I believe there are entire companies right now under AI psychosis

Original Article ↗ Hacker News Discussion ↗

What “AI psychosis” is (and isn’t)

  • Many interpret it as companies outsourcing judgment to AI and rationalizing reckless behavior (“agents will fix bugs later”).
  • Others say that’s just another hype‑cycle / cargo‑cult phase, not literal psychosis, and object to misusing a clinical term.
  • Some distinguish between mass groupthink / reality distortion vs genuine AI‑induced psychotic episodes (chatbot delusions, parasocial relationships).

MTTR vs MTBF mindset and software quality

  • Analogy: shift from optimizing “don’t fail” (MTBF) to “recover fast” (MTTR) in cloud ops.
  • Concern: leaders now apply this to AI‑written code — ship quickly, let agents patch production — ignoring hard‑to-detect, long‑running or data‑corrupting bugs.
  • Several argue bug metrics must be “defects introduced per defect fixed”; speed alone is meaningless or harmful.

Experiences with AI coding tools

  • Positive side:
    • Many report 2–5x speedups for small tools, reports, scripts, refactors, and test writing.
    • LLMs can explain complex code, surface subtle bugs, and help cross large codebases.
    • Some teams claim stable or improved incident rates with AI‑assisted workflows plus strict code review and tests.
  • Negative side:
    • “Vibe coding” produces verbose, incoherent, redundant code with hidden coupling and architectural drift.
    • LLMs often “look busy”: plausible fixes that don’t change behavior, ignore specs/tests, or reintroduce bugs.
    • Test suites and “100% coverage” generated by AI can be shallow and miss real defects.
    • Several anecdotes of AI‑rewritten libraries and APIs becoming less reliable.

Management, incentives, and forced adoption

  • Reports of executives mandating “AI everywhere,” measuring token usage, requiring AI in every repo, and pushing “AI-only code review.”
  • Some employees fake AI usage or generate meaningless work to hit AI KPIs.
  • AI is used to justify layoffs and pressure remaining staff to do more with less.

Long‑term risks: debt, security, cleanup

  • Fear of massive cognitive/technical debt: codebases so complex no human understands them, with defect‑fixing agents eventually net-negative.
  • Security worries: AI‑written slop, AI‑poisoned dependencies, and prompt‑injection risks in agentic systems.
  • Expectation that “AI rescue consulting” / “AI janitors” will emerge to clean up failed AI‑built systems.

Profession, economics, and culture

  • Split between “AI already better than average devs” and “AI magnifies mediocrity 10x faster.”
  • Concern that junior devs won’t learn fundamentals if they start by prompting rather than coding.
  • Some hope this crisis will push software toward real engineering discipline; others think short‑term greed will win.