The Benchmark Illusion: How Bounded AI Performance Becomes Unsupported Institutional Proof

AI benchmarks are useful. They compare systems, expose weaknesses and show what an artefact achieved under defined test conditions. The problem begins when that result is made to prove something much larger: general capability, reliability, safety or readiness for consequential use. This paper calls that the Benchmark Illusion. It traces Benchmark Warrant Inflation - how a bounded score travels from an evaluation into model cards, procurement papers, risk assessments and deployment decisions while the conditions that gave the score meaning fall away. The number remains the same. Its institutional function does not. The score itself may be accurate. A provider-run evaluation may also be technically valuable. The problem is what the evidence is permitted to support, particularly where first-party evidence is treated as independent assurance. A second gap appears when the artefact that ships is not the artefact that was evaluated. Different model version, system configuration, scaffolding or tool access - yet the score can follow the product name rather than the system that earned it. The paper calls this Artefact Inflation. For engineering, procurement, governance and assurance teams, the paper provides a fourteen-part Benchmark Warrant Assessment Matrix for testing whether evaluation evidence actually supports the consequence being proposed. A score cited in a procurement paper becomes part of the institution's own record. If the decision is later challenged, that number is what the file contains. The score may be entirely accurate. It still cannot do what the file is asking it to do. A benchmark score may evidence performance. It cannot authorise reliance.