🧠 Context of the Editorial:

• The editorial discusses India’s lack of AI compute infrastructure — a core requirement for training and deploying artificial intelligence models.
  
  
• It evaluates the policy vacuum, infrastructure gaps, and the need for public-private collaboration to position India as a competitive AI leader globally.
  
  
• The article coincides with the Indian government’s increased interest in rolling out a national AI strategy.
  
  

📌 Key Themes & Analysis:

1. 🧮 What is “AI Compute”? Why It Matters?

• AI Compute refers to the processing power (data centres, GPUs, TPUs, supercomputers, cloud infrastructure) required to train and deploy large AI models.
  
  
• AI innovation, especially in generative AI, demands massive compute resources. Lack of access = barrier to development.
  
  

UPSC GS Paper III: Science and Tech – Emerging Tech like AI, Cloud Computing, Data Infrastructure.

2. 🔍 Current Challenges in India’s AI Compute Ecosystem:

a. Low Availability of Compute Infrastructure:

• India lags behind the U.S. and China, where AI innovation is backed by high compute capacity.
  
  
• Indian researchers often rely on foreign cloud providers, increasing costs and data security risks.
  
  

b. High Costs and Lack of Domestic Capacity:

• Setting up compute infra needs:
  
  
  • Huge capital investment,
    
    
  • Reliable electricity,
    
    
  • Cooling systems,
    
    
  • Skilled workforce.
    
    
• Startups and academia suffer the most from the lack of shared access to affordable compute.
  
  

c. Underutilization of Existing Facilities:

• Public infrastructure (like C-DAC’s Param supercomputers) is underused due to bureaucratic bottlenecks and lack of coordination.
  
  

UPSC GS Paper II & III: Issues in governance, federal coordination, digital infrastructure.

3. 🏛️ Policy Gaps and Institutional Inertia:

• Despite a push for Digital India, India lacks a dedicated AI compute policy or funding mechanism.
  
  
• No national registry or mapping of existing public infrastructure for AI use.
  
  

GS II: Government policy and coordination mechanisms; GS III: Scientific innovation policy.

4. 🌍 Comparative International Experience:

• S.: Heavily funded via Department of Energy, Pentagon; collaboration with NVIDIA, OpenAI.
  
  
• EU & China: Strategic investments to build sovereign AI capability, reduce dependence on foreign cloud players.
  
  

GS II – International Comparison of Public Policy (UPSC asks such angles in Mains).

✅ Way Forward: Policy Recommendations

1. National Compute Policy

• India urgently needs a National AI Compute Mission with:
  
  
  • Clear targets for compute capacity,
    
    
  • Funding support for infrastructure,
    
    
  • Access provisions for startups, academia.
    
    

2. Public-Private Partnerships (PPP):

• Encourage co-investment models with tech firms (NVIDIA, AMD, Google, etc.).
  
  
• Use case-driven infrastructure development (e.g., for healthcare AI, climate AI, agriculture).
  
  

3. Sovereign AI Strategy:

• Strategic compute capacity = technological sovereignty.
  
  
• Avoid dependency on foreign compute for national security-sensitive applications.
  
  

4. Shared Compute Cloud for India (like GigaMesh/AI Foundry):

• Allow Indian startups and researchers access to pooled infrastructure at low or subsidized cost.
  
  

5. Use of PLI and Semiconductor Mission:

• Extend PLI schemes to support AI chip design and compute infra, not just manufacturing.

📘 GS Paper II – Governance, Policies, and International Relations

“The absence of a dedicated AI compute policy in India undermines its technological sovereignty.” Discuss the importance of state intervention in shaping AI infrastructure in India.