Ask HN: Should "I asked $AI, and it said" replies be forbidden in HN guidelines?

Hacker News Discussion ↗

Perceived Problem with “I asked $AI, and it said…” Replies

  • Many see these as the new “lmgtfy” or “I googled this…”: lazy, low-effort, and adding no value that others couldn’t get themselves in one click.
  • AI answers are often wrong yet highly convincing; reposting them without checking just injects fluent nonsense.
  • Readers come to HN for human insight, experience, and weird edge cases, not averaged training‑data output.
  • Some view such posts as karma farming or “outsourcing thinking,” breaking an implicit norm that writers invest more effort than readers.

Arguments for Banning or Explicitly Discouraging

  • A guideline would clarify that copy‑pasted AI output is unwelcome “slop,” aligning with existing expectations against canned or bot comments.
  • Banning the pattern would push people to take ownership: if you post it as your own, you’re accountable for its correctness.
  • Some argue for strong measures (flags, shadowbans, even permabans) to prevent AI content from overwhelming human discussion.
  • Several note that moderators have already said generated comments are not allowed, even if this isn’t yet in the formal guidelines.

Arguments Against a Ban / In Favor of Tolerance with Norms

  • Banning disclosure doesn’t stop AI usage; it just incentivizes hiding it and laundering AI text as human.
  • Transparency (“I used an LLM for this part”) is seen as better than deception, and a useful signal for readers to discount or ignore.
  • Voting and flagging are viewed by some as sufficient; guidelines should cover behavior (low effort, off‑topic), not specific tools.
  • In threads about AI itself, or when comparing models, quoting outputs can be directly on‑topic and informative.

Narrowly Accepted or Edge Use Cases

  • Summarizing long, technical papers or dense documents can be genuinely helpful, especially on mobile or outside one’s domain, though people worry about over‑broad or inaccurate summaries.
  • Machine translation for non‑native speakers is widely seen as legitimate, especially if disclosed (“translated by LLM”).
  • Using AI as a research aide or editor is often considered fine if the final comment is clearly the poster’s own synthesis and judgment.

Related Concerns: Detection and Meta‑Behavior

  • “This feels like AI” comments divide opinion: some find them useless noise, others appreciate early warnings on AI‑generated articles or posts.
  • There’s skepticism about people’s actual ability to reliably detect AI style; accusations can be wrong and corrosive.
  • Several propose tooling instead of rules: AI flags, labels, or filters so readers can hide suspected LLM content if they wish.