Minimum Standards for AI Transparency Below are the core tenets we believe should guide AI transparency policy:

Limit Application to the Largest Model Developers: AI transparency should apply only to the largest frontier model developers that are building the most capable models - where frontier models are distinguished by a combination of thresholds for computing power, computing cost, evaluation performance, annual revenue and R&D. To avoid burdening the startup ecosystem and small developers with models at low risk to national security or for causing catastrophic harm, the framework should include appropriate exemptions for smaller developers. We welcome input from the start-up community on what those thresholds should be. Internally, we've discussed the following examples for what the threshold could look like: annual revenue cutoff amounts on the order of $100 million; or R&D or capital expenditures on the order of $1 billion annually. These scoping thresholds should be periodically reviewed as the technology and industry landscape evolves. Create a Secure Development Framework: Require covered frontier model developers to have a Secure Development Framework that lays out how they will assess and mitigate unreasonable risk in a model. Those risks must include the creation of chemical, biological, radiological and nuclear harms, as well as harms caused by misaligned model autonomy. Secure Development Frameworks are still an evolving safety tool, so any proposal should strive for flexibility. Make the Secure Development Framework Public: The Secure Development Framework should be disclosed to the public, subject to reasonable redaction protections for sensitive information, on a public-facing website registered to and maintained by the AI company. This will enable researchers, governments, and the public to stay informed about the AI models deployed today. The disclosure should come with a self-certification that the lab is complying with the terms of their published Secure Development Framework. Publish a System Card : System cards or other documentation should summarize the testing and evaluation procedures, results and mitigations required (subject to appropriate redaction for information that could compromise public safety or the safety and security of the model). The system card should also be publicly disclosed at deployment, and updated if the model is substantially revised. Protect Whistleblowers by Prohibiting False Statements: Explicitly make it a violation of law for a lab to lie about its compliance with its framework. This clarification creates a clear legal violation that enables existing whistleblower protections to apply and ensures that enforcement resources are squarely focused on labs that have engaged in purposeful misconduct. Transparency Standards: A workable AI transparency framework should have a minimum set of standards so that it can enhance security and public safety while accommodating the evolving nature of AI development. Given that AI safety and security practices remain in their early stages, with frontier developers like Anthropic actively researching best practices, any framework must be designed for evolution. Standards should begin as flexible, lightweight requirements that can adapt as consensus best practices emerge among industry, government, and other stakeholders.