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A theoretical framework exploring two intertwined properties of advanced AI: generativity (the ability to produce novel, human‑level or superhuman outputs across domains) and recursion (the ability to improve itself, generating better AI that generates even better AI, in a feedback loop). The theory examines how recursive self‑improvement could lead to rapid capability gains (an intelligence explosion) and how generative AI systems might produce training data for their successors, creating closed‑loop evolution. It also considers risks: loss of control, value misalignment, and the difficulty of verifying recursive safety. The theory is central to discussions of artificial general intelligence and existential risk.
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