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Preprint reveals a mathematical framework enhancing adaptability in AI systems, suggesting implications for survival.
This preprint introduces a mathematical adaptability‑first framework inspired by Darwinian survival principles and applied to modern AI systems. It formalizes adaptability as the capacity of a system to reconfigure under environmental change at bounded cost, and presents the Rate‑Mismatch Theorem: adaptation fails when the rate or acceleration of environmental change exceeds the system’s ability to adapt. The work is unique in bridging evolutionary biology, applied mathematics, and artificial intelligence. It highlights how humans have historically survived through slow but holistic adaptability—biological, cultural, and social—while robots and AI systems may surpass humans in rapid technical reconfiguration. This raises critical questions about the future of survival: will machines hold the edge in environments defined by accelerating change, or will human adaptability remain unmatched in its depth and breadth?
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Pravesh Pandey (2026) studied this question.
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