When the Model Grades Itself, "It Passed" Means Less: On Independent AI Evaluation
vishalmisra · x · 2026-09-16
Wrapping up a thread on AI evaluation, vishalmisra argues that evaluation is only as strong as its independence: if the model, its lineage, or the same infrastructure helps produce the test, the evidence, and the interpretation, then "it passed" may mean far less than we think.
He borrows the lesson from Thompson's self-replicating compiler case — Wheeler's diverse double-compiling broke the circle via an independent path outside the lineage — and proposes the AI analogue: independent evaluators, independent infrastructure, independent evidence. A methodological critique of vendor self-benchmarking.
Related event: Scholars warn AI evals lose validity when models grade themselves(2 posts)→
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