Owens argues that emerging AI commerce agents risk taking advantage of urgent consumer crises to charge inflated rates. Because these automated tools possess detailed personal data, she advocates enforcing strict fiduciary standards and conflict-of-interest regulations to ensure agents serve shoppers rather than retailers.
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.
Owens notes that automated surveillance pricing creates massive discrepancies, charging different consumers up to 23 percent more for identical items at the exact same moment. This opacity prevents consumers from comparison shopping effectively and undermines their ability to budget predictably.
Marta Tellado highlights that 97 percent of surveyed consumers assume manufacturers are legally mandated to test products for safety before selling them. In reality, almost no mandatory testing requirements exist, allowing hazardous consumer goods to enter the market until outside organizations intervene.
Tellado explains that the belief that technology eliminates human bias has proven false. Automated facial recognition and algorithmic decision-making tools frequently preserve and automate historical prejudices, reinforcing disparities across credit, insurance premiums, and access to medical care.