Can India become an AI leader by 2040 despite its late start?

Understanding the reality gap

The world's leading AI powers did not emerge overnight. The United States built an ecosystem over several decades through companies such as Google, Microsoft, Amazon, Nvidia, Meta, Oracle and numerous research universities. China followed a similar path through companies such as Tencent, Alibaba, Baidu and Huawei, supported by large-scale government investment and domestic digital ecosystems.

India's technology success story has historically been different. For nearly four decades, the country built a reputation as a global services and outsourcing hub. Indian IT companies excelled in software services, consulting, implementation, maintenance and backend operations. While this generated employment and foreign exchange, it did not necessarily create globally dominant technology platforms.

Why building AI leadership is difficult

Artificial Intelligence is not a single industry. It consists of multiple interconnected layers:

• Semiconductor design and manufacturing
• Data centres and computing infrastructure
• Cloud platforms
• Foundation models
• Application software
• Consumer platforms
• Enterprise solutions
• Research and talent development

Most countries attempting to enter AI are trying to compete across all layers simultaneously. That is extremely difficult because each layer requires massive investment, specialised talent and long-term policy support.

The Sam Altman challenge

In 2023, OpenAI CEO Sam Altman expressed skepticism about countries attempting to build frontier AI models with limited resources, implying that competing directly with the largest AI players would be extremely challenging.

Whether one agrees or disagrees with that assessment, the underlying point remains important: creating cutting-edge AI capability requires years of investment, experimentation and ecosystem development. There are very few shortcuts.

What India is trying to do differently

According to statements from policymakers including Ashwini Vaishnaw, India's strategy appears to be more pragmatic than attempting to immediately challenge global AI leaders in every segment.

The focus has been on building foundational capabilities first:

• Expanding semiconductor manufacturing capacity
• Creating a domestic chip ecosystem
• Attracting global electronics manufacturing
• Building hyperscale data centres
• Developing AI computing infrastructure
• Encouraging startups and research initiatives
• Creating digital public infrastructure

This approach recognizes that AI leadership cannot exist without the underlying industrial ecosystem.

Lessons from previous industries

India's experience in mobile phone manufacturing offers an important lesson. A decade ago, India was heavily dependent on imports. Today, the country has become one of the world's largest mobile manufacturing hubs.

Similarly, semiconductor investments, electronics production and data centre expansion are beginning to scale. These developments do not automatically make India an AI leader, but they establish critical building blocks for future growth.

Can India become an AI leader by 2040?

The answer depends on how leadership is defined.

• Becoming the world's dominant AI platform creator may be difficult.
• Becoming a major AI deployment and application economy is achievable.
• Building a significant semiconductor ecosystem is possible.
• Creating one of the world's largest AI talent pools is highly likely.
• Becoming a major data centre destination appears realistic.
• Developing globally competitive AI startups remains possible.

The most realistic path may be for India to become a critical component of the global AI value chain rather than attempting to dominate every layer simultaneously.

Investor takeaway

Final thoughts

India carries both strengths and historical limitations into the AI era. The country has immense talent, a large digital economy and growing infrastructure investments. At the same time, it faces the challenge of catching up in areas where global leaders have enjoyed decades of head start. Expectations should therefore remain ambitious but realistic. The journey toward AI leadership is likely to be measured in decades rather than years.

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