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Stack Overflow’s forum is dead but the company’s still kicking

Stack Overflow’s forum is dead thanks to AI, but the company’s still kicking... thanks to AI

sherwood.news

May 26, 2026

2 min read

🔥🔥🔥🔥🔥

57/100

Summary

Stack Overflow's forum usage has declined significantly due to the rise of AI, leading Elon Musk to label the situation as “death by LLM.” Despite this decline, the company continues to generate millions in revenue, leveraging AI as a new source of income.

Key Takeaways

  • Stack Overflow's Q&A forum traffic has plummeted, recording only 6,866 questions last month, similar to its launch volume in 2008.
  • The company's annual revenue has doubled to $115 million despite declining engagement, primarily due to monetizing its content through enterprise solutions and licensing data to AI companies.
  • Stack Overflow's generative-AI add-on, "Stack Internal," is now utilized by 25,000 companies globally.
  • Complex coding questions continue to be asked on Stack Overflow, as large language models struggle with such inquiries without curated human data.
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Community Sentiment

Mixed

Positives

  • Some users still find better solutions on Stack Overflow than from AI models like Claude Code, highlighting the unique value of human-generated content.
  • Asking for multiple solutions from LLMs can lead to creative problem-solving, demonstrating the potential for AI to enhance human decision-making.
  • The ability of AI to learn from open-source code and documentation shows promise for improving its coding capabilities, especially in handling bugs.

Concerns

  • The decline of Stack Overflow raises concerns about the future of knowledge generation for training AI models, as fewer platforms exist for community-driven learning.
  • LLMs may struggle to access diverse, up-to-date knowledge without forums like Stack Overflow, potentially limiting their effectiveness in providing innovative solutions.
  • The culture issues on Stack Overflow have been detrimental, as they may discourage users from contributing valuable knowledge, impacting the quality of training data for AI.