AI for Social Good Using Technology to Create Real-World Impact

20 Feb 2026 10:00h - 11:00h

Session at a glance

Summary

This discussion at the India AI Impact Summit focused on how open networks and digital public infrastructure (DPI) can enable AI to deliver transformational change at population scale, particularly in developing countries. James Manyika from Google moderated a panel featuring leaders who have pioneered digital infrastructure solutions in India and are now scaling them globally.

The conversation centered on India’s success with open network systems like UPI for payments and how similar approaches can democratize AI access. Nandan Nilekani, architect of India’s digital infrastructure, emphasized that open networks allow multiple innovators to build AI applications that remove complexity for users, enabling farmers and other citizens to interact through AI agents in their local languages. This approach has proven successful with systems like Bashini, which supports over 100 Indian languages.

The World Bank’s Sangbu Kim discussed the AgriConnect initiative in Uttar Pradesh, which provides multilingual AI agents to help farmers with everything from credit to crop prediction. This model is being replicated in countries including Brazil, Nigeria, Ethiopia, and Kenya, demonstrating how locally successful solutions can scale globally through standardized, open architectures.

Kiran Mazumdar-Shaw highlighted AI’s potential to transform healthcare through India’s emerging health data stack, enabling risk profiling at demographic scale and supporting universal healthcare delivery. She also discussed the convergence of biological and artificial intelligence, noting how biological systems achieve complex processing with minimal energy consumption.

Sunil Wadhwani shared concrete examples from Wadhwani AI’s work, including AI systems that diagnose tuberculosis from cough sounds and assess children’s reading abilities in 20 seconds, both enabled by government DPI platforms. These solutions have achieved remarkable scale, with reading assessment tools reaching millions of students across multiple Indian states.

The panelists agreed that the key to population-scale AI impact lies in dramatically reducing inference costs, maintaining open and decentralized networks, and ensuring solutions work in local languages. The discussion concluded with each speaker outlining their vision for the next 12 months, emphasizing the need to demonstrate AI as a force for good and to scale successful Indian models globally through organizations like the Networks for Humanity Foundation.

Keypoints

Major Discussion Points:

– AI-powered Digital Public Infrastructure (DPI) as a foundation for global impact: The discussion emphasized how open networks and digital public infrastructure can serve as coordination layers that allow AI to translate human intent into real-world action at population scale, with India’s UPI and Bashini network cited as leading examples.

– Language accessibility and multilingual AI agents: Multiple speakers highlighted the critical importance of making AI accessible in local languages, including mixed-language conversations common in India, to ensure true inclusion and remove barriers for farmers, healthcare workers, and other users.

– Scaling successful models globally through open networks: The conversation focused on how proven solutions developed in India (like AgriConnect in Uttar Pradesh) can be adapted and scaled to other countries including Brazil, Nigeria, Ethiopia, and Kenya through standardized, interoperable frameworks.

– Convergence of biological intelligence and artificial intelligence: Kiran Mazumdar-Shaw presented a vision of learning from biology’s distributed data centers and energy-efficient processing to improve AI systems, while using AI to unlock deeper insights into biological processes for healthcare transformation.

– Cost-effective AI inference for population-scale deployment: The panel discussed the necessity of dramatically reducing AI inference costs to make solutions viable for serving millions of users in the Global South, particularly for applications in healthcare, education, and agriculture.

Overall Purpose:

The discussion aimed to explore how open networks and digital public infrastructure can create globally interoperable systems powered by AI to deliver population-scale impact, particularly in transforming education, healthcare, and agriculture across developing nations.

Overall Tone:

The tone was consistently optimistic and collaborative throughout, with speakers demonstrating genuine enthusiasm for AI’s potential to solve societal challenges. The conversation maintained a forward-looking, solution-oriented approach, with panelists building on each other’s ideas and sharing concrete examples of successful implementations. There was a strong sense of urgency about ensuring AI benefits reach everyone, not just privileged populations, and the tone reflected both ambition and responsibility in pursuing these goals.

Speakers

Speakers from the provided list:

– Speaker 1: Role not specified, appears to be a moderator/host for the India AI Impact Summit

– James Manyika: Senior Vice President at Google, leading research, labs, and technology in society; served as co-chair of the UN’s High-Level Advisory Board on AI

– Nandan Nilekani: Co-founder and chairman of Infosys; global leader in digital public infrastructure; co-founder of Networks for Humanity

– Sangbu Kim: World Bank’s Vice President for digital and AI, leading efforts to drive digital economy growth in developing countries by strengthening infrastructure, cybersecurity, data privacy, while modernizing government services

– Kiran Mazumdar-Shaw: Chairperson of Biocon Group; pioneering biotech entrepreneur, healthcare visionary, and philanthropist committed to expanding access to healthcare through affordable innovation

– Sunil Wadhwani: Visionary entrepreneur and philanthropist who co-founded the Wadhwani Institute for Artificial Intelligence to drive systematic and systemic social transformation through AI solutions and innovation in public systems across healthcare, education, and agriculture

Additional speakers:

None identified in the transcript.

Full session report

This discussion at the India AI Impact Summit represented a comprehensive examination of how open networks and digital public infrastructure (DPI) can serve as foundational coordination layers for delivering AI-powered transformation at population scale, particularly across developing nations. The conversation, moderated by Google’s James Manyika, brought together leading voices who have pioneered India’s digital infrastructure revolution and are now working to scale these innovations globally.

The Foundation: Open Networks as AI Enablers

The central thesis of the discussion was that AI’s transformative potential can only be realised through coordinated systems built on open, interoperable networks. The moderator established this context by emphasising that whilst AI holds tremendous promise for transforming education, healthcare, and agriculture, this impact requires coordination built into the system from the ground up. This coordination layer enables AI to translate human intent into real-world action across borders and sectors.

James Manyika highlighted Google’s commitment to ensuring that the digital divide does not become an AI divide. He presented compelling evidence of AI’s current impact, citing AlphaFold’s protein structure prediction breakthrough, which has been used by over 3 million researchers across 190 countries, with India being the fourth largest adopter and user of the protein database. This example demonstrated how freely available AI tools can drive scientific advancement when proper infrastructure exists to support widespread access.

Manyika also announced Google’s collaboration with the Uttar Pradesh government to create a Gemini-powered open network for agriculture, providing farmers with multilingual AI agents that can facilitate everything from credit access to crop prediction. This initiative exemplifies how open networks can enable AI solutions to reach populations at scale.

India’s Digital Infrastructure as a Global Model

Nandan Nilekani, architect of India’s digital transformation, provided crucial insights into how open networks enable massive technology diffusion. Drawing from India’s success with UPI, which became the world’s largest payment system through its open architecture, Nilekani argued that open networks allow multiple actors and innovators to build AI applications that remove complexity for end users. His vision centres on AI agents operating on open networks as the fundamental construct for massive technology diffusion, particularly when these agents can interact with users in their local languages.

