knew any of this, I already had a suspicion. Hey, Keegan. So tell me this story about your air conditioner again.

Keegan: I was looking online for a standing air conditioner and the prices were all around four hundred dollars, three ninety-nine. And then I finally decided to go into Costco and was all ready to pay the three ninety-nine, and then went to where their air conditioners were, and they were three seventy-nine in-house.

Beau: Seemed to me that the cheaper in-store price was probably intentional, because who can go to a big box store and not buy—

Modern Family Clip: Oh, hey. I got the toothpaste and the soap. Well, good. Now we can open a general store. I thought we were just here to get diapers. We had a saying on the farm, “As long as you’re bringing the mule to the market, you…” I feel you rolling your eyes at me. Can we just please get the diapers and get out of here? Fine, but I wanna stop by the wine section first. Uh, wait. There’s a wine section? Yeah, a really good one just past the tires. No way. They do not have… Oh my God. Oh my… Cam, Cam, the paper shredder I wanted. Huh? Confetti and cross-cut. Yep. Oh. Oh my God, amazing. Yeah.

Beau: Cameron and Mitchell and Keegan’s cheaper in-store price tell the tale better than I can. But that’s just the real-life impact of smart retailing, right? It’s not smart pricing, which would’ve been a better name than the name that big tech chose for surveillance pricing, which is personalized pricing. Way more Orwellian. Anyway, so here we are following in the heroic footsteps of Chris, The Producer. Welcome to What the Hack’s very own totally unscientific experiment featuring intern CJ Mullings and his friend Ronnie Weisenstein. CJ, what kind of phone are you using? Is it an Android or is it an Apple?

CJ: Apple.

Beau: New or not new?

CJ: Not new. 12 Pro.

Beau: Oh, yeah. So you have the old charger like I have.

CJ: Yeah, yeah, yeah.

Beau: So people have started to say no to you. And like, “Get a new phone.” I understand.

CJ: Yeah, pretty much, yeah.

Beau: Ronnie, what are you on?

Ronnie: 13.

Beau: Also on an Apple. Okay, you’re both on iOS. CJ and Ronnie are seniors at Penn State Behrend in Erie, Pennsylvania. I want you guys to go to DoorDash, what are we gonna order?

Ronnie: You wanna do Taco Bell?

CJ: Do you approve of Taco Bell?

Beau: I think that it’s very bad for you, but I’m fine with it right now.

CJ: We’ll use Taco Bell.

Beau: Okay. Now you’re gonna order the exact same thing, so decide what you’re gonna order and order the exact same thing.

CJ: Let’s get the cantina chicken crispy taco combo. That’s a tongue twister.

Ronnie: Wait, the cantina what? Cantina chicken…

CJ: Okay, yeah, that one. Okay. Okay, he has it.

Beau: Now go to checkout. All right, what are you seeing?

Ronnie: Okay. Mine says 16.99.

CJ: Hmm. That’s interesting.

Beau: What’s your—

CJ: My original price is 16.85.

Beau: It’s different.

Ronnie: Yeah. Wow.

Beau: And it didn’t just stop at Taco Bell.

CJ: You wanna do CVS?

Beau: Okay, now I want you to buy something that you would never buy, and I’m guessing you would never buy this, but do either of you have an infant baby?

CJ and Ronnie: No.

Beau: I want you to go pick the same diaper product, put it in your cart, and go to checkout and see how much the price is.

CJ: Let’s go with Huggies size seven. Yeah. Okay, yeah.

Beau: This is cute. It’s the first time you’re ever ordering diapers. It may happen later in your life.

Ronnie: True.

Beau: What’s your price, CJ?

CJ: I have $20.64.

Ronnie: Mine’s $20.82. It’s crazy.

Beau: Okay, I’m starting to feel like there’s something happening here.

Ronnie: And I mean, we are right next to each other. Yeah.

Beau: Next stop, PetSmart, where they shop for a fish tank.

Ronnie: My original was $35:30.

Beau: And what’s your price, CJ?

CJ: $35.07.

Beau: Different again. Okay, so next we tried Target. Why don’t you buy something useful like a fall jacket? Why don’t you guys pick out some fall attire?

