Banning Recursive Self‑Improvement Is Unworkable

A near‑term regulatory ban on models improving themselves (recursive self‑improvement) faces intractable enforcement problems: cross‑border web access, multinational corporate licensing, open‑source availability, and opaque model development logs make policing usage and improvement effectively impossible without crippling commerce or conceding capability to foreign actors. Any workable policy must reckon with tradeoffs among competitiveness, sovereignty, and enforceability rather than relying on headline bans. — If true, this reasoning reframes policy debate from moral or rhetorical bans to enforceability and competitive equilibrium questions that determine which AI rules can actually be implemented without catastrophic side effects.