在专访中，《Gouged》作者林赛·欧文斯（Lindsay Owens）指出，维持了约一个半世纪的固定定价机制正因现代科技而瓦解。大型科技公司透过忠诚度应用程式搜集大量消费者个资，并借由「监控定价」（Surveillance Pricing，即第一级价格歧视）根据个人的支付意愿动态调整价格，使消费者因忠诚度或急迫需求反而承担更高费用。

新兴的生成式人工智慧与代理人商业模式（Agentic Commerce）进一步加剧了消费者面临的定价不公，这些工具往往被企业用来诱使顾客追加购买或支付更高总额。面对定价混乱，美国目前在跨党派推动下于州层级陆续立法管制监控定价，但要落实要求商业AI代理人承担维护消费者最佳利益的受信责任，仍处于关键的立法转折点。

欧文斯强调，防范演算法剥削不应全由消费者承担，但购物者短期内仍可采取跨管道比价策略应对，例如比较登入与登出状态、应用程式与实体店、或是透过VPN更换地理位置。此外，消费者也可以与亲友同时查看同一商品以识破企业的A/B测试定价差异，尽管这些反制方法极为耗费个人的宝贵时间与心力。

In the interview, Lindsay Owens, author of 'Gouged', explains that the century-and-a-half-old norm of fixed pricing is unraveling due to modern technology. Big tech companies utilize loyalty apps to harvest extensive consumer personal data, enabling 'surveillance pricing'—also known as first-degree price discrimination—to tailor charges according to an individual's estimated willingness to pay, frequently penalizing customer loyalty and urgency.

Emerging generative AI and agentic commerce threaten to exacerbate these pricing inequities, serving as corporate mechanisms engineered to drive up cart totals and pressure shoppers into add-on purchases. In response to these opaque practices, bipartisan legislative momentum is taking shape primarily at the state level across the United States, highlighting an urgent need to establish fiduciary and best-interest standards for AI shopping agents.

Owens contends that outmaneuvering discriminatory algorithms should not fall solely on consumers, yet buyers can mitigate overcharging by comparing prices across modalities, including logged-in versus logged-out accounts, mobile apps versus brick-and-mortar stores, or varying locations via VPN. Shoppers can also team up with friends to expose A/B testing price variations, although these defensive tactics unfortuately demand significant amounts of valuable time.