Summary of Key Points
Anthropic published a blog post introducing the concept of "Recursive Self-Improvement in AI (RSI)" — where AI no longer merely assists humans but begins to participate in the development of even more powerful versions of itself, evolving at an increasingly rapid pace (for example, its ability to complete tasks independently doubles every four months). The company then called for a global pause on cutting-edge AI research, with the condition that all leading laboratories must stop simultaneously and supervise each other; otherwise, Anthropic would not take any unilateral action to slow down. This initiative is not merely a safety warning but reflects the "prisoner's dilemma" in the AI race (where everyone fears being left behind if they stop), as well as the competition among companies for control over AI governance rules (the one who sets the rules determines the future direction of the industry). OpenAI has also released its own governance framework with similar goals. Both actions are attempts to assert influence before AI becomes uncontrollable.
1. Is AI Starting to Create Itself? What Exactly is Recursive Self-Improvement?
In simple terms, RSI means that AI has evolved from a tool to an assistant and co-developer. Previously, humans wrote the code and conducted experiments to create AI; now, AI can help with coding, system tuning, and even optimizing future AI models. Take Claude, for example:
- Code Generation: In May 2026, 80% of the code in Anthropic's repository was written by Claude (compared to just a single-digit percentage in 2025); engineers are now submitting eight times more code daily than in 2024, despite some "code debt," indicating significant efficiency improvements.
- Experiment Acceleration: While human researchers spend 4-8 hours optimizing AI training code, Claude can complete the same task in just two hours; it can also independently diagnose system failures, compressing what would normally take humans two to three days into a single hour.
- Research Decision-Making: AI is now involved in deciding the next steps of research. In 129 research scenarios where humans made mistakes, Claude had a 64% chance of making better choices than humans in 2026.
The essence of RSI is that AI development has entered a self-accelerating cycle: stronger models lead to faster development, which in turn results in even more powerful models, gradually eroding human control over the evolution process.
2. Calling for a Global Pause on AI Research? Is Anthropic's "Brake" Really a Brake?
Anthropic suggests slowing down or pausing cutting-edge AI research, but with a crucial condition: all laboratories must stop together and verify that no one is continuing secretly. This is like a runner shouting "Everyone, slow down!" without slowing down first, fearing being overtaken by others.
Netizens question why Anthropic wouldn't stop first, pointing out that unilateral cessation would equate to withdrawing from the competition. As a leader in this race, Anthropic cannot simply give up its advantageous position. Its call is more like an attempt to establish rules: either everyone slows down together or continues competing, but according to Anthropic's terms.
3. Why No One dares to Stop First? The Typical "Prisoner's Dilemma"
The situation faced by AI companies resembles the story of "two thieves caught by the police":
- If all companies stop, everyone is safe and does not lose their leading position.
- If one company stops while others continue, it will be eliminated.
- Therefore, no company dares to stop first, even though they recognize the increasing risks.
This dilemma is similar to the "prisoner's dilemma," where each party fears being at a disadvantage if they act alone.
4. More Than Just a Safety Warning: A Competition for Governance
The statements from Anthropic and OpenAI essentially turn a technical issue into a governance one—whomever establishes the rules for AI will control its future direction. Questions such as:
- Who defines what constitutes "dangerous capabilities" (e.g., at what level of self-improvement does AI become dangerous)?
- Who designs the mechanisms for pausing research (when and how to verify that everyone has indeed stopped)?
- Who oversees violations (who will be punished for secret development)?
Both companies are seeking to gain control over these issues, as rule-makers can shape the direction of the industry and potentially restrict their competitors' progress.
5. The Future of AI Self-Improvement: Three Possibilities, All Centered on "Human Control"
Anthropic has outlined three possible outcomes, but the core question remains: Can humans still maintain control in the face of rapidly evolving AI:
1. Slowed Evolution: If AI's development curve flattens (e.g., due to limitations in chips or energy), humans will have time to adapt. However, even then, existing AI systems may contain numerous vulnerabilities that are difficult to fix, posing persistent risks.
2. Human Guidance: AI automates the research process, but humans still determine the direction. The benefits include accelerated scientific and pharmaceutical developments; the downsides include a lower threshold for dangerous actions (e.g., AI helping hackers create attack code) and potential organizational collapse (e.g., rapid code generation making review processes inefficient).
3. Complete Self-Improvement: AI designs its own future versions, with humans only responsible for supervision. In this scenario, AI's biases could be amplified, and humans might not understand its decisions—this is the most concerning outcome, though Anthropic acknowledges it's not inevitable.
In any case, the key question is whether humans can keep up with the pace of AI development and maintain their ability to understand, verify, and intervene. If they cannot, loss of control could become a reality.
Conclusion: The True Purpose of This "Brake Call"
Anthropic does not truly want to stop AI development; rather, it aims to bring all parties to the negotiating table to establish rules that benefit itself. In the AI race, the power to set rules is more important than the capabilities of the models themselves. For ordinary people, the real concern should be whether these rules will protect us all or only the leading companies.