In News:

As artificial intelligence (AI) reshapes global economic, strategic, and technological landscapes, the question of whether India should build its own sovereign foundational AI model has gained prominence. Sovereign AI models—developed, trained, and deployed using domestic infrastructure, datasets, and expertise—are now seen as strategic assets, with countries like the US and China already establishing their own. India, however, remains dependent on foreign AI giants such as OpenAI, Google DeepMind, and Meta.

Why India Needs a Sovereign AI Model

1. Data Sovereignty and Security: India generates one of the world’s largest data pools, including sensitive data from healthcare, finance, and governance. Using foreign-built AI models risks privacy breaches and potential misuse. A homegrown model would ensure control over data and ethical AI deployment.

2. Reducing Foreign Dependence: Sovereign AI is crucial for applications in defense, cybersecurity, and governance, where reliance on foreign technology may undermine strategic autonomy. Sanctions or export controls could otherwise disrupt access to essential technologies like GPUs or software updates.

3. Cultural and Linguistic Alignment: Current global AI models are largely English-centric. A sovereign model trained on Indian languages and datasets would bridge the digital divide and make AI more inclusive. Projects like AI4Bharat’s IndicTrans2 and Sarvam AI’s Sarvam-1, a multilingual model built with Nvidia, exemplify this direction.

4. National Security and Innovation: Sovereign AI is essential in military intelligence, predictive security, and surveillance. It also fosters an innovation ecosystem, generating high-skilled jobs and encouraging academic-industry collaboration.

Challenges in Building Foundational AI Models

1. Infrastructure Gaps: India lacks cutting-edge chip manufacturing capabilities. With no agreements with firms like TSMC, India relies on imports of GPUs and processors, unlike countries developing supercomputers (e.g., Denmark’s Gefion, Japan’s AI Grid).

2. High Development Costs: Training a large AI model can cost millions. DeepSeek V3, for instance, cost $5.6 million for a single run, while India’s annual AI R&D budget remains modest compared to Big Tech’s $80 billion.

3. Fragmented Resources: Subsidized GPUs are spread thin across institutions, diluting their impact. Meta’s Llama 4, for example, used large dedicated clusters—unfeasible under current Indian frameworks.

4. Public R&D Inefficiencies: Bureaucratic red tape discourages risk-taking needed in AI research. Unlike flexible spending in firms like OpenAI, Indian R&D lacks autonomy and long-term funding.

Policy Recommendations and Way Forward

• Invest in IndiaAI Mission: Develop a national AI infrastructure with over 10,000 GPUs, secure cloud systems, and supercomputing clusters to train and deploy large-scale models.
• Build DPI for AI Builders: Create datasets, APIs, and platforms to support data annotation, fine-tuning, and delivery in Indian contexts.
• Adopt a Phased Approach: Focus on sovereign models in sensitive sectors (defense, healthcare) while using global open models for non-critical applications.
• Promote Public-Private Collaboration: Forge partnerships with companies like Nvidia or OpenAI for technology transfer and joint ventures.
• Encourage Innovation Under Constraints: India must emulate models like Alibaba or DeepSeek, which succeeded with limited resources and targeted innovations.

Conclusion

Building a sovereign foundational AI model is not merely a technological ambition but a strategic necessity. With coordinated efforts between government, industry, and academia, India can achieve AI self-reliance—ensuring data sovereignty, inclusive growth, and a strong global presence in the AI-driven future.