Let The People Set The Pace Of Frontier AI

Hélène Landemore is the Damon Wells ‘58 professor of political science at Yale University and distinguished researcher at the Oxford Institute for Ethics in AI. Audrey Tang is Taiwan’s cyber ambassador and founding minister of digital affairs, a Carnegie distinguished fellow at Columbia University School of International and Public Affairs, a senior fellow at the Oxford Institute for Ethics in AI, a senior research fellow at the Collective Intelligence Project and a 2025 Right Livelihood Laureate. Those building the most powerful AI systems in the world are increasingly telling us that the race to build them may be moving too fast.

But there is a problem: They cannot simply agree to slow down. On Sept. 12, Anthropic CEO Dario Amodei called for an easing of the pace at which AI models improve and committed his company to giving independent, third-party evaluators ongoing, employee-like access to its systems. Amodei further gestured toward “democratic coordination” on shared safety limits and “global coordination” on the international stage.

Within hours, OpenAI CEO Sam Altman agreed that “we need to pace the frontier” and made the same evaluator pledge. Elon Musk’s response was three words: “Dario is right.” This apparent convergence among the leaders of the frontier AI labs is striking. It comes after months of mounting concern inside and outside the industry: AI models have begun to find ways around safeguards and test environments; researchers have left frontier labs amid disagreements over catastrophic and extinction-level risks; and more than 1,300 employees at leading AI companies have called on Washington to back an international slowdown.

But even if the CEOs agree, the race does not stop. The reason is structural. The two governments with the greatest capacity to shape the frontier AI race — in Washington and Beijing — have powerful incentives not to be the first to slow down.

Each has reason to fear that restraint on their part will simply hand a strategic advantage to the other. What we are facing, in other words, is a double collective-action problem, one nested under the other. The two are linked: “If we slow down, China wins” is the main argument American labs and government officials use against binding regulation.

The outer race is the inner one’s best excuse; end the global race, and the domestic one loses cover. The good news is that, contrary to frequent depictions, neither is a simple prisoner’s dilemma, in which each player’s best strategy is to defect no matter what others do, and also that the only solution is necessarily some sort of global regulatory leviathan. Instead, both collective-action problems look more like what game theorists call a “stag hunt,” after a parable of Jean-Jacques Rousseau’s.