Artificial intelligence systems often assume stability and minimized uncertainty as hallmarks of success, yet real-world dynamics defy such ideals. This paper proposes the Tension Principle (TTP), a theoretical framework for self-regulating AI that tracks "tension"—the gap between predicted and actual reliability—as a second-order signal. Formalized as T = max(|PPA - APA| - M, ϵ + f(U)), TTP enables dynamic confidence adjustment, learning rate tuning, and resistance to drift, addressing limitations in first-order error correction. Through a detailed derivation and critique of methods like RLHF and Constitutional AI, TTP emerges as a foundational requirement for adaptive, self-aware systems. Uploaded as a preprint, this work invites empirical testing to explore its impact on stability and alignment. Keywords: Tension Principle, self-regulation, AI alignment, second-order feedback, adaptive intelligence.