Nilekani provided compelling examples of how open networks enable rapid capability integration and new economic opportunities. He described how Google’s improved weather models could be instantly plugged into agricultural networks, immediately providing millions of farmers with access to the latest weather predictions. He also outlined how farmers could sell excess rooftop solar energy through simple interfaces, with AI agents handling the complex energy trading processes in the background whilst presenting users with straightforward options.

Language Accessibility and Multilingual AI

The language accessibility challenge emerged as a critical theme throughout the discussion. India’s linguistic diversity—with users commonly mixing English, Hindi, and regional languages within single sentences—requires sophisticated AI systems capable of processing this multilingual complexity. Manyika highlighted Google’s Project Vani, which has completed its second phase covering every Indian state and making speech data for over 100 Indic languages freely available, including 20 languages that had never been recorded digitally before.

This multilingual capability is essential for ensuring that AI benefits reach all populations, not just those comfortable with English or other dominant languages. The speakers emphasised that true population-scale impact requires AI systems that can understand and respond in the languages people actually use in their daily lives.

Concrete Impact: Education and Health Solutions at Scale

Sunil Wadhwani from the Wadhwani Institute for Artificial Intelligence provided concrete examples of how digital public infrastructure enables AI solutions to achieve remarkable scale and impact. Opening with humour about being invited “for his good looks,” Wadhwani outlined how his organisation has developed over 25 AI platforms across education, healthcare, and agriculture, with striking examples demonstrating the power of DPI-enabled AI deployment.

In healthcare, Wadhwani AI addressed tuberculosis—which kills close to 2 million people globally and close to half a million people annually in India—through AI solutions that diagnose TB from cough sounds captured on smartphones. This innovation, enabled by access to the government’s NICSHA database containing data on all detected TB cases, has increased national TB detection by 25% whilst providing same-day results. The system also predicts which patients are likely to discontinue treatment, allowing healthcare workers to focus their limited resources on high-risk cases.

In education, the organisation developed a system that diagnoses reading difficulties in children within 20 seconds through speech analysis, providing personalised remediation plans at a cost of just 5 paise per student. This solution addresses the primary cause of high dropout rates in early grades—reading difficulties that affect performance across all subjects. The system has been scaled through Rajasthan’s state DPI called “Rakshak,” reaching 400,000 schools and 8 million students, with plans for broader deployment.

These examples illustrate how DPI provides both the data pipelines necessary for AI development and the distribution channels required for population-scale deployment. Without these government-managed platforms, such solutions would be prohibitively expensive and impossible to scale effectively.

Global Scaling: From Nigeria to Brazil

The World Bank’s Sangbu Kim presented concrete examples of how locally successful solutions can achieve global scale. He described initiatives in Nigeria where handheld ultrasound devices are reducing baby death rates, and educational interventions that compressed what typically takes one year of academic achievement into six weeks. Kim positioned the World Bank as a “sommelier” in this process—not creating innovations but identifying successful models and matching them to different countries’ specific needs and capabilities.

This approach recognises that whilst core principles may be universal, implementation must account for local conditions and requirements. The AgriConnect model is being expanded to Brazil, Nigeria, Ethiopia, Kenya, and the Philippines, demonstrating how India’s blueprint can be localised across diverse contexts.

Healthcare Transformation and Biological Intelligence

Kiran Mazumdar-Shaw, chairperson of Biocon Group, presented perhaps the most visionary perspective on AI’s potential in healthcare. She outlined how India’s emerging health stack could collect comprehensive health data—phenotypic, genomic, demographic, and radiological—to enable risk profiling at demographic scale. This data foundation, combined with AI analysis, could enable universal healthcare delivery whilst reducing disease burden and increasing lifespan.

Mazumdar-Shaw’s most forward-looking contribution involved the convergence of biological intelligence and artificial intelligence. She described how biological systems demonstrate remarkable efficiency and capability, citing Arctic terns that can navigate 70,000 kilometres on their first flight using navigational intent embedded in their DNA through generational learning. Biological systems operate through distributed data centres that connect and disconnect using minimal energy—”sips of energy, not gigawatts”—whilst achieving rapid information processing and decision-making.

This biological intelligence operates through principles that could revolutionise AI: distributed processing, energy efficiency, rapid multimodal data integration, and generational memory storage. Her vision extends to reprogramming cells, particularly converting cancer cells into non-malignant ones, and advancing regenerative science through the convergence of biological and artificial intelligence.

Economic Sustainability and Cost Challenges

A critical theme throughout the discussion was the economic dimension of AI deployment at scale. Nilekani emphasised that AI inference costs must drop dramatically for global south applications, noting that serving customers with expensive queries would be unsustainable for population-scale deployment. He predicted that as the focus on training larger models stabilises, attention will shift to making inference cheaper—a crucial requirement for widespread adoption.

This economic reality connects to broader questions about global scaling and the need for AI solutions that are not only technically effective but also economically viable for developing economies. The speakers demonstrated that achieving population-scale impact requires solutions that can operate profitably at very low per-user costs.

Data Sharing and Global Collaboration Challenges

Mazumdar-Shaw highlighted a significant scaling challenge: resistance to data sharing beyond India’s model. She noted that most organisations worldwide are reluctant to share data due to intellectual property concerns, creating data silos that limit AI effectiveness. India’s unique approach to consent-based secure data sharing, already established through systems like UPI, provides a potential model for addressing this challenge globally.

This tension between data protection and AI effectiveness represents a key challenge for scaling successful models across different regulatory and cultural contexts.

Future Commitments and Vision

The discussion concluded with each speaker outlining their vision for the next 12 months. Nilekani emphasised the need for massive diffusion of applications on open networks to demonstrate AI as a force for good. Mazumdar-Shaw called for sustainable, high-quality universal healthcare emerging from AI efforts and health stack development. Wadhwani highlighted the opportunity to scale India’s 25 AI platforms globally, responding to significant demand from developing countries. Kim focused on disseminating successful use cases to help developing world populations understand AI’s affordable capabilities.

Manyika concluded by noting the example that India and the panelists are setting for the world, encouraging attendees to visit the Google.org Impact Challenge booth to learn more about ongoing initiatives.

Implications for Global AI Development

This discussion represents more than a showcase of Indian innovations; it presents a comprehensive framework for AI deployment that prioritises inclusion, accessibility, and population-scale impact. The emphasis on open networks, multilingual capabilities, and user-centric design offers an alternative to proprietary, centralised approaches to AI development.

The conversation reveals how digital public infrastructure can serve as a force multiplier for AI impact, providing the coordination layers necessary to translate technological capability into social benefit. The integration of biological intelligence principles, the focus on dramatic cost reduction for inference, and the commitment to maintaining open, decentralised architectures all point towards a more sustainable and inclusive approach to AI advancement.

