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

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.

Here’s what we’re doing:

- 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” ⁠ (opens in a new 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 ⁠ (opens in a new window) .

Preparing for recursive self-improvement

Fully autonomous recursive self-improvement—in which AI systems independently drive successive generations of increasingly capable AI—is not happening today. We should not pursue it unless and until it can be done safely.

However, AI is already accelerating parts of the research used to develop and align the next generation of models. Our latest research ⁠ shows that AI agents can perform some tasks that would take skilled researchers several days. This is not recursive self-improvement, but it is evidence of the direction of travel.

That acceleration can and must also be directed toward safety. Our aim is to safely build automated AI researchers that work under human supervision to advance both deep learning and alignment—using each generation of AI to help make the next one safer, more aligned, and easier to control, not simply more capable.

Governments should develop common ways to measure this progress, preserve meaningful human control, and establish shared safety bars for when and how development should slow or stop. If we cannot meet certain safety bars without slowing down capability growth, we should prioritize the former. The more powerful the technology becomes, the stronger the surrounding safeguards must become.

Working with Congress on mandatory national AI safety requirements

The prospect of AI-accelerated AI development demands more than voluntary commitments. The United States needs mandatory, capability-based national regulation that can evolve as the technology does.

Our Blueprint for Democratic Governance of Frontier AI ⁠ lays out a path toward a durable federal framework: common testing and independent-assessment requirements, stronger cybersecurity protections, clear incident-reporting rules, greater national preparedness, and shared measures for tracking progress toward recursive self-improvement.

Several serious frontier safety proposals are now taking shape in Congress. We will continue to engage constructively and expect to support legislation that materially raises the safety bar. With stakes this high, we cannot let the perfect become the enemy of the good. Congress should act before it adjourns.

A national framework should be strong but carefully targeted. Frontier safety requirements should apply to the handful of well-resourced laboratories developing the most capable systems—not to startups, small developers, or researchers operating nowhere near the frontier. Obligations should be proportionate to capabilities and risks.

Nor should frontier safety policy become open-weights policy by another name. Open models can be part of the solution, particularly in cybersecurity and where sovereignty, security, or data-residency needs favor local deployment. Most compete not at the frontier, but on cost, control, and latency. As we affirmed in signing the Open Weights and American AI Leadership letter ⁠ (opens in a new window) , America needs both open and closed models. A federal framework should address frontier capabilities and risks without weakening competition, entrenching incumbents, or driving innovation overseas.