「我們的意見算意見還是異見？」劉先生這樣笑說。「我當然可以關注『 兩會』，但是有意義嗎？」
- "I need to see problems, not everything."
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Manjit Sangha, who worked seven days a week before her illness, returned home on a Sunday afternoon in July last year, feeling unwell.
People increasingly use large language models (LLMs) to explore ideas, gather information, and make sense of the world. In these interactions, they encounter agents that are overly agreeable. We argue that this sycophancy poses a unique epistemic risk to how individuals come to see the world: unlike hallucinations that introduce falsehoods, sycophancy distorts reality by returning responses that are biased to reinforce existing beliefs. We provide a rational analysis of this phenomenon, showing that when a Bayesian agent is provided with data that are sampled based on a current hypothesis the agent becomes increasingly confident about that hypothesis but does not make any progress towards the truth. We test this prediction using a modified Wason 2-4-6 rule discovery task where participants (N=557N=557) interacted with AI agents providing different types of feedback. Unmodified LLM behavior suppressed discovery and inflated confidence comparably to explicitly sycophantic prompting. By contrast, unbiased sampling from the true distribution yielded discovery rates five times higher. These results reveal how sycophantic AI distorts belief, manufacturing certainty where there should be doubt.
"It's not young people's failure ... It's the system's failure, both in the labour market and in the schools, skills, employment support, mental health and welfare system that is letting young people down."