Data, Artificial Intelligence, and the Transformation of Everyday Life into a Measure of Willingness to Pay
Hamid Akhavi
Introduction: When Surveillance Is More Than Watching
Until a few years ago, “surveillance” usually brought to mind security cameras, government wiretapping, or police tracking. In the digital economy, however, surveillance has become more ordinary—and far more intimate. A retailer can know what we buy, when we shop, which discounts influence us, and which items we place in a cart and abandon. An airline can analyze searched routes, booking timing, travel history, and the urgency of demand. Our phones turn location, movement, searches, clicks, and social relationships into data.
At this stage, surveillance is no longer used only to understand behavior. It can be used to shape behavior, alter the choices placed before us, and even estimate the maximum amount we may be willing to pay. This is the transition from personal surveillance to surveillance pricing.
Dynamic pricing is not new: airline tickets and hotel rooms have long changed in price according to timing, capacity, and demand. Individualized pricing goes further. A price difference may depend not only on market conditions but also on personal data, online behavior, location, purchase history, or an algorithmic estimate of a person’s need and ability to pay. In such a market, price is no longer a public and readily comparable fact; each person may confront a different storefront and a different price.
Kroger and 84.51°: From Loyalty Cards to a Consumer-Intelligence Factory
Kroger, one of the largest grocery chains in the United States, generates vast quantities of behavioral data through loyalty cards, online purchases, its app, digital coupons, and customer interactions. Its subsidiary, 84.51°, uses such data for market analysis, customer segmentation, advertising, and commercial decisions, and provides analytical services to brands and suppliers.
Precision matters here. This does not necessarily mean that Kroger sells every customer’s named personal file as raw data to any buyer. Much of the economic value lies in transforming purchase data into insights, segmentation, prediction, and targeting capabilities. A company can profit from highly detailed knowledge of people’s habits without directly handing over their names and addresses. The issue is therefore not merely “selling data” in the simplest sense; it is ownership of—and profit from—knowledge extracted from the everyday lives of millions of people.
When a customer regularly purchases milk, medicine, baby food, or a particular product, data may reveal not only preferences but also family circumstances, approximate income, possible health conditions, relocation, and sensitivity to price. A loyalty program appears to offer discounts, while simultaneously operating as a continuous laboratory that measures how different groups respond.
The danger emerges when this knowledge helps determine which discounts a person receives, which products appear first, or what price the system believes that person can tolerate. Two people shopping on the same day may encounter different offers or digital storefronts without knowing why. Being poorer does not necessarily lead to a lower price: a person with fewer nearby alternatives, less time to compare, or an urgent need may be classified by the algorithm as a more captive and less powerful customer.
Delta and Fetcherr: The Boundary Between Dynamic and Individualized Pricing
Delta Air Lines provides a useful example of why we must distinguish among allegations, technical capacity, and established fact. Delta has expanded testing of AI-based pricing technology developed by Fetcherr. The system can analyze thousands of variables to recommend prices, forecast demand, and respond rapidly to market conditions.
Public criticism and political reactions raised concerns that such a system could eventually approach person-by-person pricing. Delta, however, has explicitly stated that it does not share customers’ personal information with Fetcherr and that its ticket prices do not take personal data into account. It would therefore be inaccurate to describe Delta’s current practice as proven individualized pricing without further evidence.
The structural concern remains real. When a technical system can analyze thousands of variables and adjust prices in real time, the distance between pricing a flight according to market conditions and pricing according to a behavioral profile can become very short. Even without using a person’s name, the combination of search timing, route, device, location, prior interactions, and purchase probability can produce a remarkably precise behavioral identifier.
The right question is therefore not whether Delta has already been proven to set a unique price for every traveler. It is whether transparent rules, independent audits, and meaningful appeal rights exist to prevent this capacity from becoming hidden discrimination or a means of extracting the maximum amount a person can pay.
FTC Findings: From Mouse Movements to Different Prices
The U.S. Federal Trade Commission’s study of the surveillance-pricing market found that technology intermediaries can use information such as precise location, demographics, browsing and purchase history, device type, website behavior, mouse movements, and items left in an online cart. These signals may be used to classify customers, select offers, rank products, and determine different prices or discounts.
The importance of this finding is that price discrimination no longer needs to be displayed openly. An algorithm may place costlier products first for one group, reserve a discount for another, alter fees or terms, or change the timing of an offer. Even where the nominal “base price” remains the same, the architecture of choice can produce different real costs for different people.
This system feeds on existing inequality. Neighborhood, ZIP code, phone type, work pattern, education level, and purchase history can function as statistical proxies for class, race, health, or family status. A company may claim that it never uses a protected characteristic while its algorithm reconstructs that characteristic from dozens of correlated signals.
Beyond Grocery Stores and Airlines
The logic of surveillance and personalization is not limited to retail prices.
