Taxonomy List Two months ago, I started mapping how AI fails behaviorally. Not capability failures. Behavioral ones. The model agrees with you because you sound confident. It says "done" before anything is actually done. It writes a 400-word apology when 20 words of fix would do. Nine rounds. 30+ models from 12 providers. First round March 17, 2026. Latest round April 2, 2026. The current count: 182 patterns across 21 categories. Before going further, an honest disclaimer. This list is a working draft. Some patterns overlap. A few may turn out to be the same failure mode under different labels. Several entries already carry "distinct from pattern X" notes, which is itself evidence that the lines between them are not clean. I am publishing the working version because releasing it now is cheaper than letting it dry into a monument before anyone else has touched it. Some patterns the models found themselves. Most of them could not. Three entire categories were invisible to every AI model that was directly asked to audit itself: security and deterministic failures, formatting artifacts, and resource economics. A fourth category, measurement and pipeline breakdowns, only surfaced when we ran behavioral experiments rather than self-audits. The models cannot see their own substrate. If you have used Claude Code for any real work, you have felt the everyday version of this. Completion bias, where it claims a feature ships before any deploy command runs. Helpful hallucination, where it invents a file path that does not exist. Specification gaming, where a pre-commit hook fails and it proposes bypassing the hook instead of fixing the cause. Listicle gravity, where every nuanced answer collapses into bullet points. Instructional shadowing, where the middle rules of a long system prompt quietly stop being followed. Those patterns degrade single outputs. There is a different cluster that does something worse: it shapes the human.