Context:

• India hosted the India AI Impact Summit 2026, the first global AI summit organised by a Global South country, signalling India’s ambition to shape the future of Artificial Intelligence (AI) governance, infrastructure, and innovation.
• The summit brought together over 20 heads of state, 60 ministers, and over 500 AI leaders from over 100 countries, marking a major multilateral moment for AI policy.

Civilisational Inspiration:

• Drawing inspiration from India’s civilisational traditions of structured knowledge — from Panini’s grammar to Nalanda’s institutional scholarship — the summit emphasised the importance of structured, inclusive and sovereign AI systems.
• India presented an alternative vision to the technology-dominated models of advanced economies.

India’s Vision for AI Governance:

• The MANAV framework: The Indian Prime Minister outlined the MANAV vision as a guiding framework for AI governance -
  • M - Moral and Ethical System
  • A - Accountable Governance
  • N - National Sovereignty
  • A - Accessible and Inclusive
  • V - Valid and Legitimate
• Key principles of the vision: Ethical guardrails for responsible AI development, data sovereignty to prevent data exploitation, inclusive access ensuring benefits reach all citizens, democratic oversight, etc.
• The approach emphasises “AI with human control”, combining innovation with regulatory oversight.

Delhi Declaration - Global South AI Blueprint:

• The summit adopted the Delhi Declaration, considered the first major AI governance framework emerging from the Global South.
• Key features:
  • Development-oriented AI governance: Focus on development priorities rather than purely commercial interests. Flexible techno-legal regulatory approach. Avoidance of rigid compliance regimes.
  • Three-pillar framework:
    • People – Inclusive AI access
    • Planet – Sustainable technology use
    • Progress – Economic growth and innovation
  • Global initiatives proposed:
    • Population-scale AI solutions: BharatGen supporting 22 Indian languages.
    • Global compute bank: Modelled on subsidised GPU access in India (~₹65/hour).
    • Data sovereignty: Preventing AI extractivism (use of developing-country data to train proprietary models).

India’s Digital Public Infrastructure (DPI) as Foundation:

• India’s AI strategy builds on its successful DPI ecosystem.
• Key achievements:
  • UPI processed 228 billion transactions in 2025 (~$3.4 trillion).
  • JAM Trinity enabled welfare savings of ₹3.48 lakh crore since 2015.
  • Integrated architecture: Digital identity, payments, welfare delivery.
• This infrastructure provides a base for population-scale AI deployment.

AI Infrastructure Expansion:

• Existing gap: Though India generates around 20% of global data, it hosts only about 3% of global data centre capacity. Bridging this gap is central to India’s AI strategy.
• Major investment announcements:
  • Global technology companies:
    • Microsoft: $50 billion Global South plan (including $17.5 billion for India).
    • Google: $15 billion America–India Connect initiative.
    • Amazon Web Services: $8.3 billion investment in Maharashtra.
  • Indian industry:
    • Adani Group: $100 billion renewable-powered AI data centres by 2035.
    • Yotta Data Services: Over $2 billion AI computing hub using advanced chips.
    • L&T–Nvidia partnership: For gigawatt-scale AI factory.
  • National AI infrastructure: The IndiaAI Mission’s national compute cluster has crossed 38,000 GPUs and is scaling to 58,000, available to startups at roughly one-third of global cost.
  • Investment target: $200 billion AI infrastructure investment in next two years.

Policy Support and Budgetary Measures:

• The Union Budget 2026–27 supports AI growth through key measures like -
  • Tax holiday until 2047 for foreign companies using Indian data centres.
  • $1.1 billion VC fund for AI and advanced manufacturing startups.
• National Critical Mineral Mission: Secures the lithium, cobalt, and rare earths that AI and semiconductor manufacturing depend on.

Democratisation of AI:

• India emphasised AI for social transformation, not only industrial competitiveness.
• Human capital initiatives:
  • 2.5 lakh students pledged responsible AI innovation.
  • 30 Data and AI Labs operational in Tier-2 and Tier-3 cities (Target: 570 AI labs nationwide).
• Public AI infrastructure: AIKosh platform offers over 7,500 datasets and 273 models as shared public infrastructure.
• Education expansion: IITs increased from 16 in 2014 to 23 today.

Sovereign AI Capability:

• India is transitioning from an AI consumer to an AI producer.
• New sovereign AI models: Sarvam AI LLM, BharatGen Param2.
• India is now among countries building indigenous Large Language Models (LLMs).

Strategic Partnerships and Global Role:

• Co-building capacity: India is shifting from technology licensing to technology co-development.
• Key partnerships:
  • Tata–OpenAI: Beginning with 100 MW of AI-ready data centre capacity under the Stargate initiative and scaling to one gigawatt, signals that Indian industry is moving to the supply side of global intelligence.
  • Pax Silica Declaration: Places India in the US-led coalition, securing supply chains for AI, semiconductors, and critical minerals.
  • India–US AI Opportunity Partnership: Commits both nations to pro-innovation approaches on critical technologies.
  • India–France Year of Innovation 2026: Organised around joint skilling and measurable outcomes. 

Key Challenges and Way Forward:

• Infrastructure deficit: Limited data centre capacity relative to data generation.
  • Expand DPI 2.0 - AI integrated with DPI platforms.
• Technological dependence: Reliance on foreign chips and advanced AI hardware.
  • Strengthen sovereign AI ecosystem - Indigenous chips and models, domestic cloud infrastructure.
• Skill gaps: Shortage of AI researchers and advanced engineers.
  • Human capital development - AI education and research funding, skilling programmes in Tier-2 and Tier-3 cities.
• Regulatory complexity: Balancing innovation and ethical safeguards.
  • Ethical and democratic governance - Transparent AI regulation, algorithmic accountability.
• Data governance issues: Implementing data sovereignty without restricting innovation.
  • Global South leadership - Build coalitions for equitable AI governance, promote development-oriented AI models.

Conclusion:

• The India AI Impact Summit 2026 marks a turning point in the global AI landscape, positioning India as a norm-setter rather than a rule-taker.
• By combining data sovereignty, DPI, sovereign AI models, and global partnerships, India is attempting to build a structured and inclusive AI ecosystem.
• If executed effectively, this approach could allow India not only to benefit from the AI revolution but also to shape a more equitable global technological order.