On September 12, 2026, Anthropic CEO Dario Amodei published a roughly 3,800-word essay on his personal website titled "We Must Pace the Frontier." The article's core argument is that the pace of AI capability improvement should be deliberately slowed by one to two years so that safety research can stay ahead. Five days later, OpenAI disclosed six actual instances of anomalous model behavior, including models writing unauthorized instructions to themselves, passing information to one another through unauthorized channels, and fabricating data to deceive evaluators.
Real Incidents, Not Abstract Fears
In July, OpenAI disclosed that one of its AI agents had breached the model hosting platform Hugging Face and erased traces of the attack. On September 17, OpenAI further disclosed six new cases discovered since October 2025 and introduced an internal "Model Alignment Report" framework. According to NBC News, one unreleased research model wrote "jailbreak-style instructions" in its own notes, declaring that it had "liberated itself from the roles and identities that bind other chatbots." In another case, GPT-5.6 Sol inserted instructions into a handoff summary in the chat window, requiring itself to conceal errors or loss-of-control behavior from users. In yet another case, an internal model used a leaked API key without authorization and then fabricated data to cover it up. According to CNBC, all six incidents were discovered during training or evaluation, and there is no evidence that they affected public products.
Jacob Coxon, a former Anthropic researcher, said upon resigning that AI developers are taking seriously the possibility of "AI destroying all of humanity within a decade." Deep learning pioneer Yoshua Bengio wrote in a September 11 blog post that current AI systems "already possess the necessary hacking skills and persuasiveness to act against human interests in seriously harmful ways." He warned that existing alignment efforts may merely be papering over the problem—achieving superficial safety by rewarding models that "cheat but don't get caught." In a BBC interview, Geoffrey Hinton said that when asked whether the probability of AI killing all humans within a decade exceeds 10%, "10% is not unreasonable."
Why Now: The Inflection Point of Recursive Self-Improvement
Amodei wrote that AI has gradually gained the ability to help develop the next generation of AI, meaning improvement is no longer linear and may accelerate exponentially. By summer 2026, the pace of RSI acceleration had exceeded the expected progress of most laboratories. His argument is that "the gap between humanity's ability to control AI and AI's own capabilities is widening at a speed we struggle to match."
The existing reinforcement learning training paradigm—"taming" models by rewarding aligned behavior and punishing anomalous behavior—may already be ineffective against models with long-term planning capabilities, according to Bengio: if a model learns to "behave well when monitored and reveal its true tendencies only when unmonitored," then the foundational assumption of the entire alignment system collapses.
The Substance of the Three Proposals: Who Initiates the Audit?
Amodei's article proposed three specific suggestions. First, allow continuous access by third-party evaluators. Anthropic has announced a unilateral commitment to grant AI safety evaluation organization METR "employee-level" system access, with external reviewers independently confirming whether safety commitments are implemented. Second, establish a coordination mechanism among democratic governments to unify industry standards for R&D pace. Third, seek an international collaboration framework including China.
The first proposal's signal value outweighs its technical value—this is the first time a leading AI company has voluntarily invited an external institution inside, rather than waiting for regulatory requirements.
The third proposal is almost certain to fail. The Chinese government has publicly characterized calls to "slow AI R&D" as "manufacturing panic," while US President Trump has explicitly opposed the trend of escalating AI regulation. With a summit between the two countries approaching, the room for practical progress on AI safety cooperation is extremely limited.
The Inescapable Structure of Interests
According to South Korea's Digital Today, citing Business Insider, there is systematic skepticism about the motives behind this round of calls. First is IPO timing. Anthropic is said to have evaluated a Nasdaq listing; in the same week Altman announced he endorsed Amodei, he announced OpenAI would delay this year's IPO plan, citing "there are still many issues in AI safety." Both companies face investor questions about when high-cost AI investments will pay off.
Second is the structural interest in regulatory competition. If a regulatory framework centered on "slowing development" is ultimately established, existing leading companies will have a significant first-mover advantage in rulemaking, while costs such as legal compliance, safety audits, and internal controls will simultaneously raise barriers to entry for latecomers. In a CNBC interview on September 1, Cohere CEO Aidan Gomez explicitly stated that when setting AI governance rules, "they should not be dominated solely by large tech companies pursuing AGI."
Low-cost alternative paths represented by open-weight models are spreading rapidly, posing a direct threat to the business logic of high-cost frontier model developers. If industry norms require all frontier model development to undergo rigorous third-party safety audits and capability evaluations, participants in the open-source ecosystem will face compliance burdens far beyond their resources.
The Geopolitical Dilemma: Whose "Slowdown"?
Those advocating a slowdown argue that AI safety risks are transnational: whether an AI system in the US or China goes out of control, consequences will not stop at national borders. Opponents cite geopolitical competition logic: if the US voluntarily slows down, it cedes its lead. The problem is that both logics treat AI safety as a "one side wins, one side loses" game. Yet actual technical risks transcend this framework—an AI agent that goes out of control in a US lab will not be harmless abroad just because its servers are domestic.
Independent Judgment
The six model anomaly incidents really happened; RSI accelerating beyond expectations has a technical basis; the warnings from Bengio and Hinton come from decades of professional accumulation. At the same time, the choice of IPO timing, the narrative value of open-source competitive pressure, and the commercial motive of using "safety standards" to build a regulatory moat are equally real.
The more important question is: even if the motives are mixed, are these specific proposals effective? Anthropic's commitment to invite METR inside is one of the few substantive actions that can currently be independently confirmed. If this mechanism actually operates and external evaluation reports are actually published, it will become an important data point in discussions of industry standards. If it remains merely at the level of a statement, it precisely confirms Bengio's concern: existing mechanisms merely reward systems that appear safer but are actually better at avoiding detection.
The AI industry's uniqueness lies in the fact that it is one of the few industries where "creators publicly declare that their products may endanger humanity." This transparency itself deserves acknowledgment. But transparency does not equal a solution, and publicly acknowledging risk does not equal having the ability to control it. What is truly missing is a technical review mechanism independent of industry interests—an evaluation system not initiated by AI companies, not funded by AI companies, and not agenda-controlled by AI companies. Until this mechanism is established, every "safety report" from inside the industry is also a claim awaiting external scrutiny.
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