The only viable path for AI regulation is to treat it as a dual-use technology, similar to nuclear energy. Governments must clearly delineate and control 'weapons-grade' AI while fostering innovation in 'civilian use' AI. A failure to do so risks either falling behind in a global arms race or allowing dangerous capabilities to proliferate.

Early nuclear scientists like Oppenheimer acted as influential advisors, while actual regulatory work was done by engineers and technicians. This separation of "futurist thinkers" from "practical implementers" could reduce conflicts of interest and politicization in AI regulation, making the task more achievable and less ideological.

The belief that AI development is unstoppable ignores history. Global treaties successfully limited nuclear proliferation, phased out ozone-depleting CFCs, and banned blinding lasers. These precedents prove that coordinated international action can steer powerful technologies away from the worst outcomes.

The debate on AI regulator qualifications is misplaced. Rather than seeking futurists, the government can train technically competent individuals to enforce safety protocols, mirroring how the Navy trains young personnel to manage nuclear submarine reactors. This separates the job of day-to-day safety from high-level threat prediction.

The US nuclear weapons industry operates as a hybrid: the government owns the IP and facilities, but private contractors like Honeywell and Boeing operate them and build delivery systems. This established public-private partnership model could be applied to manage the risks of powerful, privately-developed AI.

Ben Horowitz revealed that Biden administration officials defended the idea of regulating AI—which he framed as "regulating math"—by citing the precedent of classifying nuclear physics in the 1940s. This suggests a governmental willingness to treat core algorithms as controlled, classifiable technology, potentially stifling open innovation.

For AI safety, Demis Hassabis advocates for an international regulatory body, similar to the International Atomic Energy Agency. This body would have technical experts who audit frontier models against agreed-upon benchmarks, checking for undesirable properties like deception and ensuring public confidence through independent verification.

The history of nuclear power, where regulation transformed an exponential growth curve into a flat S-curve, serves as a powerful warning for AI. This suggests that AI's biggest long-term hurdle may not be technical limits but regulatory intervention that stifles its potential for a "fast takeoff," effectively regulating it out of rapid adoption.

As governments increasingly rely on AI for rapid decision-making, they will need AI advisory systems. A critical gap exists for non-profit or public-good 'AI chief of staff' tools. This prevents a conflict of interest where governments depend on AI built by the very companies they are tasked with monitoring.