Building Scalable AI Through Global South Partnerships

20 Feb 2026 11:00h - 12:00h

Building Scalable AI Through Global South Partnerships

Session at a glanceSummary, keypoints, and speakers overview

Summary

The session began with Sunil Wadhwani explaining that he and his brother founded the Badwani Institute for Artificial Intelligence in 2018, at a time when AI was still nascent and few resources were directed toward societal challenges [17-20]. After early setbacks they refocused on partnering with government ministries to identify priority problems, leading to a national tuberculosis (TB) initiative prompted by the health ministry’s focus on TB as the leading infectious-disease killer in India [41-44][45-48]. The institute developed a smartphone-based cough-analysis tool that instantly estimates a person’s TB risk, which has become a national standard and is the only such solution worldwide, prompting WHO to call it a potential game-changer [56-62]. They also automated sputum-test analysis across 64 government labs, cutting result turnaround to one day, and created AI models that predict which patients will abandon treatment so that 2,000 caseworkers can target the most at-risk individuals, contributing to a 25 % rise in TB detection last year [63-70][71-73]. In education, they addressed high dropout rates among primary students by deploying an AI-driven reading-proficiency suite that generates personalized exercises; after a successful pilot, the Rajasthan state mandated the tool for three million children [75-84][87-90].


From these experiences Sunil distilled key lessons: scaling must be built with government from day one, solutions need to be designed for large-scale deployment early, and they must integrate with existing digital public infrastructure such as the TB case-management system Nikshay and the school platform Rakshak [92-100][111-124]. He emphasized that tools only succeed when they make frontline health workers’ and teachers’ jobs easier, otherwise adoption stalls [125-127]. Ankur highlighted the gap between innovation and impact and noted the Gates Foundation’s new “Advantage India for AI” pledge to support such collaborations [131-136]. Sunil reported that the institute now serves over 100 million Indians annually, has built more than 25 AI platforms, and is expanding to Africa-launching operations in Rwanda, Ethiopia and Kenya and offering capacity-building for civil servants on AI governance [143-150][155-156].


Lacina Kone added that Africa’s Smart Africa initiative seeks to replicate India’s digital public-infrastructure model, citing India’s Aadhaar and UPI as examples that can inform a continent-wide digital market [198-207][211-218]. Shalini Kapoor described “AI diffusion” as sharing playbooks across borders so that solutions built in one country can be adapted elsewhere, reinforcing the South-South learning loop [179-190][191-194]. S. Krishnan outlined India’s AI Mission model, which provides low-cost compute, sovereign models, and open data pipelines, and pledged to share these resources globally, underscoring the summit’s role in democratizing AI [305-319][320-327]. He also noted the Gates Foundation’s partnership in curating sessions and establishing an international cooperation centre to help other nations implement digital public infrastructure [329-333].


The participants agreed that coordinated government engagement, frugal scaling, and cross-regional collaboration are essential to translate AI innovations into widespread societal benefit for the Global South [92-100][125-127][179-190][305-319].


Keypoints


Major discussion points


AI-driven health and education solutions in India and the importance of government partnership – Sunil described AI tools for tuberculosis (cough-based detection, automated sputum analysis, adherence prediction) that are now national standards, and an AI-based reading-proficiency suite that has been mandated for millions of children [56-70][80-88]. He emphasized that scaling only succeeded after they began working directly with ministries, planning deployment at scale from day one, and leveraging existing digital public infrastructure such as Nikshay and Rakshak [92-106][111-124]. He also warned that tools must make frontline workers’ lives easier, otherwise adoption stalls [125-127].


Key lessons learned for impact at scale – The team realized that technical brilliance alone is insufficient; success requires (1) early and humble engagement with senior civil servants, (2) designing for mass deployment (training, logistics, hardware) from the outset, and (3) integrating with government-owned platforms that provide data pipelines and distribution channels [92-106][111-124][125-127].


South-South collaboration and export of the Indian model – After impacting over 100 million Indians, the institute began fielding requests from Kenya, Rwanda, Ethiopia, Indonesia, Egypt, Mexico, etc., and set up a foundation to support global deployments [139-156][160-168]. Panelists from Smart Africa and Kala highlighted the need for shared playbooks, joint investment, and regulatory harmonisation to replicate successes across the Global South [177-194][198-226][259-267].


India’s Digital Public Infrastructure (DPI) and AI Mission as a replicable framework – The discussion highlighted Aadhaar, UPI, and sector-specific platforms (Nikshay, Rakshak) as the backbone that made AI scaling possible, and S. Krishnan outlined India’s frugal AI-mission model (low-cost compute, sovereign models, open data) that the UN and other countries can adopt [111-124][289-321][322-327].


Democratizing AI and the summit’s role in fostering inclusive participation – Both hosts and panelists stressed that the summit aimed to move AI from elite circles to “the rooms” where youth and diverse stakeholders can see and shape the technology, aligning with the “people, planet, progress” mantra and the Gates Foundation’s partnership [128-136][289-298][327-334][357-365].


