"India's AI Strategy: Smaller Models, Compute Investment, and a Decade-Long Race"
Horizontal technology
A technology that applies across all sectors of the economy simultaneously, not just one industry.
Large Language Model (LLM)
An AI system trained on vast text data to understand and generate human language.
Artificial Intelligence has emerged as a horizontal technology, one that cuts across every sector of the economy, from agriculture and healthcare to finance and manufacturing. Unlike earlier technology waves that were largely sector-specific, AI reshapes the entire production frontier simultaneously. This makes it both an extraordinary opportunity and a governance challenge of the first order.
India's engagement with AI has been shaped by its experience with Digital Public Infrastructure (DPI). Systems like Aadhaar, UPI, and the ONDC network created large-scale, interoperable digital layers that AI can now be built on top of. The IndiaAI Mission, launched with a significant budgetary allocation, focuses on compute infrastructure, curated datasets, and AI applications in public services. India has not yet enacted a unified AI law. Instead, it relies on sector-specific regulators and a principles-based approach that emphasises responsible innovation.
Globally, the regulatory landscape is shifting. The EU AI Act introduced the world's first comprehensive, legally binding AI framework, using a risk-based classification of AI systems. The EU AI Omnibus subsequently extended compliance deadlines and simplified obligations for certain providers, recognising that even well-designed regulation must be capable of iterative revision. India's own regulatory posture remains more permissive, prioritising supply-side investment over binding rules.
India's AI strategy rests on a distinct logic: rather than competing with frontier labs on the largest, most expensive models, India aims to build smaller, cheaper, domain-specific models optimised for Indian languages, contexts, and use cases. This approach has both economic and strategic rationale.
The cost argument is central. Training a single run of a 100-billion-parameter model is orders of magnitude cheaper than training a 10-trillion-parameter frontier model, whose single training run can cost billions of dollars. For the vast majority of enterprise AI use cases, estimated at 90 to 95 per cent, models below 100 billion parameters are sufficient. Indian companies like Sarvam have demonstrated this: their voice AI models are reportedly five times cheaper than comparable offerings from leading global labs, making them commercially viable for Indian enterprise customers.
Voice AI is identified as the first billion-dollar revenue category for Indian enterprise AI. …
Consider the following statements with respect to the AI Impact Summit, 2026 held in New Delhi:
1.The Summit's intellectual framework was based on three foundational Sutras: People, Planning, and Progress.
2.The Preamble of the Summit stresses Democratising AI Resources, which acknowledges the Charter for Democratic Diffusion of AI as a binding framework to support locally relevant innovation and strengthen resilient AI ecosystems while respecting national laws.
3.The New Delhi Declaration on AI Impact was structured around seven Chakras (Pillars), which included Access for Social Empowerment, AI for Science, and Secure and Trusted AI.
Which of the statements given above is/are correct?
Consider the following statements regarding the AI Action Summit held in Grand Palais, Paris in February 2025:
I.Co-chaired with India, the event builds on the advances made at the Bletchley Park Summit held in 2023 and the Seoul Summit held in 2024.
II.Along with other countries, US and UK also signed the declaration on inclusive and sustainable AI.
Which of the statements given above is/are correct?
How can Artificial Intelligence (AI) and drones be effectively used along with GIS and RS techniques in locational and areal planning? (Answer in 250 words)
Consider the following statements regarding India's AI model ecosystem:
1.Sarvam is an Indian AI company that has launched models trained ground-up in India.
2.BharatGen is an AI model developed by IIT Bombay.
3.Indian voice AI models are currently more expensive than comparable offerings from leading global frontier labs.
4.India's current semiconductor fabs are capable of producing advanced AI chips at the 7-nanometre node.
How many of the above statements are correct?
Consider the following statements regarding the AI Impact Summit, 2026 held in New Delhi:
1.The Summit's intellectual framework was based on three foundational Sutras: People, Planning, and Progress.
2.The New Delhi Declaration on AI Impact was structured around seven Chakras (Pillars).
3.'Access for Social Empowerment' and 'Secure and Trusted AI' were among the seven Chakras of the New Delhi Declaration.
Which of the statements given above is/are correct?
With reference to India's position in global digital payments, consider the following:
1.India was not among the top 100 countries in digital payments in 2016.
2.India currently accounts for 55–60 per cent of global real-time payment transactions.
3.India's rise in digital payments is primarily attributed to the launch of the Unified Payments Interface (UPI).
Which of the statements given above is/are correct?
Match the following AI models/initiatives with their associated institutions:
List I (Model/Initiative) — List II (Institution/Company)
A.Sarvam — 1. IIT Bombay
B.BharatGen — 2. Indian private AI company
C.IndiaAI Mission — 3. Government of India
D.EU AI Act — 4. European Union
Consider the following statements regarding India's semiconductor and AI infrastructure:
1.India's current semiconductor fabrication plants operate at the 28-nanometre node.
2.28-nanometre fabs are suitable for manufacturing advanced AI training chips.
3.India's renewable energy expansion is cited as a cost advantage for AI data centre operations.
Which of the statements given above is/are correct?
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