China’s recent launch of DeepSeek, an advanced artificial intelligence (AI) model, has once again demonstrated the country’s growing prowess in AI research and development. This raises an important question: Why has India, despite its strong technological base and significant investments in AI, not been able to launch a comparable model?
Given that the Chinese claim to have built DeepSeek with just $7 million—an amount that is well within India’s AI budget—the issue is not one of funding. Instead, the answer lies in deeper structural, strategic, and policy-related challenges. This article explores these challenges and proposes a roadmap for India to strengthen its AI ecosystem.
Challenges Hindering India’s AI Development
Fragmented AI Research and Lack of Centralized Strategy: India’s AI research efforts are spread across various government agencies, private enterprises, and academic institutions, but there is no unified strategy to ensure their seamless collaboration. Unlike China, where AI development is heavily centralized and directed by state policy, India’s efforts are often fragmented, leading to inefficiencies.
Talent Drain and Skill Gaps: India produces some of the world’s best AI researchers, but many of them migrate to global tech hubs like Silicon Valley due to better research opportunities, funding, and work environments. While India has a vast pool of software engineers, expertise in deep learning, computational linguistics, and AI-specific hardware engineering remains limited.
Computational Infrastructure Limitations: Building large AI models like DeepSeek requires massive computational resources, including access to high-performance GPUs and TPUs. India lags behind China in developing indigenous AI-specific hardware and cloud infrastructure. This dependency on foreign semiconductor companies slows down AI advancements.
Data Accessibility and Privacy Regulations: China’s AI boom has been fueled by its access to vast datasets, which are relatively easy for companies and research institutions to collect and utilize due to lenient data privacy laws. In contrast, India’s data governance framework is more restrictive, making it harder to compile the massive datasets needed for training large AI models.
Industry-Academia Disconnect: In countries like China and the U.S., there is a seamless collaboration between academia, industry, and government. India’s AI development is hindered by a lack of strong partnerships between universities and private enterprises. This prevents the commercial application of research and slows down innovation.
What India Can Do to Improve Its AI Ecosystem
Establish a Central AI Task Force: India needs a centralized AI task force that coordinates efforts across government bodies, private sector players, and research institutions. A national AI roadmap, with well-defined short-term and long-term goals, can help bridge the gap between research and real-world applications.
Invest in AI Talent and Prevent Brain Drain: India should create competitive research environments to retain top AI talent. This includes increasing funding for AI PhD programs, creating more AI-focused institutes, and providing lucrative incentives for researchers to stay and contribute domestically.
Develop Indigenous AI Hardware and Compute Infrastructure: Building AI models like DeepSeek requires significant computational power. India must invest in AI-specific semiconductor research, build its own AI cloud computing facilities, and reduce dependence on foreign companies for computing resources.
Enhance Data Accessibility While Ensuring Privacy: The government should create a balanced data-sharing framework that allows researchers and companies access to large-scale datasets while ensuring user privacy and ethical AI practices.
Strengthen Industry-Academia Collaboration: To encourage innovation, the government should facilitate stronger partnerships between universities and AI startups. Joint AI research labs, incubators, and funding grants for applied AI research can help bridge the gap between theoretical research and industrial applications.
Conclusion
India has the potential to compete with global AI leaders, but achieving this goal requires a strategic and collaborative approach.
By centralizing its AI efforts, investing in talent and infrastructure, and fostering stronger industry-academia collaborations, India can accelerate its AI growth and develop models comparable to DeepSeek. With the right policies and initiatives, India can position itself as a global AI powerhouse in the coming years.