Overall purpose / goal of the discussion


The conversation was designed to showcase how AI can be democratized and scaled in India to address critical health and education challenges, extract concrete lessons about government-led deployment, and use those insights to catalyze South-South collaboration. By highlighting the role of digital public infrastructure and the Gates Foundation partnership, participants aimed to build a shared playbook that other Global-South nations can adopt, thereby accelerating AI-driven development across the region.


Overall tone and its evolution


Opening (0:09-14:54) – Optimistic and celebratory, with Sunil proudly recounting breakthrough AI products and their national impact.


Mid-section (14:55-18:40) – Reflective and instructional; Ankur and Sunil shift to “lessons learned,” acknowledging earlier missteps and emphasizing humility and systematic scaling.


Panel segment (19:46-32:24) – Collaborative and forward-looking, with participants from Smart Africa, Kala, and the Indian ministry exchanging ideas on pathways, regulatory alignment, and mutual learning.


Closing (32:25-48:45) – Appreciative and inspirational, highlighting the summit’s success in bringing diverse Global-South voices together and ending on a hopeful, “we can do this together” sentiment.


The tone moves from proud reporting of achievements, through candid self-critique and strategic guidance, to a unifying, hopeful call for collective action across the Global South.


Speakers

Ankur Vora – Chief Strategy Officer and President, Africa and India Office, Gates Foundation; expertise in AI for development, global-south partnerships. [S9][S10]


Sunil Wadhwani – Co-founder & Chairman, Wadhwani Institute for Artificial Intelligence; expertise in AI-driven health, education, and social impact solutions. [transcript]

S. Krishnan – Secretary, Ministry of Electronics and Information Technology (METI), Government of India; expertise in AI policy, digital public infrastructure, and national AI mission. [S1][S2]


Shalini Kapoor – Chief Strategist, XSTEP Foundation; expertise in AI strategy, partnership building, and scaling AI solutions in the Global South. [S14]


Lacina Kone – Director General and CEO, Smart Africa; expertise in continental AI policy, digital market integration, and public-private AI collaboration. [S11][S12][S13]


Shikoh Gitau – CEO, Kala; expertise in AI implementation for health and education, private-sector AI solutions in Africa. [S6][S8]


Additional speakers:


Nandan Hillikini – (mentioned as announcing “100 Pathways to 2030”; role not specified in the transcript). [transcript]

Full session reportComprehensive analysis and detailed insights

The session opened with Ankur Vora asking Sunil Badwani to elaborate on the institute’s work in India, noting that his own speeches had highlighted AI-driven tools for oral-reading fluency and tuberculosis (TB) screening that were “just amazing” and deserved wider attention [1-7].


Sunil Badwani: He recounted how he and his brother Ramesh founded the Badwani Institute for Artificial Intelligence in 2018, when AI was still a niche field and before the advent of ChatGPT [17-20]. While serving on the Carnegie Mellon University board, he observed billions of dollars flowing into AI research [21-23] but recognised a stark gap: the majority of the world’s population lacked access to quality health care and education [24-26]. After an uninspiring early phase in which prototypes failed to scale, the institute refocused on direct collaboration with government ministries, identifying national priorities and redesigning its approach [31-38].


In health, the Ministry of Health identified TB as the country’s top infectious-disease killer, accounting for nearly two million deaths globally and half a million in India [41-44]. The institute mapped the TB cascade and pinpointed three critical bottlenecks: lack of functional X-ray equipment, slow sputum-test turnaround across 64 government labs, and poor adherence to toxic medication regimens [49-55]. Their AI responses were threefold: a smartphone-based cough-analysis app that instantly estimates a person’s TB risk and provides a probability score (now a national standard and the only solution of its kind worldwide, praised by the WHO as a potential game-changer) [56-62]; an AI model that fully automates sputum analysis, reducing result time to one day [63-66]; and predictive algorithms that flag patients likely to abandon treatment, enabling 2 000 caseworkers to focus on the most at-risk individuals and raising TB detection by 25 % in the previous year [67-73].


In education, Sunil described the pervasive dropout problem among primary-school children, especially in grades 1-5, where early ill-literacy triggers a cascade of failure, frustration and early entry into labour [75-84]. The institute built an AI-driven reading-proficiency suite that generates personalised home-reading exercises for each child. After a successful pilot, the Rajasthan state mandated the tool for three million pupils, illustrating how a targeted AI solution can achieve massive scale [85-90].


From these experiences Sunil distilled four key lessons. First, scaling is impossible without early, humble engagement with senior civil servants; the institute must approach ministries with humility and a willingness to learn [95-99]. Second, solutions must be designed for mass deployment from day 1, with explicit plans for training, logistics and hardware distribution [100-106]. Third, integration with existing Digital Public Infrastructure (DPI) is essential – the TB tool plugs into the Nikshay case-management platform and the reading suite uses Rajasthan’s Rakshak system, both of which provide data pipelines and distribution channels [111-124]. Fourth, tools must make frontline workers’ lives easier; otherwise adoption stalls, regardless of technical merit [125-127].


Ankur Vora highlighted the “innovation-to-impact” gap, noting that progress is not a straight line and that the Gates Foundation’s new “Advantage India for AI” pledge aims to fund such collaborations [131-136].


