India stands at a transformative crossroads in artificial intelligence, with experts urging strategic focus on foundational infrastructure rather than rushing to replicate global models like ChatGPT or China’s DeepSeek. Professor B Ravindran, head of the Wadhwani School of Data Science and AI at IIT Madras, emphasizes that India’s AI success must be built on robust data ecosystems and advanced computational infrastructure, not headline-chasing developments.
Strategic Foundation Over Quick Wins
Ravindran points to China’s decade-long strategic investment in DeepSeek as a blueprint for sustained AI advancement. “It didn’t happen momentarily. The Chinese government deliberately started investing in it over a decade ago,” he explains. India requires similar long-term commitment and deliberate policy support to achieve breakthrough AI capabilities.
Abhishek Singh, additional secretary at the Ministry of Electronics and IT and CEO of the IndiaAI Mission, highlights India’s core strength in human capital while acknowledging infrastructure gaps. The availability of nearly 40,000 GPUs marks a significant milestone, but scaling remains critical for competing globally.
Three Critical Gaps Demanding Immediate Action
According to the Carnegie Endowment research, India faces fundamental gaps in three areas: talent development, data infrastructure, and research capabilities. These missing pieces must be addressed urgently for India to compete effectively in the global AI race against superpowers like the United States and China.
The talent gap extends beyond basic AI skills to cutting-edge research capabilities. While Indian IT services firms will naturally upskill existing workforce, India needs to attract, nurture, and retain top-tier AI research talent to ensure breakthrough innovations emerge domestically.
Data represents another strategic challenge. Despite being among the world’s largest smartphone and digital transaction markets, most Indian digital data remains locked within platforms owned by global tech firms. India must build “digital public data” infrastructure and proliferate multilingual datasets to power India-specific AI models.
Infrastructure Surge Shows Promise
Sunil Gupta, CEO of data center provider Yotta, reports dramatic progress from zero GPUs to over 20,000 currently operational. However, India’s scale demands over 100,000 GPUs just for training purposes. This infrastructure scaling will enable commercial AI applications tailored to local needs.
CP Gurnani, former CEO of Tech Mahindra and founder of AIonOS, emphasizes that India isn’t trying to catch up but leapfrog entirely. Constraints often drive faster innovation, positioning India to solve unique challenges like judicial backlogs and agricultural productivity through AI applications.
Market Momentum Building Rapidly
AI inferencing platforms like Groq report massive Indian developer demand without local marketing presence. India represents the second-largest source of demand on Groq’s platform, demonstrating tremendous appetite for AI tools and applications.
The next six to nine months will witness launches of Indian AI models trained on local context and languages, according to Singh. These aren’t ChatGPT replicas but purpose-built solutions for India-specific problems in healthcare, agriculture, and governance.
Global Competition Context
The AI arms race intensifies as countries beyond the US-China duopoly develop national strategies. DeepSeek-R1’s January 2025 release sparked panic in American AI sectors, highlighting how quickly competitive dynamics shift in this space.
India must balance its “AI for All” approach with an aggressive competitiveness strategy. The Carnegie Endowment emphasizes that without significant resource allocation to data, talent, and R&D, India risks falling short of stated leadership ambitions.
Regulatory Balance Critical
Panelists stress that regulating powerful AI technology remains essential as misinformation, deepfakes, and espionage present huge challenges. India must develop frameworks that encourage innovation while protecting national interests and citizen privacy.
Litmus7’s chief scientist Shiju SS suggests India should explore AI architectures that address infrastructure constraints creatively. This approach could yield entirely new technological solutions rather than copying existing models.
Strategic Patience Pays Off
Ravindran advocates strategic patience over reactive development. Rather than replicating others’ successes, India can differentiate through AI applications addressing unique national challenges in healthcare, agriculture, and social services.
Four Indian firms currently build foundation models tailored to local languages and cultural nuances. This localization strategy positions India to serve its massive domestic market while developing exportable AI solutions.
The Path Forward
India’s AI future hinges on building strong foundations rather than chasing headline-making models. Success requires aligning AI growth with national strengths, societal needs, and economic priorities. The country must create sustainable ecosystems for AI research and commercialization while maintaining ethical practices and inclusive benefits.
Solidifying data infrastructure, computational capabilities, and research talent will empower India to emerge as a prominent AI leader ready to seize its deserved role on the world stage. The leapfrog moment approaches, but only through strategic patience and foundational investments.
What’s your take on India’s AI strategy—should the focus be on building infrastructure or racing to create the next ChatGPT? Share your perspective on this critical moment.