The discussion ultimately demonstrates that achieving AI’s beneficial potential requires not just technological innovation but also institutional innovation, economic sustainability, and a fundamental commitment to ensuring that the benefits of AI reach everyone. The coordination layers provided by open networks and digital public infrastructure emerge as essential infrastructure for this inclusive AI future, with India’s model of “develop in India, deliver to the world” representing a new paradigm for technology leadership that emphasises practical solutions for developing economies.

Session transcript

Moderator

Because we believe that AI’s true potential lies in its ability to deliver population -scale impact, transforming education, healthcare, and agriculture for every citizen. However, that impact can only be possible when there’s coordination that’s built into the system. And therefore, today, we are here joined by global leaders to explore how open networks and digital public infrastructure can create a global, interoperable coordination rail, powered by AI to translate intent into action across borders. To set the stage, it’s my honor to introduce James Manika. James is the Senior Vice President at Google, leading research, labs, and technology in society. He also served as the co -chair of the UN’s High -Level Advisory Board on AI. James, welcome. The floor is yours to set the stage.

James Manyika

Thank you, Ashwani. Good morning, everyone. It’s a real pleasure and privilege to be back in India and to join all of you here at the India AI Impact Summit. At Google, we believe that access to AI is essential for unlocking opportunities and expanding the innovation capacity for people everywhere. The rapid technological progress that we’re seeing in AI’s development is really quite breathtaking and represents an extraordinary opportunity to solve problems and empower people, power economies, advance science, and tackle some of society’s greatest challenges. Indeed, we’re beginning to see the impact of this, so it’s not just in the future, but we’re already starting to see some of these benefits. benefits and impacts materialize today. Take science, for example.

Five years ago, our AlphaFold system, which is our Nobel Prize winning innovation, solved the 50 -year grand challenge of protein structure prediction. And since then, the freely available AlphaFold protein database has been used by more than 3 million researchers in over 190 countries. And in fact, India is actually the fourth largest adopter and user of the protein database, where people are working on a variety of problems, everything from neglected diseases all the way to even breeding resistance, soya beans, and a whole range of things that are incredibly beneficial to people in India and beyond. But to take full advantage of this potential, we need to collectively expand access right from the beginning. As you may have heard our CEO Sundar Pichai say yesterday, we need to ensure that the digital divide does not and AI divide.

Digital public infrastructure and open networks are an important part of making this possible. They provide the coordination layer that allows AI to translate human intent into real -world action. And India has been leading the way with systems like UPI and Bashini network and infrastructure, bringing the capabilities of AI into the daily lives of people across the country and at population scale. At Google, we’ve been a very committed partner in this journey by helping to build the foundations that help to scale it. For instance, our collaboration with the Indian Institute of Science, and in particular on Project Vani, has now completed its second phase, where we’ve been covering every Indian state, making speech data for over 100 Indic languages available for free.

And we’ve been able to do this through the government of India’s Bashini mission. In fact, this includes 20 languages that had never been spoken before. been recorded before digitally that we’re now building onto these systems in ways that truly try to attempt to reflect India’s true linguistic and cultural richness and diversity. And we continue to build on our commitment to drive scaled impact at the grassroots level. This commitment to scaled impact is reflected in our recent partnership with the World Bank, and I’m sure we’ll talk about this later today. Together we’re taking a blueprint born right here in India and scaling it by localizing it across the globe to countries from Brazil to Nigeria, Ethiopia, and Kenya.

And the heart of this blueprint began with our partnership with the government of Uttar Pradesh. There we piloted a Gemini -powered open network for agriculture that provides farmers with multilingual AI agents to facilitate everything from credit to crop prediction. By taking the lessons we learned in Uttar Pradesh, where digital tools drove real… measurable impact. We’re proving that a small holder farmer can compete and execute on the value that they create rather than the platforms that they’re on. This isn’t just a regional success. It’s now a global architecture and a model that can be taken everywhere for global digital inclusion. The success of these networks depend on a single fundamental principle. It must remain decentralized and open.

This is the driving force behind our support for the Networks for Humanity Foundation. Again, one of the things we’ll talk about this morning. And through a $10 million Google .org grant that we announced last year, the Network for Humanity Foundation is building the universal tools for tomorrow from the FinInternet, for asset tokenization, to the BEK and open networks. And by establishing innovation labs from Singapore to Switzerland, they’re ensuring that the that the infrastructure of opportunity is a global standard and not just a local exception. Having this type of infrastructure in place is what will allow all of us to collectively achieve population scale change. That’s why we’re supporting change makers like Wadwani AI through Google .org grants that try to embed intelligence directly into the digital rails for millions of Indians to be able to use.

In healthcare, for example, this means empowering something like 1 .4 million frontline workers with multilingual AI assistance, providing early warnings to combat child malnutrition across the country. In agriculture, it means integrating AI into the national pest surveillance system to protect India’s most important crops at a national scale. And in education, it means integrating AI into the national pest surveillance system to protect India’s most important crops at a national scale. And in education, it means delivering high -quality learning experiences through AI -led transformation of government government -owned education and development platforms. And this is an initiative that’s already reached 10 million students and educators with the goal of empowering as many as 75 million students and nearly 2 million educators by the end of 2027.

Ultimately, to fully capture AI’s beneficial potential, we must be bold and responsible and be committed to building all of this together. We must pursue AI’s most ambitious possibilities while ensuring that we build the coordination layer necessary to bridge and close the AI divide. With that, it is now my great pleasure and honor to welcome an extraordinary group of incredible leaders and innovators to the stage. We’ve been doing this for an extraordinarily long time with incredible impact. First, I’d like to invite Nandan Nilikani. Nandan is the…

Nandan Nilekani

Thank you.

James Manyika

Nandod is the co -founder and chairman of Infosys. He’s a global leader in digital public infrastructure and the co -founder of Networks for Humanity, an initiative building open, interoperable digital infrastructure for the intelligence age. I should say I’ve known Nandod for a very long time. When he first told me what he was working on 15 years ago, I’m not quite sure I quite believed him, but here we are. Next, joining us is Sang -Boo Kim. Sang -Boo is the World Bank’s vice president. Sang -Boo is the World Bank’s vice president for digital and AI, leading efforts to drive digital economy growth in developing countries by strengthening infrastructure, cybersecurity, data privacy, while modernizing government services and also touching many areas like health, education, and more.

Our third guest… is Kiran Mamzouma -Shaw. As chairperson of Biocon Group, Kiran is a pioneering biotech… Kiran is a pioneering biotech entrepreneur, health care visionary, and a passionate philanthropist committed to expanding access to health care through affordable innovation. And finally, please welcome Sunil Wadhwani. Sunil is a visionary entrepreneur and philanthropist who co -founded the Wadhwani Institute for Artificial Intelligence to drive systematic and systemic social transformation through AI solutions and innovation in the public systems across health care, education, and agriculture. So we’re now going to have a conversation. I can’t wait to do a conversation with these extraordinary leaders. Thank you. Nanda, let me start with you. You’ve been championing digital decentralized ecosystems for a very long time, building open networks, taking things to extraordinary scale in India, and recently with Bakken and Finantech.

