Large language models have become a focal point of U.S.–China political tension, but they also represent a rare opportunity where cooperation—rather than rivalry—could yield outsized global benefits. At the core of this opportunity is the alignment problem: ensuring that increasingly capable AI systems reliably act in ways that are safe, interpretable, and beneficial to humanity. While current discourse often frames frontier models as instruments of geopolitical competition, a cooperative approach between U.S. and Chinese researchers could meaningfully accelerate progress on alignment while reducing the risks associated with unchecked escalation.

U.S. frontier models such as ChatGPT, Gemini, and Claude are typically deployed as closed, service-based systems. Their developers emphasize centralized control, continuous updates, and safety guardrails enforced through APIs. This structure enables consistent policy enforcement and rapid mitigation of harmful behaviors, but it also limits transparency and concentrates decision-making power within a small number of firms and governments. On the flip side, Chinese models like DeepSeek have gained attention for their comparatively open technical posture, including the release of model weights and detailed training disclosures. This openness allows broader experimentation and local deployment, albeit often under different state-aligned constraints regarding content and use.

Rather than viewing these approaches as incompatible, cooperation could make them complementary. Alignment research benefits from diversity: different cultural values, evaluation benchmarks, and failure modes reveal weaknesses that a single national ecosystem might overlook. Joint research on interpretability, robustness, and value-learning could help identify universal alignment principles, while disagreements would force clearer articulation of assumptions about safety, governance, and acceptable risk. Historically, scientific collaboration during periods of political tension—from nuclear arms control to climate science—has reduced misunderstanding and improved global stability; AI alignment could follow a similar path.

Nvidia’s GPUs and the export controls surrounding them further complicate this landscape but also underscore the case for cooperation. Current restrictions aim to slow China’s access to cutting-edge compute, reinforcing a zero-sum dynamic in which hardware scarcity drives secrecy, duplication, and workaround strategies. In a cooperative framework, shared safety research and agreed-upon limits on the most dangerous training runs could reduce incentives to race for scale at any cost. This would allow both countries to redirect resources from redundancy toward joint standards, evaluation methods, and safety infrastructure—areas where progress benefits everyone and does not meaningfully undermine national security.

Ultimately, framing LLM development purely as a strategic competition risks producing models that are powerful but poorly aligned and deployed hastily to secure advantage. Greater cooperation between U.S. and Chinese scientists would not eliminate political differences, but it could lower mistrust, slow destabilizing races, and accelerate solutions to alignment challenges that no single country is likely to solve alone. In doing so, both nations—and the world—stand to gain far more from AI than they would from rivalry alone.