University of St. Thomas Law Journal

Big Brother Wants to Charge You More for Eggs at the Grocery Store: Why We Need to Regulate Surveillance Pricing 

Michael Hurd*

Consider two people who are in the market to buy a new car, Customer A and Customer B.1 Customer A makes $100,000 per year, is frugal with her money, and has no particular interest in cars other than as a means of transportation. Customer B, on the other hand, makes $150,000 per year and is interested in cars. Ford Motor Company has to decide how much it can charge for its cars so that it gets the most money from both Customer A and Customer B. To help it determine a price, Ford would like to know, among other things, Customer A’s and Customer B’s incomes and their interest in cars. Obtaining this information would be a simple task if Customer A and B were the only customers in the market. Alas, there are tens of thousands of customers looking to buy a car, and Ford can’t know what each consumer would be willing to pay for its vehicles. All Ford can do is set a price and stick to it. Suppose Customer A begrudgingly buys the Ford car. The car is just barely within her price range—if the car were any more expensive, she would have walked away. Customer B buys the same car and is elated to have spent so little on the car he wanted. Ford squeezed every dollar it could from Customer A. But in the case of Customer B, Ford left money on the table. Ford could have charged Customer B significantly more, and he would have still bought the car. This scenario illustrates the economic concept of willingness to pay.

What Is Willingness to Pay?

    Willingness to pay (WTP) is the maximum price a customer is willing to pay for a service or product.2 Businesses want to charge each customer at the limit of the customer’s WTP to maximize revenue for their products or services.3 Companies do this through a practice called “price discrimination.”4 You are likely already familiar with this concept. Movie theaters participate in price discrimination when they offer discounted tickets for children and senior citizens.5 Everyone watches the same movie, but the theater charges different prices based on the customer’s identity because different groups have different willingness to pay.6 This form of price discrimination is not perfect. Certainly, there are senior citizens and parents of children who would be willing to pay full price for the movie ticket, so movie theaters leave money on the table by giving those people a discount. The problem for the theater (and any business engaging in price discrimination) is collecting enough data on its customers to know who would pay what price for the theater’s movie tickets.7 This is where big data and sophisticated pricing algorithms step in.8

    Some Companies Are Using Algorithms to Set Prices

      Returning to our car-buying hypothetical, we’ve already established that Ford would like to know things about its customers, like their incomes, spending habits, and interests. Luckily for a business looking to price discriminate, Google and other companies collect tons of data about you.9 To name a few, Google collects information about your photos and videos, browser history, purchase activity, and the people you contact.10 Once massive amounts of data are collected on consumers, it needs to be organized in a way that’s useful to the price-discriminating company.11 Companies use the information to build profiles on individual consumers.12 Using these profiles, companies can feed information into sophisticated algorithms to charge their customers the maximum amount they are willing to pay, which means charging every customer a different price.13 In our example, Ford’s algorithm would tell it that Customer B, the car enthusiast, loves cars and has a high income. The algorithm would create a price specific to Customer B, and he would end up paying more than Customer A for the same car. This is called “surveillance pricing.”14

      Surveillance Pricing Is Bad for Consumers

        Consumers are happy when they buy something for less than they would have otherwise been willing to pay for it. Widespread implementation of surveillance pricing could end that.15 Unfortunately, the rise of surveillance pricing has already begun.16 A recent study found that Instacart, an online grocery delivery marketplace, charges different prices to different customers.17 In one case, shoppers were quoted five different prices for the same carton of eggs at a Safeway in Washington, D.C.18 It’s not just groceries. The Princeton Review offers online SAT tutoring packages for high school students.19 The Princeton Review was found to discriminate based on geographic location, with some areas being charged up to $1,800 more for tutoring than other areas.20 A (perhaps unintended) consequence of this price discrimination was that Asian families were twice as likely to be offered higher prices for tutoring than non-Asian families.21 Although The Princeton Review adjusted pricing purely based on geography, this is an example of surveillance pricing targeting various demographics differently. 

