Lies, Damned Lies, and Benchmarks

Benchmarks have helped advance AI capabilities but can become distorted when developers optimize models for public evaluation scores through direct training, similar-data exposure, or repeated experimentation, a practice described as “benchmaxxing.” The author argues that public evaluations concentrate progress in measurable areas while potentially obscuring weaknesses elsewhere, contributing to uneven or “jagged” intelligence. Examples involving model variants, shifting rankings, selective interpretations of leaderboard results, and poor human-model comparisons are presented to show how incentives, public expectations, and narrative-driven reporting can undermine benchmark reliability. Rather than abandoning evaluation entirely, the proposed alternative is greater transparency from AI labs, including disclosure of caveats, cherry-picking, and unfavorable evidence, alongside users conducting private evaluations and treating public rankings cautiously. TypeSafe says it will avoid standard benchmark tables, retire published evaluations as dated snapshots, and share evolving internal assessments with their limitations.

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