Theorem of Methodological Non-Neutrality
Theorem T6
Existing SDLC methodologies (Waterfall, Scrum, Kanban, SAFe) are not neutral substrates for AI augmentation. They contain structural assumptions — about cadence, review, specification granularity, and role boundaries — that make them partially incompatible with AI-native execution, requiring purpose-built methodology.
Implication
You cannot "add AI to Scrum" and achieve the full economic benefit predicted by T4. Scrum was built for a world where code production was the bottleneck. When specification and verification become the bottleneck, the ceremonies and roles need to be rebuilt for that reality. This is the case for RACE Programming.
Full proof and discussion
A complete derivation — including proof sketch, worked examples, edge cases, and references to supporting research — is in progress. This page will be expanded as the theory matures.
In the meantime, the statement and implication above are the compressed form; they are sufficient to derive the main methodological consequences worked out in RACE Programming.