Iteration Affordance

The iteration affordance transforms the economics of exploration. In the pre-AI environment, each cycle through intention-implementation-evaluation-adjustment consumed hours of implementation labor, imposing a natural limit on the number of cycles a builder could complete. The cost created selection pressure toward upfront planning, detailed specifications, conservative architectural choices — getting the implementation right the first time. The AI-augmented environment collapses cycle time to seconds or minutes. The builder can afford to be wrong: she can specify loosely, see what emerges, learn from the discrepancy between intention and output, and adjust. In Gibson's framework, this is an affordance for exploratory behavior — the active, probing engagement with the environment that Gibson identified as the primary mechanism of perceptual learning. The density of exploratory sampling increases, and with it the probability of detecting non-obvious affordances in the problem space that upfront analysis could not have revealed. Segal's account of the laparoscopic surgery insight — the moment when his question collided with Claude's associative capacity and produced a