India's Economic Survey on AI Development

Summary Notes on Economic Survey 2025-26: AI Development in India

1. Proposed Initiatives:

• AI-OS Initiative:

  • Government to act as a monetary shareholder in AI infrastructure, akin to UPI and Aadhaar.
  • Aim: Transform AI into a public good with a centralized code repository under the IndiaAI mission for developers and researchers.

• Sector-Specific AI Applications:

  • Emphasis on developing small, application-specific AI models rather than large, resource-intensive models.

• Education Restructuring:

  • Introduction of an ‘Earn-and-Learn’ initiative from Class 11 to gain academic credits and paid work through apprenticeships.
  • Collaboration between private sector and educational institutions to co-design industry fellowships.

2. Economic Context:

• India ranks second globally in AI literacy, only after the US (Source: Stanford AI Index Report 2025).

• Importance of sectoral job mapping: Emphasis on high-skill jobs outside white-collar sectors to mitigate AI impact on employment.

• Structural Challenges:

  • Limited access to cutting-edge compute infrastructure, with India taking only 2% of global training data.
  • 70% of data centers are in high-income countries; India only has 3% (World Bank data).
  • Concerns regarding technological dependence and market power concentration in AI infrastructure.

3. Job Creation and Skills:

• Identification of high-skill jobs that are understaffed, e.g., nursing, geriatric care, culinary sciences, advanced metalwork.
• Focus on enhancing education and skilling infrastructure to meet labour supply gaps.

4. Challenges and Trade-offs in AI Adoption:

• Resource Allocation:

  • Need to choose between investing in large-scale models versus sector-specific applications aligned with economic priorities.

• Labour Displacement vs Efficiency:

  • AI adoption poses risks of job displacement; necessity to balance productivity gains with employment opportunities.

• Open vs Closed Models:

  • The trade-off between maintaining proprietary AI models versus fostering open-source initiatives for transparency and adaptability.

• Compute Strength vs Resource Allocation:

  • Developing AI infrastructure could stress shared resources like electricity and water; smaller models on distributed networks are recommended.

• Regulatory Framework:

  • Need for balanced regulatory compliance that supports innovation without stifling early-stage ventures, particularly in critical sectors.

• Global Integration vs Strategic Autonomy:

  • Tension between foreign dependence on critical AI systems and the need for strategic autonomy in technological development.

5. Strategic Recommendations:

• Foster decentralized AI development to leverage local talents and resources.
• Emphasize regulatory clarity that safeguards innovation.
• Promote shared innovation while retaining economic value domestically.

These delineations reflect the Indian government's strategy to shift from traditional AI development models towards a more inclusive and sector-focused approach, keeping in consideration the challenges and global dynamics surrounding technology and workforce development.