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India's AI Advantage May Come From Execution, Not Models
Rajesh Nambiar says India should not measure its AI success only by whether it creates the biggest AI model.
He believes more value will come from using AI in banks, hospitals, governments and companies.
India has a very large community of software developers.
Many Indian businesses are already putting AI systems into real-world use.
India also has digital systems for identity, payments and data that work for hundreds of millions of people.
These strengths can help Indian companies build AI products for customers around the world.
The article points to companies such as Sarvam AI, Krutrim and Qure.ai as examples of this ambition.
Nambiar says India still needs to invest in infrastructure and research.
He argues that learning and adapting quickly may be as important as having the most powerful model.
Rajesh Nambiar argues that the biggest AI race is making systems work at scale, not building the largest frontier model.
India has more than 27 million GitHub developers and over 7.5 million contributions to AI open-source projects.
About 25% of companies surveyed by Nasscom have moved agentic AI from pilots into production.
Deloitte's 2026 survey ranked India first among 15 countries for at-scale AI adoption, with 40% reporting significant or full use.
McKinsey estimates AI and agentic AI could create a $300-400 billion opportunity by 2030 across enterprise technology and governance.
- Who
- Rajesh Nambiar, president of NASSCOM, discusses India's position in the AI industry.
- What
- The article argues that AI execution, deployment and governance could create more value than competing only to build frontier models.
- Where
- The focus is India and its ability to build AI products for global customers.
- When
- The article cites figures for 2026, FY26 and FY27, including Deloitte's 2026 survey.
- Why
- India combines a large developer base, enterprise AI adoption, digital public infrastructure and decades of IT-services experience.
Execution-First View
Frontier-Model-First View
Where AI value will come from
Execution-First View
Nambiar argues that the consequential competition is making AI work reliably in enterprises, governments and other real-world settings.
Frontier-Model-First View
A frontier-model-focused view would prioritize building the largest or most powerful models, which Nambiar says will remain a contest among a small number of heavily funded global labs.
What India should prioritize
Execution-First View
India should use its developer ecosystem, IT-services experience, digital infrastructure and enterprise relationships to deploy and adapt AI at scale.
Frontier-Model-First View
India's position could instead be judged mainly by whether it develops globally competitive frontier models and the infrastructure needed to train them.
How AI teams should work
Execution-First View
The article favors forward-deployed engineering, with technology teams working alongside customers to implement, secure and adapt AI systems.
Frontier-Model-First View
A product-handoff approach would provide completed AI tools to customers rather than embedding teams deeply in implementation and adaptation.
Key facts
- Indian GitHub developers
- More than 27 million, including 2 million who joined in 2026.
- AI open-source contributions
- More than 7.5 million contributions by Indian developers.
- Technology industry outlook
- India's technology industry is projected to exceed $315 billion in FY26, growing 6.1%.
- AI services revenue
- Estimated at $10-12 billion and expanding at a mid-teen pace.
- Agentic AI production use
- Around 25% of companies in a Nasscom survey had moved agentic AI from pilots into production.
- At-scale adoption
- Deloitte reported significant or full AI use among 40% of Indian respondents, compared with a 28% global average.
- Potential 2030 opportunity
- McKinsey estimates a $300-400 billion opportunity from AI and agentic AI.
Sources
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