Source: before the Hugging Face incident, OpenAI was negotiating a legally binding deal with Anthropic for the companies to stress-test each other's models
The Information:
Context & Ripple Effects
The reported talks build on OpenAI and Anthropic's 2025 publication of joint model-safety tests, which was designed to expose gaps in each lab's internal evaluations. They also sit alongside the companies' 2024 agreement to give the US AI Safety Institute early access to major models.
OpenAI said on September 15 that it had been working with Anthropic and Google on AI safety without needing an antitrust waiver. A legally binding bilateral arrangement, if completed, would make the OpenAI-Anthropic part of that cooperation more formal; the negotiations themselves remain reported, not confirmed.
First-order effects
- A binding agreement would give OpenAI and Anthropic a contractual framework for reciprocal stress-testing, extending their earlier voluntary joint evaluations.
- OpenAI and Anthropic would each face outside scrutiny from a direct frontier-model rival in addition to their own internal testing and US government evaluation channels.
Second-order effects
- The reported pact would reinforce OpenAI and Anthropic's lobbying position that government model reviews should apply to lagging competitors as well, making shared evaluation a more visible baseline for frontier labs.
- Other AI developers seeking comparable safety credibility would face pressure to show either independent access for evaluators or similarly credible cross-lab testing arrangements.
Third-order effects
- If formal reciprocal testing spreads, frontier-model governance would rely less exclusively on company self-assessment and more on overlapping private peer review and state-access mechanisms.
- The emerging structure favors labs able to grant controlled model access to trusted counterparts and public evaluators, making evaluability part of competition among leading developers.
The trend: Frontier AI safety is moving toward institutionalized external evaluation, combining cross-lab scrutiny with government access to advanced models.