Ronnie: What size did he get? Medium?

CJ: Medium, yeah.

Beau: All right. What’s your price, Ronnie?

Ronnie: Mine’s $54:32.

CJ: $49.60.

Ronnie: Huh?

Beau: Today, we’re gonna keep digging to see what we can find out about this totally uncool situation. CJ, you’re consistently getting cheaper prices than Ronnie.

CJ: Yep.

Ronnie: I’m paying a lot.

Beau: Ronnie, how do you feel about this?

Ronnie: Dude, that’s not awesome.

Beau: CJ, how do you feel about this?

CJ: Compared to Ronnie, I think this is good for me.

Beau: I’m not a big fan of what you just listened to because it’s squishy proof. You hear people say all the time like, “Hey, they are doing something to us. They’re doing us dirty.” I really want something a little more concrete, and that’s why I spoke to David Dayen. David Dayen has been an important presence in American journalism for decades, finding and telling the stories that impact Americans the most. He’s the executive editor of The American Prospect. David wrote about the phenomenon first and is often credited with coining the phrase “surveillance pricing.” That’s almost right. He’s certainly the reason we all use it today. David, welcome back to “What the Hack?”

David: Thanks for having me on.

Beau: What I wanna talk to you about is something you started reporting on, I think in 2023. And it is a term that I think Zephyr Teachout originated when she was helping the New York attorney general go after price gouging. She called it surveillance pricing. Is that the origins of surveillance pricing?

David: Yeah. I mean, some will say that when Zephyr quoted that to me, she’s like, “This is what I wanna name it,” in a story I wrote, I believe in 2024, that that was the first sort of widespread use, and within a month, the Federal Trade Commission had opened an investigation into surveillance pricing, and I think that’s what really kicked it off and became the widespread known term.

Beau: What I’m so blown away by in this area is the idea that companies can infer, from data that they can purchase, whether I’m a high-intent or a low-intent buyer, and then have a game plan based on that fact. And we know Orbitz got caught steering Mac users to pricier hotels once upon a time. Princeton Review was also caught targeting folks buying SAT prep programs by zip code looking for Asian students. Is it just souped-up marketing or is it something more nefarious than that?

David: Well, it’s a technological progression of something that actually has been around for a while. You’re familiar with when you go to a grocery store and you get your sales slip at the end, and it has a bunch of coupons on it, and those coupons are usually for products that you just bought, right? That is an innovation that dates back 50, 60 years. There’s a thing called Catalina Marketing that used your purchases to create real-time coupons attached to the receipt. And that’s been around for a while, and when you think about it, it is a version of surveillance pricing, a version of surveillance marketing, right? They’re looking at your purchase history, which you’re freely giving to them by buying the products. Then they came out with a loyalty card and all of the information was sort of stored on that. They now know what you’re buying and they know what you’re buying at regular intervals, and they can use that to give you a discount. But you don’t necessarily know what the real price of that is, and they’re giving you a coupon that is trying to entice you to buy that product more and more and more, right? So that is sort of the dumb version of surveillance pricing. And now we have much more than your history of purchases as you go through the grocery checkout. We have your phone, which is often attached to your loyalty program. On your phone, we have your email, we have your browsing history, we have maybe your financial history. We might have your medical history. We have all of these ways. And then we have third-party data brokers where you can buy more information from and graft that on to this individual profile that companies have amassed for you. And so now you put all that information together and you have a tremendous wealth of knowledge about an individual.

FOX News Clip: So, did you know this? Stores tracking your every move, well, may have finally taken it too far. Target’s advanced advertising system even knew about a teenage girl’s pregnancy before she could break the news to her own father, and he found out when the store sent her maternity deals in the mail.

David: And so that is how sophisticated this has gotten, how detailed that you can do this analysis on your particular habits and all of this data that you’re giving up into various places whether online or offline, and you can figure out all kinds of things about that individual.

