The copyleft moat was never purely legal. It was economic: compliance was cheaper than reimplementation. AI collapsed that cost.

AI models reviewing AI-generated code share systematic blind spots with the generator, creating gaps that neither side detects.

AI equalizes the defender's security cost across open and closed source but varies the attacker's cost by source availability. This inverts Linus's Law.

Alerts should fire at or near the threshold where an SLO breach would occur, not well before.

Architecture orphaning is the phenomenon where architectural decisions fall between layers that neither specs nor AI agents can govern.

When a framework leaks implementation details (like serializing arguments at schedule time but loading code from HEAD at execution time), changing a function signature breaks the assumption that old code calls old signatures.

AI agents resist relational stabilization because their social cues are rich but inconsistent.

When iterating on a spec, convergence against a model's confabulation pattern is indistinguishable from convergence against intent.

Users of LLMs unconsciously oscillate between five relational modes — Director, Trainer, Partner, Student, Consumer — within single interactions, often within minutes.

Using the model as observer that describes deviations rather than judge that declares conformance.