AI for Viksit Bharat: Google’s historic $15 Billion investment for an AI Hub – Convergence of Policy, Capital, and Infrastructure.

Context: The technological landscape in India has witnessed a paradigm shift with three major developments: the launch of the “AI for Viksit Bharat Roadmap”, Google’s historic $15 Billion investment for an AI Hub in India, and the strategic proposal to use Small Modular Reactors (SMRs) to power energy-intensive AI data centers. This triad represents the convergence of Policy, Capital, and Infrastructure.

Basics: Core Concepts

• Artificial Intelligence (AI): Simulation of human intelligence processes by machines, especially computer systems.
  • Generative AI: A subset of AI (like ChatGPT) that can create new content (text, images, code) rather than just analyzing existing data.
• Robotics: The intersection of science, engineering, and technology that produces machines (robots) to substitute (or replicate) human actions.
• Small Modular Reactors (SMRs): Advanced nuclear reactors with a power capacity of up to 300 MW(e) per unit (about 1/3rd of traditional reactors).
  • Relevance: AI Data centers are “energy guzzlers.” SMRs provide a clean, 24×7 baseload power source that renewables (solar/wind) cannot consistently guarantee.

Strategic Roadmap: AI for Viksit Bharat

This roadmap, released in late 2025, aligns with the goal of a $30 Trillion economy by 2047.

1. Objective: To position India not just as a consumer but as a creator of Sovereign AI.
2. Key Pillars:
   • Sovereign Compute Infrastructure: Expanding GPU capacity (Graphics Processing Units) for domestic startups.
   • India Datasets Platform: creating non-personal, diverse datasets for training AI models in Indian languages.
   • FutureSkills: Re-skilling the demographic dividend to avoid job displacement.

Constitutional & Legal Framework

To write a substantive answer, you must cite the evolving legal architecture:

Instrument	Key Provision / Relevance
India AI Governance Guidelines (Nov 2025)	Released by MeitY; focuses on “Safe, Trusted, and Ethical AI.” It prioritizes User Safety over unconditional innovation.
Digital Personal Data Protection (DPDP) Act, 2023	The bedrock for AI privacy. It mandates that AI models cannot process personal data without explicit consent.
IT Rules Amendment (Proposed 2025)	Mandates “Watermarking” and “Labelling” of AI-generated content (Deepfakes) to curb misinformation.
Digital India Act (Upcoming)	Will replace the IT Act, 2000. It is expected to classify AI models into “High Risk” (healthcare, law enforcement) and “Low Risk” categories for regulation.

Case Studies (Value Addition for Mains)

• Case Study 1: Healthcare – Yashoda Hospitals & Qure.ai (2025)
  • What Happened: Yashoda Hospitals (Hyderabad) launched an AI-powered “Lung Nodule Clinic” using Qure.ai technology.
  • Impact: The AI analyzes chest X-rays in seconds to detect early signs of lung cancer, which human eyes might miss in early stages.
  • Lesson: AI acts as a “Force Multiplier” for doctors, not a replacement.
• Case Study 2: CEREBO (Healthcare)
  • ICMR developed CEREBO, a handheld, non-invasive device using Near-Infrared Spectroscopy (NIRS) and Machine Learning to detect Traumatic Brain Injuries (TBI) in 1 minute
  • Lesson: Indigenization and Demonstrates India’s ability to create low-cost, high-impact medical devices for rural areas.
• Case Study 3: Agriculture – ‘Saagu Baagu’ (Telangana)
  • What Happened: The Telangana government partnered with the World Economic Forum (WEF) to deploy AI tools for chili farmers.
  • Impact: Farmers received real-time alerts on “pests” and “sowing cycles” via WhatsApp. Resulted in a 21% increase in yield per acre.
  • Lesson: Digital Public Infrastructure (DPI) can democratize access to high-tech solutions for the poor.

Government Initiatives & Investments

1. IndiaAI Mission: A ₹10,372 crore outlay to build a public AI infrastructure (Compute capability of 10,000 GPUs).
2. Google’s $15 Billion Investment (2026-2030):
   • Establishment of a massive AI Hub in Visakhapatnam.
   • Focus on “Make in India” for Google Pixel devices and AI-skilling for Indian youth.
3. AIRAWAT (AI Research Analytics and Knowledge Assimilation): NITI Aayog’s cloud computing platform for researchers.
4. Bhashini: An AI-led language translation platform to break the language barrier in digital services.

Issues & Challenges

• Energy Famine: AI models are energy-hungry. A single ChatGPT query uses 10x more energy than a Google search. India’s power grid is already stressed. Despite the Google investment, India lacks the massive GPU clusters required for training foundational models (sovereign AI).
• Data Blindspots: Most Global AI models (LLMs) are trained on western data. They often fail to understand Indian cultural nuances or languages (Bias).
• The “Black Box” Problem: AI makes decisions (e.g., rejecting a loan or a job application) without explaining why. This violates the principle of Natural Justice.
• Adversarial AI (Deepfakes): rapid spread of realistic fake videos threatens election integrity and individual privacy (Article 21).
• Job Polarization: Automation threat to the BPO/KPO sector, which employs millions of Indians in routine coding/data entry jobs. Automation in manufacturing (Cobots) and agriculture might displace unskilled labor in a labor-surplus economy.

International Landscape & Comparative Models

• European Union: Leads in regulation (EU AI Act). Focuses on “risk-based” categorization.
• USA: Leads in innovation and private investment (Google, Microsoft, OpenAI). Minimal regulation to foster growth.
• China: Leads in state-surveillance AI and manufacturing robotics.
• India’s Position: India advocates “AI for All”—using AI as a Digital Public Infrastructure (DPI) for social good (health, agritech), acting as a voice for the Global South.

Way Forward: Visionary Recommendations

• Nuclear-AI Hybrid: Fast-track the policy for Captive SMRs (Small Nuclear Reactors) specifically for Data Centers to ensure green, reliable power.
• “Lab-to-Land” Approach: Move beyond chat-bots. Incentivize AI startups that solve “Bharat’s problems”—water management, crop disease, and judicial pendency.
• Algorithmic Audits: Establish an independent “AI Safety Institute” (similar to the UK model) to audit high-risk AI algorithms for bias before public release.
• Sovereign AI Clouds: India must build its own “AI Cloud” so that sensitive government and citizen data does not reside on foreign servers.

UPSC CSE Prelims: Key Pointers 2026

• Global Partnership on AI (GPAI): India was the Lead Chair for 2024. It is a multi-stakeholder initiative (not UN-based).
• SMR Regulation: Nuclear power is a central subject. Only NPCIL (or JVs with PSUs) can currently operate reactors; private players cannot own reactors yet (reform debated).
• C-DAC: The nodal agency implementing the “IndiaAI Compute Platform”.
• Compute Unit: Measured in Petaflops or Exaflops.

UPSC Mains: Answer Writing Framework

Mock Question: “The deployment of Artificial Intelligence offers a leapfrog opportunity for India’s development but brings unique energy and ethical challenges. Discuss the need for an integrated policy framework.” (15 Marks)

Previous Year Questions (PYQs) on Technological Developments, AI, Internet Technologies

• Mains 2023: “Introduce the concept of Artificial Intelligence (AI). How does Al help clinical diagnosis? Do you perceive any threat to privacy of the individual in the use of Al in healthcare?”
• Prelims 2022: Question on “Web 3.0” (Concepts of decentralization/Blockchain).
• Prelims 2020: Question on applications of “Artificial Intelligence” (Power consumption, text-to-speech, etc.)