A recent study published in the PNAS nexus Journal suggests that the rise of AI models such as ChatGPT could challenge the dominance of traditional knowledge-sharing sites such as Reddit and programming forum Stack Overflow. This shift could have consequences for the availability of freely accessible public information.
The research, conducted by Maria del Rio-Chanona and her colleagues, shows that Stack Overflow saw a 25% drop in user activity within just six months of launching ChatGPT.
This decline was not observed on similar sites where access to ChatGPT is limited, highlighting the significant impact of the rapid adoption of the AI model. According to the study, users may turn to AI-generated responses instead of seeking out human-driven content, changing the way people obtain information online.
“LLMs are so powerful, have such high value and have a huge impact on the world. You start to wonder about their future,” says Del Rio-Chanona, who is also an associate professor at the Complexity Science Hub (CSH). The findings raise concerns that a growing reliance on AI could reduce the number of contributions to public forums, leading to a shortage of diverse and authentic data needed to train future models. “This has quite major consequences. This means that in the future there may not be enough public data to train models,” she warns.
Python and JavaScript
This trend could disrupt the open web ecosystem, as AI tools like ChatGPT rely on publicly shared knowledge for training data. “Even AI models like ChatGPT are trained on human-generated content like Stack Overflow posts,” explains Johannes Wachs, a faculty member at CSH. Ironically, the quality of training data may deteriorate over time as AI displaces these platforms.
The impact has been especially pronounced in posts about commonly used programming languages such as Python and JavaScript, where activity has dropped significantly. The study suggests that this shift is not limited to beginners but affects users at all experience levels, indicating a broad shift from public to private interactions on AI platforms.
With fewer people contributing to public platforms, AI models may end up relying on lower quality data, which could degrade their performance. The researchers advocate a balanced approach that maintains the open exchange of knowledge while embracing advances in AI.