Opinion At Paris AI Action Summit, much is riding on how India positions itself in the global AI landscape

The question many have now is: Where does India stand in the global landscape? With Prime Minister Narendra Modi and other major world leaders attending the AI Action Summit in Paris, much is riding on how India positions itself in the global AI landscape.

DeepSeek’s approach holds a lesson for India, besides being a source of hope. It shows that it is possible for India to kick-start its AI development journey on its own terms, without investing billions of dollars into building massive data centres or flooding the process with vast amounts of data to improve results. The architectural advancements made by China demonstrate that similar, or even better, AI models can be developed with much less money. This challenges several fundamental assumptions about AI progress — assumptions the US has been so confident about, treating its approach as the “gold standard”.

Some may call the Chinese lab’s attempt at a world-class AI model a fluke, because the US is still dominant in AI. But India needs to take this seriously and start working on a plan to focus more on research and development. DeepSeek’s success shows how the emphasis on research has played a critical role in the development of an AI model comparable to US companies like OpenAI and Meta, despite being trained with fewer resources. DeepSeek founder Liang Wenfeng avoided the traditional route of China copying the US in the past, such as building apps or making cheap smartphones. Instead, he focused on talent that could be used for research and resources on creating a model that could outperform OpenAI.

India has not yet had a golden moment in tech. We have never built global companies on the scale of Google or Apple, nor have we seen our tech go global. We have recently made some progress in fintech, with our Unified Payments Interface (UPI) becoming a global payment system — but that’s about it.

For India to succeed in AI, it needs to focus on two key areas. First, adopting a long-term AI-first approach, with an emphasis on developing AI technologies and creating tech platforms locally, while aiming to export these innovations globally. This means building AI technology from scratch — completely indigenous and not reliant on OpenAI or Google. India can score a lead in AI only if the government, institutions, and domestic companies come together to invest in research, create an ecosystem, and develop the right AI infrastructure. AI is complex and technical and requires a mix of programming, mathematics, and statistics. These skills are necessary to analyse data, develop efficient algorithms, and implement AI models. It requires smart people with the right technical skills, patience, and a long-term vision.

Similar to what China has done, India needs to set multiple smaller goals after identifying the sectors that require the most attention. However, achieving these goals may take years, and the right policies can accelerate progress.

In the immediate future, India’s greatest opportunity (which China does not have) lies in its geopolitical strategy, particularly in the context of AI. India stands to benefit the most by being a strategic ally of the US, ensuring a continued semiconductor supply and kickstarting the development of AI technologies. The second step, and perhaps the most important, is to collaborate on developing AI technologies, meaning India must work closely with the US and Europe and establish joint ventures (JVs) so that its local companies have equal representation.

However, while developing AI, India also needs to be a part of creating a specific framework around responsible AI development and use.
Perhaps it is also time for India Inc. to come together, create AI research labs spread across the country, and operate them independently. They should hire PhD-level students from IITs and other top technical institutions, pay them well so they stay in India, and let them develop and build foundational AI models while working on future AI technologies beyond LLMs.

India needs to realise that any nation that manages to scale up AI, democratise it, and generate developer interest — so they can reproduce and modify model weights and methods — is the one that will control the narrative and lead the AI arms race. Not to mention, LLMs are a sort of operating system — similar to Apple’s iOS and Google’s Android — on which developers can build new applications. With China aspiring to be the global leader in open-source AI, and the US’s best models being closed-source, the former has a better chance to expand AI at a much lower cost. But in tech, anything is possible, and India could jump in if it plays its cards right.