  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.
