During the discussion at the World Economic Forum in Davos, Switzerland, the MD of IMF released a new index of countries on AI readiness. In this he divided the countries into 3 categories. These include those countries which are bringing change. Those who are only watching and who are unaware of the change. America, Denmark and Singapore topped this list. Whereas in the list, India was kept in the second category along with emerging markets like Saudi Arabia. Appreciated the increasing investment in India's IT sector but did not give it a place in the top group. Union Minister Ashwini Vaishnav got angry on this.

In fact, when Ashwini Vaishnav was asked, does India need coordination with America and China to come in the category of top category countries? On this he rejected the ranking of IMF and said that India is not in the second category but is part of the first group. India is part of the first group of leading countries in the world of AI. He bluntly told this forum that India should not be underestimated under any circumstances.

Showed a mirror to the world from the stage of Davos

Application Layer: AI creates real-world value

• The top layer of the AI ​​stack, closest to citizens and industry
• The country's strategy focuses on the spread of AI on a population scale. Value comes from mass adoption
• Impact in Agriculture, Healthcare, Education, Manufacturing, Governance

Model layer: the brain of the application

• AI model intelligence and decision-making capabilities.
• Frontier models showed the potential of AI but were very expensive in terms of compute and capital.
• Open-source model lowers costs and barriers to entry
• India models enable localization for Indian languages, sectors and regulations
• Sovereign Model – Data Security, Cultural Relevance, Strategic Autonomy

Chip/Compute Layer: Chips running AI

• Compute enables training and inference of AI models – e.g. GPUs, TPUs, NPUs
• Affordable and easily available computers are essential for startups and researchers.
• Subsidized GPU access under National Mission to make AI development accessible to all
• (Over 38,000 GPUs, priced at 1/3 the global average)
• Semiconductor Fabs and ATMP Units to Build Chip Development Capabilities

Data center layer: the backbone of digital infrastructure

• Data centers host AI models, data, and compute resources
• Massive investments are increasing domestic data-center capacity (big companies like Google, Microsoft, and Amazon have invested $70 billion so far)
• Innovation improves cooling, water efficiency and energy use
• Strengthens digital sovereignty and creates high-value jobs

Energy Layer: Powering AI at scale

• AI infrastructure needs reliable, 24/7 power
• Power needs increase rapidly with AI and data-center expansion
• Renewable energy essential but intermittent for 24×7 AI workloads
• Nuclear energy provides clean, stable baseload power
• PEACE Act – Nuclear-based AI infrastructure through small modular and micro reactors, public-private partnerships, and foreign investment