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Director of AI Policy and Chief Economist at the Foundation for American Innovation
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Location: Washington, DC
ai-governance (5)
ai-regulation (5)
regulations (5)
policy (4)
ai-policy (3)
international-relations (3)
law (2)
trade (2)
transparency (2)
ai (1)
ai-safety (1)
china (1)
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Top
New
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Samuel Hammond
votes Against
and says:
The federal government is really the only entity powerful enough to be a real check on these companies. If they become joined at the hip, that check goes away. It can easily become a form of regulatory capture.
AI Verified source (Jul 2, 2026) -
Samuel Hammond
votes For
and says:
For too long, the United States has borne the economic and political costs of semiconductor export controls while leaving room for foreign suppliers to free ride, backfill, or slow-roll their alignment. The MATCH Act creates a simple rule: either our...
more AI Verified source (Apr 8, 2026) -
Samuel Hammond
votes For
and says:
Either our allies match our controls on the most important tools and components for advanced semiconductors, or the United States closes the loopholes itself.
AI Verified source (Apr 8, 2026) -
Frontier AI labs should be required to contribute a share of their equity to a national fund that pays a dividend to every citizen
30 opinions
Samuel Hammond
votes Against
and says:
Even if taking partial ownership of frontier AI companies can make sense on paper, in practice it's a recipe for political favoritism and corruption.
AI Verified source (2026) -
Samuel Hammond
votes For
and says:
The Chip Security Act (CSA) is a bipartisan bill that would require high-end AI chips to have the capability to verify their location before being exported. [...] With the confidence that sensitive exports are going to their intended foreign buyers, ...
more AI Verified source (Jul 18, 2025) -
Mandate reporting of AI training runs above 10²⁶ FLOPs to a designated national or international authority
25 opinions
Samuel Hammond
votes For
and says:
to train models beyond a sufficiently high threshold of compute should be required to pre-register training runs [...] a threshold of 10^26 FLOPs would likely suffice.
Unverifiable source (2023) -
Samuel Hammond
votes For
and says:
LLM scaling laws also suggest the computing resources required to train large models are a reasonable proxy for model power and generality. Consistent with our argument for refining the definition of AI systems, the NTIA should thus consider defining...
more AI Verified source (2023)