The Interpolation Trap

The interpolation trap is the phenomenological and epistemological error produced when sophisticated combinations within the convex hull of existing knowledge are mistaken for genuine discovery outside it. The trap operates through surface features — fluency, coherence, the feeling of pieces fitting together — that trigger the human reward circuits associated with comprehension, regardless of whether the underlying content is genuinely novel or structurally hollow. Because next-token prediction is optimized for exactly these surface features, AI output systematically passes through the selective retention filter without activating it. The user feels the satisfaction of insight. The insight may or may not exist. The difference is invisible from inside the collaboration and detectable only by a retention function calibrated by deep domain expertise.