Building on the Indian successes, Sunil announced that the institute now serves over 100 million Indians annually through more than 25 AI platforms [143-150] and is expanding to the Global South. Over the past year, governments from Kenya, Rwanda, Ethiopia, Indonesia, Egypt and Mexico have requested assistance. In response, a dedicated foundation was created, a team was dispatched to Africa, and operations are slated to begin in Rwanda, Ethiopia and Kenya [154-156]. The institute’s long-term ambition is to impact 500 million people by 2040, echoing Prime Minister Modi’s call to “design in India for the world and deliver these solutions to the world” [161-168].


The panel then turned to the broader theme of AI diffusion. Shalini Kapoor framed diffusion as the creation of “rails” that allow AI solutions to travel across borders, much like the digital public infrastructure that enabled India’s rapid adoption of Aadhaar and UPI [179-190]. She argued that shared playbooks would let a solution built in Kenya be reused in India and vice-versa, reducing the need for each country to reinvent the wheel [191-194].


Lacina Kone, representing Smart Africa, expanded on this vision, describing the continent’s ambition to become a single digital market of 1.4 billion people. She noted that Africa can learn from India’s DPI successes-digital ID, UPI and large-scale election management-and that a harmonised regulatory “cloud” is the prerequisite for investment, likening finance to rain that falls only when the clouds are right [198-206][231-236]. The Smart Africa AI Council, launched in 2025, brings together ministers and private-sector leaders across 49 countries to coordinate computing, data, skills, regulation, market and investment themes [217-225].


Shikoh Gitau added that political goodwill is essential for AI to become both a technological and an economic issue. She defined the “collaboration tax” as the effort, resources, and coordination required for cross-border AI projects, and urged governments to absorb this cost [259-273]. She also cited a resonant comment from CV Madoka … CBC, which underscored the need for shared responsibility [259-273].


S. Krishnan, speaking for the Indian Ministry of Electronics and Information Technology, outlined the AI Mission model that underpins the country’s scaling success. The model provides compute at roughly one-third of global prices [305-307], sovereign AI models (the “AI Kosh” model, as we call it, the AI treasury) [327-329], and open-source data pipelines, all subsidised by the state [322-324]. He affirmed that these resources are available for sharing with the UN and other Global-South nations, and that the AI Kosh is a sovereign model built with taxpayer resources [320-327]. Krishnan stressed that democratising AI means opening the “rooms” and “halls” to youth and diverse stakeholders, allowing them to see and shape the technology directly [289-298][327-329].


In closing, Ankur thanked Sunil and highlighted the Gates Foundation’s role in curating the sessions [128-136]. Krishnan reflected that the summit succeeded in moving AI out of elite circles, placing people, planet and progress at the centre, and announced the establishment of an International Cooperation Centre under the National Institute of Smart Governance to support DPI implementation worldwide [329-334]. He concluded by reaffirming India’s commitment to open, secure and sovereign AI infrastructure [340-345].


The rapid-fire segment captured the overall sentiment: panelists expressed optimism that AI’s collaborative spirit-evident in the collective photograph of partners from Italy, Kenya, Anthropic, Google, Carnegie and the Gates Foundation-demonstrates that AI is about partnership, not competition [391-410].


Synthesis of shared pillars


1. Early, humble partnership with government ministries [95-99].


2. Design for mass-scale deployment from day 1 [100-106].


3. Embed solutions in existing DPI (Nikshay, Rakshak, etc.) [111-124].


4. Ensure tools ease frontline workers’ workflows [125-127].


These pillars, reinforced by concrete Indian case studies and the broader policy context of India’s AI Mission and Africa’s Smart Africa initiative, set a clear agenda for expanding AI-driven health and education impact across the Global South [92-106][111-124][179-190][305-311].


Session transcriptComplete transcript of the session
Ankur Vora

The first question around India. One of the things you’ve done and your organization has done is you found ways of taking the power of AI, democratizing it, and making sure it solves problems that we all care about. In my speeches, I’ve talked about you. I’ve talked about oral reading fluency, the tool whereby for less than, and if I’m stealing your thunder, sorry, but he’ll tell you a little bit more. But I’ve been talking about it because it’s just amazing. I’ve been talking about the fact that your TB screening, you can do things that we couldn’t imagine being done before. So can you tell us more about your work in India?

Sunil Wadhwani

Sure. Hi, everyone. Thank you. Welcome. Thanks for being here. Thank you for having me. I suspect the way I got over here was they needed, Gates Foundation needed someone for this chat. They looked around. They found this guy wandering around with two badges. They figured that means he’s important. Let’s get him. And next thing I’m sitting over here. But thank you so much, Ankur. So, you know, my brother and I launched the Badwani Institute for Artificial Intelligence here in India about eight years ago, 2018. Back then, AI wasn’t a thing. ChatGPT hadn’t come out. But I happened to be serving on the board of trustees of Carnegie Mellon University in the U .S., where I had studied, gotten my master’s.

