Stack Overflow is almost dead

Role of AI vs Other Decline Factors

• Many say LLMs “ate SO’s lunch”: they now ask ChatGPT/Perplexity instead of visiting or posting.
• Others argue the decline started ~2014, long before modern LLMs, due to better docs, smarter tools, GitHub issues, official vendor forums, YouTube, and tutorials.
• Some claim SO simply “answered most common questions,” so new-question volume naturally fell. Critics counter that much content is outdated and it’s hard to see what’s still valid.

Licensing, Copyright, and AI Training

• Discussion notes SO content is under Creative Commons, but there’s debate whether AI companies respect attribution/obligations.
• Several commenters share anecdotes of LLMs reproducing SO posts or comments verbatim, suggesting more than abstract “learning.”
• Others argue such snippets are de minimis legally and that CC applies to presentation, not facts.

Moderation, Culture, and “Toxicity”

• A major thread is hostility toward SO’s aggressive closing, downvoting, and editing culture, especially from ~2014 onward.
• Many describe good, novel questions being closed as duplicates or “off-topic,” discouraging participation and pushing people to Reddit/Discord.
• Defenders argue SO was never meant as a helpdesk or chat forum but as a tightly curated, searchable knowledge base; strict closure and anti-chitchat policies are seen as essential, not “power trips.”
• There’s deep disagreement over whether this gatekeeping preserved quality or killed the community.

Duplicates, Completeness, and Site Purpose

• Curators emphasize that duplicates are linked, not forbidden, and that merging into a single canonical Q&A improves search and avoids repeated low-value answers.
• Critics say “duplicate of vaguely related question with different context” became common, making SO feel hostile and useless for real, current problems.

Future of LLMs and Knowledge Sources

• Several worry that if SO and similar sites atrophy, LLMs will lack fresh, vetted training data for new languages/frameworks, leading to self-cannibalizing, lower-quality answers.
• Others think future models can learn more directly from code, docs, and repositories, or from new Q&A platforms.
• Some foresee SO (or successors) becoming primarily a structured data source for LLM training, which others view as a dystopian “humans-labeling-for-AI” future.

Business, Infrastructure, and Alternatives

• Commenters note SO’s question volume is down to ~2009 levels but still far from “zero”; traffic might remain high enough for it to function as a static reference.
• Private equity ownership, attempts to bolt on AI products against community consensus, and the sale to an LLM vendor are seen as signs of strategic drift.
• Many now rely on GitHub issues, project Discords/Slacks, and official forums, though these are fragmented and often not search-indexed.