Between-country issues: global inequality
There is an even greater divide between the countries currently leading in AI and those falling behind. While AI is widely considered a national priority, with almost 40% of countries having created an AI strategy [437], the implementation of these strategies depends on scarce resources, including trained STEM talent and computing power. These resources are predictably concentrated: 59% of leading AI researchers currently work in the US, and another 20% in China and Europe [372]. Figure 9 shows post-college migration among AI researchers who have published at one top conference, as of 2019.
ENTITY
3 - Other
INTENT
3 - Other
TIMING
3 - Other
Risk ID
mit887
Domain lineage
6. Socioeconomic and Environmental
6.1 > Power centralization and unfair distribution of benefits
Mitigation strategy
1. Establish and fund context-specific AI Capacity Building Programs in developing nations to cultivate indigenous research, development, and governance capabilities. This strategic investment in local STEM talent and educational infrastructure is essential for shifting countries from being AI consumers to sovereign AI builders, directly counteracting the concentration of human capital. 2. Implement global resource-sharing mechanisms, such as subsidized "Green Compute-as-a-Service" models and open-source AI initiatives, to ensure equitable access to high-performance computing infrastructure and locally representative, inclusive training datasets. This directly addresses the material resource scarcity that currently impedes the implementation of national AI strategies. 3. Mandate and enforce transparent international governance and regulatory frameworks designed to monitor and mitigate the risks of global AI power centralization and monopolistic practices. This systemic approach is necessary to ensure the equitable distribution of AI benefits and align technological progress with Sustainable Development Goal \#10 (Reduced Inequalities).