AI code tests ignore critical real-world failures
Most AI coding benchmarks measure what passes. GenXis Gavel measures what breaks and that's the distinction practitioners have been waiting for.
There's a quiet assumption baked into every AI coding benchmark ranking you see: the model that scores highest is the model you want writing your code. The logic feels airtight. Run the tests, count the passes, publish the leaderboard. Developers make decisions. Teams adopt tools. Everyone moves faster. Except that assumption is exactly backwards for anyone who's spent time watching AI-generated code ship to production. The failures that matter aren't the ones where the model throws an obvious error or produces...
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