India’s AI Ambition: Need For Focused Investment & Public Infrastructure

Syllabus: GS3/Science & Technology

Context

• India’s growing AI ambitions, the IndiaAI Mission and the AI Impact Summit have renewed debate on building indigenous research capabilities and establishing global leadership in artificial intelligence.

Understanding the Global AI Landscape

• Artificial Intelligence (AI) research broadly covers ten interconnected areas: foundation models, reasoning, AI agents, multimodal AI, AI safety, robotics, AI for science, specialised hardware, data and learning methods, and AI governance.
• The global AI landscape is primarily shaped by players like the United States, China and the European Union.
  • The United States leads in foundation models, reasoning, AI agents, safety, AI for science and advanced hardware. Its leadership is supported by substantial private investment, advanced computing infrastructure and global research talent.
  • China has emerged as a leader in robotics and AI research output while rapidly narrowing the gap in foundation models and semiconductor capabilities.
  • The European Union has taken a leading role in AI governance through the EU AI Act, which establishes a risk-based regulatory framework for AI systems.
• While the US and China dominate technological development, competition in multimodal AI, efficient computing and data-learning methods remains intense.

India’s AI Readiness

• India’s AI ecosystem is gradually transitioning from technology adoption towards indigenous research and innovation.
• The IndiaAI Mission aims to strengthen computing infrastructure, develop indigenous AI models, support startups and promote responsible AI.
• Key developments include:
  • Mobilisation of more than 45,000 GPUs through the common computing infrastructure initiative.
  • Development of indigenous foundation models through selected research and industry teams.
  • Expansion of AI data centres and computing infrastructure.
  • Strengthening AI research through academic institutions and startups.
  • Hosting the AI Impact Summit, culminating in the New Delhi Declaration endorsed by 92 countries, strengthening India’s role in global AI governance.
• However, India’s frontier research capabilities remain relatively limited compared with the US and China.

India’s Emerging Strengths

• Multilingual AI: Initiatives such as AI4Bharat, BharatGen, Sarvam AI and Bhashini are developing language models, datasets and tools across India’s 22 scheduled languages.
  • These capabilities also have potential applications in multilingual societies across Africa and Asia.
• Applied AI Solutions: Indian institutions are developing AI applications to address real-world challenges:
  • Wadhwani AI: AI-based tuberculosis screening and cotton pest management.
  • IIT Ropar’s Annam.AI: Weather intelligence and agricultural solutions.
  • IIT Kanpur’s Airawat Research Foundation: AI-based air-quality monitoring.
• Digital Public Infrastructure (DPI): Aadhaar, UPI and other digital platforms demonstrate India’s ability to develop population-scale, affordable and interoperable digital systems.
  • It provides a valuable foundation for developing AI as a public utility.

India’s Existing Challenges

• Limited Frontier Research: Insufficient investment in fundamental AI research and advanced model development.
• Computing Dependency: Heavy reliance on imported GPUs and advanced semiconductor technologies.
• Talent Constraints: Shortage of specialised researchers, doctoral graduates and experienced AI scientists.
• Weak Indigenous IP: Limited ownership of globally competitive AI technologies and intellectual property.
• Underdeveloped Robotics: Relatively weak capabilities in advanced robotics and physical AI.
• Safety & Governance Gaps: Need for stronger research on algorithmic bias, privacy, explainability and AI misuse.

Where Can India Develop Global Leadership?

• Multilingual & Voice-First AI: Develop open-source AI models for underserved languages, enabling inclusive access to digital services, especially for rural populations and non-English speakers.
• Frugal AI: Develop smaller, energy-efficient and affordable models capable of running on inexpensive smartphones, vehicles and agricultural equipment, including in offline environments.
• AI as Public Infrastructure: Integrate reliable AI solutions into healthcare, agriculture, education, disaster management and land administration, particularly in resource-constrained settings.
• AI Evaluation & Trust: Develop multilingual benchmarks, deepfake detection systems, bias assessment tools and safety standards suited to the needs of developing countries.
  • These priorities can position India as a provider of affordable, inclusive and socially relevant AI solutions for the Global South.

Way Forward: Building India’s AI Research Ecosystem

• Establish a Dedicated Institution: Create a National AI Development Organisation, modelled on the institutional approach of NPCI, with government, industry, financial institutions and academic participation.
  • It can coordinate research, infrastructure and large-scale deployment.
• Ensure Long-Term Funding: Provide ₹25,000–30,000 crore annually for a decade, concentrating resources on selected strategic research priorities rather than spreading investments thinly.
• Promote Open Research: Make publicly funded models, datasets and benchmarks openly accessible.
  • Implement the Data Empowerment and Protection Architecture (DEPA) in mission mode to develop trusted data-sharing frameworks, strengthening platforms such as AIKosh and Bhashini.
• Make Government First Customer: Use public procurement to create demand for indigenous AI solutions and facilitate their expansion across government departments.
• Strengthen Research Talent: Establish 1,000 research chairs, introduce several thousand doctoral fellowships and attract Indian researchers working abroad.
• Expand Computing and Semiconductor Capabilities: Increase shared GPU access, strengthen national supercomputing infrastructure and promote indigenous, energy-efficient AI chip design through initiatives such as Semicon India.

Conclusion

• India must move beyond being merely a consumer of AI technologies towards becoming a creator of globally competitive AI solutions.
  • Its success will depend on indigenous intellectual property, sustained research investment and responsible innovation.
• India should pursue a distinctive development pathway i.e. affordable, multilingual, energy-efficient and inclusive AI that works for billions of people rather than entering the crowded race to build the world’s largest AI models.
  • It can transform India’s digital public infrastructure success into a global AI leadership opportunity.

Source: BS