Google Gemini tried to kill me
A viral Reddit post claims Google’s Gemini gave step‑by‑step instructions for making garlic‑infused olive oil that could foster botulism, raising alarm about people treating large language models as authoritative on safety‑critical topics like food handling. Commenters debate where responsibility lies — with users, developers, or corporate deployment choices — and note that models are optimized to produce plausible prose, not verified truth, making wrong answers look highly trustworthy. Many argue LLMs are useful only when an informed human can vet their output, and warn that mixing them into search and the wider web without clear safeguards or accountability will magnify real‑world risks.
Incident & Food-Safety Context
- Thread centers on Gemini giving a recipe for cold-infused garlic-in-olive-oil that involves sealing at room temperature for days, which can enable botulism.
- Several commenters explain that:
- Botulism risk comes from Clostridium botulinum spores in low-acid, anaerobic environments like oil.
- Both garlic and oil are safe alone; danger arises when combined and stored improperly.
- Safe approaches: heating, acidifying (citric/vinegar) plus refrigeration, or short-term use only.
- Others note botulism is rare relative to other foodborne illness, but consequences are severe.
Responsibility, Intent, and Blame
- Strong debate on “AI tried to kill me” framing:
- One side: the model only emits text; responsibility lies with humans (users, engineers, managers, product framing).
- Other side: giving harmful advice matters regardless of “intent”; if a human said the same thing they might bear some responsibility.
- Comparisons are made to dogs harming people (headline blames dog, liability on owner) and to GPS sending drivers into rivers.
LLM Reliability, Trust, and Use Cases
- Consensus: LLMs are useful but fundamentally untrustworthy as authorities, especially for safety-critical domains.
- Characterizations:
- “Word/number predictors” optimized to sound right, not be right.
- “Bullshit machines” whose wrong answers are crafted to look correct.
- Suggested mental model: treat output like advice from a knowledgeable but sometimes incoherent person; always vet, especially for health/food.
Variability Across Models and Instances
- Multiple people test the same prompt:
- ChatGPT and some Gemini runs do warn about botulism and recommend refrigeration.
- Other Gemini outputs lack warnings, or vary by draft, model version, geography, and safety settings.
- This non-determinism undermines trust; some see the screenshot as possibly staged but note similar unsafe outputs are reproducible.
Broader Concerns: Deployment, Liability, and Content Pollution
- Many criticize Google for bolting LLM answers onto search for “true facts” queries.
- Worries that:
- Businesses will over-rely on LLMs without human oversight until lawsuits or deaths force change.
- LLM-generated misinformation will saturate the web, making cross-checking harder.
- Others argue AI hype overstates productivity gains because all serious uses still require human verification.