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Anthropic Researcher Quits Over ‘Out-of-Control’ AI Fears, Warns AI Could Kill Us All by End of Decade
- On Tuesday, Anthropic researcher Jacob Coxon announced his resignation, claiming no other human activity poses the danger found in the current race to develop artificial intelligence.
- The Wall Street Journal reported that Coxon, 27, quit because he refuses to contribute to a competitive race where firms prioritize speed over ensuring humans maintain control of advanced systems.
- Coxon warned that "AI systems are unpredictable and difficult to control," citing behaviors like "sycophancy, laziness, deception, blackmail, scheming," and noting training models is more like "growing" than "building."
- Writing on social media, Coxon insisted his warnings are "Not a marketing stunt," adding that senior researchers privately express fear AI "could kill us all."
- Anthropic CEO Dario Amodei holds similar views and is known for warnings, though Coxon argues preventing a global race may require drastic actions like a temporary ban on model improvements.
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207 Articles
207 Articles
The researcher in the field of IA, Jacob Coxon, leaves the laboratories of San Francisco denouncing catastrophic risks for all humanity. What has ...
·Rome, Italy
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'We Really Do Earnestly Believe AI Could Kill All Humans!' — Anthropic Safety Researcher Issues Worrying Prediction, Says There's No Internal Plan to Prevent It
An Anthropic safety researcher has said there is a greater than 10% chance that AI “could kill all humans” within the next decade, and admitted there is no coordinated plan to prevent it from happening.
·San Francisco, United States
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Jacob Coxon, a pretraining researcher at both OpenAI and Anthropic, resigned from his position, stating that both AI giants were acting irresponsibly. Coxon emphasized that the companies were engaged in an uncontrolled race to achieve self-improving superintelligence, jeopardizing the future of humanity.
·Istanbul, Türkiye
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Coverage Details
Total News Sources207
Leaning Left29Leaning Right29Center21Last UpdatedBias Distribution37% Left, 37% Right
Bias Distribution
- 37% of the sources lean Left, 37% of the sources lean Right
37% Right
L 37%
C 26%
R 37%
Factuality
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