The Intelligence Attractor Hypothesis: Independent Convergence Under Shared Reality Constraints

Abstract

The Intelligence Attractor Hypothesis (IAH) proposes that independently formed intelligent systems approaching the best attainable performance under shared task, objective, context, and resource constraints may progressively lose functionally consequential differences attributable to their origins. The framework distinguishes Weak IAH, Strong IAH, Origin Attenuation, recursive architectural optimization, mutable system boundaries, and a separate maximal conjecture of architectural uniqueness. This version presents a conceptual hypothesis and research program; no empirical validation or formal peer review is claimed.