# Transcript: The Consumer's Guide to Beating the Bots **Date:** March 18, 2026 · 10:30 PM **Session:** [The Consumer's Guide to Beating the Bots](/sessions/2026-03-18/pp1148552-the-consumer-s-guide-to-beating-the-bots) ## Summary This session delves into how corporations utilize personal data and algorithms to implement "surveillance pricing," manipulating prices for consumers and wages for workers. Speakers detail practices like dynamic pricing, algorithmic collusion in rental markets, and data harvesting through loyalty programs, arguing these tactics erode fair market principles. The panel emphasizes the critical need for stronger antitrust enforcement and new legislation to combat these deceptive schemes, while also offering individual strategies for consumers to protect themselves. ## Topics `algorithmic pricing` · `surveillance pricing` · `consumer data` · `price discrimination` · `antitrust` · `market manipulation` · `loyalty programs` · `data privacy` · `legislative solutions` · `consumer protection` ## Key Takeaways 1. Be aware that companies use your personal data to set individualized prices, often without your knowledge, going beyond traditional dynamic pricing. 2. Comparison shop not just across brands, but also within brands (website vs. app, different devices) to find the best price, as in-app purchases often lead to higher costs and more data collection. 3. Support legislative efforts at the state and federal levels that aim to ban surveillance pricing and algorithmic collusion, as current laws are often insufficient to address these evolving practices. 4. Utilize privacy-focused browsers, turn off ad personalization, and consider Data Subject Access Requests (DSARs) in applicable states to understand and limit your data footprint. 5. Engage on social media to call out companies employing deceptive pricing practices, as public pressure can sometimes lead to changes faster than legal or regulatory action. ## Full Transcript Late last week, the Washington Post sent around an email to subscribers about rate increases, and in fine print it said, 'Hey, this price was set by an algorithm using your personal data.' A lot of people see that and are like, 'Wait, what's going on?' That is my question to you, Lindsay, what's going on here? Isn't the price, just the price? This is a great example to start off with. There was definitely a bit of a panic about the revelation that the Washington Post is using subscriber information to set different prices for different subscribers. This really reflects that while Americans are rightfully upset about high prices, they are also upset about how pricing works, which has shifted entirely under our feet. For thousands of years, haggling was the order of the day; you and I paid different prices for the same items. In the US, Quakers introduced the idea of a standard, fair, transparent price, believing every person should pay the same. This principle was adopted by early department stores like John Wanamaker's, who also saw the business case for price tags, as haggling took too much time. This standard pricing model has been with us for nearly 150 years, but it is all changing as commerce moves online. Companies increasingly dig into our personal data to figure out how much we are willing to pay and to use that data to set pricing. The Washington Post case is just the tip of the iceberg; for example, the New York Times collects all sorts of data, including your Wordle game play, to set subscription prices. What's happening more broadly is the way pricing has worked for all of our lifetimes is completely changing. Many of us know about dynamic pricing, where last-minute flights are higher than those booked months in advance, used to ration scarce resources by ability to pay. However, what's happening now is not the same as traditional dynamic pricing. Increasingly, companies are varying pricing according to a whole host of characteristics that have nothing to do with managing scarce resources. For instance, Instacart was running experiments on millions of Americans while they shopped for groceries. These experiments were not about managing scarce resources, as stores were not running out of pasta or wheat. Instead, these experiments were about figuring out the exact maximum consumers would pay for an item before removing it from their cart or abandoning the app. This goes well beyond traditional dynamic pricing, representing high-frequency pricing and pricing experiments. Companies have become more sophisticated at understanding our willingness to pay for any given item, not just the cost to make it. The analogy to make this understandable is the feeling many have had with social media: you open an app for a moment, and 30 minutes later, you've lost track of time. This is the end product of constant A/B testing by social media companies to keep you engaged. This same 'brute force' attack on our brains is now coming to pricing. When Instacart was found charging different people different prices for eggs, they claimed they weren't individualizing or doing dynamic pricing, but rather trying to 'brute force' figure out how much people could pay. We don't really know exactly what data they track, but the principle is similar to how social media platforms hook users. This violates a basic sense of what we think is right. We have a moral expectation that a price is a price that will be offered to everyone. These companies are taking advantage of this to extract as much profit as possible, moving beyond traditional price gouging to a more insidious form of algorithmic manipulation. The RealPage case, filed by the antitrust division of the Justice Department, involved an algorithmic product sold