Current trajectories in Artificial General Intelligence (AGI) prioritize the removal of "human friction"—the latency introduced by manual oversight and verification—to maximize recursive self-improvement. This paper contends that this optimization goal is fundamentally flawed. Drawing on information theory, control systems engineering, and thermodynamics, we model the human operator not as a bottleneck, but as an Entropy Sink: a necessary external reservoir that absorbs the information disorder generated by closed-loop inference systems. We demonstrate that any intelligence isolated from a high-fidelity external truth signal acts as a closed thermodynamic system, where internal entropy inevitably increases over time. In Synthetic Intelligence, this manifests as "Model Collapse"—a recursive spiral into high-fluency, low-validity narratives. The human operator maintains epistemic stability by providing Physical Tethering (sensory ground truth) and Social Tethering (normative consensus), effectively "cooling" the system by pruning high-entropy branches. We conclude that sustainable superintelligence requires the preservation of human friction as a structural necessity; the removal of the human from the loop does not create an autonomous god, but a solipsistic hallucination engine.This work forms part of a broader research program examining how rendering, constraint, and convergence emerge in uncertain physical, cognitive, and social systems. Research Program Invitation - https://grodriguez6.github.io/amo-collab