From AlphaGo to Peripheral AI: Why a Narrow, High-Fidelity AI Trained on the Periphery May Outperform Systemic Reprogramming

This preprint is Part 3 of the “Peripheral Vicious Cycle” series. The two previous papers in this series proposed that many hallmarks of aging, including epigenetic dysregulation, arise largely as downstream consequences of a self-perpetuating Peripheral Vicious Cycle (PVC) originating in the musculoskeletal system. Here we argue that the most effective way to interrupt this cycle is through a narrow, high-fidelity AI system trained specifically on peripheral signals. Because the periphery generates highly structured and repetitive patterns (local ischemia, myofascial tone, group III/IV afferent activity, and sympathetic outflow), such an AI can learn rapidly, undergo grokking, achieve high reliability with minimal errors, and operate locally with inherent safety. Unlike broad downstream approaches that attempt to reprogram the epigenome or screen trillions of compounds, this peripheral AI creates a self-reinforcing data flywheel by continuously learning from real physiological data in situ. Early development of such localized AI architectures is already underway. This upstream strategy may offer a safer, faster, and more scalable path toward mitigating the drivers of aging than current attempts to override cellular programs directly.