If you are asking for human attention, demonstrate human effort

Original Article ↗ Hacker News Discussion ↗

Perceived problem: “AI slop” and asymmetry of effort

  • Many describe being flooded with long, verbose, obviously‑LLM text (emails, specs, PRs, docs).
  • Core complaint: trivial effort by the sender creates large effort for the reader/reviewer.
  • People resent being forced into the role of “human in the loop” to debug or fact‑check others’ AI output.
  • This is framed as antisocial and disrespectful: “If you couldn’t be bothered to write it, why should I be bothered to read it?”

Effort, respect, and attention

  • Human effort is seen as a signal of care, ownership, and accountability.
  • Several argue for reciprocity: match your effort to the effort shown by the other side.
  • Others push back: what matters is usefulness and quality, not how hard it was to produce.
  • Tension: labor‑theory‑of‑value (“effort = value”) vs “value = outcome” is repeatedly debated.

Impact on workplaces and code review

  • Common pattern: coworkers pasting large, barely‑reviewed AI PRs or specs, then expecting serious human review.
  • Reviewers report spending more time than the “author,” who sometimes can’t explain the code (“Claude added that”).
  • This erodes trust, slows teams, and nudges reviewers toward ignoring or rubber‑stamping work.
  • Counterpoint: manual PR review “doesn’t scale” in an agentic world; some suggest heavier automation and tests instead.

Proposed norms and coping strategies

  • Require authors to self‑review AI output and take responsibility (“you commit it, you own it”).
  • Keep PRs small and well‑explained; invest more effort in making work easy to consume.
  • Some advocate:
    • Label AI‑generated content and allow filtering, with strict penalties for deception.
    • Default “no” on low‑effort, high‑volume submissions.
    • Use AI to review AI‑generated PRs as a first pass.
    • Refuse to read obvious slop or escalate to management.

Attitudes toward AI itself

  • Enthusiasts: AI is great for summarization, editing, boilerplate, research assistance, and even civic activism; tool choice doesn’t matter if output is good and checked.
  • Skeptics: many outputs remain brittle, fluffy, or incorrect; some find AI content and art viscerally off‑putting or “soulless.”
  • Widespread concern about an AI‑to‑AI arms race (spam, hiring filters, support, governance) that shifts costs onto humans caught in the middle.