An analysis of ChatGPT's answers to 517 programming questions on Stack Overflow: 52% of answers contain misinformation, 77% are more verbose than human answers
Not So Smart — In recent years, computer programmers have flocked to chatbots like OpenAI's ChatGPT to help them code …
Context & Ripple Effects
Stack Overflow had already temporarily restricted ChatGPT-generated posts after moderators said many were wrong; this analysis supplies a focused measure of that concern in programming help through the earlier restriction on ChatGPT-generated answers.
The findings also fit prior coverage of chatbots filling knowledge gaps with plausible-sounding text: fluency can obscure technical errors, particularly when answers are longer than the human alternatives.
First-order effects
- Developers using ChatGPT for programming guidance have evidence that its output needs independent testing and source-level verification rather than acceptance on presentation alone.
- Stack Overflow and its contributors face a clearer quality-control problem when AI-written answers are both frequently inaccurate and more verbose, making errors harder to spot quickly.
Second-order effects
- Coding-assistant providers are pressured to improve grounding, uncertainty signaling, and evaluation on real technical questions; concise but verifiable answers become a product-quality differentiator.
- Community-maintained technical forums may have to devote more moderation effort to AI-assisted submissions, while contributors and readers place greater value on reproducible examples and cited documentation.
Third-order effects
- If this pattern persists, software development workflows will treat generative answers less as authoritative reference material and more as drafts that require tests, review, and provenance.
- The broader market may increasingly compete on mechanisms that make AI output auditable in high-stakes knowledge work, not merely on conversational fluency.
The trend: Generative AI is moving from novelty assistant to work-surface tool, making reliability, verification, and editorial overhead central constraints on adoption.