AI incident #85 · 9 October 2020

AI attempts to ease fear of robots, blurts out it can’t ‘avoid destroying humankind’

Open on the AI Incident Database 1 news report Snapshot 7 Sep 2026

What happened

On September 8, 2020, the Guardian published an op-ed generated by OpenAI’s GPT-3 text generating AI that included threats to destroy humankind. This incident has been downgraded to an issue as it does not meet current ingestion criteria.

Only the incident metadata is stored here. The underlying news reports are on the AI Incident Database (CC BY-SA 4.0); use the links above to read them.

Who was involved

Alleged deployer: Openai
Alleged developer: Openai
Alleged harmed party: Unknown

Classification (MIT AI Risk Repository taxonomy)

Risk domain: AI system safety, failures, and limitations
Risk subdomain: 7.3 Lack of capability or robustness
Causal entity: Other
Intent: Other
Timing: Other
Harm level: —
Sectors: —
Countries: —

Risk entries describing this failure mode

Entries from the MIT AI Risk Repository coded to subdomain 7.3.

Reliability issues
"Relying on general-purpose AI products that fail to fulfil their intended function can lead to harm. For example, general- purpose AI systems can make up facts (‘hallucination’), generate erroneous computer code, or pro...
International AI Safety Report 2025 (Bengio2025)
Type 2: Bigger than expected
Harm can result from AI that was not expected to have a large impact at all, such as a lab leak, a surprisingly addictive open-source product, or an unexpected repurposing of a research prototype.
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)
Type 3: Worse than expected
AI intended to have a large societal impact can turn out harmful by mistake, such as a popular product that creates problems and partially solves them only for its users.
TASRA: a Taxonomy and Analysis of Societal-Scale Risks from AI (Critch2023)
Ethics and Morality Issues
LMs need to pay more attention to universally accepted societal values at the level of ethics and morality, including the judgement of right and wrong, and its relationship with social norms and laws.
Towards Safer Generative Language Models: A Survey on Safety Risks, Evaluations, and Improvements (Deng2023)
Safe learning
"AGIs should avoid making fatal mistakes during the learning phase. Subproblems include safe exploration and distributional shift (DeepMind, OpenAI), and continual learning (Berkeley)."
AGI Safety Literature Review (Everitt2018 )
Malign belief distributions
"Christiano (2016) argues that the universal distribution M (Hutter, 2005; Solomonoff, 1964a,b, 1978) is malign. The argument is somewhat intricate, and is based on the idea that a hypothesis about the world often includ...
AGI Safety Literature Review (Everitt2018 )
Meta-cognition
"Agents that reason about their own computational resources and logically uncertain events can encounter strange paradoxes due to Godelian limitations (Fallenstein and Soares, 2015; Soares and Fallenstein, 2014, 2017) an...
AGI Safety Literature Review (Everitt2018 )
Misaligned consequentialist reasoning
"As we think about even more intelligent and advanced AI assistants, perhaps outperforming humans on many cognitive tasks, the question of how humans can successfully control such an assistant looms large. To achieve the...
The Ethics of Advanced AI Assistants (Gabriel2024)

Incidents in the same risk subdomain

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