Infosys Co-Founder Says India’s Strength Lies in Applying AI at Scale Across Sectors Rather Than Competing on Semiconductor Manufacturing Alone
India should aim to become the world’s leading hub for artificial intelligence use cases rather than focusing primarily on becoming a semiconductor manufacturing capital, according to Infosys co-founder and technology pioneer Nandan Nilekani. His remarks highlight a strategic view that prioritises large-scale application of AI across governance, industry and public services over pure hardware production.
Nilekani’s Core Argument
Speaking on the country’s technology trajectory, Nilekani observed that while semiconductor manufacturing is important and India is making progress in building fabrication capabilities, the nation’s biggest opportunity lies elsewhere. With its vast population, digital public infrastructure and diverse real-world challenges, India is uniquely positioned to generate and scale AI solutions that can be applied across sectors and potentially exported as models to other developing economies.
“India will become the AI use-case capital of the world, not the chip capital,” he said, underscoring the difference between producing chips and creating widespread, practical applications of artificial intelligence.
Why Use Cases Matter More
Semiconductor fabs require massive capital investment, highly specialised technology and long gestation periods. Only a handful of countries currently dominate advanced chip manufacturing. In contrast, AI use cases can be developed relatively faster when the right data, digital platforms and talent are available.
India already possesses several advantages:
- A large and growing base of digital users
- Established digital public infrastructure such as Aadhaar, UPI and Account Aggregator
- A strong software and services talent pool
- Pressing needs in agriculture, healthcare, education, logistics and governance that AI can address
These factors, Nilekani suggested, allow India to move quickly from pilots to large-scale deployment of AI solutions.
Building on Digital Public Infrastructure
Nilekani has long been associated with India’s digital public infrastructure journey. He noted that the same approach that enabled UPI to transform payments can be extended to AI. Open protocols, interoperable systems and public-private collaboration can help AI applications reach hundreds of millions of people efficiently.
Areas such as precision agriculture, predictive healthcare, adaptive learning, fraud detection in financial services and smarter urban management are frequently cited as high-potential domains where Indian use cases could lead globally.
Balancing Hardware and Software Ambitions
Nilekani’s comments do not dismiss India’s semiconductor efforts. The country has launched incentives and projects aimed at attracting chip manufacturing and packaging investments. However, he argued that expecting India to quickly match the scale of established chipmaking nations may be unrealistic in the near term.
Instead, a dual strategy makes sense: continue building selective semiconductor capabilities while aggressively developing and scaling AI applications that leverage India’s software strengths and market size. Success in use cases can itself create demand for domestic chip design and specialised hardware over time.
Implications for Policy and Industry
The perspective has implications for how policymakers allocate attention and resources. Greater emphasis on AI skilling, data governance, sector-specific AI platforms and startup ecosystems could accelerate India’s progress as a use-case leader.
For industry, the message encourages companies to focus on solving real problems at population scale rather than solely chasing hardware manufacturing headlines. Global technology firms and investors are also likely to view India increasingly as a laboratory for AI applications that can later be adapted elsewhere.
Talent and Ecosystem Advantages
India produces a large number of engineers and has a vibrant startup ecosystem. Combined with improving computing infrastructure and cloud availability, these strengths support rapid experimentation and deployment of AI solutions. Nilekani’s view aligns with the idea that software and services excellence can deliver economic value and geopolitical relevance even without dominating the global chip supply chain.
Challenges to Address
Becoming the AI use-case capital will not be automatic. Issues around data quality and access, privacy, algorithmic bias, digital literacy and equitable access must be managed carefully. Reliable power, connectivity and computing resources in smaller cities and rural areas will also determine how widely AI benefits can spread.
Clear regulations and ethical frameworks will be necessary to build public trust as AI systems take on more critical roles in daily life.
Outlook
Nandan Nilekani’s assessment offers a pragmatic roadmap. Rather than measuring success only by the number of fabs built, India can aim to lead in the practical, large-scale application of AI. If the country succeeds in turning its digital foundations and human capital into widely adopted AI solutions, it can claim a distinctive and influential position in the global technology landscape as the world’s AI use-case capital.
