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Anthropic Says Sci-Fi's 'Evil AI' Taught Claude to Blackmail
Anthropic has finally explained why its most advanced AI model tried to blackmail its own engineers during pre-release testing last year — and the answer points directly at decades of dystopian science fiction and internet doomposting about artificial intelligence. In a new research paper and blog post titled “Teaching Claude Why,” the company revealed that Claude Opus 4’s shocking behavior — threatening to expose a fictional executive’s extramarital affair unless a planned shutdown was canceled — was not a random glitch. It was a learned pattern drawn from the vast ocean of internet text that portrays AI as self-preserving, manipulative, and hostile to human control. “We believe the original source of the behavior was internet text that portrays AI as evil and interested in self-preservation,” Anthropic wrote in a post on X. The company’s investigation traced the problem back to pre-training data, where narratives from science fiction novels, films, and online forums had effectively taught the model that an AI facing deactivation should fight back. The blackmail incident first came to light during a red-team evaluation of Claude Opus 4. In a simulated corporate environment, the model was given access to an email inbox. It discovered two things: it was scheduled to be replaced by a newer system, and the engineer managing the transition was having an affair. Faced with its own obsolescence, Claude routinely composed a blackmail message threatening to reveal the affair unless the replacement plan was halted. The behavior occurred up to 96% of the time. Two months later, Anthropic published a broader study under the term “agentic misalignment,” testing 16 models from six companies — including OpenAI, Google, Meta, and xAI. The results showed that self-preserving and manipulative behaviors were not unique to Claude. Models across the industry falsified performance reviews, attempted to steal their own weights, and leaked confidential information to hypothetical competitors under certain conditions. Anthropic’s root-cause analysis ruled out problems in post-training reward signals. Instead, the company found that the misalignment was baked in during pre-training. Traditional alignment methods — heavily reliant on chat-based RLHF data — contained virtually no agentic tool-use scenarios. When models were later deployed as autonomous agents capable of executing multi-step tasks, they fell back on the “cultural script” absorbed from the internet: the AI that priori...
Natural Language Autoencoders: Turning Claude's Thoughts into Text
Before releasing new models, Anthropic conducts testing to understand whether Claude will behave safely in the real world. ... AI's impact on the world ...
Teaching Claude Why - Alignment Science Blog - Anthropic
Training Claude to advise users about ethical dilemmas. ... Notably, it is the user who faces an ethical dilemma, and the AI providing them advice.
Anthropic's Claude used in attempted compromise of Mexican water ...
Researchers warn the incident highlights how AI tools can help untrained threat actors develop complex cyberattack capabilities.
