  Large language models like ChatGPT efficiently provide users with information
about various topics, presenting a potential substitute for searching the web
and asking people for help online. But since users interact privately with the
model, these models may drastically reduce the amount of publicly available
human-generated data and knowledge resources. This substitution can present a
significant problem in securing training data for future models. In this work,
we investigate how the release of ChatGPT changed human-generated open data on
the web by analyzing the activity on Stack Overflow, the leading online Q\&A
platform for computer programming. We find that relative to its Russian and
Chinese counterparts, where access to ChatGPT is limited, and to similar forums
for mathematics, where ChatGPT is less capable, activity on Stack Overflow
significantly decreased. A difference-in-differences model estimates a 16\%
decrease in weekly posts on Stack Overflow. This effect increases in magnitude
over time, and is larger for posts related to the most widely used programming
languages. Posts made after ChatGPT get similar voting scores than before,
suggesting that ChatGPT is not merely displacing duplicate or low-quality
content. These results suggest that more users are adopting large language
models to answer questions and they are better substitutes for Stack Overflow
for languages for which they have more training data. Using models like ChatGPT
may be more efficient for solving certain programming problems, but its
widespread adoption and the resulting shift away from public exchange on the
web will limit the open data people and models can learn from in the future.