So and CMU was ranked then as ranked number one in the world for artificial intelligence research and teaching. So being on the board, I could see all the billions of dollars coming into AI from Google and so on and so forth, even in those days. And it always pained me that none of this money was going into AI for society. You know, three, four billion people in the world out of eight billion don’t have access to decent health care, decent education. AI could be transformative. And that’s what we’re talking about today. But at that time, nothing was going on. So I spoke to my brother Ramesh. We decided, let’s launch this Institute for AI in India.

Prime Minister Modi came, inaugurated it, etc. So we hired a really good team of AI machine learning people, spoke to government, identified use cases, started working, and nothing happened. A couple of years went by, we were developing this, what we thought was really neat stuff, but it wasn’t scaling up. So we took a look at the issues, etc. And then we started realizing, look, we’re not approaching it quite right. We’ve got great AI solutions, but there is a lot more, a lot more to actually having impact than just having a nice technical solution. So I’ll, in a couple of minutes, tell you what we’re doing. So the key lessons that we’ve learned. But once we started figuring out, OK, what we were not doing and that we needed to be doing, then things started happening.

So just to give you two or three examples, as Ankur mentioned, we identify our problems, our challenges that we want to focus AI on by working directly with government. We talk to the health ministry about their national priorities for the next three, four, five years. What should we do? We talk to the education ministry and so on. So three years ago, the health ministry told us that tuberculosis is a very high priority for us. It’s the largest infectious disease killer in the world, kills close to two million people a year. Largest infectious disease killer in India kills close to half a million people over here. And for each person that dies, there are 20 others that don’t die, but they live miserable lives and they are infecting lots of other people as they go on.

So the government, the health ministry said, can you help? So we took a look at the whole. cascade of care in tuberculosis? What’s the patient journey like? Where are the three or four or five key pain points? And we identified, okay, diagnosis is number one, because in these economically vulnerable communities where TB happens, you need x -ray machines, you need sputum analysis, and in these communities, you don’t have all this stuff. You don’t have x -ray machines that work and are calibrated and so on. Problem number one. Problem number two, sputum analysis is another way of diagnosing TB, but these samples go to 64 government labs around India where they are ranulized, et cetera, and it takes time for the results to come back to the patient.

And for those that have TB, you’ve lost a lot of valuable time. Third big challenge is there’s a number of patients with TB who are on the medication regimen, but these are very toxic medicines. They really destroy your body while they’re trying to cure you of TB. So a lot of people stop taking these medicines. and they developed drug -resistant TB, which is much worse, 50 % mortality rate, etc. So we started applying AI to each of these issues. On the diagnosis, we’ve come up with a way of detecting tuberculosis from the sound of a cough into a smartphone. It’s instant. It’s quick. We don’t just say yes or no. We give the risk of this person having TB, what’s the probability, etc.

That is now rolling out nationally, and it is becoming the national standard. And by the way, we’re the only country that has this. It doesn’t exist anywhere. World Health Organization has told us this could be a game changer globally. For the sputum analysis, we’ve developed an AI model. So now the sputum analysis in the 64 government labs, totally automated. Results come out within a day, go back to the patient, treatment starts. Perhaps the most challenging thing. These patients who will fall off their medication. We’ve developed AI algorithms that predict well ahead of time which TB patients are likely to fall off the medication. So then the 2 ,000 TB caseworkers in India, which is a very limited number for 4 million TB patients, they can focus on the right people.

This is impacting now tens of millions of people. Just in the last year, the rate of TB detection, thanks to our cough against TB, has gone up by 25%. You may think that’s bad news, you know, higher numbers, but now we can treat these patients. We can get them on the right, you know, clinical care protocols. That’s one example. Education. Throughout the global south, there is a very high dropout rate of young children from schools, very high, in grades 1 through 5. Problem in India, problem everywhere. We got a call from a very large state government in India that said, we’ve got this issue, can you help? We sent a team in. We’ve got a call from a very large state government in India that said, we’ve got this issue, can you help?

and we had to analyze what’s causing this high dropout rate. We learned that the single biggest reason for this high dropout rate is an inability of these very young children, 7, 8, 9 years old, to be able to read If you can’t read, it affects how you do in every subject, right? Science, history, geography, you struggle, you start failing, you get frustrated, and these are, again, poor communities. Your parents say, forget school, what’s the point? Come work in the field or work in the kitchen, and that affects the rest of their lives. We’ve come up with an AI -based suite of tools that… …and for the child that goes to the teacher, and for the child, we come up with personalized exercises…

stories that they can read at home, but which help them to get better at their specific area of weakness. Each child is different. We were in pilot with the state. They were so impressed, they made it mandatory for all 3 million school kids in that state, in that age group. State of Rajasthan saw it recently. Here, in that age group. So that’s the kind of scale one can get. What’s the difference between what we were not doing in our first 2 or 3 years versus what we’re doing now? What we learned is, number one, the only way to scale is government. You have to work with government from day one. Working with government isn’t easy, right? It’s easy to say it’s challenging, it can be frustrating at times.

but you have to understand how to navigate it. How do you work with senior civil servants? You know, start with an, you know, approach them with humility, not like you have the answers. You’re trying to understand the problem. You want to work with them. Secondly, think scale from day one. You can’t develop an AI solution and then say, oh, I want to use it on one million people. There are issues you have to think in right in the beginning as to how will the scale out? How will large scale training happen in the field? How will frontline health workers or, you know, teachers or other governments? How will they use this? So thinking that way is very upfront.

