Companies across the world are turning to Chinese open-weight models that now deliver near-frontier capability at a fraction of the cost, trailing the frontier (Anthropic’s) by roughly eight months. Even this claim is changing fast with Moonshot AI’s Kimi K3 beating Anthropic’s Fable 5 on some coding-related benchmarks. These developments have amplified the question of AI sovereignty for India: In the absence of a domestic frontier model, are Chinese open-source models just as problematic as US proprietary models?

Our argument is that the sovereignty debate should not treat AI as a monolith. A proprietary model reached through a foreign application programming interface (API) and an open-weight model running on Indian hardware create fundamentally different dependencies. Despite structural tensions between India and China, the cheaper Chinese option might be the more sovereign one. We explain why.

When a ministry or a bank builds a workflow on Claude or ChatGPT, every query is processed by a company subject to US jurisdiction. When access to frontier models is being turned on and off based on national policy, this matters as the dependence is continuous — if an export control or sanctions decision were to remove access tomorrow, the workflow would break.

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Now consider an alternative. An Indian financial services firm runs a Chinese open-weight model on graphics processing units (GPUs) rented from data centres in India. What exactly is the sovereignty risk here? The model weights are a static file of parameters sitting on Indian hardware. The data never leaves Indian territory. The Chinese creator of the model has no ongoing connection to........

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