The AI Divide

Why It Matters

The concentration of AI talent and resources in two superpowers threatens to reshape global competitiveness, forcing other economies to rely on external technology and policy decisions. Policymakers must address this imbalance to preserve sovereignty and innovation.

Key Takeaways

• US and China hold 70% top AI researchers.
• They control 90% global AI computing capacity.
• They attract over half of worldwide AI funding.
• Other nations risk technological dependency on AI superpowers.
• Diversifying talent and infrastructure safeguards national AI sovereignty.

Pulse Analysis

The United States and China have amassed an unprecedented share of artificial‑intelligence assets. Together they employ roughly 70 percent of the world’s elite machine‑learning researchers, own about 90 percent of the high‑performance computing infrastructure, and attract more than half of global AI venture capital. This concentration is not merely a statistical curiosity; it reflects a structural shift where the most advanced models, data pipelines, and talent pipelines are tightly bound to two geopolitical blocs, leaving the rest of the world with limited access to cutting‑edge capabilities.

For nations outside the U.S.–China axis, the emerging AI divide translates into strategic vulnerability. Dependence on foreign AI platforms can dictate domestic policy choices, constrain critical‑infrastructure security, and erode competitive advantage in sectors ranging from finance to defense. Moreover, the speed of AI model iteration and the scale of training data required make it increasingly difficult for lagging economies to develop homegrown alternatives, effectively turning them into technological vassals. This dynamic reshapes trade relationships, amplifies geopolitical leverage, and raises urgent questions about global governance of AI ethics and standards.

Addressing the imbalance calls for coordinated policy action and investment. Regional AI hubs, public‑private research consortia, and targeted education programs can nurture homegrown talent and reduce reliance on external compute resources. Multilateral frameworks that promote data sharing, joint standards, and equitable access to AI tools are essential to prevent a monopolized landscape. By diversifying talent pipelines and building sovereign compute capacity, countries can safeguard their economic autonomy while contributing to a more inclusive, resilient global AI ecosystem.