When AI Improves AI: Governing Recursive Intelligence Acceleration

Recent work on intelligence explosions has made an important distinction between very rapid capability growth and a mathematical singularity. Toby Ord shows that super-exponential growth does not necessarily imply a finite-time vertical asymptote and identifies generation time — the time required to go around the feedback loop — as a central variable [1]. William MacAskill and Fin Moorhouse examine the institutional and social challenges that could arise if AI-accelerated research compressed technological progress into a much shorter period [2]. This article connects those arguments to governed autonomous AI. Its central claim is simple: capability is not authority, and previously valid assurance is not automatically valid after a material capability change. A system that becomes better at research, planning, tool use, coordination, or self-modification should not silently receive broader permission to act. Instead, material capability change should trigger renewed evaluation, explicit governance, and evidence-based decisions about continued, limited, suspended, or revoked operation. This is a SGAEIA governance proposal, not a finding established by either paper. The article does not claim that an intelligence explosion is inevitable or that current systems have achieved open-ended recursive self-improvement. It presents a governance principle for systems that may become more capable faster than ordinary oversight processes can adapt.