And in fact, with government, what we do now is once we identify a problem, even before we work on the technical solution, we plan that deployment to scale as to what will happen. And we make government accountable for a lot of it. Just as we’re accountable for the technical side. The other really key learning has been. And that government, and this relates, Ankur, to what you were just saying, has developed a lot of digital public infrastructure. Aadhaar is like the great example that we’re all aware of, right? UPI, United Payment Interface, incredible example of that. So there are lots of things in health care, in education and agriculture where the government has developed this digital public infrastructure.

And it’s critical. We didn’t know this. This was probably our key finding. It’s critical to find a government platform that you can integrate into. So the examples I’ve given of TB, government has a wonderful platform called Nikshay. It’s like a case management system for tuberculosis patients. We’ve integrated everything. We developed algorithms into that platform. The education, this early childhood reading proficiency, each state has a platform. Rajasthan, as an example, has a platform called Rakshak. Rakshak for 70 ,000 schools, 400 ,000 teachers, 8 million students. we plugged our algorithms into that platform. So if these platforms hadn’t been there, we’d be struggling to scale any of this up. The final learning that we’ve had, and maybe this is the most important, is all these technical solutions are great at a macro level to bring down TB, to improve reading proficiency, etc.

But at the end of the day, if the person using this tool, the frontline health worker, the teacher, if it doesn’t make life easier for them, in addition to improving education for the child or healthcare for the patient, it won’t happen. You can push all you want from the top that, oh, you must use this, but there’s got to be pull. They’ve got to want to use it, and that happens only when you make life easier for them.

Ankur Vora

Thank you very much, Sunil. We’ll do the next question a little bit quickly. But I do want to just… acknowledge a few things, call out a few things. One is this journey between innovation and impact. I love what the learnings you talked about, because we keep on sometimes focusing on the innovation part, and we think that the road from innovation to impact is a straight road, and it’s not. It’s possible, it’s probable, but it’s not guaranteed, and we need to work hard at it. And so your learnings get to it. It’s also one of the reasons why we love our seven -year partnership, and hopefully it’ll be much more as we think about, as some of you know, the Gates Foundation yesterday announced a new initiative, a new pledge around AI for AI, which is Advantage India for AI.

And the idea is to make investments in India for the global south, and we’re looking forward to partnering with you. So, Sunil, one of the places where we do partner, and we’re talking about things, in fact, earlier today, we were talking about work in Ethiopia and Rwanda and Kenya. Can you talk a little bit more about… how you think about your work in the context of South -South partnership and how do you take the learnings you have from one place to the other place?

Sunil Wadhwani

Sure. So when we got started, our goal was only India, right? My brother and I, we are from India, our hearts are still here. So we weren’t thinking about any place else. But what’s happened is as our AI solutions have been scaling up in India quite dramatically, and we are today impacting over 100 million people a year, we’ve developed over 25 AI platforms in partnership with government. We’ve started over the last year getting a lot of inquiries from governments around the world in the global South saying what you’re doing in India, we need in Kenya or in Rwanda or in Indonesia or Egypt or Mexico. By the way, in India, we don’t just develop these solutions. We also do a lot of capacity building, meaning training of senior civil servants on how you can use AI.

What it’s good for, not good for, etc. We help ministries develop data governance standards, use case frameworks and so on. Then we do the actual solutions development. That’s the biggest chunk of what we do. But then we have a big deployment team. We have close to 100 people making sure that these things get deployed. So we were thinking only India. But over the last year, we started getting all these inquiries. And we finally said, look, we set up this foundation to have impact globally. So now we’ve we’ve have we sent a team out to Africa to meet with several countries. We are starting operations this month in Rwanda, Ethiopia and Kenya. And I’m glad to see a colleague over here from Smart Africa.

We will be partnering in this work. We’re very excited about that. And then beyond that, we expect to be going to a number of other places today. As I said, we’re impacting maybe 100 million people in India. Our goal is by the year 2040. To be impacting 500 million people. We are very excited about our partnership with you at the Gates Foundation. So the Prime Minister Modi, if you heard him yesterday in his speech, he gave a brilliant speech. As part of that, he said he said for the last several years, I’ve been saying make in India for the world. He said, no, I want to add to that in this age of AI design in India for the world, develop in India for the world and then deliver these solutions to the world.

And that is what we’re trying to do. That’s the evolution now thinking. And again, we’re excited, very excited about the partnership with you.

Ankur Vora

Thank you, Sunil. Are we I think we have a change of plans. Thank you so much. And Sunil, if you could please stay on stage and if I could invite our panel up. Shalini Kapoor, Chief Strategist from the XSTEP Foundation. Lacina Kone, Director General and CEO of Smart Africa. And Shikoh Gitau, CEO of Kala. Thank you.

