Up the Stack: How AI's Escape From the Commodity Trap Risks Enterprise Lock-in

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• Argue frontier AI labs face a “commodity trap” in inference pricing (undifferentiated models, low switching costs, similar capital costs) that pushes prices toward marginal cost per the Bertrand paradox, echoing how railroads and telecom infrastructure builders failed to capture the value they created.
• Labs are escaping by moving up the stack: building embedding moats (customer data/workflows), developer ecosystems, multi-year commercial lock-in, and outcome-based pricing — evidenced by ChatGPT’s revenue dominance over the API and Claude Code’s growth.
• Note cloud computing and TSMC as historical partial exceptions that escaped commoditization via software-like properties or near-monopoly.
• Practical takeaway: urge competition regulators to act early — establishing interoperability standards and switching-cost transparency before lock-in compounds — and urge enterprises to evaluate portability before committing.