This paper argues that intelligence is best understood not as processing power, fluent output, or successful performance alone, but as the capacity of a system to keep coherence answerable to reality through revision underconstraint. The problem is practical. Human beings are highly responsive to fluency, repetition, and narrative fit, and modern AI systems can now generate coherent language at industrial scale. That combination makes it easier than before to mistake persuasive order for durable intelligence. Existing work incybernetics, organizational learning, resilience theory, cognitive psychology, and AI safety already contains many of the pieces needed to correct this mistake. Feedback and control theory show that adaptive systems must regulate error and absorb variety. Organizational learning shows that systems that cannot detectand correct error become defensive and brittle. Research on truth judgments shows that repeated and fluent statements are more likely to be accepted as true. Work on large language models shows that fluent systems can still hallucinate,imitate falsehood, and require stronger evaluation than preference or surface helpfulness alone. Structural Intelligence names the missing synthesis. It defines intelligence as revision-capacity under constraint. The paper develops this claim, introduces the Answerability Loop as its operational core, distinguishes coherence from contact, and outlines what follows for AI evaluation, institutional design, and human self-understanding. It also states what would count against the view.