And obviously you bring a lot of credibility to both users of these systems and to regulatory bodies. How do you see AI as a multiplier or a factor as you think about open networks and the kind of transformational change you’ve been pursuing?

Nandan Nilekani

No, I think AI is very fundamental, and I’ll explain how open networks and AI come together. I think what some of us have been thinking about is if AI is a general purpose technology, what is the fastest way of diffusing the use of AI in a productive way for people? And, you know, ultimately all this, it only makes sense if you can do it. people’s lives improve. And I think we have a lot of experience with open networks. I mean, in some sense, UPI was an open network for payments, and the open architecture led to the massive growth and became the world’s largest payment system. So a lot of those principles are embedded in Beckon, and we have other examples.

But I think open networks allows many actors, many innovators to build applications on the edge using AI. And I think we keep talking about agents, but I think the real power of agents is in removing complexity for the user. So if a user is there who is a farmer or somebody who is producing a little bit of electricity, if they can very easily transact with somebody else through an agent, which is in their own language, then suddenly this is inclusion at massive scale. So I see really AI… agents on an open network as the fundamental construct for massive diffusion of technology.

James Manyika

And also the importance, as you mentioned, of doing that in languages, in local languages.

Nandan Nilekani

Oh, totally. I think you talked about what you’re doing at ISE. I think there are many initiatives in India which essentially are driving to make language completely accessible. Because language is not just pure language. I mean, the way Indians speak, they mix the English, Hindi, and Tamil in one sentence. So how do you deal with that? How do you recognize that? So I think all that is getting addressed. There are many initiatives, voice AI, there’s Bhashini of the government, there’s AI for Bharat, there’s the Google project. So I think there’s lots of stuff. But fundamentally, I think language as a barrier will go away. So if you combine language, so a person talks to the agent in their own language, and then the agent does some transaction with hiding all the complexity behind it, then, you know, that’s the holy grail.

We can get everybody on the system, and that’s how AI will get diffused.

James Manyika

And then speaking of, you mentioned farmers and agriculture. Sangbo, let me come to you. I mean, the World Bank recently launched AgriConnect initiative. First of all, I’d like you to describe that a little bit. I think it’s intended to make what smallholder farmers do much, much more efficient and scalable. But I’m curious, what has that work taught you so far about the type of global standards that are going to be needed to scale local solutions?

Sangbu Kim

So, if you just go to look about the AgriConnect in Uttar Pradesh for now, so it is a very farmer -oriented approach to provide very coherent and consistent services at the same time with open stack, open network. That means, if you think about the previous day, from the… computer innovation, mobile innovation, now we are seeing AI innovation. I would interpret this evolution from the supplier -oriented service environment to the customer, user -oriented environment. In that sense, some open standard and open network is a really crucial part to make sure user -centric service. So it is very efficient and affordable solutions for an AI era to fully provide quality of service to the user. In that sense, this AgriConnect project is really important, but it is not only for agriculture project.

With that, we are really looking forward to expanding to another sector like healthcare and education. So it can be a very… universal network in the future.

James Manyika

That’s pretty powerful. In fact, speaking of health care, you mentioned you’re taking this to health care. Kieran, you’ve been an incredible advocate and innovator when it comes to thinking about medicine as a whole. And you’ve talked about this idea that we need to move beyond the industry of medicine. And tell me, say more about what you have in mind about what we need to move to, and in particular, how you can connect what’s going on maybe with AI and data sets with fundamentally transforming medicine.

Kiran Mazumdar-Shaw:

So I think I have to answer this in two parts. The first part is how do we basically leverage what Nandan refers to as the digital stack to a health stack. That is the first big opportunity we have. And I think India is a country that can uniquely create a global reference model when it comes to… The use of AI in the kind of… health data that we are collecting. So, for instance, India is beginning to collect a lot of health data in its health stack, and it’s phenotypic, it’s genomic, it’s demographic, and radiological data, and, of course, treatment and treatment outcome data. Now, when you start collecting this data, I think the whole objective, again, which is a holy grail, which is universal healthcare delivery at scale in a sustainable way, and how do we reduce the disease burden, I mean, and increase lifespan, all these are big challenges and a very complex set of solutions.

But I think this is a starting point where you get this huge digital stack of health data. And because India has this open source and the consent -based kind of secure data share, already established in UPI. I think we should quickly apply this to healthcare. And when you do that, you will start risk profiling your population at a demographic level, which I think is very exciting and at scale. And if you can integrate insurance into that, that will be even more powerful. That can only be done by AI. So AI has the opportunity to risk profile very fast, to try and find interesting insurance models, to see how we can marry the risk profile with the insurance instrument.

Not easy, but I think it’s a good challenge because AI can be given a lot of exclusion -inclusion criteria, which it can adopt. So I personally am very excited with what AI can do for health, digital health, and the whole universal healthcare delivery model. Additionally, of course, India has this unique model of ASHA work, because if ASHA work is not done, can be empowered with AI that is even more powerful. So I think you know deploying AI for the common people, the common man is very important to both Nandan and Samguth’s point of view. Now coming to my excitement about your second question about what is it that I’m looking beyond this. Beyond this I’m looking at advancing medicine using AI.

Now biology on its own was limited because it didn’t have the kind of power of technology to get deeper insights. AI like what you’ve just done, alpha fold, alpha genome is going to give it immeasurable opportunities to understand biology and to me biological intelligence is just amazing. And if you combine it with artificial intelligence and convert bring that convergence I think we are in for huge And when I look at biological intelligence, when I just look at cell biology, how cells signal, how cells create circuits, how cells regulate, how cells connect and disconnect. I mean, the human body, living systems, have distributed data centers. And these data centers are connecting, disconnecting with sips of energy, not gigawatts of energy.

And they’re actually translating that into instant information and decision making. If we can learn that and apply it to AI, I think it’s going to be transformational. I am really looking forward to reprogramming cells, right? That’s the holy grail. How do you convert a cancer cell into a non -malignant cell? How do you basically look at regenerative science? How do you look at lifespan? I mean, your biggest question today. Right. How do we shift from… from hospital -centric care to primary and community care. That can happen with AI, with predictive and preventive medicine. That’s, I think I’ve said enough. Yeah.

James Manyika

Well, it would actually take us, in effect, Kiran, it would probably take us from kind of treating diseases to preventing diseases. And I like it. You and I were talking earlier. You and I and Demis were talking about this idea of someday we should be able to try to build virtual cells, models of virtual cells, and be able to do kind of cell -based biology, basically.

Kiran Mazumdar-Shaw:

Absolutely. That’s one of the exciting things. It’s very exciting. Yeah.

