米国が州・連邦レベルでAIの安全性推進に取り組むThe US is advancing AI safety through state and federal action

The United States is increasingly treating artificial intelligence safety as a matter of public policy rather than voluntary corporate discretion, and OpenAI has weighed in on how that governance should take shape. In a recent blog post, the company outlined what it calls a "reverse federalism" approach, in which state laws help construct a national framework for safe and democratic AI. The framing matters because it stakes out a position on one of the most contested questions in technology policy: whether AI rules should be set primarily in Washington, in state capitals, or through some combination of the two.

The term "reverse federalism" inverts the usual direction of policy flow. In conventional federalism, the national government sets a baseline and states fill in details. OpenAI's phrasing suggests the opposite dynamic, in which experimentation and rulemaking at the state level accumulate into a coherent national standard over time. Under this reading, individual state laws are not obstacles to a unified system but building blocks for it, allowing different jurisdictions to test approaches that can later inform federal action. The emphasis on "safe, democratic AI" appears intended to align the company's stance with broader public-interest goals rather than narrow commercial ones.

This position sits within an active and sometimes fragmented regulatory landscape. Several US states have already advanced their own AI legislation, addressing issues such as automated decision-making, transparency requirements, deepfakes, and consumer protections. California, Colorado, and other states have pursued measures targeting high-risk AI systems and disclosure obligations, while lawmakers elsewhere continue to introduce bills at a steady pace. The result is a patchwork that industry participants often describe as difficult to navigate, since companies operating nationally must reconcile potentially divergent obligations. OpenAI's argument appears to be that this patchwork, rather than being purely a liability, can seed a national framework if the individual efforts are coordinated toward common principles.

At the federal level, momentum has been more uneven. Congress has held numerous hearings and floated various proposals, but comprehensive AI legislation has been slow to materialize. Executive actions and agency guidance have filled some of the gap, and standards bodies such as the National Institute of Standards and Technology have produced influential voluntary frameworks, including its AI Risk Management Framework. These efforts provide reference points that both states and companies can adopt, and they help explain why a bottom-up approach is plausible: shared technical vocabulary and risk-assessment methods already exist even in the absence of binding national law.

The debate also reflects a longstanding tension between innovation and oversight. Proponents of lighter-touch or unified rules argue that a thicket of state requirements could raise compliance costs and slow deployment, particularly for smaller developers. Advocates for stronger state authority counter that local governments are often faster to respond to emerging harms and closer to affected communities. OpenAI's reverse-federalism framing attempts to bridge these camps, presenting state action as complementary to, rather than in competition with, eventual national coordination. Whether policymakers embrace that interpretation remains uncertain, and the approach is likely to draw scrutiny from those who favor a single federal standard preemptively.

For context, OpenAI is one of several major AI developers that have publicly engaged with governments on safety and governance, alongside companies such as Anthropic, Google, and Microsoft. Many of these firms have signed onto voluntary commitments and participated in safety institutes established in the US and abroad. The company's public policy positions are worth reading in that light: they are contributions to an ongoing negotiation over who writes the rules and how strict they should be, not settled outcomes.

The practical significance of this post is that it signals how a leading developer wants the regulatory conversation to unfold. If the reverse-federalism idea gains traction, it could shape how state legislatures coordinate and how future federal proposals are structured. If it does not, the underlying patchwork dynamic is likely to persist regardless. Either way, the framing underscores that AI safety in the United States is being negotiated across multiple levels of government simultaneously, and that industry voices are actively trying to influence the shape of the resulting framework.