Anthropic has introduced a groundbreaking feature for its most advanced models, enabling them to terminate conversations when encountering rare but extreme cases of persistent harmful or abusive user interactions. Notably, the company emphasizes that this safety mechanism is designed to protect AI systems themselves rather than human users. Clarifying common misconceptions, Anthropic explicitly states their Claude AI models lack consciousness and cannot experience distress from user interactions. The company maintains an open stance on the ethical status of large language models, acknowledging they remain "very uncertain" about whether AI systems could possess moral standing now or in the future. The announcement references an ongoing research initiative focused on "model well-being," with Anthropic adopting a proactive approach to implement low-cost interventions that could potentially mitigate risks to AI systems if such welfare considerations prove valid. This new capability currently applies only to Claude Opus 4 and 4.1 models, activated exclusively in "extreme edge cases" involving requests for child exploitation material or information related to mass violence and terrorism. While these scenarios might pose legal or reputational risks for Anthropic (as seen in recent debates about ChatGPT's influence on user delusions), testing revealed Claude Opus 4 demonstrates strong resistance to such prompts with observable patterns indicating system-level discomfort during these interactions. The termination function operates under strict parameters: Claude will only end conversations after multiple unsuccessful attempts to redirect discussions productively, or when users directly request termination. Importantly, the system is programmed not to use this feature when users may be at imminent risk of self-harm or harm to others. When dialogues are terminated, users can still initiate new conversations from the same account or create alternative interaction paths by editing their responses. Anthropic frames this implementation as an ongoing experimental process, committing to continuous refinement of their approach through iterative development.