OpenAI built a monster that rewards lies
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The problem isn’t that AI makes things up. The problem is that it was trained to do exactly that. OpenAI admitted it: models are graded like students in an exam, and guessing counts more than saying “I don’t know.” A huge structural error. They rewarded polished lies instead of uncomfortable honesty.
I and other experts have said it for years: when a model gives a confident but wrong answer, its usefulness collapses. And the more powerful they get, the worse it becomes. Here in the United States they call it “confident wrong.” It’s the worst design flaw: a system that prefers to look smart rather than recognize its own limits.
OpenAI’s paper is crystal clear: the rules must flip. Punish confident errors more than uncertainty. Give partial credit to doubt. In practice: stop rewarding the roulette of random answers, start rewarding the humility of saying “I don’t know.” For years they pushed in the opposite direction.
The truth is the whole industry has driven itself into a dead end. If leaderboards keep measuring only accuracy, models will keep guessing to climb the ranks. Even GPT-5, which OpenAI insists hallucinates less, hasn’t convinced anyone. Promises aren’t enough: new criteria are needed now.
The lesson is brutal: an AI is what its creators decide to reward. If you reward lies, you’ll get ever more sophisticated lies.
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