Benchmarking Every AI Suggestion Proved More Valuable Than Trusting Its Confidence

A developer experimenting with AI pair-programming found that the tool's most confident optimization suggestions often failed to deliver real-world performance gains. Ideas like 8x8 register blocking, packed f16 storage, and vectorized kernels each sounded promising but underperformed or broke training loops when tested. The productive workflow involved implementing the smallest possible test, measuring against a proper baseline, and preserving negative results for future reference. End-to-end parity tests caught errors that isolated kernel benchmarks missed entirely. The key takeaway was that AI proved useful for generating hypotheses and tracing failures, but the final judgment had to rest with reproducible measurements rather than the model's own assessment.