James Manyika

We’ll come back to that. But I want to come to you, Sunil, which is, you know, I’m curious, Sunil, as you think about what you’ve been doing, what role does DPI and open networks pay in developing and scaling the kinds of solutions to some of society’s most pressing problems? I mean, you’ve been thinking about this for a very long time. I mean, from way back. When you set up the AI. Institutes, way before most people were thinking about these things. But I want to hear what your perspectives and experiences have been.

Sunil Wadhwani

Thanks, James. Good morning. Just so we’re all clear, there’s a lot of intellectual horsepower on the stage, and it’s all on this side of me. I’m basically here for my good looks, so just so we manage expectations. But when I set up Vadbani AI back in 2018, and Prime Minister was good enough to come and inaugurate that, basically we had a huge benefit, which is the following, to your point, that over the last 20 years, the government of India has developed a set of DPI, Digital Public Infrastructure, that is broad and that is deep. And this DPI are basically digital building blocks that are going to be used to build a system of infrastructure that connect policy, program implementation, public service workers, and citizens.

in the country. And I’ll give you a couple of examples. But these, again, these digital public, this DPI provides two key, very practical, down -to -earth functions and benefits, as I see it. Number one, they provide data and data pipelines. And for AI, you couldn’t build AI for the social sector without the kind of data and the data pipelines that they provide. Secondly, this DPI provides distribution channels so that once your inference models are ready, these AI platforms, again, developed and managed by government, provide a distribution channel to get our AI models out at scale. Without these, trust me, the usage of any model in the public sector, in the social sector, would cost incredibly more and wouldn’t scale anywhere near what we see.

So, two quick examples. One in healthcare, one in… education. So one of the challenges, one of the national health priorities for the government of India for the last several years has been the elimination of tuberculosis, TB. It’s the largest infectious disease killer in the world, kills close to 2 million people a year. It’s the largest infectious disease killer in India, kills close to half a million people a year in India. And for each person that unfortunately dies, 20 other survivors live miserable lives and it impacts their life, their ability to earn a living. So the government asked us to come in and see what we could do. And we identified with the government what are the three or four key pain points in the patient’s journey.

First one is diagnosis and diagnosing TB in economically vulnerable communities isn’t easy. X -ray machines, sputum analysis, etc. These are all challenging, they’re expensive, they’re time -consuming, they’re tedious. So that’s challenge number one. Secondly, if you do sputum analysis, these have to go to 64 government labs around the country. There’s throughput time, and by the time the patient gets the results back, you’ve lost some time in initiating treatment for the people who have TB. And finally, there’s a huge problem that there’s a subset of TB patients who stop taking their medication because it’s a very toxic regimen of medications, which has a very toxic effect on the body. The problem is once you stop taking them, you develop drug -resistant TB, then the mortality rate goes up dramatically to 50%, and then you infect a lot more people and so on.

So fortunately, the government has a DPI called Nixia. It’s a very large data platform. It’s a patient management system that has data on all of the TB. They detected TB cases in the country. Government gave us access to that database. We developed a range of models. And… And to address these challenges that we saw in the patient care journey, for diagnosis, we’ve come up with a way of diagnosing TB from the sound of a cough into a smartphone. It’s instant. It’s quick. It shows you what the risk is of that patient having TB, and so government workers can focus on those patients. In the one year or so that this program has started getting rolled out, the diagnosis part using the sound of a cough, detection of TB patients has gone up by 25 % nationally.

You may think that’s bad news because now there’s more TB cases, but they were there. But now we can make sure they get treated. On the labs and the turnaround time, we’ve come up with an AI way to automate a lot of this testing, so now literally you get the results the same day. Patient and the doctor finds out instantaneously, and then they can start treatment. On the issue of patients who develop drug -resistant TB, we’ve come up with algorithms that predict. predict which TB patients are likely to fall off their medication regimen. And so the 2 ,000 or so TB caseworkers in the country, which is a small number for the 4 or 5 million TB patients that we have, but now they can target their time and bandwidth on their subset of patients that really needs help.

But all of that was enabled by this DPI called NICSHA, this database, which enables all of this. One other quick example in the education space. In the global south, including in India, there’s a very high dropout rate of students in early grades, grades 1 to 5. So we got a call from a large state government in India about a year back saying, can you help? We took a look at it, and it turns out that the single key reason for this very high dropout rate in early grades, grades 1 through 5, is the inability of these young children to read proficiently in that environment. And so we’re going to have to do a lot of research.

if you can’t read properly it will affect how you do in all your subjects geography, history, science, everything you start failing you get frustrated, your parents say come back home, work on the farm work in the kitchen, etc. and that affects the rest of their life so we’ve come up with a system to diagnose within 20 seconds for each child by just speaking into a phone into our model in 20 seconds we figure out exactly where they are struggling what words what phrases, what sentences and what will help them get on the right track and this is being done at the cost of 5 paise per student I think cost is another very big part of scaling that doesn’t get discussed too much but cost is very important so 5 paise per student while this was in pilot the suite of solutions we came up with so we’ve got a way like I said of assessing in 20 seconds where the patient where the student is struggling where the patient is struggling We come up with a diagnostic and then come up with a remediation plan and exercises for each student to practice at home to improve their reading.

The state was so impressed with the pilot, they made it mandatory for all 3 million kids of that age in school. Three or four other states, including the state of Rajasthan, just made it mandatory for all 8 million kids over there. And by the way, all of this, again, enabled by DPI. So Rajasthan has a state -level DPI called Rakshak. Our system, our models sit on top of that system. It reaches 400 ,000 schools, 8 million students, and it’s spreading. So now the government of India, by the end of next year, wants to make this standard across the country. All 75 million children of that age group will get their reading improved and strengthened through the systems that we have.

Bottom line, all enabled by DPI.

James Manyika

No, I mean, those are very… APPLAUSE Thank you. Those are incredibly powerful examples. In fact, the case of TB is actually one that’s super important because something like 40 % of people in the world go undiagnosed with TB. And, in fact, most of them are in the global south. But it also just brings me back to maybe a question of scale. I mean, in what you’re doing, you mentioned a few countries, but I think your goal is to get with some of these education and health solutions to, like, 25 countries or more. How are you thinking about kind of taking that to kind of multi -country scale? And what are some of the ideas you have about how you do that?

Sangbu Kim

achieve the same you know academic goal within six week which usually takes more longer than a year long process so that’s one course one example in Nigeria also not only the TV some very small handheld ultrasound device can scan the the pregnant woman and then easily diagnosis some some problem for a baby and then it drastically reduced the birth that the baby death rate so it is one another example how can we scale this up another good example as you said we are expanding the current India model to other three African countries and in Brazil we added one more in Philippine okay and then the one of the way is that you how can you find some very standardized and scalable model, but this is not easy.