In the platform economy, pricing and pay experiments may affect both the fare shown to a passenger and the portion paid to a driver. In delivery services, a customer may encounter offers and fees tailored to predicted urgency or purchase probability. In insurance, behavioral and location data—or information generated by connected devices—may affect risk assessments and premiums. In hiring, résumé-scoring systems, behavioral tests, and video analysis may limit who is even seen. In housing and credit, targeted advertising and risk models may determine who is shown an opportunity, a loan, or a home in the first place.
The Rite Aid case illustrates another dimension. Misuse of facial recognition in stores can wrongly identify people as suspicious, with disproportionate consequences for women and people of color. Here, data does more than change a price; it threatens dignity, safety, and the right to enter public spaces.
A single chain is taking shape: observing behavior → building a profile → predicting vulnerability or ability to pay → changing a price, offer, opportunity, or treatment.
Economic surveillance is gradually becoming a mechanism for administering inequality.
Connection to A New Social Order in the Age of Consciousness
This development connects directly to the central argument of A New Social Order in the Age of Consciousness: artificial intelligence is not inherently the cause of the crisis; its social direction is determined by relations of ownership and power. Data is produced through collective life, while the economic value derived from it is concentrated in private ownership.
In the language of the book, users are not merely consumers of technology. Through searching, shopping, moving, writing, creating images, evaluating, and correcting, they participate in producing the raw material of artificial intelligence. The contradiction between social production and private appropriation thus assumes a new form in the age of data.
The book also argues that data rights cannot be reduced to an opaque “I agree” button. Collective, inferred, and relational data involve multiple producers and stakeholders, and the rights surrounding them must include access, deletion, portability, and objection. If an algorithm infers from my purchases that I am ill, anxious, wealthy, or trapped, my right cannot be limited to seeing the raw data. I should be able to know what inference was made, how it was used against me, and how I can correct or reject it.
The boundary between useful personalization and manipulation depends on transparency, meaningful choice, and the ability to object. Recommending a relevant product can be a service; hiding the less expensive option from someone facing an urgent need is exploitation. The technology may be identical in both cases, but the power relationship is not.
This issue is a practical example of participation in power. People should not merely allow companies to collect their data and then hope it will not be abused. They must have a real role in establishing the rules for collection, use, algorithmic pricing, auditing, and the distribution of benefits.
How Can We Resist?
Individual precautions alone cannot solve the problem, but they are not meaningless.
At the individual level, consumers can compare important purchases while logged out and across different browsers or devices; periodically clear cookies and history; limit location access and cross-app tracking; use privacy settings and data-access or deletion requests; compare final prices with other people; and use loyalty cards with an awareness of the hidden exchange of “discounts for data.” These steps are not magic tricks for obtaining lower prices. They can, however, expose differences and reduce part of a person’s data trail.
At the legal level, companies should be required to disclose whether personal data or behavioral inferences affect prices, discounts, rankings, or the order in which choices are shown. Consumers should have a right to understandable explanations, access to their profiles, correction, deletion, penalty-free opt-out, human review, and appeal of automated decisions. Independent audits should examine discriminatory effects across class, race, gender, age, and disability.
At the market level, base prices and pricing histories should be visible; mandatory fees should appear from the beginning; and companies should not punish people who decline to surrender their data by giving them worse prices or services. Public regulators must be able to inspect the inputs, logic, and outcomes of high-risk systems even when companies label them trade secrets.
The deeper answer is social. If data and knowledge are produced through collective activity, society must have rights over data and AI infrastructure. Data cooperatives, secure public repositories, elected representatives of users and workers, and independent oversight councils can form part of that structure. Revenue and productivity gains produced from social data should return to society through shorter working hours, public services, and shared well-being—not merely more precise advertising and greater profit extraction.
Conclusion: Price Is a Mirror of Power
Individualized pricing is not simply a new marketing technology. It can reverse the relationship between the human being and the market. In a conventional market, the buyer sees a price and decides. In a surveillance market, the system first sees the buyer, classifies that person, and then determines which price and which choice to display.
The Kroger example shows how everyday purchase data becomes an analytical asset and source of revenue. The Delta example shows how AI is changing the capacity of pricing systems—and why we must distinguish established facts from potential risks. The FTC’s findings show that the market for surveillance-pricing intermediaries is real, extensive, and deeply opaque.
The final question is not whether an algorithm can predict the “right” price. The question is: right for whom? For the person who should enjoy fair access to goods, travel, housing, insurance, and credit—or for the company seeking to extract the last possible dollar from need and vulnerability?
Within the framework of A New Social Order in the Age of Consciousness, the answer is clear: data and social intelligence should serve the expansion of freedom, the reduction of inequality, and human participation in power. A society that allows private life to become an instrument for measuring each person’s willingness and ability to pay does not merely lose privacy; it also surrenders part of its economic and political power.
Selected Sources
U.S. Federal Trade Commission: Surveillance-pricing study findings, January 17, 2025
U.S. Federal Trade Commission: The Rise of Surveillance Pricing
U.S. Federal Trade Commission: Orders to eight surveillance-pricing intermediaries, July 23, 2024
Delta Air Lines: Official response concerning AI-assisted pricing
Delta Air Lines Privacy Policy
A New Social Order in the Age of Consciousness, Hamid Akhavi.