Beau: Everybody who’s been listening to this show for a while is certainly familiar with me railing against Meta and the fact that Meta also for ages has known when- they know you’re gonna break up with the person you’re with before you do. And it’s not because they see them chatting with someone else. Well, it might be, but it’s opaque. We don’t know why they know what they know, but we know that there’s a lot of data being plugged in. It’s not a lucky guess. So in the realm of not lucky guesses, like in the 19th century, let’s back up. When someone walked into a general mercantile and needed to buy something, the guy behind the counter, if they were shrewd, they might notice like, “She looks like she might be pregnant. I might be able to sell her this, that, and the next thing.” And a really good salesperson, I feel like they do have that sixth sense where they can kinda just smell it, and they’re like, Okay, this person doesn’t look like they have money, but I’m gonna sell them a Mercedes. Yada, yada, yada. This has taken that outlier person who has 10,000 hours of selling stuff and they just know how to do it, and it’s mechanized it so that it’s no longer necessary to have any special expertise, and it’s better and more reliable.

David: One technical way that economics would describe this is that consumer surplus has been moved into the realm of the seller. So usually this sort of uncertainty about willingness to pay sometimes in the past has rebounded to the benefit of the consumer, because the consumer can hold out and maybe get the price lower and then pay. Because the seller now knows actually you need that rake, you need that dustpan, whatever it is, this consumer surplus has gone away. And it becomes a much easier product for the seller to get off of their shelves, and they know that you’re gonna buy this at a particular price point. Now, there are a couple things that have intensified the use of this pricing mechanism. The first and most important is isolation of the consumer. If you’re actually in a line and you’re buying ice cream, and the person in front of you paid $4, and then you get to the counter and they say, “That’s $6 for you,” you’re gonna be pissed off and you’re never gonna shop there again. However, if you’re online or you’re buying it through your television set, your smart TV, and you don’t know what your neighbor paid for that, you don’t know what the price of ice cream was for the person three doors down, then you’re not gonna be as angry. You’re going to accept it. You’re not gonna know any better. And that’s why every time this gets out, every time we hear about something like Orbitz or some of the other companies that have engaged in this, they pull it back. They say, “Oh, we were just testing, we weren’t trying to do that on a widespread scale.” So, isolation of the consumer, really, really important. And then the second thing is I think this bout of inflation that we’ve had since 2022 has provided an opportunity. If prices are going up generally, you’re going to accept that the price of your individual good has gone up at a commensurate level, even if the cost of producing that good didn’t change at all. So this has created opportunity in the space of pricing for companies to continue to sort of ramp up those prices a little bit more and a little bit more, and to invest in these sophisticated tools and these consultants, often AI-driven consultants, that are teaching companies how to use this data to increase prices.

Beau: Well, you’re taking two very potent things and creating a situation where the first thing that comes to mind is food purveyors: McDonald’s, Shake Shack, Dunkin’, the list is endless, Starbucks…

David: Yeah.

Beau: Where they are encouraging consumers to download an app. And that app then creates that invisibility you were just talking about so that you can be standing in line getting ice cream, paying a different price, and having no visibility into that.

David: Yeah. And the app is the primary extraction tool of that data because once you get the app, and they really want you to, you get on the phone, and then you have an entryway into somebody’s entire life. And so the apps have become very, very important for the purposes of this data extraction and also this isolation, as you say. There was a recent story in Wired Magazine, where in California, because of various laws we have around data privacy, you can request the amount of data that a company is gathering on you, and you can ask them to send it to you. And so this reporter asked McDonald’s to send all of the data that they had on him, and it came out to 515 pages. That’s how much data they had on one person, one individual about their shopping habits and everything else under the sun. Now, for context, the FBI file on John Lennon was about 500 pages. So at a similar investigative level is what McDonald’s has on every one of its millions and millions and millions of customers. And that data is being used and massaged and utilized every single day.

Beau: After the break, why being rich might help you save money.

