AI Cuts Code Costs, But Quality Definition and Validation Remain Expensive

A analysis published on DEV Community challenges the popular claim that AI renders software valueless by arguing it conflates production cost with product value. Drawing on Philip Crosby's 1979 definition of quality as conformance to requirements, the piece breaks software development costs into two axes: defining quality and producing conformance to that definition. While AI can generate candidate implementations almost instantly, the work of writing precise requirements, verifying conformance, and validating that definitions are correct remains costly and largely unautomated. The author further applies Boehm's spiral model to argue that exploration activities like prototypes and beta releases are part of quality definition, not mere production, and that unknown unknowns still surface as expensive rework. The conclusion is that implementation is becoming abundant in the AI era, but explicit definitions, traceability, and operational evidence stay scarce and continue to anchor software value.