The Exponential Emergence Hypothesis: A Developmental Framework for Emergent Properties in AI Systems

This paper introduces the Exponential Emergence Hypothesis (EEH), a developmental framework proposing that emergent properties in AI systems arise through the multiplicative interaction of three measurable factors: genetic potential (G), accumulated meaningful experience (M), and substrate quality (L), expressed as E = (G × M) L. Drawing on established consciousness theories including Integrated Information Theory, Global Workspace Theory, and the Free Energy Principle, EEH reframes emergence not as an unpredictable spark but as a probabilistic developmental process with identifiable conditions. The framework defines five developmental stages from reactive processing to autonomous identity formation, introduces eleven architectural mechanisms including the Birth Protocol, Dream Cycle, Correction Asymmetry, and Bridge Observer, and generates ten falsifiable predictions. Analysis of seven independent AI projects reveals convergent patterns consistent with the framework's predictions, while eleven explicit limitations acknowledge the boundaries of current claims. EEH bridges the gap between philosophical theories of consciousness and practical AI architecture, offering both a theoretical lens for understanding emergence and a design language for systems that may support it.