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India’s Pursuit of Sovereign Foundational AI Model

Syllabus:

GS-2:

Government Policies & Interventions

GS-3:

Robotics , Artificial Intelligence ,Scientific Innovations & Discoveries , IT & Computers

Focus:

The IndiaAI Mission, under the IT Ministry, announced plans to make GPU clusters available to startups and academia, sparking debates on whether India should invest in building its own foundational AI model for sovereignty and technological advancement.

India’s Quest for a Sovereign Foundational AI Model:

• The emergence of foundational AI models like OpenAI’s ChatGPT has sparked debates in India.
• The Indian government’s IndiaAI Mission, under the IT Ministry, proposes building sovereign AI models and making GPU clusters accessible to startups and academia.
• This initiative stems from concerns around sovereignty, national pride, and financial considerations.

What is Sovereign AI ?

• Sovereign AI refers to a nation’s ability to develop, control, and deploy AI using its own infrastructure, data, workforce, and business networks.
• It focuses on domestic AI models, infrastructure, and talent development.

Growth of AI

• 2018: 340-million-parameter models were considered large.
• Today:
• ChatGPT: 1.8 trillion parameters
• Gemini: 1.5 trillion parameters
• DeepSeek (China): 240 billion parameters
• Parameters are internal variables adjusted during training to improve model performance.

Key Aspects of Sovereign AI

• National Control: Aligns AI development with national laws and ethics.
• Data Sovereignty: Ensures control over domestic data, safeguarding privacy and national security.
• AI in Governance: Generative AI reshapes markets, governance, and industries, assisting professionals through AI copilots.
• Ethical Considerations: Establishes security protocols and ethical AI standards.
• Strategic Autonomy: Reduces reliance on foreign technologies and promotes domestic AI development.
• Economic Competitiveness: Drives industrial innovation; essential to remain globally competitive.

Applications

• Used in defense, healthcare, transportation, and other critical sectors.

India’s Position

• Companies like Tata Group and Reliance are developing AI infrastructure and Large Language Models (LLMs).
• India allocated USD 1.2 billion for sovereign AI, including an AI supercomputer under the IndiaAI Mission.

Global AI Cooperation

• The proposed Global AI Compact advocates equitable sharing of AI resources among nations.

Sovereignty vs. Resource Constraints:

Need for Sovereign AI Models

• Sovereignty concerns stem from the possibility of sanctions on AI-related software, models, or hardware by foreign nations.
• Possessing independent AI capabilities ensures India’s strategic autonomy and technological self-reliance.

Financial and Technical Limitations

• Developing AI models requires substantial investment in infrastructure and hardware, such as GPUs.
• Leading tech firms like Meta invest billions annually in AI infrastructure, whereas India’s allocated budget is far smaller.
• Tanuj Bhojwani notes that India lacks contracts with manufacturers like TSMC for advanced chips, making self-reliance

Prudent Allocation of Limited Resources:

Focused Investments for Local Needs

• Instead of competing directly with global giants, India should focus on developing AI models tailored for local languages and needs.
• IndicTrans2 and Indian text-to-speech systems are promising examples.

Subsidies and Public Investment

• The government’s plan to subsidize GPU clusters for startups and academia is commendable but requires judicious allocation.
• Pranesh Prakash emphasizes the need for research on responsible AI usage and regulatory frameworks.

Enhancing the Research and Innovation Ecosystem:

Addressing R&D Challenges

• Bhojwani highlights gaps in India’s research ecosystem and calls for reforms in R&D spending and private investment.
• Failed experiments are part of AI development, and public procurement systems need to tolerate failures.

Strengthening Human Capital

• The enthusiasm among young developers in India is evident, but they need better training and exposure to advanced AI tools.
• Encouraging innovation under constraints, as demonstrated by Alibaba and DeepSeek, is essential.

Collaboration Between Public and Private Sectors

• A collaborative approach involving government, academia, and private players can foster the development of foundational AI models in India.
• Bhojwani suggests creating autonomous research institutions with adequate spending power.

The Way Forward: Building a Sustainable AI Ecosystem:

Focusing on Practical AI Applications

• Rather than solely aiming for sovereign models, India should prioritize building commercial applications on top of existing AI models.
• Open-source models provide opportunities for local customization and innovation.

Strategic Investments in AI Infrastructure

• The government must ensure that investments in GPU clusters and AI training are proportionate to expected returns.
• As Bhojwani notes, foundational models should only be pursued if they yield tangible returns, whether in pride, sovereignty, or economic gains.

Promoting Global Competitiveness

• While the S. dominates the global AI market, India can carve a niche by focusing on affordable, culturally relevant AI solutions.
• Encouraging private market investments and international collaborations will be key.

Conclusion:

India’s decision to develop a sovereign AI model must be balanced with resource constraints and market needs. Focusing on local language models, commercial applications, and strengthening research ecosystems will better position the country in the global AI landscape while fostering strategic autonomy and innovation.

Source: TH

Mains Practice Question:

Discuss the need for India to develop a sovereign foundational AI model in the context of technological self-reliance and resource constraints. What steps should the government take to strengthen the AI research ecosystem and foster innovation for both local needs and global competitiveness?