India stands at a critical juncture in the global AI race. With 15 per cent of the world’s AI talent, a vibrant start-up ecosystem, and increasing investments from major tech companies, the country has all the ingredients to become an AI powerhouse. Yet, despite these advantages, India is struggling to keep pace with global leaders like the US and China. The absence of a home-grown foundational AI model, limited research infrastructure, and inadequate government funding raise pressing concerns about India’s ability to lead in the AI revolution. While the government has promised rapid development of an indigenous AI model, the challenges are immense.
Countries like the US and China have spent years building robust AI ecosystems, investing heavily in research, academia, and military applications. The US has committed an astronomical $500 billion to AI infrastructure, while China has set aside $137 billion to establish itself as a global AI hub. In stark contrast, India’s AI mission is worth only $1 billion ~ a fraction of what is required to compete at a global level. Without significant state backing, it is unrealistic to expect Indian start-ups and universities to bridge this gap on their own. Another fundamental issue is the lack of high-quality, India-specific da – t asets. AI models require vast amounts of data to train, and India’s linguistic and cultural diversity add an extra layer of complexity.
Training models to understand and respond accurately in multiple Indian languages requires extensive resources, but little has been done to address this gap. Without access to well-structured datasets, even the most advanced AI models will struggle to be effective in the Indian context. Additionally, India’s AI sector faces a persistent brain drain. While Indian talent is sought after globally, many top AI researchers and engineers prefer to work in the US or Europe due to better funding, research facilities, and career opportunities. Unlike in China, where the government has built a strong academic and corporate AI research ecosystem, India’s research environment remains weak. Universities and private companies must work together to build long-term R&D capabilities rather than focusing solely on short-term commercial applications.
However, all is not lost. India has already demonstrated its ability to leapfrog technological gaps, as seen in the success of the Unified Payments Interface (UPI). The digital payments revolution was made possible through a strong partnership between the government, industry, and academia. This model needs to be replicated for AI ~ where state-backed initiatives provide critical infrastructure, private companies invest in innovation, and universities drive fundamental research. In the short term, India can accelerate its AI development by leveraging open-source models and existing global platforms. But in the long run, strategic autonomy in AI will require deep investment in semiconductor manufacturing, computational infrastructure, and local R&D. If India fails to act decisively, it risks becoming a mere consumer of AI technologies developed elsewhere rather than a leader in the field.