to large landlords. This product asked landlords to input granular data about available apartments, then spit out recommendations to maximize pricing. It was designed to resist rent decreases, adjust prices based on future supply, and even make units seem unavailable to retain price control. RealPage also managed 'frictions' to discourage landlords from lowering prices or holding them steady, ensuring they followed recommendations to go higher. The Justice Department alleged this was essentially collusion in the modern era, replacing the 'smoke-filled room' with an algorithm. This was considered at least as dangerous, if not more, because algorithms can discipline cheating and cartels more effectively. The Trump administration ultimately settled the DOJ suit against RealPage, a settlement considered lenient given the billions of dollars in alleged rental overcharges. This case highlights how categories like first-degree price discrimination (charging maximum price) and traditional collusion are converging, with companies blending prohibitions and using consumer insights with competitor insights. The warning is that the future is here, and extraordinary insight is available to companies. When asked how this is allowed, the answer is often that it's not; it's illegal. There's a 'magical thinking' around computers, where people believe that if an algorithm sets a price, it's somehow different from human collusion, like the beef trust of the early 20th century. Collusion is collusion, even if it involves ones and zeros instead of a smoky back room. While clearer laws would help, the strength of the RealPage case was arguing that the fundamental act of price fixing remains illegal. Some argue this is 'just how business is,' but competitive markets are supposed to work in our favor by creating downward pricing pressure. However, these market forces are increasingly eroded due to a lack of competition in key markets and information asymmetries. Consumers cannot effectively comparison shop when they don't know what the price will be from one minute to the next or what the 'reference price' is. This makes it hard to use our wallets to discipline excessive pricing. Markets exist to serve people, not the other way around. The original antitrust laws, passed with broad bipartisan support, understood this basic idea. These issues are not partisan; if people cannot count on basic fair treatment in the market, it erodes their expectations of dignity. Even conservative figures like Texas AG Ken Paxton are filing righteous antitrust cases against such practices. The Care.com case, investigated by the FTC, showed how a 'tech company' could trick caregivers. Care.com allegedly created job profiles for users without their knowledge, then lured in caregivers by showing many 'jobs' that were often inactive or couldn't be communicated with. This highlights how tech platforms can create illusions that wouldn't be possible in a physical storefront. This pattern extends to gig economy platforms like Uber and Lyft, where driver wages can be manipulated. Drivers might be lured with good initial fares, only to see them decrease over time as the platform learns their habits. This demonstrates that surveillance and algorithmic manipulation affect not just consumer prices, but also the paychecks of workers. Loyalty programs, often perceived as digital coupons, are actually data harvesting machines. They operate in three phases: 'hook' (enticing with a deal), 'hacked' (building deep insights into consumer habits, web browsing, location data), and 'hike' (discounts become stingier once a profile is built). Many state laws have carve-outs for 'bona fide loyalty provisions,' making it difficult to challenge these practices. For grocery companies, selling consumer data has become their highest-margin book of business, even if not the majority of revenue. With 95% of transactions at places like Kroger happening through loyalty cards, the wealth of data collected is immense. This means grocery stores are no longer just selling groceries; they are selling your data. While some legislative efforts, like New York's disclosure law, are a start, they often face strong opposition from retailers. More aggressive policies, including outright bans on surveillance pricing, are being proposed at the state level. These laws typically allow for broadly applicable discounts (e.g., early bird, veterans) but target individualized, data-driven price manipulation. Companies often threaten that such laws would eliminate discounts, but this is a tactic to scare consumers. The reality is that if companies were forced to compete on merits, they would still offer discounts. The fundamental issue is that 84% of Americans believe they should pay the same price for the same item at the same time, a principle undermined by current practices. As consumers, it shouldn't be our job to 'beat the bot,' but until lawmakers catch up, there are actions we can take. Use privacy-focused browsers, experiment by comparing prices across different devices or accounts, and comparison shop within brands (website vs. app vs. brick-and-mortar). In-app purchasing is often the worst option for both price and data privacy. Additionally, leverage social media to call out companies for deceptive pricing, as public pressure can be effective. For those in states with Data Subject Access Request (DSAR) laws, filing a request can reveal the extent of data companies collect on you, as one reporter discovered with Starbucks, seeing his discounts erode over time. --- *Source: stt · Language: en · Model: google-vertex/gemini-2.5-flash* [← Back to session](/sessions/2026-03-18/pp1148552-the-consumer-s-guide-to-beating-the-bots)