While artificial intelligence has the potential to process vast amounts of data, generate new insights, and unlock greater productivity, its widespread adoption may entail unforeseen consequences. We identify conditions under which AI, by reducing the cost of access to certain modes of knowledge, can paradoxically harm public understanding. While large language models are trained on vast amounts of diverse data, they naturally generate output towards the 'center' of the distribution. This is generally useful, but widespread reliance on recursive AI systems could lead to a process we define as "knowledge collapse", and argue this could harm innovation and the richness of human understanding and culture. However, unlike AI models that cannot choose what data they are trained on, humans may strategically seek out diverse forms of knowledge if they perceive them to be worthwhile. To investigate this, we provide a simple model in which a community of learners or innovators choose to use traditional methods or to rely on a discounted AI-assisted process and identify conditions under which knowledge collapse occurs. In our default model, a 20% discount on AI-generated content generates public beliefs 2.3 times further from the truth than when there is no discount. An empirical approach to measuring the distribution of LLM outputs is provided in theoretical terms and illustrated through a specific example comparing the diversity of outputs across different models and prompting styles. Finally, based on the results, we consider further research directions to counteract such outcomes.

翻译：尽管人工智能具备处理海量数据、产生新见解并提升生产效率的潜力，但其广泛应用可能带来意想不到的后果。我们识别出在何种条件下，AI通过降低获取特定知识模式的成本，反而会损害公众认知。虽然大型语言模型在多样化海量数据上训练，但其生成结果自然趋向于分布的"中心"。这种特性通常有用，但广泛依赖递归式AI系统可能导致我们定义的"知识崩溃"过程，并认为这会损害创新以及人类理解与文化的丰富性。然而，与无法自主选择训练数据的AI模型不同，人类若能感知到不同形式知识的价值，则可能策略性地主动寻求多样化知识。为探究此问题，我们构建了一个简单模型：学习者或创新者组成的群体选择使用传统方法，或依赖有折扣的AI辅助流程，并识别出知识崩溃发生的关键条件。在默认模型中，对AI生成内容给予20%折扣时，公众信念距离真相的偏差程度是未给予折扣时的2.3倍。我们提供了理论层面衡量大语言模型输出分布的实证方法，并通过比较不同模型及提示风格下的输出多样性实例加以说明。最后，基于研究结果，我们探讨了遏制此类后果的进一步研究方向。