In a public reply on X, JetBlue's official account advised a user complaining about a fare increase to 'try clearing your cash and cookies or booking with an incognito window.' This is a rare, direct confirmation from an airline that dynamic pricing based on user tracking is real and that consumers can bypass it using common privacy tools.

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'.

Unlike airlines with limited seats, media has no supply constraints for digital content. Implementing dynamic pricing based on a user's perceived wealth or location could damage brand trust and attract regulatory scrutiny without a clear justification.

Post-pandemic, companies have shifted from setting prices on a fixed schedule to "state-dependent pricing." They now adjust prices more frequently in direct response to rising costs, causing inflation to pass through to consumers more quickly and persistently.

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.

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.

Current regulatory focus on privacy misses the core issue of algorithmic harm. A more effective future approach is to establish a "right to algorithmic transparency," compelling companies like Amazon to publicly disclose how their recommendation and pricing algorithms operate.