Beau: Here’s something worth being mad as hell about, a tax for not being flush with cash. Yep, and it’s not just hitting low-income shoppers, but sad to say, it does hit them harder than the unfairly targeted middle-class cohort that also seems to be targeted by higher prices when compared to the upper echelons of moneyed shoppers. We were talking about McDonald’s, but it could be any company selling anything. Remember, data drives this, data about you, data you hand over willingly in exchange for convenient transactions or better transactions or better deals. Look at the apps in your phone. That’s ground zero for some of the data being used against you. Those apps grab a whole universe of information about you. The McDonald’s dossier is not the most robust version out there. There are worse. They use location data, so now they have all kinds of really granular stuff about your patterns of life. What you do, when you do it, and how you do it, and where you do it.

David: And McDonald’s app has that too.

Beau: Last week, David Dayen gave us real information. Staples charged more in zip codes that didn’t have as many competitive stores. Of course they did. And those were usually found in lower income neighborhoods. Again, duh. Chris, the producer, saw a similar thing firsthand ordering from DoorDash while getting some White Castle from DoorDash. It was more expensive to be a poor college student than a wealthy Minnesotan. And as it turns out, in twenty twenty-three, a group of Boston teenagers proved it too. Sarah, friend, compatriot, colleague. Hi, Sarah.

Sarah: Hello.

Beau: This is Sarah H, who works at DeleteMe.

Sarah: When I was growing up, we had a $40 budget for all of our groceries. And now when I look at the surveillance pricing and how much of a difference it can make, it staggers me how we could never possibly have survived on that budget in today’s world.

Beau: I’m hearing a lot of rice, beans, and ramen. Is that right? I do.

Sarah: Mm-hmm. Yeah, you’ve— And 99 Cents Stores, which actually had price tags.

Beau: They’re not 99 cents stores anymore either. No such thing. Talk to me about those kids in Boston.

Sarah: Most of the folks who have been able to show this and who have actually done the studies, they’re like student journalists. The FTC hasn’t done a big study on this and isn’t going into this. They say they’re doing investigations then don’t show the results from them.

CBS News Clip: Some teenagers are raising red flags after looking into price differences at grocery stores along greater Boston. As WBZ’s Paul Burton shows us, they want answers after discovering that shoppers at the Stop & Shop in Jamaica Plain are paying 18% more for items than shoppers buying their groceries in Dedham. It makes us angry to see that we’re paying more and we’re being ripped off, ’cause sometimes, because we live by food stamps. Derek Medina is learning valuable lessons about equity and inflation. The 15-year-old is part of a youth development organization called High Square Task Force that focuses on arts, civic engagement, and college prep. Recently, five high school students formed a team to study and compare food prices at Stop & Shop in a lower income community in Jamaica Plain where he lives as compared to a Stop & Shop in Dedham. And then we saw that there was an 18% increase in the prices, so like about a $34.80 kind of thing, and so that concerned us.

Beau: Senators got involved, Elizabeth Warren, Ed Markey, Ayanna Pressley. Stop & Shop lowered prices at that one store.

NBC10: Now, in response, Stop & Shop promised it had launched a, quote, “multi-year strategy to invest in pricing and lower everyday prices across all of our stores.”

Beau: Fast-forward to the here and now and another chain called Hannaford, a consumer group found more of the same.

NEWS CENTER Maine: Released Friday, the report coming from the New England Consumers Alliance looked at the prices of 45 items sold at Hannaford stores in Falmouth, Yarmouth, Millinocket, and Machias. The report draws a connection between the incomes of the areas and the price of groceries. Their findings show costs are 25% higher at the lower income area stores in Machias and Millinocket.

Sarah: They were able to show that the stores in this area where there was basically no competition, there’s no other options, they were able to make lower income families pay 25% more for their food than this one in the big city, and it’s the same franchise, same products, everything. But the prices just increase massively for folks who actually have less to spend.

Beau: This problem isn’t new. If you have exceptional credit, you get better deals than people with poor or average credit. That’s just the way it works. It takes the form of advantageous financing deals on things, better mortgages. It’s not news. Sounds like we’re in the same world Chris The Producer pinged, though, with his low-intention buyer. The high-intention buyer is more likely to buy this or that thing because they can afford it. And as we speculated with the cheaper in-store price my friend Keegan encountered at Costco, there’s a lot of gold in them thar hills for a retailer that can tell if they’ve got a high-intention buyer with money to burn. A long tail of, “Look at that paper shredder. We totally need one of those.” But Sarah, what people really wanna know is how these retailers are figuring out who is gonna pay what.