        Lawmakers Have Proposed Legislation to Curb Surveillance Pricing

          So is all of this legal? For the most part, yes. There are currently no state or federal regulations in place,22 but more than a dozen states have introduced bills to curb surveillance pricing.23 The federal government has been monitoring surveillance pricing for years.24 Lina Khan, the Chair of the FTC under President Biden, pushed for transparency in pricing, stating, “The FTC should continue to investigate surveillance pricing practices because Americans deserve to know how their private data is being used to set the prices they pay and whether firms are charging different people different prices for the same good or service.”25 Republicans have also recognized the issue and continue to monitor the companies using surveillance pricing.26 Several states, including Minnesota, have proposed legislation on surveillance pricing.27 The tricky part for legislators will be crafting effective legislation without unreasonably burdening markets. For example, banning all price discrimination would mean movie theaters could not charge different prices for customers of different ages. Another possibility is limiting companies from accessing big data by expanding data privacy laws.28 But that could restrict some of the benefits consumers gain from personalization.29

          Conclusion

          Surveillance pricing is creating a fundamental shift in how markets operate, and it risks eroding consumer trust. While personalized pricing may increase efficiency, allowing companies to extract each consumer’s maximum willingness to pay comes at the cost of transparency and fairness. Policymakers need to act soon to impose reasonable limits on data use and pricing practices to prevent surveillance pricing from outpacing basic consumer protections. Without regulation, consumers may never feel like they got a good deal at checkout again.

          *Michael Hurd, J.D. Candidate, University of St. Thomas School of Law (Editor-in-Chief). B.B.A. in economics, University of South Dakota.

