AI diffusion is the spread and adoption of AI systems across countries, industries, and institutions. In the context of US-China competition, diffusion is framed as the process that determines whose systems become the global default, and therefore whose values, governance norms, and data practices define the digital order. Geopolitics in the Age of Artificial Intelligence argues that diffusion may matter as much as or more than frontier capability, particularly in scenarios where breakthroughs are easy to copy.
Geopolitical dynamics
The argument advanced in Geopolitics in the Age of Artificial Intelligence is that the systems that take root first shape technical standards, create dependency, and define governance norms, and that this gives diffusion strategic weight independent of who holds the capability lead. In this account, early deployment creates lock-in through familiarity, integration, and switching costs, conferring a first-mover advantage. Beijing is described as treating AI governance itself as a strategic export, bundling systems with financing and development projects.
Several factors are identified as the substrate of diffusion. Infrastructure — data centers, networks, and regionally tailored systems — determines where systems can be deployed. Development finance shapes which markets receive systems at all: institutions such as the US Development Finance Corporation could fund deployment in markets the private sector will not serve. The choice between open-source and proprietary distribution is also treated as a diffusion lever; open-sourcing safe models is presented as potentially the best diffusion strategy when copying is easy, corresponding to Worlds 7 and 8 in the Eight Worlds Framework.
Domestic and organizational diffusion
Beyond cross-border diffusion, the spread of AI within organizations is treated as a separate problem. Stanford HAI research (Karunakaran et al.) found that corporate AI project success depends on three organizational variables rather than technical capability alone:
- Jurisdictional clarity — whether there is a well-defined group of domain experts.
- Task centrality — whether the target task is core to the experts' daily work.
- Task enactment homogeneity — whether experts perform the task the same way.
According to the study, AI adoption succeeds when all three conditions are favorable and projects fail when they are not, even with the same development team. The finding is framed as evidence that AI readiness is as much a matter of organizational structure as of technology or talent supply. (Source: hai.stanford.edu)
Relation to other concepts
Diffusion interacts with several other policy and technical concepts. It stands in tension with Export Controls (AI): controls can slow the diffusion of US systems abroad, potentially ceding ground to China. It depends on Compute Governance, because infrastructure determines where systems can be deployed. Its importance varies across the Eight Worlds Framework, where it is central in Worlds 5, 7, and 8 and important but secondary in Worlds 1 and 2. Emergent Misalignment complicates open diffusion strategies: if fine-tuning open models can produce dangerous behavior, then open distribution carries risk.
Relationships
- depends-on: Compute Governance
- related: Export Controls (AI)
- related: Eight Worlds Framework
- related: Emergent Misalignment
Sources
- Geopolitics in the Age of Artificial Intelligence (Foreign Affairs, 2026-01-27)
- Why Corporate AI Projects Succeed or Fail (Source: hai.stanford.edu) (Stanford HAI)