What Should Be Internationalised in AI Governance?

As artificial intelligence (AI) advances, states increasingly recognize the need for international governance to address shared benefits and challenges. However, international cooperation is complex and costly, and not all AI issues require cooperation at the international level. This paper presents a novel framework to identify and prioritize AI governance issues warranting internationalization. We analyze nine critical policy areas across data, compute, and model governance using four factors which broadly incentivize states to internationalize governance efforts: cross-border externalities, regulatory arbitrage, uneven governance capacity, and interoperability. We find strong benefits of internationalization in compute-provider oversight, content provenance, model evaluations, incident monitoring, and risk management protocols. In contrast, the benefits of internationalization are lower or mixed in data privacy, data provenance, chip distribution, and bias mitigation. These results can guide policymakers and researchers in prioritizing international AI governance efforts.