But from the one concrete example, like in India case for Agricultural Connect, we are figuring out what would be the best way and lighter model we can quickly replicate to other countries. This is our role. The World Bank is trying very hard to figure out what that means and then how they can really replicate this model to other countries. And what would be the really critical component to be replicated. So this is our role. So we are just working on what would be the best simple model is. So in that sense, usually I’m not sure it is really right analogy. I’m using this analogy as a sommelier. We are not innovation creator. There’s a bunch of really good wine producers, but I would say that this is a very good model.

customers is not really aware of which wine is really fit for their taste. So as a sommelier, the World Bank is trying very hard to understand the wines and then find some better, recommend better wines for our customers’ taste.

James Manyika

Yeah, I think on this question of scale, I mean, those are great ways to… Do you need any help on quality control with these wines? Yeah, but I think on this question of scale, I mean, Nandan, I’ve heard you say that, you know, we won’t get to true population scale unless we actually scale things like inference in AI and how we do that at massive scale. And I’m just curious for you to expand on that a bit more, but also what lessons and implications it might even have for people like us who are building frontier models. Say more about why the inference part of this really matters.

Nandan Nilekani

No, I think broadly speaking, I think, especially in the global sub… the cost of AI inference has to drop dramatically because if you’re serving a customer with one query and that costs, you know, 500 rupees or something, it’s not going to work. So we have to make it really, inference has to be, which I think, you know, you’ll do that because, I mean, there’s a lot of focus today on the training side, you know, getting bigger and bigger models and launching them. But I think as that sort of stabilizes, I think the focus will shift on the inference side to make inference cheap. But I think the, I’ll give you an example of, a very tangible example of open networks, even AgriConnect.

Yesterday I was talking to Demis. And Demis was saying that Google is improving its weather models. Yeah. They’re making it better, more efficient, more predictive, more granular in where it, you know, area by area and so on. Now, if you had an open network, if you had a network for Agri, like AgriConnect, which suppose it has millions of… then all we need to do is just plug in the latest weather model of Google into that open network, and suddenly 10 million farmers have access to the latest weather data. That’s a good example of why this open network thing is important, because it allows you to plug new models, new sources of capability, new ideas, and so on.

And I think that’s what we’re doing. And to give you another example of how it reduces the complexity, there’s a very interesting demo here of energy trading. Now, we never thought of energy as something that you traded because you bought it from the utility. But today, with millions of people producing energy, they’re able to – somebody has a rooftop solar, there’s some extra energy they can sell it to somebody else. But how does a farmer in UP learn how to sell energy? It’s a whole new concept. It’s only by an agency. It’s a classic commerce interface that is simple. So I think low -cost inference combined with – with agents that hide complexity is the key to massive diffusion.

James Manyika

Yeah, and in fact, I like the example you brought up on weather because in some ways, thanks to the, quite frankly, the forward thinking of part of the Indian government and the Ministry of Agriculture, they’ve set up that infrastructure. And in fact, last year, they used one of our models, Neural GCM, which predicts monsoons. And we’re actually able to deliver, I think, to something like 38 million Indian farmers predictions about the monsoons. But that only worked because the Indian government actually set up that kind of infrastructure where you could plug in these models. Kiran, I want to come back to you because, I mean, in some ways, you raise some more foundational questions here about the future of biology and health overall.

And I’ve heard you say, for example, that, in fact, AI doesn’t replace biology, that biology is much, much more fundamental and foundational. Say more about that. that and what can AI learn from biology and vice versa and what do you imagine what needs to happen to fully take advantage of?

Kiran Mazumdar-Shaw:

Yeah so I think first and foremost biology works through distributed data centers okay and when it wants to build intelligence retrieve memory and inference data it does so with sips of energy not with gigawatts of power that our data centers. So we could learn something from biology. So I think we can learn something from that. I think more fundamental to that is that biology also has generational learning you know if you think about how our DNA stores generational memory I think that’s fascinating how does the Arctic turn fly out of its nest for the first time travel 70 ,000 kilometers to the Antioch and then travel to the Arctic and then travel to the Arctic and then back it has navigational intent embedded in its DNA.

How does that work? So I think we have to learn a lot from biology and use AI to learn that biology. Because without AI, you cannot have any insights into biology. So I just feel that the future is going to be about the convergence of biological intelligence and AI. And that is going to be a very powerful transformative process. Because biology has a lot to teach AI in terms of how to do it with less energy, how to do it rapidly, and how to multiplex multimodal data very rapidly. Now that is something which I think is very exciting. And I think to go back to what you’ve just been discussing with Nandan and others, I think what makes it very exciting right now is the volumes of data you can collect.

And I think that is the reason why we are so excited to be here. And I think that is the reason why we are so excited to be here. And I think that is the reason why we are so excited to be here. Thank you. on open networks. I think we have to talk about, I mean, I know I work with a lot of, you know, organizations around the world in my field. There’s a huge reluctance to share data. You know, there’s a lot of wariness about IP being, you know, fragmented. And therefore, I think, except for India, there’s a lot of this resistance to share data. Now, when you don’t share data, you’re going to silo it.

India has this unique opportunity because of its, you know, this open networks and the public digital infrastructure to share volumes of very important data like Nandan just illustrated in terms of the, you know, the environmental data that he was talking about, the climate data, and farmers taking huge advantage of that. That is what we have to really, really, really focus on because India. is uniquely positioned in terms of its open networks. And if we can actually keep generating data and then make

James Manyika

I’m being told that we’re going to have to wrap this up. But before we do that, though, I want to just see if we can do a quick lightning round, so to speak. I mean, this summit has been extraordinary. The example that India is setting for the world, quite frankly, is extraordinary. If you could say, each of you say, one thing you’d like to see happen in the next 12 months, particularly with this idea of open networks and change at population scale, what would that be? I don’t know who wants to go first.

Nandan Nilekani

I think I’d like to see massive diffusion where all these applications that are just rolling out on open networks reach millions of farmers and farmers in the world. I think that’s going to be a big deal. I think that’s going to be a big deal. I think that’s going to be a big deal. I think that’s going to be a big deal. I think that’s going to be a big deal. I think that’s going to be a big deal. I think that’s going to be a big deal. and so on, and actually show the world that AI is a force of good. I think we have an obligation to show

James Manyika

That’s good.

Kiran Mazumdar-Shaw:

Yeah, I definitely want to see a sustainable standard of care, high -quality universal health care coming out of this AI effort and the health stack.

James Manyika

That’s preventative, presumably.

Kiran Mazumdar-Shaw:

Absolutely. Diagnostic, preventative, predictive, and precision, because you can’t do away with treatment. But how do you basically stage it up front?

James Manyika

Sunil or Sangut?

