Digital platforms can algorithmically change rules, prices, and recommendations on a per-user, per-session basis, a practice called "twiddling." This leverages surveillance data to maximize extraction, such as raising prices on payday or offering lower wages to workers with high credit card debt, which was previously too labor-intensive for businesses to implement.

Walmart is replacing all paper price stickers with digital shelf labels and has patented an algorithmic pricing system. This isn't just an efficiency upgrade; it's a fundamental infrastructure shift that brings dynamic, algorithm-driven pricing—common in e-commerce—to the aisles of brick-and-mortar stores, heralding an era of 'price extraction'.

Lindsay Owens explains that corporations no longer rely on clandestine meetings to coordinate prices. Instead, businesses utilize centralized algorithmic tools that aggregate and analyze competitor data, creating software-driven coordination schemes that effectively enforce collusion and sustain high profit margins.

Owens reveals that customer loyalty programs often function primarily as sophisticated data-harvesting operations. Retailers use discount incentives to collect extensive personal browsing and purchase histories, which they then repurpose to profile shoppers and determine the maximum price each consumer will tolerate.

Uber Eats' use of personalized pricing was only confirmed because a New York state law requires companies to disclose it. This highlights that without specific, localized regulation, controversial corporate practices fueled by algorithms can remain hidden from the public and regulators in other jurisdictions.

Contrary to the common view, algorithms charging different prices based on a consumer's wealth can be beneficial for market efficiency. The real harm occurs when algorithms exploit a lack of information or behavioral biases, not simply when they adjust prices based on a person's ability to pay.

Instacart's AI-driven personalized pricing created a PR crisis because it directly conflicts with the grocery industry's core value proposition of low, consistent prices. This was especially damaging during a period of high inflation, making the company appear exploitative in a price-sensitive market.

Brands like Uber and JetBlue are tracking user data—such as the type of credit card used or browsing history—to secretly charge wealthier or less price-sensitive customers more for the same service.

AI tools and peer communities allow buyers to easily see pricing discrepancies. This transparency makes it unsustainable for companies to offer different prices based on negotiation skill or timing, forcing a shift to consistent, economically justifiable models.