Shalini Kapoor

yeah good good to start okay thank you so much thank you anchor. Thanks for your time uh and here we are and thanks uh Sunil for you know spending some more time with us, uh thanks Shikoh we have been meeting bumping into each other and thanks thanks Lacina thanks for being here So we’ll get into some of the discussions on the South -South collaboration that is spurring innovation and that is diffusing AI into all the sectors. AI diffusion is about the routes and the rails which need to be laid in AI the similar way digital rails were laid in the DPI time. And now they can be shared. They are playbooks. They can be shared.

And AI diffusion actually concept came from it started with generation. Jeffrey Hinton talking about it. He’s a professor in Georgetown in D .C. When he talked about that, how actually electricity was created in. Germany, but it was diffused across India, across the USA, where USA made so much of strides into it. So like electricity, AI is a general purpose technology. It’s a GPT, which is there. And between invention and impact, there’s a big layer of adoption and diffusion, which needs to be there so that AI gets diffused into society. And when it gets diffused into society, something which can be built in Kenya can come to India, something which is built in India can go to Kenya, because there are playbooks which could be leveraged.

Not everybody needs to build everything. Not everybody needs to build the entire stack. How do we learn from each other? And how does that South -South collaboration happen? That’s what is the focus. So I’ll start with the pathways. What are the pathways to scale? and I’ll start with Lesina, that you are leading Smart Africa and you help coordinate across a lot of nations, different stages of AI, somebody in pilot, somebody in production, somebody has solved data, somebody has solved language, somebody has solved voice AI. What do you think are the opportunities for the South -South collaboration in building these pathways together?

Lacina Kone

Yeah, thank you very much for inviting me. In fact, that collaboration, first of all, before we even talk about South -South, the collaboration is a sense of the creation of a Smart Africa. Because if you look at Africa through just on the Kenya, which is 50 million, Ghana, which is 30 million, or Nigeria, which is 240 million, you’re missing the point. But if you look at Africa as a 1 .4 billion people, but be able to leverage that 1 .4, you need a collaboration. and the scale. So coming from that, when you look at the continent of Africa, which is technically speaking the global south, I don’t want to get there, in the global south, and the south -south collaboration is very important because we do not need to reinvent the wheel.

India has shown the world what the DPI actually means for 1 .4 billion people. It’s working digital ID, a country which is able to organize an election for 850 million people to vote. You have to actually kudos. So we don’t need to reinvent the wheel. Africa can learn a lot from India, even with the use cases. But why use cases particularly? Because there’s a lot of similarity between India and Africa in terms of culture, in terms of value, that we all know we are into the AI, we are into digital transformation. It’s not just… You create a… But the luxury of the luxury of the society is to be able to have a population, inclusion of the population, ethical of the technology, exactly.

So coming from that, that’s one of the reasons to reboost, actually, Smart Africa Initiative. The creations of the Africa AI Council came into play where last April, on April 4th, 2025, 49 countries came together to actually sign the declaration. And subsequent to that, the AI Council came to life on November 12th, 2025 in Guinea after our board of directors, which are represented by the head of the state, announced that, accepted that we could. We had a first meeting already. Then the council consists of 15 members. is not only driven by public, but it has seven ministers coming from seven different countries and eight private sector members. Why? Because in our constitution of Smart Africa, private sector first. We do believe that the government should be creating a conducive environment for the private sector to excel.

It cannot be dominated by public sector. And underneath of the council, we have, of course, six thematic groups, mainly computing, so we can look at the collaboration of South Asian computing power. We can look at it in the data set. We can look at the skills. We can look at the regulation, which is the governance. We can look into the market and we can look at investment. And when it comes in terms of investment, something we need to know. The investments in the prior technologies, the investment cycle is too slow for the AI. Just look back 12 months ago. Where were we? And where we are today? So this is something we need to look at carefully.

We are looking at the three aspects. One, the government needs to be creating a conducive environment for private sector to chip in. The private sector needs to be executing, but they can only execute, as I said, everyone’s cry because they said finance is the issue. I always said finance is not the issue. Finance is the last thing you should think about. You know why? Because I said financing is like the rain. For rain to fall, you need certain condition of the cloud. Those clouds are the regulatory environment, the conducive environment for business private sector because private sector does not like unpredictability. So the third thing, the philanthropics. The reason why I want to speak about it, the philanthropics need to serve as a 2D risk area because these are some of the things government is the last thing to invest in a technology because they want to make sure it’s going to work.

But if you don’t throw business, it’s time for them to accelerate, to use that as a de -risking while the private sector can chip in and so on and so forth. Thank you.

Shalini Kapoor

Yeah, thank you so much. And you talked about DPI, you talked about the private sector, public coming together. It’s the entire ecosystem. And on 18th, actually, Mr. Nandan Hillikini actually announced 100 Pathways to 2030, which is a clarion call. If you ask me, it’s a clarion call for people to join to create pathways simply because, you know, if you climb Mount Everest, suppose Edmund Hillary has climbed Mount Everest. And do you think he’ll come back and he’ll say, I’m not going to tell you how I climbed? What is the route I took? Where did I go? Where did I go? Where did I not go? What did I see? He will talk about, right? He will talk about them so that it is easy for other travelers to come in.

