Achieving high reliability in AI systems—such as autonomous vehicles that stay on course even in snowstorms or medical AI that can diagnose cancer from low-resolution images—depends heavily on model robustness. While data augmentation has long been a go-to technique for enhancing this robustness, the specific conditions under which it works best remained unclear—until now.