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. Finally, based on
the results, we consider further research directions to counteract such
outcomes.

通过降低获取知识成本，人工智能可能对公众理解产生逆效应，最终导致知识塌缩，损害创新和人类理解与文化的丰富性。我们提供一个简单模型来研究学习者或创新者选择使用传统方法还是依赖打折的人工智能辅助过程的条件，并发现人工智能生成内容的打折将使公众信仰与真实事实相差 2.3 倍。最后，我们考虑进一步的研究方向来对抗这种结果。