Generative AI Shifts Technical Knowledge Production Toward Recombinant Novelty

Abstract

Public debate about generative AI often centers on the role of humans in knowledge work. We study this question by examining the questions people continue to ask on the largest technical Question and Answer community, even after the broad availability of LLMs. We find that while overall question volume declines, the questions that persist are those that combine knowledge domains in new ways, referred to as novel questions. More importantly, these novel questions are primarily driven by users combining existing niche domains, rather than new technical domains. This shift in demand for knowledge is also reflected in the hollowing out of the core of the community's knowledge network. These shifts are driven by both selection among new entrants and by incumbent users adapting what they ask. Finally, our results raise concerns for the sustainability of the open knowledge and data pipeline that supports the continued development of LLMs.