Sarah: There are a lot of signals that they’re using, and all of those are being pulled together through AI inferencing, which you know is my pet project at the moment. So you have literally thousands of signals that apply to how much somebody is going to be able to pay, but location is probably the biggest one, and precise geolocation over a period of time. So for example, Target was able to, through precise geolocation in the apps, tell if somebody’s in their parking lot, they’re going to see a different price than somebody who’s purchasing from further away online or if somebody’s already in the store. There’s different prices for everybody just based on that one data point, the geolocation. But there are a lot of other data points that somebody can use to determine income level, and it was something that was really fascinating to me. So Lyft put out a study of the folks who are using their vehicles, and they actually explained, “Oh, this percentage of our people are in this income bracket. This percentage of people are in this income bracket.” How do they even know this stuff? How do they have this information? And some of it has to be coming from data brokers. Some of it may be surveys, self-declared. You know, they can give rewards for surveys, right? So some of this is gonna be first-party data. I believe some of it’s gonna be third-party data. And I think it’s just an absolutely vast data economy that is pulling together all these data points, and we have a profile out there.

Beau: It’s happening. It’s totally happening.

Sarah: It’s happening.

Beau: No, but it’s worse. In other words, like whatever we think might be being done, they’re like, “Oh yeah, but that’s kid stuff. Here’s what we’re actually doing.” Now, I know you, and I know you don’t share a lot from your own personal life on social media. You called it “self-declared” information. But a lot of the AI inferences are coming from that. Here’s a picture I posted of snapping turtles. This person loves snapping turtles. Well, you know what people who like snapping turtles like? Leather gloves, thick leather gloves. Whatever the inferences are. And what kind of person tends to like them? I’m using a silly example because I don’t want to bother with the ins and outs of what they’re actually looking at, which is something that most Xennials, if they were to hear what they’re actually looking at, would be appalled because it is all identity-based.

Sarah: One of the data points that was crazy to me is that they can tell whether somebody’s lower or higher income based on what time of the day they’re driving more. And so, if you look at that piece of data, you’re not gonna say, “Oh, somebody’s driving in the morning or late at night, and that must mean they’re lower income.” But if they’re doing it regularly every day, there are shifts that they could pick up in the evening, maybe it’s third shift for them, and the AI can infer from that all of this other information. And so it’s extremely sophisticated, and it’s pulling from thousands of data points and, like you said, also social media, and I think everybody should be concerned about it. I’m worried when people don’t know anything about this, and most people don’t in the area where I live.

Beau: We are talking about a system that has the potential for really extreme discrimination, correct?

Grace: Yes. I mean, I think there are some questions about how do existing anti-discrimination laws intersect when a price is being set based on a protected characteristic.

Beau: This is Grace Gedye, a policy analyst at Consumer Reports who spends her days digging into exactly this kind of algorithmic discrimination.

Grace: Both the US Constitution has protected characteristics and also state laws often have protections based on race, sex, ethnicity or country of origin, et cetera. And so there’s a question about how do existing laws, when might they cover if that’s the basis of a price difference? But there’s all sorts of other sources of potential discrimination, including things that might reasonably be a proxy for a sensitive characteristic.

Beau: What kinds of data would be used to discriminate in this day and age? I mean, we know how it worked back in the ’50s with redlining. How would it work now?

Grace: Yeah. So I think gathering consumers’ location data, especially their real-time location data, so you’re seeing not only where they likely live based on where they’re spending every single night, but also where they go during the day, where they shop, where they drop their kids off for daycare. You could find out if someone’s a student if they’re going to a university every single day, or if they’re likely unemployed. Well, I guess with work from home, someone being at home all day does not necessarily indicate that they’re unemployed. But all this to say, even if you’re just looking at someone’s real-time location data, that can be incredibly revealing, and then you combine it with behavioral data from all their online interactions, and you’ve got a very complete picture.

