This preprint presents The Metabolic Sieve, a theoretical and computational framework that reconceptualizes artificial intelligence as an adaptive physical system governed by thermodynamic, metabolic, and evolutionary principles. The work introduces a multi-layer control architecture integrating geometric interaction mapping, computational metabolism, immuno-evolutionary policy selection, and the human observer as a dynamic boundary condition. The framework proposes a paradigm shift from task optimization and static alignment toward emergent symbiotic evolution between human and artificial agents.