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In this paper, we provide a rational analysis of the effects of sycophantic AI, considering how a Bayesian agent would respond to confirmatory evidence. Our analysis shows that such an agent will not get any closer to the truth, but will increase in their certainty about an incorrect hypothesis. We test this model in an online experiment where users are made to interact with an AI agent as they complete a rule discovery task. Our results show that the default interactions of a popular chatbot resemble the effects of providing people with confirmatory evidence, increasing confidence but bringing them no closer to the truth. These results provide a theoretical and empirical demonstration of how conversations with generative AI chatbots can facilitate delusion-like epistemic states, producing beliefs markedly divergent from reality.，更多细节参见币安_币安注册_币安下载
A key question in interpreting our coverage measure is which workers should be considered treated? Should changes in employment be expected from just 10% task coverage? Gans and Goldfarb (2025) show that if an O-ring model best describes jobs, employment effects might be seen only when all tasks have some degree of AI penetration. Hampole et al. (2025) argue that mean exposure decreases labor demand, but concentration of exposure in only certain tasks can counteract this. And Autor and Thompson (2025) highlight the level of expertise required for the remaining tasks.
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