As India advances toward its Viksit Bharat 2047 goals, 2026 is emerging as a pivotal year for the country’s artificial intelligence strategy. With the AI Impact Summit 2026 set to be hosted in less than a month, policymakers and technologists are mapping a transition from the broad promise of “AI for All” to a more focused push toward “AI for Science.”
That shift was on display last week in New Delhi, where the policy paper India AI Gambit: Navigating the Global Race was released by the Strategic Foresight Group, in collaboration with NatStrat and Founding Fuel. The paper argues that India’s AI agenda in 2026 should concentrate on two fronts: accelerating scientific discovery and addressing national security risks.
Sundeep Waslekar, President of the Strategic Foresight Group, said that leading AI powers are already moving aggressively on both counts. While governments in the US, UK and China are investing heavily in AI for defence and security, they are also using advanced systems to reshape the scientific process itself. According to Waslekar, tasks that once “relied on humans and required years of research are now being given to complex LLMs,” particularly in domains such as biology, chemistry and physics.
He pointed to neuromorphic computing—hardware and software designed to mimic the structure and function of the human brain—as a critical frontier. “Other functions such as mathematical reasoning models, material prediction and developing new materials, especially rare earth extraction, must also be focused on. These are the places the money should go,” Waslekar said.
The paper contrasts this with India’s current trajectory. Rather than competing head-on in foundational AI models, India is positioning itself as a global hub for applied AI. Its ambition, the authors suggest, is to become the world’s “use case capital,” with a strong emphasis on social and economic applications. Much of the domestic AI ecosystem remains state-driven: entrepreneurs build government-facing large language models, funded by the public sector and deployed across public enterprises and departments, with benefits extended downstream to citizens. Large IT firms, meanwhile, focus on AI solutions for enterprise productivity rather than mass-market products.
Making that model viable at scale will require deep investments in efficiency. Manan Suri, a professor at IIT Delhi, argued that the long-term differentiator will be cutting the energy cost of AI itself. “From a long-term planning point of view, the key differentiator will be to make it energy-efficient AI,” he said, pointing to “low-power compute, chips, optical computing, photonic computing, neuromorphic computing—basically, anything that cuts down the energy budget of generating AI training and inferencing.”
On governance, India is also charting a distinct path. Preeti Banzal, senior scientist and adviser at the Office of the Principal Scientific Advisor to the Government of India, said the country has opted against a single, overarching AI regulator. “Instead of having a single AI actor, let us have central regulators improving their existing regulation, tweaking it if required to fit in AI nuances, and secondly, helping coordination between them to have a whole-of-government approach,” she said.
The paper also endorses softer policy measures, including a dedicated parliamentary debate on AI to define India’s long-term national position, broader institutional participation from agencies such as DRDO and ISRO, and the creation of an AI council. Such a body, it argues, could take a wider view of AI’s implications for national security and its potential to shape human destiny.
As India prepares to host the AI Impact Summit, India AI Gambit makes a clear case: the country’s place in the global AI race will depend not just on adoption at scale, but on how efficiently it computes—and how strategically it governs.
