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India AI Impact Summit: Spotlight on open networks, digital public infrastructure to scale AI across the Global South
At the India AI Impact Summit, James Manyika of Google and Alphabet, Nandan Nilekani of Networks for Humanity, Sangbu Kim of the World Bank Group, Kiran Mazumdar-Shaw of Biocon Group and Sunil Wadhwani of Wadhwani AI discussed how open networks and digital public infrastructure can enable AI to reach farmers, patients and students at a population scale.
The most striking aspect of the India AI Impact Summit was not the technology on display. It was the clarity of intent. This was a conversation about how to ensure that artificial intelligence (AI) does not remain confined to labs, large enterprises or high-income markets. It was about how to design systems that work for farmers, frontline health workers and school children.
James Manyika, President, Research, Labs, Technology and Society at Google and Alphabet, framed AI as both an extraordinary opportunity and a distribution challenge.
“At Google, we believe that access to AI is essential for unlocking opportunities and expanding the innovation capacity for people everywhere,” he said. The pace of AI progress, he noted, is “quite breathtaking” and already delivering tangible impact.
He cited AlphaFold, Google’s Nobel Prize-winning breakthrough in protein structure prediction, now used by over 3 million researchers across more than 190 countries. India, he pointed out, is the fourth largest user of the AlphaFold protein database, applying it to everything from neglected diseases to crop resilience.
But scientific achievement alone is not enough.
“We need to ensure that the digital divide does not become an AI divide,” Manyika warned, arguing that digital public infrastructure and open networks provide the coordination layer that translates human intent into real-world action.
AI agents, low-cost inference and diffusion
That emphasis on diffusion was echoed by Nandan Nilekani, Co-Founder and Patron, Networks for Humanity.“If AI is a general-purpose technology, what is the fastest way of diffusing the use of AI in a productive way for people?” Nilekani asked. “Ultimately, all this only makes sense if people’s lives improve.”
Drawing on India’s experience with open networks in payments, he argued that open architecture enables multiple innovators to build applications at the edge. AI agents operating on these networks can remove complexity for users.
“The real power of agents is in removing complexity for the user,” he said. A farmer or a small electricity producer should be able to transact in their own language, with the agent handling the backend intricacies.
Nilekani also focused on economics. “The cost of AI inference has to drop dramatically,” he said. In many parts of the Global South, high per query costs would make widespread adoption unviable. Low-cost inference, combined with agentic interfaces that hide complexity, is essential for population-scale impact.
Agriculture as a template
For Sangbu Kim, Vice President for Digital and AI at World Bank Group, agriculture offers a working template. Describing the AgriConnect initiative in Uttar Pradesh, Kim called it a “farmer-oriented approach” built on an open stack and open network. The broader shift, he suggested, is from supplier-oriented systems to user-oriented environments.Open standards are critical to ensure that services remain coherent, consistent and affordable in the AI era. The ambition is not limited to agriculture. The World Bank is exploring similar models in healthcare and education across multiple countries.
Kim cited examples from Kenya, where AI-powered math tutoring significantly accelerated learning outcomes, and Nigeria, where handheld ultrasound devices combined with AI are helping reduce infant mortality. The challenge now is replication.
“How can you really find some very standardised and scalable model?” he asked, noting that the World Bank’s role is to identify the critical components that can be adapted across developing economies.
Building a health stack
Healthcare may prove to be the most complex and consequential frontier. Kiran Mazumdar-Shaw, Chairperson, Biocon Group, argued that India is uniquely positioned to create a global reference model by building a comprehensive health stack on top of its existing digital infrastructure.She pointed out that the country is already generating large volumes of phenotypic, genomic, demographic and radiological data, along with detailed treatment and outcome records. With a consent-based and secure data-sharing framework in place, this foundation could enable AI systems to risk-profile populations at scale and design more responsive insurance frameworks. In her view, the larger objective is to use AI to advance universal healthcare delivery in a sustainable and scalable manner, bringing predictive and preventive capabilities to the forefront of care.
Mazumdar-Shaw also broadened the lens beyond deployment. Biology itself, she argued, offers insights for AI.
“Biology works through distributed data centres,” she said, pointing out that living systems process information with minimal energy compared to modern data centres. The convergence of biological intelligence and artificial intelligence, in her view, will be transformative.
“The future is going to be about the convergence of biological intelligence and AI,” she added.
DPI as the invisible backbone
If infrastructure is the theme, Sunil Wadhwani, Founder, Wadhwani AI, offered the most grounded examples of how digital public infrastructure (DPI) makes AI deployable.India’s DPI, he explained, provides two essential functions. First, it offers data and data pipelines. Second, it provides distribution channels to deliver AI models at scale once they are ready.
In tuberculosis care, access to the government’s Nikshay patient management system enabled Wadhwani AI to develop models that diagnose TB risk from the sound of a cough, automate lab testing workflows and predict which patients are likely to discontinue treatment. Detection rates have risen as a result.
In education, the organisation built a system that assesses a child’s reading proficiency in their mother tongue within seconds and generates a personalised remediation plan. Pilots led to statewide mandates covering millions of students at an extremely low cost per child.
“All enabled by DPI,” Wadhwani said, underscoring that without these digital rails, scaling such interventions would have been prohibitively expensive and slow.
From national model to global blueprint
Nilekani called for “massive diffusion” to demonstrate that AI is a force for good. Mazumdar-Shaw emphasised sustainable, high-quality universal healthcare built on predictive and preventive models. Kim spoke of disseminating practical use cases to countries that are still grappling with how to harness AI. Wadhwani pointed to growing demand from governments across Africa and Asia for solutions developed in India.Taken together, the discussion suggested that India’s AI strategy may not be defined by who trains the largest model. It may instead be judged by whether open networks and digital public infrastructure can turn AI into a public utility that works at a population scale.
If that experiment succeeds, it could reshape how the Global South approaches artificial intelligence.
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Source: EconomicTimes
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