This Article examines dynamic pricing on Artificial Intelligence (AI)-driven platforms through a theoretical framework. It explores how AI enables platforms to engage in first-degree price discrimination, allowing them to perpetually impose “consumer-surplus-exhausting prices” based on personalized data—a significant shift from pre-AI price differentiation toward a more systemic and actual discrimination. This raises concerns about consumer welfare, as AI-driven dynamic pricing may eventually lead to worse outcomes than traditional monopolies, with full consumer surplus extraction despite competitive transaction volumes. In addition, this Article introduces the concept of multiple platforms in the same relevant market functioning as an “AI-driven de facto single entity.” It also explores consumers’ “profiling lock-in” and the erosion of the “invisible hand” by AI-driven dynamic pricing. Furthermore, the Article examines how a state-owned platform’s “quasi-taxation effect” could be exacerbated by AI-driven first-degree price discrimination. It argues that even in purely private markets, AI-driven pricing may generate a similar quasi-taxation effect. In some cases, however, a “quasi-subsidy effect” may also occur for consumers with lower reservation prices. The Article also briefly discusses regulatory responses, including the importance of obtaining enhanced consumer consent for the use of personal information in AI-driven pricing strategies.