So pathway is like that. that if someone has done the AI pathway, others should learn from it, benefit from it. So, Shiko, you were with us on the stage when you joined and you said that, you know, from the Global South, 100 Pathways to Scale, you would like to join us. Please tell us, how can that diffusion help? How can this, you know, we can work, collaborate to, you know, to get the AI use cases from pilot to production?

Shikoh Gitau

I can finish? Okay. It’s that collaboration. As I was saying, this idea of how do we bring this multiplicity of thinking together, given that we have the same exact challenges. We have challenges around, we have multiple languages. We have culture and diversity. We have things that we need to be able to work together. How do we collaborate together? And for us, the biggest takeaway is how do we make AI not just a technology, but a political and economical issue? Yeah? That was the biggest one, because the people are there. The builders were there. The researchers were there. The policy makers were there. But we need that political goodwill to be able to make this work together.

And something that CV Madoka from I’ve forgotten the organization. CBC said that struck really a chord to everybody including myself is we need to start having a conversation on what is called the collaboration tax and it’s something DG when we were coming in we were talking about I’ll define collaboration tax as this effort resources and things that you need to put together to be able to collaborate with each other. It’s what the government should be doing it’s what that political part of AI should be doing to bring the collaboration together and how do we make these people come together without the effort of I mean not the effort, the pain of collaboration and that’s what we need to be talking about because the resources, the people are there people are willing to collaborate and work together we saw it this week and as the minister said I said while you’re chasing the Guinness World Record of having the most number of people also chase for the diversity that these countries are seeing thank you so much for bringing Africa and the world to India Yeah.

Shalini Kapoor

Thank you, Shikoh. Thank you so much. We’ll take a small break in the panel discussion and we’ll have Mr. Krishnan come here and talk about how scale and collaboration can help in the South -South and what is transferable from India. I know he’s like busy and across all. So over to you, Mr. Krishnan.

Ankur Vora

I was just going to do one more thing, which is thank you, Shalini, and thank you to the panel for allowing us this small break. For those of you who don’t know, Secretary Krishnan from the Ministry of Métis is over here. He’s had probably one of the most amazing, successful weeks this week. So please join me and give him a big round of applause for. Secretary Krishnan, thank you so much for being here. As a proud Indian, I’m quite excited about the fact what happened this week. I’m also, as somebody who cares about the agenda of this global South. everything that happened in India this week put the Global South agenda right front and center.

So thank you for doing that. There were so many announcements made this week about how we’re going to make progress in the months and years to come together. Would love to welcome you to give a little bit more context of the announcements that were made and the achievements that were achieved this week. Thank you. Welcome.

S. Krishnan

Let me first apologize to the panel for having sort of stepped in abruptly, but between juggling many things going on across the summit, I think this is a very important session as far as I was concerned, because if this particular summit was about one thing, it was about the Global South. The fact that… India representing the Global South… could actually dare to host this event and also dare to host it on this scale. The one thing we were very clear about is summits thus far have basically been about country leaders. It’s been about CEOs and it’s been about some experts getting together in closed rooms and not really having the opportunity to do what or to actually showcase to people as to what the possibility of the technology is.

And this particular event gave us that opportunity. I think we were very clear that what we wanted to do was to let people into the rooms. We wanted to make sure that people, especially youth, had this opportunity to come and listen to the best minds, there are on artificial intelligence as technology. and to every possible perspective on how this technology can work for everybody. And the second thing, of course, as you’re well aware, we said people, planet, progress. And two aspects of it were very important. One was, or three, if I can. One is democratizing access to AI, all the AI infrastructure and resources. That was one key aspect. The second key aspect was including those who are not ordinarily given access to this, those who are excluded.

And the third key aspect is putting humans at the center of this process to make sure that this is a technology that works for people. And I think the prime minister was very clear and emphatic in his address yesterday where he put people, or manna, right at the heart of AI. So to enable this to happen, of course, multiple things have to happen. We have to find frugal ways to innovate. In order to make these resources available, we have to make sure that we have the resources. We believe that our own AI mission model, the India AI mission model, is one of those frugal ways in which both the compute infrastructure and the model infrastructure and the data set infrastructure can be created for each country because some of this needs to be on a specific basis for regions and for countries.

In India, we are a subcontinental scale. There are 22 official languages and many other languages which need to be taught or which need to be understood. And we understand this cultural and linguistic diversity better than any other region in the world. And we can, at a continental scale, we can contribute in that effort. That is one key element. The second key element, of course, is to create compute in a way so that it doesn’t, I mean, people are not enabled. I mean, people are not able to build moats. around it. That, you know, the implication that you need the kind of resources to do this, that nobody else can do it and only we can do it is not an approach we wanted to take.

So we created this model where the private sector is encouraged to invest. We created this model where access to it is something which the government subsidizes. In the process today, AI compute in India is available at a third of the price that it is available in the rest of the world. I think that has been the significant achievement. The United Nations asked us saying, would it be possible to, once you build it out on scale, would it be possible to share with the rest of the world? This is something that we have committed to them saying, as in when the size is adequate so that we can meet other requirements, we will be happy to share them.

