AI Saturation-Splitting Hypothesis: Structural Limits of Self-Improving AI

Abstract

Recursive self-improvement is often discussed as though an AI system could increase its intelligence without limit. This paper proposes a different possibility. If intelligence includes not only performance but also the capacity to preserve operational identity while processing ambiguous, underdetermined, or value-laden problems, then self-improvement may approach a structural upper bound. Near this limit, local capabilities may continue to improve even when global integration no longer improves at the same rate. Optimization pressure may then shift toward locally measurable objectives, producing apparent progress while the system becomes less clear about what is being improved as a whole. This paper calls this possible failure mode saturation-splitting.