When industrial machines fail, the consequences ripple outward in ways that simple sensor readings cannot fully anticipate. A team of researchers, working with a widely used industrial benchmark dataset, asked whether encoding the physical laws of mechanical failure directly into a machine learning system could outperform the raw data approaches that dominate the field. Their findings — strong predictive accuracy where training data was rich, and instructive silence where it was sparse — remind us that intelligence, artificial or otherwise, is bounded by the quality of what it has been taught