Donald Gainsborough is a preeminent voice in the intersection of technology and public policy, currently serving as a leader at Government Curated. With a career dedicated to deciphering the complexities of legislation, he has become a go-to expert on how emerging technologies like artificial intelligence reshape the economic landscape for everyday citizens. In this discussion, we explore the rise of “surveillance pricing”—a practice where retail algorithms leverage personal data to set individualized costs. Gainsborough offers a deep dive into the legal shift across various states, the friction between corporate efficiency and consumer privacy, and the potential for these digital tools to inadvertently dismantle long-standing promotional structures like loyalty programs.
The following conversation examines the nuances of algorithmic pricing, the logistical hurdles of state-level transparency mandates, and the broader debate over whether individualized pricing or behind-the-scenes collusion is the true culprit behind rising grocery bills.
In some cities, shoppers at the same store have been charged significantly different prices for identical items like crackers due to AI algorithms. How do these algorithms determine a specific customer’s “willingness to pay,” and what metrics do retailers use to ensure these discrepancies don’t alienate their customer base?
The mechanics of “willingness to pay” are rooted in an exhaustive analysis of personal data points that most consumers don’t realize they are sharing. Algorithms can track your specific purchase history, the device you are using, and even your location to build a profile of your price sensitivity. For example, an investigation in Seattle found that a grocery app was charging some customers as much as 23% more for the exact same box of Wheat Thins. Retailers walk a razor-thin line here; while the goal is to maximize profit per transaction, the risk of “pissing people off” is immense once these disparities become public. Consequently, many companies use these metrics to test the waters of consumer tolerance, but as we saw with Instacart, they often retract the technology once the public backlash threatens their overall brand reputation.
New York now requires disclosures when algorithms set prices using personal data, while other states aim to ban the practice for essential goods like milk. How do these transparency mandates impact a store’s operational costs, and what specific steps must a retailer take to comply with varying state laws?
The operational burden of these mandates is substantial because it forces a complete overhaul of the digital and physical interface between the store and the shopper. In New York, retailers must now display specific disclosures stating that a price was set by an algorithm using personal data, which requires back-end integration to trigger these alerts in real-time. For a national chain, this means managing a patchwork of regulations where Illinois might allow an “opt-out” while New Jersey might ban the practice for supermarkets entirely. Compliance involves not just legal oversight but a significant investment in software that can recognize a user’s jurisdiction and adjust the pricing logic and disclosure visibility accordingly. This new regulatory structure is undeniably costly, and some trade groups argue these expenses will eventually be passed back to the consumer.
Some states are proposing bans on electronic shelf labels to prevent remote, real-time price changes for in-store shoppers. What are the logistical challenges of reverting to manual labeling, and how does this technology specifically facilitate personalized pricing compared to traditional paper tags?
Electronic shelf labels are the “bridge” that brings online surveillance pricing into the physical aisle, allowing a store to update a price tag remotely in a matter of seconds. If states like Oklahoma or Nebraska successfully ban these digital tags, retailers face the massive logistical hurdle of manual labor—employees would have to physically swap out thousands of paper tags every time a price fluctuates. This technology is controversial because it creates the possibility of a price changing the moment a customer walks toward a shelf, similar to an incident where a major retailer’s app prices changed based on a customer’s location within the store. Traditional paper tags provide a static “anchor” for the consumer, whereas digital labels allow for a fluid, data-driven pricing environment that many lawmakers feel is inherently deceptive.
Legislation banning the use of purchase history or device data to set prices could potentially disrupt grocery loyalty programs and digital coupons. How can companies distinguish between “surveillance pricing” and standard promotional discounts, and what are the legal risks of using broad data definitions in these bills?
This is the central tension in current legislative battles, particularly with bills like the one in Tennessee that defines personal data very broadly as anything linked to a specific consumer or device. From a corporate perspective, a “loyal customer discount” is essentially a form of personalized pricing based on purchase history, so a ban on using that data could effectively kill the rewards programs people actually enjoy. The legal risk lies in the lack of a clear distinction between a “surcharge” for one person and a “discount” for another; if the law prohibits using a device ID to set a price, it might inadvertently ban a digital coupon sent to a smartphone. Industry advocates warn that these broad definitions will create a “compliance nightmare” that prevents businesses from offering any targeted promotions at all.
While much focus remains on consumer-facing algorithms, some argue that behind-the-scenes collusion between suppliers is a bigger driver of high grocery costs. How does algorithmic price fixing differ from individualized surveillance pricing, and what metrics suggest which practice has a larger impact on a household’s monthly budget?
Surveillance pricing is a “one-to-one” tactic aimed at squeezing extra cents from an individual based on their specific habits, whereas algorithmic price fixing is a “many-to-one” strategy where competitors use the same software to keep prices artificially high across the board. We are seeing major legal actions, such as the Department of Justice suing large landlords for using algorithms to collude on rent, which suggests that the “macro” impact of price fixing is far more damaging to a household budget than a 50-cent hike on crackers. Many economists argue that while individualized pricing is annoying and feels invasive, the real “energy” of regulators should be spent on closed-door deals and supplier collusion that prevent prices from cooling down even when inflation slows. One involves personal privacy and “junk fees,” but the other involves the fundamental erosion of market competition.
What is your forecast for algorithmic grocery pricing?
I expect we will see a significant retreat from “individualized” pricing in the grocery sector as the reputational risks and the costs of state-by-state compliance begin to outweigh the marginal profits. However, the use of algorithms for general dynamic pricing—adjusting for supply and demand—will only become more sophisticated and harder for the average consumer to detect. We are likely heading toward a “transparency era” where the most successful retailers will be those who pivot away from surveillance and instead use data to offer genuine, transparent value. Ultimately, the pressure from at least 11 states currently considering restrictive legislation will force a national standard that prioritizes consumer privacy over the high-tech “squeezing” of the American shopper.
