AnthropicAnnouncement
Ben Bernanke appointed to Anthropic’s Long-Term Benefit Trust
Summary
Anthropic’s Long-Term Benefit Trust (LTBT) appointed Dr. Ben Bernanke, former Federal Reserve Chair and Brookings Institution Distinguished Fellow, as its newest member. The independent body helps hold Anthropic to its mission of responsibly developing advanced AI for humanity’s long-term benefit, and its Trustees advise the board and leadership on critical decisions involving AI’s risks and societal impacts. Bernanke brings research and policy experience spanning the Great Depression, financial crises, and his 2006–2014 Federal Reserve tenure; he will contribute to Anthropic’s economic research, including work on how AI changes economies and workforces. The LTBT can appoint members to Anthropic’s board, while its Trustees hold no company equity or profit share and are compensated only for their service.
Context
The LTBT exists to help Anthropic maintain a long-term balance between commercial success and social and public good as a Public Benefit Corporation. It provides an independent check on how Anthropic develops and deploys AI, including decisions involving potential risks and societal impacts.
Approach / What changed
The LTBT appointed Ben Bernanke as a Trustee. He will contribute his economic and policy expertise to Anthropic’s economic research and other work, while advising the board and leadership on critical decisions involving advanced AI.
Takeaways
Ben Bernanke served as Federal Reserve Chair from 2006 to 2014, studied the Great Depression and financial crises, and received the 2022 Nobel Prize in Economic Sciences.
LTBT Trustees are independent of Anthropic’s management and investors: they hold no equity, do not share in profits, and are compensated only for their time and service.
The LTBT has authority to appoint members to Anthropic’s board and advises the company’s leadership on AI risks and societal impacts.
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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