India’s Pursuit of a Sovereign Foundational AI Model

Context

In 2023, discussions arose regarding India's necessity to develop its own sovereign foundational AI model, particularly in response to remarks on the high cost of training AI models like those that power ChatGPT. The debate revolves around whether India should invest hundreds of millions of dollars into developing its own AI capabilities or leverage existing open-source models. The IndiaAI Mission, launched by the IT Ministry, has committed to advancing AI infrastructure, raising key questions about sovereignty, economic viability, and technological self-reliance.

Need for a Sovereign Foundational AI Model

• Technological Sovereignty: Ensuring independence from external AI providers, avoiding reliance on proprietary models controlled by foreign entities.
• National Security Concerns: Potential export controls and sanctions on AI-related technologies, such as high-performance chips, could impact India’s AI ecosystem.
• Economic and Strategic Advantage: Developing a domestic AI model can foster local innovation, employment, and industry growth.
• Customization for Local Needs: Creating AI solutions tailored for Indian languages, governance systems, and societal needs, reducing dependence on foreign technology.

However, concerns exist about the feasibility of such an investment. Developing a foundational AI model requires massive computational power, high financial investment, and sustained research efforts.

Challenges in Building a Foundational AI Model

1. High Cost of Training AI Models

   • Training large language models costs hundreds of millions of dollars.
   • Example: DeepSeek V3’s training cost was $5.6 million, and top AI labs invest $80 billion annually in infrastructure.
   • The cost of hiring AI experts, maintaining data centers, and running high-performance computing clusters adds to the financial burden.

2. Hardware Dependency

   • AI training requires high-end GPUs, primarily manufactured by Nvidia, AMD, or Huawei.
   • India lacks domestic semiconductor manufacturing capabilities and depends on imports for AI hardware.

3. Market Size Constraints

   • The AI market is heavily dominated by the U.S., where businesses readily invest in AI.
   • In India, AI adoption remains limited due to lower per capita income and business investment in automation.

4. Research and Development Gaps

   • India’s AI research ecosystem lacks institutional autonomy, funding, and long-term investment planning.
   • Public procurement policies do not support high-risk, high-reward AI research.

Government’s Role and Policy Initiatives

The IndiaAI Mission has announced several measures, including:

• Providing subsidized GPU clusters to startups and academia to encourage AI research.
• Focusing on AI for Bharat, which prioritizes local language AI models and speech recognition systems.
• Promoting responsible AI development by ensuring ethical and legal frameworks for AI governance.

While subsidized access to AI infrastructure is a positive step, India’s AI investments are significantly lower than global AI leaders, making it difficult to compete with firms investing billions in AI research.

Conclusion

India must balance technological ambition with economic pragmatism. While a sovereign foundational AI model may offer strategic advantages, the investment must be justified by clear economic returns and technological feasibility. Rather than directly competing with global AI giants, India should focus on niche AI applications, local language processing, and AI-driven governance solutions. Developing a strong AI research ecosystem, incentivizing private investments, and ensuring access to critical AI hardware will be key to India’s success in the global AI landscape.