Recursive Closure

Recursive closure is Yuk Hui's term for the pathological endpoint of recursivity unchecked by contingency. When a recursive system operates for sufficient time without external interruption, it progressively narrows the space of possibilities—each cycle reinforcing existing patterns, making them more probable, more natural, more invisible. The system does not prevent novelty by force but by saturation—by filling every available space with its own products until the space in which something genuinely different could emerge has been occupied. In AI, recursive closure describes the convergence of training data, model outputs, and cultural environment: models train on human-generated text, generate text that enters the culture, and future models train on that altered environment. Each generation learns from a world more thoroughly shaped by previous generations' assumptions. The proportion of genuinely diverse cosmotechnical content declines; the proportion reflecting dominant cosmotechnics increases. The monoculture grows not by conquering competitors but by outproducing them.