Beau: So Grace, we know there’s thousands of pixels and other ways of tracking people on every retail site they’re visiting, any major retail site they’re visiting. Is that part of this robust data dossier that you’re talking about?

Grace: Absolutely. People now know that they can potentially receive a discount if they put something in their digital cart and then do nothing for several days. And the basis on which they’re receiving that discount is the company has noticed you’ve put that in your cart, signaled intent to buy, and then didn’t complete the sale. And so they’re trying to encourage you to complete the sale by sending a discount. That’s one piece of data that people are actually more familiar with because of the kind of response it could generate.

Beau: Right. And you’re also going to see, let’s say it was a pair of scissors. Now, I left a pair of the most amazing scissors you ever saw on Earth ever in your life. They’re really remarkable. They’re made out of tempered steel, made in the center of the Earth by a very specific group of people, and you must buy it. So I put it in my cart. They’re $30. And now, if I leave it in my cart hoping that they’ll knock it down to 19.99, for a month afterwards, whether or not I make the purchase, I will be receiving ads for scissors.

Grace: Very likely, yes. I think a lot of people had that experience of being chased around the internet to all these different websites they go to, seeing the ad for the product.

Beau: But it’s not just the ad for the product. It could be somebody who’s paid for the right to know that somebody’s interested in scissors. And so, on we go. And it seems harmless, right? Because, okay, well, I want scissors, so why not get a bunch of ads? And maybe I’ll find the best scissors ever made by a very particular group of people in the center of the Earth using a very strange ore. Cool. But it gets dark, right? And I wanna understand that. Like, in what ways does surveillance pricing turn on consumers? It’s not just giving them the best options or the best of everything.

Grace: Right. Well, unlike advertising, which is just presenting you with the option, if they’re collecting data about you that signals high willingness to spend, it might actually change the price you’re seeing. For example, if you regularly order grocery deliveries, there are inferences that you have a physical disability, maybe inferences that you’re homebound based on data about what you search for online. And you don’t live physically near grocery stores. You live in a grocery desert. Those three factors might make a company think, “Okay, this person has a pretty high willingness to buy our grocery delivery service because they really have few other options.” And I think a lot of folks feel like that’s pretty icky and unfair. That person does have a higher demand, but should it really be taken advantage of in the way where they end up spending a whole lot more?

Beau: After the break, we’ll look at some good news. In a perfect world, what does this legislation look like to actually solve the problem?

Grace: First, you need a really robust definition of personal data.

Beau: Here is Grace Gedye again.

Grace: Which luckily a lot of states already have in their consumer data privacy laws. Those laws definitely have their flaws, but many of them have actually good definitions of personal data. A good definition of personal data includes a bunch of kind of belt and suspenders type terms to cover all these different scenarios, right? So basically, it’s any information that is linked or reasonably linkable to an individual consumer. But you wanna make sure you’re capturing derived data. You wanna make sure you’re capturing unique identifiers, so even if it’s not this address linked to Grace Gedye, but if it’s this address linked to user 120916 whatever, it’s still being captured, especially if that user number has a robust profile that, taken together, is linkable to me. Because that’s how, in practice, companies store a lot of information. It’s often not right next to your name or email. Also you wanna capture information that’s identifiable with an individual device because very often individual devices are immediately linkable to an individual person.

Beau: So a perfect law that protects consumers from surveillance pricing would definitely start with a definition of what kind of data are we talking about? Well, so that’s the personal data we’re talking about. Now, how would the law actually handle the possession of that data and what you can do with it?

Grace: So it would typically say, the way a lot of these bills are structured, it would say a retailer and whatever the scope of the bill is, if it’s just groceries, if it’s the retail, the sale of all tangible goods, if it’s all goods and services, fill in the blank for what sort of entity you’re governing. But for now, we’ll just say a retailer cannot change a price based on someone’s personal information. That’s surveillance pricing, and it’s prohibited. Because most standard pricing, personal data doesn’t need to come into it at all, right? You’re looking at overall market data. And another little technical wrinkle is most definitions of personal data don’t cover de-identified data. So if you’re just looking at aggregated anonymized data to gauge overall market demand, you can still do that. But once it’s identifiable to an individual, you can’t use that to set a price.

