AnthropicAnnouncement
Mariano-Florentino Cuéllar appointed to Long-Term Benefit Trust
Summary
Anthropic’s Long-Term Benefit Trust has appointed Mariano-Florentino (Tino) Cuéllar as a new member, adding experience in law, governance, international affairs, and technology’s effects on public institutions. Cuéllar is a former Justice of the Supreme Court of California, former leader of Stanford’s Freeman Spogli Institute, current President of the Carnegie Endowment for International Peace, and chair of the William & Flora Hewlett Foundation; he plans to leave Carnegie in July 2026 for Stanford roles. The independent Trust selects members of Anthropic’s Board of Directors and advises Anthropic’s Board and leadership on maximizing advanced AI’s benefits and mitigating its risks, with no Trustee holding a financial stake in Anthropic. Kanika Bahl and Zachary Robinson, who served since the Trust’s founding, concluded their terms.
Context
The Long-Term Benefit Trust is intended to help Anthropic achieve its public benefit mission and advise on advanced AI’s benefits, risks, and implications for global health, international security, democratic institutions, and society.
Approach / What changed
The Trust appointed Cuéllar as a Trustee and announced that Kanika Bahl and Zachary Robinson had concluded their terms. Cuéllar will contribute experience spanning law, governance, international affairs, public institutions, and technology’s societal effects.
Takeaways
Cuéllar has served as a Justice of the Supreme Court of California, led Stanford’s Freeman Spogli Institute, and is President of the Carnegie Endowment for International Peace.
The Trust selects members of Anthropic’s Board of Directors and advises the Board and leadership on maximizing advanced AI’s benefits and mitigating its risks.
Kanika Bahl and Zachary Robinson joined the Trust at its founding and concluded their terms after contributing to its formative governance work.
Source and freshness
- Source
- anthropic.com
- Published
- Added to Yomu
Summaries are generated from the original by a language model. Check the original before relying on it.
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