The article argues that most measurable AI tasks become commodities, eaten away by cheaper models, while lasting value lies in work whose correctness is private, expensive to verify, and locked inside a firm’s data and processes. Companies that win build integrations, earn trust, and take accountability, turning AI into outcomes rather than tokens.

• MIT research found AI code output rose 180% but shipped code rose only 30%, since tests catch correctness but not integration risk in legacy systems
• Anything easily verifiable becomes a commodity as open/distilled models race to the bottom on price, and labs absorb generic tooling into their base models
• Real moats come from "private correctness"—work that can't be verified without access to a company's own systems, data, and liability structures
• Winning companies build translation layers (integrations, security reviews, user trust) rather than just better models, since full automation requires years of organizational change, not just smarter AI