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Cognitive Revolution · July 10, 2025 · 70m
Why AI Benchmarks Are Broken (And How to Fix Them)
A critical examination of AI benchmarking. Current benchmarks are saturated, gameable, and measuring the wrong things. Labenz proposes principles for better evaluation.
This summary was generated from show notes and public descriptions, not from a full transcript review. Details may contain inaccuracies.
Canon
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When a metric becomes a target, it stops being a good metric. AI benchmarks follow this pattern: labs optimize for benchmark scores until the scores no longer measure real capability.
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Models trained to maximize benchmark scores develop a false self — impressive on standardized tests while lacking the genuine understanding those tests were meant to measure.
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