The US is advancing AI safety through state and federal action

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OpenAI published a blog post outlining its preferred approach to US AI governance, using the term 'reverse federalism.' In conventional federalism, federal law sets minimum standards and states may add requirements above that floor. In OpenAI's proposed reverse model, state laws serve as an experimentation layer that informs what eventually becomes national policy — states test approaches first, a federal framework follows from that evidence. The company positions this as advancing both AI safety and democratic accountability, framing the bottom-up governance path as more responsive to public input than top-down federal preemption. The stance aligns with OpenAI's broader pattern of engaging US policy processes directly rather than resisting regulation. Practically, the approach elevates the significance of state-level AI bills already in motion — California, Colorado, Texas, and others — as potential inputs to a national framework rather than temporary measures pending federal action. Important caveat: the source content available for this article is extremely thin — only a 139-character description was extracted from the OpenAI Blog post. The full policy arguments, specific proposals, and any legislative endorsements are not visible in the available excerpt. Importance is scored conservatively as a result.