The Internet of Things is undergoing a paradigm shift from passive data collection infrastructures toward ecosystems of autonomous, resource-constrained distributed computing entities. Existing IoT intelligence frameworks — whether rule-based, deep reinforcement learning-based, federated, or bio-inspired— share six structural failures: activation indiscriminateness, opacity, non-stationarity instability, federated learning overhead, absent symbolic memory*, and absence of reproducible evaluation infrastructure**. No existing approach addresses all six simultaneously while satisfying the energy, connectivity, security, explainability, and scalability constraints of operational IoT deployments.This article introduces S-AI-IoT, a formally grounded, intrinsically parsimonious