Can ChatGPT Kill User-Generated Q&A Platforms?

Large language models (LLMs), such as ChatGPT, exhibit substantial functional overlap with user-generated knowledge ecosystems while also relying on them as critical inputs for future learning. This dual role creates a fundamental tension that calls for a clearer understanding of how LLMs reshape these ecosystems. Adopting a niche theory perspective, we examine how functional overlap and knowledge structure determine the boundary between substitution and coexistence. Using Stack Overflow, we show that LLM introduction reduces question volume by about 14% on average (and up to 27.9% over time), with stronger declines in mid- to low-quality content, in topics with richer and more structured knowledge bases, and among less experienced users. Conditional on similar question activity, topics with deeper answer-side knowledge experience disproportionately larger reductions, highlighting the role of accumulated knowledge. These patterns reflect selective substitution; routine and well-documented queries migrate to LLMs, whereas complex, context-dependent problems remain. We also document direct improvements in question quality, suggesting positive spillovers from reduced search and articulation costs. Together, the findings indicate niche partitioning rather than full displacement, with a self-reinforcing knowledge flywheel between LLMs and platforms. For practice, platforms should reposition toward high-expertise niches by integrating artificial intelligence (AI)-assisted scaffolding, strengthening expert incentives, and curating complex knowledge. For LLM development, the results point toward deeper integration, where AI systems complement community knowledge production and enable more advanced problem solving.

• 저널Information Systems Research
• 토픽Artificial Intelligence in Healthcare and Education · Health Informatics
• DOI10.1287/isre.2023.0561
• 저자Junzhi Xue, Lizheng Wang, Jinyang Zheng, Yongjun Li, Yong Tan