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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.
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.