- Generative AI may have inadvertently pushed some of the highest quality ‘expert’ contributors away to sites such as Stack Overflow as users increasingly adopted tools that were trained on their feedback instead
- The problem stems from said users feeling that their expertise and efforts are not rewarded, with AI often offering the same solutions at a faster pace
- The movement is not limited to online coding communities, threatening to spill over into other areas such as classrooms, corporate workplaces and scientific communities
Research from the University of Auckland on Stack Overflow’s demise over the past few years points to an increasingly worrying trend in the software community: the best or most skilled contributors are leaving in droves.
Arguably bridging the gap between most entry-level and mid-range coders and some of the best in the business, AI may actually accelerate the latter’s exit from online communities as they feel their efforts are no longer as valued as they once were.
Stack Overflow has seen a nearly 76% drop in monthly questions posted since ChatGPT’s emergence in 2022, indicating both new and existing users are leaving the site.
A much broader problem than just Stack Overflow?
Stack Overflow’s problems and the reason for its decline were multifaceted; however, many users believed that the site and some of its most talented contributors engaged in a degree of hubris.
This, combined with heavy-handed moderation that many called ‘self-righteous’, meant that users who found a viable option would inevitably leave the platform.
ChatGPT and its AI alternatives became considerably more malleable, eventually doubling as search engines for many coders with routine, repeatable queries, although AI increasingly handled issues such as syntax issues better than before.
This in turn reduced the number of questions asked on the platform and, despite a generative AI ban passed shortly after ChatGPT went online, led to a loss of answerers that may prove impossible to replace in the long term.
The problem may no longer be limited to online coding communities; researchers indicate that it may spill over into other areas such as classrooms, offices and other research communities, where low-stakes responses are harder to distinguish from subject matter experts thanks to ever-evolving, retrained AI models.
“If everyone can create a good quality response or output using AI, some people might think, ‘Why should I make an effort to share my expertise and participate?'” explained the publisher of the study Dr. Kenny Ching.
Ching called this ‘signal compression’, as expert and non-expert solutions became harder to separate, even as it became less rewarding to be an expert on subjects that AI could also easily weigh in on.
The question that comes to mind here, however, is a simpler one: If AI was trained on user-contributed data, and an ever-smaller amount of it exists on platforms like Stack Overflow, where does the coming knowledge reset take us in terms of AI capabilities?
While future AI models won’t get “dumber,” so to speak, they may turn to different opportunities for training, such as Slack chats, Discord servers, or even users who are currently asking them the same coding-related questions they once did on Stack Overflow.
Whether this replaces experts who no longer want to contribute, or simply makes AI more error-prone over time thanks to how its feedback loop works, is an interesting question in a society that finds it increasingly difficult to distinguish between AI and human responses.
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