Sunil Wadhwani

Yeah, so yesterday when the Prime Minister spoke at Bharat Mandapam, you know how he has been saying for years, make an India for the world. He said, in the age of AI, let’s develop in India, and let’s deliver to the world. So in our case, is just one little example at Vadvani AI. I’ve given you two or three examples of what we’ve done. But we’ve developed over 25 AI platforms in India in education, healthcare, agriculture, which are scaling up. What’s interesting is over the last year, we’ve had an incredible amount of incoming interest from governments throughout the global south, in Africa, Asia, and so on, who are hungry for these solutions. And they’re looking to India to provide these.

In fact, when PM Modi launched our institute back in 2018, you know, he was saying the U.S. is so far ahead, China is so far ahead. I said, Mr. Prime Minister, we can set the example in India for how AI can be used for societal transformation. No one else is doing that. We are showing how it can be done.

James Manyika

Samgul?

Sangbu Kim

So for the next 12 years, I really want to work more to disseminate the really good use cases to the world. To our country and people. One of the reasons is that… some big challenge for the people in developing world they do not clearly know what they can do with AI even though it can provide really much affordable and easiest way to expand their capability and productivity and intelligence so you know in a very easy way compared to the old old day so once again they can get to know oh this is a real important opportunity for them and then I can believe that they will really find some really good way to fully utilize this one in a very affordable way

James Manyika

no no thank you I mean it is what what I’m taking away is that it’s not just the example that India is setting for India and the world but also quite frankly the example that each of you is setting because all of you in your work and your organization your teams through your initiatives but also your and, quite frankly, insight. I’ve done a lot to show what leaders can do. So I appreciate the examples that you’re setting and the example that India is setting. Please join me in thanking my panelists here. Thank you, and I think with that we’ll draw to a close. Thank you.

Moderator

Just request you all to be seated 30 seconds more. First of all, could we just please have another round of applause for our esteemed panelists? Very insightful. Thank you very much for coming here. The true benefits of AI, the discussion shows, can only be realized when we build for everyone using open networks. Very insightful conversation. Thank you, James, for moderating it. And to further help drive population scale impact, we invite changemakers and researchers to apply for the two Google .org Impact Challenge. One is in AI for Science, one is for Government Innovation. There’s a QR code for you to learn more. And I encourage you all to visit us at Booths 3 and 4 in Hall 5 to see firsthand how Google AI is delivering a real -world impact.

And finally, I just request all the panelists to please join Center Stage for a photograph. Thank you, everyone. Thank you. Thank you. That was great. Thank you so much. I wish you that more this morning. No, no, no. For me personally, this is inspiring. Thank you. Thanks. Okay, all right. Okay. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Thank you.

James Manyika

Speech speed

153 words per minute

Speech length

AI in Education

Topics

Social and economic development | Artificial intelligence

Nandan Nilekani

Speech speed

181 words per minute

Speech length

Role of Open Digital Public Infrastructure (DPI) and Open Networks in Scaling AI

Topics

Closing all digital divides | Artificial intelligence

Sunil Wadhwani

Speech speed

166 words per minute

Speech length

Global Replication and Collaboration

Topics

Enabling environment for digital development | Artificial intelligence

Sangbu Kim

Speech speed

126 words per minute

Speech length

Global Replication and Collaboration

Topics

Enabling environment for digital development | Artificial intelligence

Kiran Mazumdar‑Shaw

Speech speed

Default speed

Speech length

Convergence of Biology and AI

Topics

Artificial intelligence | Environmental impacts

Moderator

Speech speed

Default speed

Speech length

Default length

Speech time

Default duration

Open networks essential for inclusive AI impact

Explanation

The discussion emphasizes that open networks and digital public infrastructure are the coordination rails needed for AI to translate intent into action for everyone, especially across borders.

Evidence

“And therefore, today, we are here joined by global leaders to explore how open networks and digital public infrastructure can create a global, interoperable coordination rail, powered by AI to translate intent into action across borders.” [11]. “The true benefits of AI, the discussion shows, can only be realized when we build for everyone using open networks.” [13].

Major discussion point

Role of Open Digital Public Infrastructure (DPI) and Open Networks in Scaling AI

Topics

Information and communication technologies for development | Closing all digital divides

Kiran Mazumdar-Shaw

Speech speed

145 words per minute

Speech length

Accelerating AI adoption in health

Topics

Social and economic development | Artificial intelligence

Agreements

Agreement points

Open networks and digital public infrastructure are essential for AI to achieve population-scale impact

Speakers

– Speaker 1
– James Manyika
– Nandan Nilekani
– Sunil Wadhwani

Arguments

AI’s true potential lies in delivering population-scale impact by transforming education, healthcare, and agriculture through coordinated systems

Open networks and digital public infrastructure are essential coordination layers that allow AI to translate human intent into real-world action

Open networks allow many actors and innovators to build AI applications on the edge, enabling massive diffusion of technology

Digital public infrastructure provides crucial data pipelines and distribution channels that make AI solutions scalable and affordable in the social sector

Summary

All speakers agree that open networks and digital public infrastructure serve as fundamental enabling technologies that allow AI to achieve meaningful impact at population scale by providing coordination layers, data pipelines, and distribution channels

Topics

Information and communication technologies for development | Artificial intelligence | The enabling environment for digital development

Language accessibility is crucial for AI inclusion and must address multilingual complexity

Speakers

– James Manyika
– Nandan Nilekani

AgriConnect provides farmers with multilingual AI agents for credit and crop prediction, proving smallholder farmers can compete based on value creation

The World Bank is expanding the India agricultural model to Brazil, Nigeria, Ethiopia, Kenya, and the Philippines using open standards

India’s approach of developing AI solutions domestically and delivering them globally represents a new paradigm for technology leadership

Summary

All speakers recognize India’s digital infrastructure and AI solutions as successful models that can be adapted and scaled to other developing countries globally

Topics

Social and economic development | Information and communication technologies for development | The enabling environment for digital development

The enabling environment for digital development | Information and communication technologies for development | Financial mechanisms

Unexpected consensus

Biology as a teacher for AI efficiency

Speakers

– Kiran Mazumdar-Shaw

Arguments

Biology operates through distributed data centers using minimal energy compared to AI data centers, offering lessons for AI efficiency

The convergence of biological intelligence and artificial intelligence will be transformative, as biology has much to teach AI about energy efficiency and rapid processing

Explanation

This represents an unexpected consensus point as it was the only speaker to raise this perspective, but it aligns with the broader theme of efficiency and sustainability that other speakers touched upon indirectly through cost reduction arguments

Topics

Artificial intelligence | Environmental impacts | The enabling environment for digital development

The critical importance of maintaining open and decentralized architecture

Speakers

– James Manyika
– Nandan Nilekani

Arguments

The success of these networks depends on remaining decentralized and open

Open networks allow many actors and innovators to build AI applications on the edge, enabling massive diffusion of technology

Explanation

The strong consensus on maintaining open and decentralized systems is somewhat unexpected given that one speaker represents a major tech company, yet both strongly advocate for open rather than proprietary approaches

Topics

Internet governance | The enabling environment for digital development | Artificial intelligence

Overall assessment

Summary

The speakers demonstrate remarkably high consensus across multiple dimensions: the critical role of open networks and digital public infrastructure as enablers for AI, the importance of language accessibility and multilingual capabilities, the need for cost-effective solutions in developing countries, the transformative potential of AI in healthcare and education, and India’s role as a global model for digital solutions. The discussion reveals strong alignment on both technical approaches (open networks, multilingual AI) and philosophical principles (inclusion, accessibility, population-scale impact).