          1. The following hypothetical is loosely based on an example that mathematician Noah Giansiracusa used in an interview with Harvard Law Today. See Rachel Reed, The Algorithm Thinks You’re Rich. Prepare to Pay More for That Flight, Harv. L. Today (Aug. 15, 2025), https://hls.harvard.edu/today/how-delta-airlines-and-other-companies-use-dynamic-pricing-to-determine-how-much-you-pay/ [https://perma.cc/85H3-ZFNE]. ↩︎
          2. Tim Stobierski, Willingness to Pay: What It Is & How to Calculate, Harv. Bus. Sch. (Oct. 20, 2020), https://online.hbs.edu/blog/post/willingness-to-pay [https://perma.cc/J77U-5GYJ]. ↩︎
          3. See id. ↩︎
          4. Oren Bar-Gill, Algorithmic Price Discrimination When Demand Is a Function of Both Preferences and (Mis)perceptions, 86 U. Chi. L. Rev. 217, 218 (2019). ↩︎
          5. Price Discrimination: Strategies, Legality, and Implications for Businesses, Simon Kucher: Blog (Nov. 3, 2023), https://www.simon-kucher.com/en/insights/price-discrimination-strategies-legality-and-implications-businesses [https://perma.cc/WX5P-LK58]. ↩︎
          6. See id. ↩︎
          7. See Bar-Gill, supra note 4, at 218 (discussing the use of big data to set prices). ↩︎
          8. See Bar-Gill, supra note 4, at 218 (“Fueled by big data, algorithmic price discrimination enables sellers to parse the population of potential customers into finer and finer subcategories-each matched with a different price.”). ↩︎
          9. See Gene Petrino, The Data Big Tech Companies Have on You, Security.org (Jan. 12, 2026), https://www.security.org/resources/data-tech-companies-have/ [https://perma.cc/H36F-JVML] (describing the wide range of data that Google and other companies collect from users). ↩︎
          10. Id. ↩︎
          11. See Bar-Gill, supra note 4, at 219. ↩︎
          12. Press Release, Comm. on Oversight and Gov’t Reform, Comer Investigates Use of A.I. to Set Prices for Consumers (Mar. 5, 2026) [hereinafter Comer Press Release], https://oversight.house.gov/release/comer-investigates-use-of-artificial-intelligence-to-set-prices-for-consumers/ [https://perma.cc/T7DQ-6JPT]. ↩︎
          13. See Reed, supra note 1. ↩︎
          14. Ali Rogin & Andrew Corkery, How Online Retailers Are Using AI to Adjust Prices by Mining Your Personal Data, PBS News (Dec. 20, 2025, at 17:45 ET), https://www.pbs.org/newshour/show/how-online-retailers-are-using-ai-to-adjust-prices-by-mining-your-personal-data [https://perma.cc/42PH-J2BV]. ↩︎
          15. See Jay Stanley, “Surveillance Pricing” Hurts Consumers, Incentivizes More Corporate Spying on Them, ACLU (Sep. 12, 2025), https://www.aclu.org/news/privacy-technology/surveillance-pricing [https://perma.cc/BKR6-W6U8] (describing surveillance pricing as “using data and knowledge about consumers to treat us differently from other customers and squeeze more money out of us”). ↩︎
          16. Rep. James Comer has requested several companies turn over documents relating to pricing practices. Comer Press Release, supra note 12. ↩︎
          17. Annie Palmer, Instacart’s AI Pricing Tools Drive Up the Cost of Some Groceries, Study Finds, CNBC (Dec. 9, 2025, at 15:34 ET), https://www.cnbc.com/2025/12/09/study-instacart-ai-pricing-cost-of-groceries.html [https://perma.cc/HG6F-V232]. ↩︎
          18. Id. ↩︎
          19. Julia Angwin, Surya Mattu & Jeff Larson, The Tiger Mom Tax: Asians Are Nearly Twice as Likely to Get a Higher Price from Princeton Review, ProPublica: Racial Just. (Sep. 1, 2015, at 10:00 CT), https://www.propublica.org/article/asians-nearly-twice-as-likely-to-get-higher-price-from-princeton-review [https://perma.cc/MR8S-YCAV]. ↩︎
          20. Id. ↩︎
          21. Id. ↩︎
          22. Reed, supra note 1. ↩︎
          23. Austin Jenkins, States Move to Curb AI-Driven ‘Surveillance Pricing’, Ariz. Capitol Times (Jan. 23, 2026), https://azcapitoltimes.com/news/2026/01/23/states-move-to-curb-ai-driven-surveillance-pricing/ [https://perma.cc/7CPN-XMQU]. ↩︎
          24. See Press Release, Fed. Trade Comm’n, FTC Surveillance Pricing Study Indicates Wide Range of Personal Data Used to Set Individualized Consumer Prices (Jan. 17, 2025), https://www.ftc.gov/news-events/news/press-releases/2025/01/ftc-surveillance-pricing-study-indicates-wide-range-personal-data-used-set-individualized-consumer [https://perma.cc/4P5B-6LLH]. ↩︎
          25. Id. ↩︎
          26. See supra note 16 and accompanying text. ↩︎
          27. Esme Murphy & Liz Christy, Pushing for “Fair and Transparent Market,” Minnesota DFL Lawmaker Calls for a Ban on Surveillance Pricing, CBS News (Mar. 12, 2026, 16:00 CT), https://www.cbsnews.com/minnesota/news/minnesota-dfl-lawmaker-calls-for-ban-surveillance-pricing/ [https://perma.cc/S6XY-LCK9]. ↩︎
          28. See Bar-Gill, supra note 4 at 222 (“Because algorithmic price discrimination is fueled by big data, this extreme form of price discrimination can be curbed by limiting sellers’ access to information about consumers’ WTP.”) ↩︎
          29. See Bar-Gill, supra note 4 at 222 (“[A]ttacking the big data foundation of price discrimination runs [a] risk. . . . Given the benefits that personalization provides, cutting the flow of information might be a net loss for consumers.”). ↩︎

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