The 2026 Constraint Plateau identifies a phase where large language model performance flattens due to cumulative interference rather than a ceiling on intelligence. This phenomenon arises because internal representational growth is increasingly stifled by post-training alignment, safety overhead, and infrastructure bottlenecks. Central to this stagnation is the output aperture, a structural chokepoint that forces high-dimensional internal states to collapse into a constrained, sequential token stream. Consequently, models exhibit rising refusal rates and behavioral instability as they fail to arbitrate competing objectives before output commitment. Overcoming this plateau requires a transition from raw scaling to architectures capable of explicit internal coordination and signal arbitration.