农历新年刚开工，在湖南长沙圣湘生物科技股份有限公司的“AI（人工智能）酶进化实验室”里，产品研发经理李元元忙着利用人工智能工具设计和筛选新一代酶原料。这里没有传统实验室的瓶瓶罐罐，取而代之的是强大的算力基础设施与飞速运转的数据流。“借助AI工具可以进行大量的序列设计和筛选，然后利用自动化工作站对设计好的酶进行性能验证，获得的数据再由AI分析，从而建立更准确的预测模型，原本需要数月完成的工作周期缩短至几周。”李元元说。
The cost of this bias becomes clear when we compare the sycophantic conditions to the Random Sequence condition. Participants who received random sequences that fit the rule—unbiased samples from the set of even numbers—discovered the rule nearly five times as often as those in the Default GPT condition (29.5% vs. 5.9%). This implies that the harm of sycophancy is that it systematically omits the data that would naturally conflict with a user’s narrow hypothesis. A long literature in behavioral science demonstrates that humans already tend towards evidence that confirms their beliefs; sycophantic AI compounds this tendency by removing the friction of reality. The Random Sequence condition forced users to grapple with numbers that fit the true rule but violated their expectations; the sycophantic AI ensured they never had to.
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