Consensus level

Very high level of consensus with no significant disagreements identified. This strong alignment suggests a mature understanding of the challenges and opportunities in deploying AI for social good in developing countries. The implications are positive for coordinated global action, as key stakeholders from technology companies, international organizations, and implementation partners share common vision and approaches. This consensus could facilitate more effective collaboration and resource allocation for scaling AI solutions globally.

Differences

Different viewpoints

Unexpected differences

Overall assessment

Summary

The discussion shows remarkable consensus among speakers with no significant disagreements identified. All speakers align on core principles of open networks, population-scale impact, and the importance of digital public infrastructure for AI deployment.

Disagreement level

Topics

Closing all digital divides | Artificial intelligence | Capacity development

The enabling environment for digital development | Information and communication technologies for development | Financial mechanisms

Takeaways

Key takeaways

AI’s transformative potential can only be realized through open networks and digital public infrastructure that enable coordination and population-scale impact across education, healthcare, and agriculture

Language accessibility is crucial for AI adoption, with India leading efforts to support over 100 Indic languages including multilingual processing capabilities

Digital public infrastructure provides essential data pipelines and distribution channels that make AI solutions scalable and cost-effective in developing countries

Healthcare transformation requires shifting from hospital-centric to preventive care using AI for risk profiling, early diagnosis, and empowering frontline workers

Agricultural AI solutions like AgriConnect demonstrate how open networks can serve millions of farmers with multilingual agents for credit and crop prediction

Education AI can diagnose learning difficulties and provide personalized solutions at extremely low costs (5 paise per student), enabling massive scale deployment

The convergence of biological intelligence and artificial intelligence offers opportunities for more energy-efficient AI systems learning from biological processes

India’s model of ‘develop in India, deliver to the world’ represents a new paradigm for AI leadership in the Global South

Cost reduction in AI inference is critical for serving populations in developing countries where expensive queries are not viable

Open networks must remain decentralized to allow easy integration of new AI capabilities and models from different providers

Resolutions and action items

Scale existing AI applications on open networks to reach millions of farmers and demonstrate AI as a force for good within 12 months

Expand AgriConnect model from India to Brazil, Nigeria, Ethiopia, Kenya, and the Philippines through World Bank partnerships

Scale reading improvement AI system to all 75 million children in target age groups across India by end of next year

Apply for Google.org Impact Challenge grants in AI for Science and Government Innovation

Visit Google AI booths (3 and 4 in Hall 5) to see real-world AI impact demonstrations

Continue building universal tools through Networks for Humanity Foundation with $10 million Google.org grant

Establish innovation labs from Singapore to Switzerland to ensure global infrastructure standards

Integrate AI into national pest surveillance systems to protect crops at national scale

Unresolved issues

How to achieve dramatic cost reduction in AI inference to make it viable for Global South populations

Resistance to data sharing in many countries outside India, creating data silos that limit AI effectiveness

Technical challenges of processing complex multilingual conversations where users mix multiple languages in single sentences

Scaling challenges for replicating successful models across different countries with varying infrastructure and regulatory environments

Energy efficiency gap between biological systems (using ‘sips of energy’) and current AI data centers (using ‘gigawatts of power’)

How to effectively transfer biological intelligence principles to improve artificial intelligence systems

Developing standardized, lightweight models that can be quickly replicated across different countries and contexts

Suggested compromises

World Bank acting as a ‘sommelier’ to match appropriate AI solutions to different countries’ specific needs and capabilities rather than one-size-fits-all approaches

Balancing the need for open data sharing with intellectual property protection concerns through consent-based secure data sharing models like those used in UPI

Focusing on user-centric service environments rather than supplier-oriented approaches to ensure AI benefits reach end users effectively

This comment provides a historical context that frames the current AI revolution as part of a broader shift from supplier-centric to user-centric technology deployment. It connects the dots between past technological transitions and current AI challenges, suggesting that open networks are not just technical solutions but represent a fundamental change in how technology serves society.

Impact

This comment provided theoretical grounding for the practical examples being discussed and helped establish why open networks represent an evolutionary step rather than just a technical choice. It influenced the flow by connecting individual use cases to broader technological and social trends.

Overall assessment

Explanation

Many people in developing countries don’t understand what AI can do for them, limiting adoption of affordable solutions that could expand their capabilities

Disclaimer: This is not an official session record. DiploAI generates these resources from audiovisual recordings, and they are presented as-is, including potential errors. Due to logistical challenges, such as discrepancies in audio/video or transcripts, names may be misspelled. We strive for accuracy to the best of our ability.

AI for Social Good Using Technology to Create Real-World Impact

Summary

Keypoints

Major Discussion Points:

Overall Purpose:

Overall Tone:

Speakers

The Foundation: Open Networks as AI Enablers

India’s Digital Infrastructure as a Global Model

Language Accessibility and Multilingual AI

Concrete Impact: Education and Health Solutions at Scale

Global Scaling: From Nigeria to Brazil

Healthcare Transformation and Biological Intelligence

Economic Sustainability and Cost Challenges

Data Sharing and Global Collaboration Challenges

Future Commitments and Vision

Implications for Global AI Development

Coordination layer for AI

Explanation

Evidence

Major discussion point

Topics

Project Vani & Bhashini speech data

Explanation

Evidence

Major discussion point

Topics

Gemini‑powered open network for agriculture

Explanation

Evidence

Major discussion point

Topics

Global replication of Indian blueprint

Explanation

Evidence

Major discussion point

Topics

Education goal – 75 million students by 2027

Explanation

Evidence

Major discussion point

Topics

Open networks enable many innovators & hide complexity

Explanation

Evidence

Major discussion point

Topics

Multilingual agents remove language barriers

Explanation

Evidence

Major discussion point

Topics

Plug‑in new weather models into AgriConnect

Explanation

Evidence

Major discussion point

Topics

Low‑cost inference & cheap model plugging

Explanation

Evidence

Major discussion point

Topics

Agents as holy grail – language + hidden complexity

Explanation

Evidence

Major discussion point

Topics

DPI provides data pipelines & distribution channels

Explanation

Evidence

Major discussion point

Topics

AI‑driven TB diagnosis & treatment adherence

Explanation

Evidence

Major discussion point

Topics

Rapid, low‑cost reading assessment in education

Explanation

Evidence

Education goal – 75 million students by 2027