The discourse on AI often highlights “hallucinations,” where language models generate confident yet incorrect statements. A recent OpenAI paper attributes this issue to statistical pressures during pre-training and misaligned evaluation incentives in post-training. To build trustworthy AI, the paper advocates for benchmark reforms that reward uncertainty rather than guessing.