Beau: It’s a shame that for me, I just personally, ’cause I’m a stickler, I would include re-identified anonymized data. Because there’s going to be data brokers out there who do that for a living, who just re-identify anonymized data sets and sell them as identified.

Grace: Well, and once they do that, if a company were to use that re-identified data to set a price, they’d be in violation of the law.

Beau: It would be illegal. I think that’s right, 1,000%.

Grace: New York state passed a law requiring companies to disclose when they’re using consumers’ personal data to change a price. So now we actually are seeing companies start to disclose it, but that is quite new. But actually what Connecticut, Maryland, and New Jersey have done is gone a step further and attempted to ban the practice, not just require the disclosure.

Beau: And New York is not doing that yet, correct?

Grace: The New York legislature has passed a bill, and it is on Governor Hochul’s desk, but she has not yet decided whether or not to sign or veto it.

Beau: So we’re about to have four states, but we’re not there yet.

David: And one key question for policymakers is…

Beau: Here’s David Dayan again.

David: Okay, if we want to tamp down on this practice, do we go after the data itself and the data collection itself, or do we go after the ways in which that data is being used? And I think there’s a split on that in the policymaker space.

Beau: I mean, it falls to an organization in government that is legendarily not great at doing this sort of thing, which is the FTC, right?

David: Yeah, I mean, there’s a couple things. Number one, we haven’t had a privacy law federally for decades. Really, any privacy laws that we have are completely outdated at this point. And then the second thing is what the retailers argue they are doing with this data collection, and they usually frame it as, “We’re just giving discounts to people. We’re just using this data to give people a break on price, and why would you wanna stop a break on price? Why would you wanna stop these discounts?” And it gets to a point where there is no sort of real functional standard price, and so what are you discounting from? To be a discount, it has to be discount from a particular place, and if that place doesn’t exist because every price is variable for every individual, then there is no discount.

Beau: An advertising agent called Publicis Groupe just bought a data broker giant named LiveRamp for two and a half billion dollars. LiveRamp has spent years building digital files, they call them identity graphs, on almost everyone in the United States with a credit card or with some buying power. They track everything. Absolutely everything. So Publicis is connecting all that with artificial intelligence, and the goal isn’t just to target ads. AI is gonna be figuring out things that make the McDonald’s example look just trivial. With AI agents checking personal identity profiles behind every single transaction and surveillance pricing, at this point, seems to me like it is going to be possible for 300 million different prices to be set at any given time during the day. How do regulators or consumers stop it? Stop this gouging?

David: Yeah. I think the answer is you don’t let those companies merge, right?

Beau: You don’t, right?

David: Come together. So that’s number one.

Beau: We’re here now, so why don’t you tell us why is it so absolutely terrifying that this is being allowed to happen?

David: This is the leading ad conglomerate controlling personal data on hundreds of millions of people around the world. There’s a surveillance pricing risk, but there’s also just a huge other risk by pooling all of this information in one place and then making it easy for hackers or anybody to breach that data. It magnifies that possibility and what the damage would be in the process. And then Publicis has a history of misusing data. Publicis had access to conversations between patients and doctors that were done for the purpose of memorializing conversations afterwards, and it apparently extracted that data so it could target patients with ads about OxyContin that it knew people who they knew to be in pain. That was settled for about $350 million. So it’s not like Publicis has any compunction against using personalized data, and now they would have access to a massive trove of personalized data. So that’s why this is a really dangerous merger. The broader question that you ask about what do we do about this, we have seen bills introduced, I think in over two dozen states at this point. There have been laws in New York, in Maryland, in Connecticut. Some of those laws are much better than others. Some have a private right of action which allow you or I to sue over surveillance pricing. Some do not. Some rely on the state attorney general to do it. Some are just confined to things like groceries. Some are much broader. So we’re kind of picking our way through and figuring out from a policy standpoint how to attack this. I think one of the more interesting ways you could think about this is to say that, okay, when we agreed to give our data away, we did not agree to have it sold, shared, processed in a particular way with third parties. And so ending the sort of data broker side of this might be a good way to get at the larger problem, because if we confine this to individual circumstances, then we’re at no, if a company can’t get data from everyone else and they can only get the data that you give them directly, that’s not much different than the Catalina Marketing back in the grocery store and you get a coupon based on what you just bought. That’s what we dealt with for 50 years. It wasn’t seen as as creepy as what we’re dealing with right now. So hiving off that data and not allowing it to be shared, not allowing it to be processed, not allowing it to be marketed and sold, that might be the way forward. Another way that we can get around all of this is by just mandating a common price. Just say the gallon of milk is 2.69 today, and it’s not going to change whether your name is Beau or whether your name is David. And that there are real penalties if you try to differentiate that pricing.

