Hamel Husain: most public benchmark failures trace back to broken tests, not models
HamelHusain · x · 2026-11-02
AI engineering consultant Hamel Husain warns against companies that publish benchmarks and sell consulting around them. Pick any failing task in a public benchmark and the first trace likely reveals: tests too narrow (grader fails a correct model), tests too broad (open to interpretation), broken harnesses, ground truth written by another model introducing bias, or grading format instead of content. The only sane way to hill-climb such benchmarks is memorizing answers — and that's just the tip of the iceberg.
Related event: Hamel Husain Slams Public AI Benchmarks as Rating-Agency-Style Business(4 posts)→
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