<p>Despite the rapid expansion of artificial intelligence (AI) research in metabolic bariatric surgery (MBS), translation into routine clinical practice remains limited, with only a small minority of published models progressing beyond retrospective evaluation. This narrative review and conceptual framework paper explores why conventional risk prediction approaches (both traditional calculators and first-generation AI models) have shown limited clinical utility in MBS. We argue that a key limitation lies in their treatment of surgical patients as static entities, insufficiently accounting for the dynamic, nonlinear, and adaptive nature of human physiology under surgical stress. MBS represents a particularly illustrative setting, characterized by abrupt metabolic transitions, autonomic recalibration, and complex perioperative physiology. We propose a complexity-aware AI paradigm that emphasizes continuous perioperative data streams, variability- and entropy-based features, temporal modeling, and rigorous prospective validation. Rather than attempting to suppress physiologic variability, future AI systems may achieve greater clinical relevance by leveraging complexity itself as a source of prognostic information.</p>