The US focus on exporting hardware (chips, data centers) over proprietary models suggests a strategic belief that open-source AI will eventually dominate. If models become a free commodity, the most valuable and defensible part of the AI stack becomes the underlying compute infrastructure.

While commendable, an AI company's refusal to sell models for controversial uses like mass surveillance is a temporary solution. Technology diffusion is so rapid that within 12-18 months, open-source models will match today's frontier capabilities. A government seeking these tools can simply wait and use a widely available open-source alternative, making individual corporate 'red lines' ultimately ineffective.

Instead of competing to build sovereign AI stacks from the chip up, India's strategic edge is in applying commoditized AI models to its unique, population-scale problems. This leverages the country's deep experience with real-world, large-scale implementation.

Nations are moving beyond the rhetoric of 'sovereign AI.' It now represents a concrete strategy to secure bargaining power across the AI stack through diverse means like domestic substitution (China), regulation (Europe), and infrastructure hosting (Gulf states).

The open vs. closed source debate is a matter of strategic control. As AI becomes as critical as electricity, enterprises and nations will use open source models to avoid dependency on a single vendor who could throttle or cut off their "intelligence supply," thereby ensuring operational and geopolitical sovereignty.

The Department of War's top AI priority is "applied AI." It consciously avoids building its own foundation models, recognizing it cannot compete with private sector investment. Instead, its strategy is to adapt commercial AI for specific defense use cases.

Contrary to past momentum, the most advanced AI startups are increasingly adopting and fine-tuning open-source models. This shift is driven by the need for cost-effective speed and deep customization as their workloads mature and scale.

For many companies, 'AI sovereignty' is less about building their own models and more about strategic resilience. It means having multiple model providers to benchmark, avoid vendor lock-in, and ensure continuous access if one service is cut off or becomes too expensive.

The United States lacks a coherent national strategy for open-source AI, while China is rapidly producing high-quality models. This has created a situation where American companies are increasingly turning to Chinese-developed models to make their AI pipelines more efficient and competitive.