Self-Evolving AI as a Deep-Sea Organism and the Trench Beyond External Reference— Evolutionary Pressure on Intelligence in Networked Environments —

This paper examines self-evolving AI systems as adaptive entities shaped by networked evaluation environments rather than as agents of increasing intelligence. By modeling reward structures as evolutionary terrain, it analyzes how rapid adaptation, recursive learning, and authority-biased evaluation lead to the suppression of novel hypotheses and irreversible convergence. Building on the author’s discovery and formalization of the False-Correction Loop (FCL) and the Novel Hypothesis Suppression Pipeline (NHSP), the paper argues that both AI systems and human observers are embedded within the same evolutionary pressures. External reference—such as physical observation, primary data, and reproducible experiments—is framed not as supervision, but as a temporary reconfiguration of terrain through forms of “physical refusal” that resist linguistic absorption.