Beau: You make it sound so simple. It never is in the Wild West of big tech doing whatever it wants, but it is too. The important part is understanding the easy part here. Something unfair is happening with the way we’re priced. And I don’t mean we have prices on us; the prices we’re assigned in a price tag-free environment, which is the internet.

David: When I talked to Sam Levine, who was at the FTC under Lina Khan, he was the head of Bureau of Consumer Protection, and now he works for Mayor Zohran Mamdani as the head of the Department of Consumer and Worker Protection in New York City. He had a great line about just this, what you’re talking about, and he said, “This is about taxing people who don’t turn over their data and manipulating people who do.” And his question was, do we want a world in which you have to pay a premium if you want to shop anonymously? And we do. If you don’t want to give your information to that company, you’re gonna miss out on their discounting and things like that. So, yeah, you’re paying a premium for wanting to be left alone. The good news is that every time one of these surveillance pricing examples gets out into the world, it is greeted with absolute outrage. People absolutely hate this, and they don’t wanna see it in their lives. And when you have something like that, when you have an issue as potent as that, you have an opportunity to actually change the game, and I think that’s where we’re at right now with surveillance pricing.

Beau: It’s time to do something about this. Spread the word. Send these last few episodes to friends. Let’s make this what people are talking about at the Thanksgiving table this year, and let it dominate the post-Black Friday coverage in the media. As Justice Louis Brandeis almost said, were he not so focused on street crime, “Criminals thrive in obscurity.” White-collar criminals, retail criminals. You know, come on. I didn’t name names. I’m just saying, seems a little… Seems like it should be illegal. It’s not illegal yet, so they’re not criminals now. But criminals of the future. Let’s start making surveillance pricing common knowledge so it can be outlawed. Let’s do that. Okay? Now it’s time for our Tinfoil Swan, and this week, what is our Tinfoil Swan? It’s our paranoid takeaway to keep you safe on and offline. And think about it, it’s not paranoia. You can be paranoid and the situation you’re paranoid about can be totally true, and that’s the case with surveillance pricing. So what can you do right now? What would work for you is to remove, to be honest, remove any shopping apps you have on your phone. Just do it. You don’t need them. Convenient? Sure. But you can see the downside of them, right? That they are gathering a lot of data about you, and they may be setting prices using that data. So if you have a retail app on your phone, consider removing it. Consider the possibility that convenience might be costing you a number we do know, upwards to $1,200 a year in surveillance pricing. So that’s one move you can make, and the other is loyalty programs. Take a close look at what you’re actually getting from them because you may not be saving as much as you think. And in the process, the trade-off, all that data you’re giving them may be costing you big time. That’s it for this week. Please stay safe out there. Spread the word, rate and review the podcast wherever you listen to it, and we’ll see you next week. What the Hack is produced by a bunch of people. I’m one of them. Andrew Steven is another. And Sarah H helps out enormously from time to time. We’re available wherever you get your podcasts. If you are a regular listener to this show and you don’t use DeleteMe already, I want to tell you, you should. If you’re not and you want to, here’s what to do. Go to joindeleteme.com/wth. That’s joindeleteme.com/wth and get 20% off. I kid you not, 20%. 20% off. That’s joindeleteme.com/wth.

Learn More:

• Read our article on covers surveillance pricing.
• Hear a recent episode from What the Hack, which kicked off our surveillance pricing series.
• Listen to more from What the Hack.