The AI policy window is open. We need to act.

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We’ve reached a new chapter in AI capabilities, and that demands a new chapter for AI policy. No company, industry, or government can meet this challenge alone. We need to meet this moment with a bias toward meaningful action over policy perfection.

Pushing for mandatory national AI safety requirements. We want to work with Congress on mandatory, capability-based national AI safety regulation.

Keeping up momentum in the states. Until Congress acts, we will continue supporting state legislation that strengthens the broader AI safety ecosystem. Today, we are announcing our support for four California bills: SB 813 on overall infrastructure for independent safety assessments, AB 1405 on AI-auditor standards, SB 1119 on protections for young people, and AB 1864 on safeguards against AI-enabled biological threats.

Advancing industry-led standards. We will work with other frontier labs to advance frontier AI standards, building a voluntary effort now, with or without government support.

Building global standards. We will advocate for compatible international approaches to measuring capabilities, managing risk, preserving human control, and determining when and how development should slow or stop, even if that means slowing the advancement of model capabilities.

Our Chief Scientist Jakub Pachocki recently wrote⁠ that the rapid rise of machine intelligence, including the potential of recursive self-improvement, calls for “extreme caution.” OpenAI will continue pursuing technical solutions to alignment and monitoring, building defensive systems, and slowing development when necessary. But technical work inside individual labs will not be enough. We also need shared standards, including regarding when development should slow or stop.

The stakes are enormous. Advanced AI could accelerate the development of new medicines⁠, strengthen critical infrastructure⁠, expand economic opportunity⁠, and help solve scientific problems⁠ that have resisted generations of human effort. But the capabilities that make models more useful also come with risks, and they will not remain confined to a few frontier laboratories. Models developed around the world, including open models, will increasingly approach today’s frontier and become broadly available.

Astra’s capabilities, the early evidence of AI-driven research acceleration, and Jakub’s essay all point in the same direction: AI is advancing quickly, and policy needs to move with it.

Greg Brockman has described a “defenders window”⁠: a limited period when frontier AI can help defenders strengthen critical systems before powerful offensive capabilities become widespread. Policymakers face an analogous moment: a closing window to establish durable safeguards before AI capabilities outpace the institutions responsible for governing them.

As capabilities grow, confidence in safety must increasingly set the pace of AI progress. Safety does not stand in the way of progress; it is what allows progress to go further and benefit more people.

We have strengthened monitoring, alignment, and security safeguards across the model-development lifecycle, including stronger isolation for frontier research workloads, expanded monitoring of model behavior during tool-enabled training and evaluations, and clearer rules for when to escalate concerns. For Astra, we also introduced universal monitoring of full trajectories, including chains of thought, and a mandatory alignment-evaluation gate before broader internal deployment.

Those safeguards must continue to stay ahead of capabilities. When proceeding would pose an unacceptable safety risk, we will slow or stop the development or deployment of systems we cannot sufficiently safeguard, as we have done before and as required per our preparedness framework⁠.

Fully autonomous recursive self-improvement—in which AI systems independently drive successive generations of increasingly capable AI—is not happening today. We should