AI Benchmarks Are Climbing the Wrong Hills: Contrary Research's Deep Dive on Benchmaxxing
rseroter · x · 2026-10-22
A 24-minute Contrary Research deep dive argues Goodhart's Law has arrived for AI benchmarks: when a measure becomes a target, it stops being a good measure.
- Benchmarks vs. reality: early benchmarks measured capabilities models genuinely lacked, so higher scores tracked real progress. Today frontier labs explicitly optimize for benchmarks ("benchmaxxing"), so gains concentrate in targeted areas rather than reflecting broad improvement.
- A growing industry: benchmarks no longer just measure models — they shape training priorities and even what capabilities models are designed to have.
- Contamination: benchmark leakage into training data further undermines credibility.
- The fix: expert-built benchmarks that measure what we actually want from AI systems, requiring strong product sense to decide what's worth measuring.
Conclusion: the next generation of benchmarks need not be flawed the way today's are; the fix starts with how we build them.
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