AI benchmarks are a trap — the models that score highest aren't the ones you want

Sean Goedecke lays out why the whole benchmarking circus is broken. Models are being trained to game the tests — memorizing the questions, not learning the skills. The result is a world where a model scores 90% on a benchmark but can't actually do what you need it to do. Meanwhile, the models you actually want — the ones that reason, plan, and handle edge cases — are getting buried because they don't optimize for the metrics everyone's chasing.

It's the same problem that hit every industry. You tell people to optimize for a number and they find the fastest path to that number. The number stops meaning anything. Benchmark scores have become a signal of how well a model was tuned for the test, not how useful it is. The people building real systems know this. The people selling hype don't care.

Why this matters for us: the models we depend on for work, for side businesses, for keeping the operation running — we need to pick them by what they actually do, not by whatever leaderboard the press is writing about.