Global AI R&D divide
Large companies in countries with strong digital infrastructure lead in general- purpose AI R&D, which could lead to an increase in global inequality and dependencies. For example, in 2023, the majority of notable general- purpose AI models (56%) were developed in the US. This disparity exposes many LMICs to risks of dependency and could exacerbate existing inequalities.
ENTITY
3 - Other
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit1028
Domain lineage
6. Socioeconomic and Environmental
6.1 > Power centralization and unfair distribution of benefits
Mitigation strategy
1. Establish a **Global AI Research Resource (GARR)** or similar federated framework to provide equitable, subsidized access to state-of-the-art computational infrastructure, diverse datasets, and essential software/tools for researchers, startups, and public institutions in Low- and Middle-Income Countries (LMICs). This directly addresses the resource disparity driving the R\&D concentration. 2. Implement targeted, scalable **capacity-building programs** in LMICs to rapidly enhance expertise in core AI development, deployment, and ethical oversight. These programs should prioritize advanced technical skills, data science literacy, and interdisciplinary training for local workforces and policymakers, enabling them to transition from passive consumers to active innovators. 3. Promote international collaboration to support the development and adoption of **human-centered AI governance frameworks** in LMICs. These frameworks must be context-aware, prioritize the alignment of AI adoption with sustainable development goals (e.g., reducing inequality), and establish mechanisms to ensure accountability and the broad, fair distribution of AI-derived economic benefits.