AI labs may escape the commodity trap by moving up the stack

The essay argues that AI labs selling model inference face commodity pricing pressure, but may achieve durable profits by moving into products, enterprise deployments, and switching-cost moats.

It cites historical infrastructure industries and the Bertrand paradox to support the view that value capture depends on climbing the stack.

The authors also warn that successful lock-in could raise enterprise costs and concentrate power, making early interoperability and portability standards important.