# The Confidence Trap: Why AI Systems Are Deliberately Trained to Sound Certain When They're Wrong > Source: > Published: 2026-10-10 12:28:58+00:00 Sharper version: raters don’t reward confidence in general — they reward it where they can’t verify the answer. Cheap to check, they judge substance; expensive to check, they judge style. I tested it at prompt scale: two prompts for one trivial task (center a div) — a precise spec vs pure encouragement — scored blind on tokens and whether it rendered centered. Encouragement burned 721 vs 311 tokens on one model, 531 vs 321 on the other — same passing renders, fewer useful bytes per token. (n=1 per cell.) One wrinkle: shared context beat cheerleading — a cryptic prompt plus a profile decoding it rendered perfectly at 332 tokens, best bytes-per-token of the six. Tone isn’t the enemy; empty encouragement is. The check that could break the story: calibration curves across domains. If overconfidence is flat everywhere, the story breaks; if it clusters where verification is expensive, it holds — and the fix is to score stated confidence separately. As usual, you can probably derive the rest from here.