India is making strides to enhance its artificial intelligence (AI) capabilities, but the nation must overcome considerable challenges to avoid falling further behind in the global AI race. Following the remarkable success of China's DeepSeek, which has driven down generative AI application development costs, India aims to create its own foundational language model to power chatbots and similar technologies. However, industry experts argue that essential infrastructure and investment gaps may hinder this ambition.
Despite official claims of progress, including providing thousands of high-end chips to startups and researchers for development within ten months, critics are skeptical. India is witnessing a surge in entrepreneurial activity, with over 200 startups engaged in generative AI technology. Still, India’s foundational model lag is evident when juxtaposed with major players like the US and China, which have made extensive investments in AI research, military applications, and large language models.
Though it ranked in the top five on Stanford's AI Vibrancy Index, India has yet to establish a comparable patent and investment landscape. From 2010 to 2022, China and the US were awarded a staggering 60% and 20% of global AI patents, while India secured less than half a percent, reflecting its challenge in fostering a research-intensive environment. State-funded AI initiatives also pale in comparison to the multi-billion dollar investments made in the US and China.
Furthermore, the outflow of AI talent, with many top professionals leaving India for opportunities abroad, exacerbates the country's struggles in transforming academic research into impactful innovations. The collaborative approach that fueled India's digital payment revolution must be replicated in the emerging AI landscape, with robust partnerships among government, industry, and academia being essential for growth.
While the success of DeepSeek demonstrates the potential for foundational AI models to be created with accessible resources, current conditions in India, including a lack of quality datasets for training models in regional languages, pose additional barriers. Moving forward, experts assert, building foundational capabilities will not only aid India's strategic autonomy but also mitigate dependencies on external entities. The need to strengthen the nation’s semiconductor manufacturing capabilities is also crucial to achieving meaningful progress in the AI sector.
Ultimately, the path ahead demands a concerted effort from all sectors to ensure India capitalizes on its inherent talent and continues to compete on the global stage in artificial intelligence development.
Despite official claims of progress, including providing thousands of high-end chips to startups and researchers for development within ten months, critics are skeptical. India is witnessing a surge in entrepreneurial activity, with over 200 startups engaged in generative AI technology. Still, India’s foundational model lag is evident when juxtaposed with major players like the US and China, which have made extensive investments in AI research, military applications, and large language models.
Though it ranked in the top five on Stanford's AI Vibrancy Index, India has yet to establish a comparable patent and investment landscape. From 2010 to 2022, China and the US were awarded a staggering 60% and 20% of global AI patents, while India secured less than half a percent, reflecting its challenge in fostering a research-intensive environment. State-funded AI initiatives also pale in comparison to the multi-billion dollar investments made in the US and China.
Furthermore, the outflow of AI talent, with many top professionals leaving India for opportunities abroad, exacerbates the country's struggles in transforming academic research into impactful innovations. The collaborative approach that fueled India's digital payment revolution must be replicated in the emerging AI landscape, with robust partnerships among government, industry, and academia being essential for growth.
While the success of DeepSeek demonstrates the potential for foundational AI models to be created with accessible resources, current conditions in India, including a lack of quality datasets for training models in regional languages, pose additional barriers. Moving forward, experts assert, building foundational capabilities will not only aid India's strategic autonomy but also mitigate dependencies on external entities. The need to strengthen the nation’s semiconductor manufacturing capabilities is also crucial to achieving meaningful progress in the AI sector.
Ultimately, the path ahead demands a concerted effort from all sectors to ensure India capitalizes on its inherent talent and continues to compete on the global stage in artificial intelligence development.