We are happy to share the model even now. And the AI Kosh model, as we call it, the AI treasury is something that we are happy to share even now. we are happy to share the fact that the models that we have supported in India and which have been built out as sovereign models in India that again is technology that we are happy to share with the global south it’s something we can enable some of it is something we’ve built with our own resources so it is in a sense completely sovereign unlike in many other places it’s something that the government has paid for from taxpayer resources and we can use and the third element of course is the data sets and how they are shared now that framework is again something we can certainly share the most important thing and I think that’s what is also showcased so eloquently in the expo is the range of applications which have been created out of this and there are close to 900 startups across all those halls who have done a variety of things even in the main hall we with the Gates Foundation we have set up a lot of applications and we have set up a lot of applications and we have set up the African village which is such a showcase even to the leaders fundamentally about applications which can work there and fundamentally for people to see applications which have worked in different parts of the world which can be taken elsewhere.

So all of those are available. Those are resources which we want to share. These are resources we want to actually give. And most importantly, as I said, I think if there is one thing that this summit reflects, for the first time, we’ve actually democratized AI. We’ve shown you what democratic AI looks like when people are let into the rooms, when people are let into the halls and they can see for themselves as to how this would work. So it gives me immense pleasure that, you know, and a very, very key partner for us in all of this has been the Gates Foundation right from the very outset, right from the planning stage of how we wanted to do this.

This particular part of the set of sessions on the Global South is something, that we work closely with, curated carefully. We have put together sessions which will be relevant to this group, and we have always made sure that in addition to this, of course, on every occasion, whether it is in the space of DPI or whether it is in the space of any of the other applications, we are in a position to support it. Under one of our organizations, the National Institute of Smart Governance, we have now put in a center which is fundamentally focused on international cooperation so that they can actually provide support to other countries where DPIs are to be actually implemented, how to ground them.

And we believe that probably the most effective way of dealing with this is to actually be able to cooperate amongst ourselves. So. So that we are able to take it out. We are able to learn from each other. We are able to contribute to each other. And that is something we are now really ready to do. India knows what it is to be deprived of or denied technology. India knows what it is to actually try and work your way past it. We have managed to do that. We have managed to democratize it. We have managed to make it available to people at scale. We have tried to keep it open source. We have tried to protect it in a number of ways from cyber attacks in each of those areas.

So in this entire technology stack, there is experience. There is the way that we leapfrog different stages. So I think if we work together in the AI space, likewise, there is so much that can be accomplished. And we undertake as a nation, I think I can say with responsibility that we will have devices and we will have structures through which we can sort of deepen this cooperation. We can deepen the support. We can enable this in a number of ways and continue to stay engaged through the Gates Foundation, through the other institutions to actually make this happen. So thank you very much. Thank you to Gates Foundation for curating this particular event. And thanks to all of you for participating in this.

I mean, it’s one thing to arrange it, one thing to organize it. But another thing for all of you to actually come up here, put up with some of the inconvenience which would have been caused. India is not a very convenient country at best of times. but India is a country with spirit and India is a country which fixes things

Shalini Kapoor

Okay, we have some time with us. How much? Five minutes. And we need to have a question to Sunil. He has been working at, you know, so many interesting, I mean, I listened to the stories of Wadwaning AI. and they inspire you thoroughly. So Sunil, I’ll give it to you. I want people to hear your message that how can these work that has been done in India, how can it help Global South?

Sunil Wadhwani

So as Mr. Kone of Smart Africa said 15 minutes ago, the challenges that we have in the Global South generally, really Africa, India, other countries are similar. The values we have, more importantly, are very similar. The strengths that we bring to the table in terms of our talent, our youth, et cetera, are similar. So I think it’s mutual learning. It’s not one way. It’s not that we’ve developed great stuff in India that can just be, you know, taken over. It’s mutual learning on both sides. It’s a mutual sharing of ideas. There are lots of very good things happening in Africa and Asia. There are lots of things happening in Africa and Asia that we can learn from over here.

On the technology side, as you were saying, Shalini, we’ve been fortunate. We’ve had a government that is very pro -technology. There’s a tremendous range of digital public infrastructure that we can access in India that enables, that provides data pipelines. It provides digital distribution systems without which none of this AI can scale. There’s been a very clear regulatory framework for AI that’s been developed in India that really helps. And most important, there’s an openness in government. And I think it’s driven by the prime minister’s vision and belief that technology and AI can truly transform societal development. So those, to me, are the big things, more than individual AI solutions that really make a difference. And I see that happening in Africa in many countries.

Shalini Kapoor

Sure. Thank you so much. I think we are literally… We are literally at the eve of the summit getting over. It’s been a fantastic week. meeting the best of the people, listening to the best of the sessions, navigating the traffic, yes. But like Secretary Krishnan said that, you know, we fix everything. So I just want to have one last question to each of the panelists. What’s the best thing you liked of the summit? And to you, Sunil, first. I mean, one moment, one feeling that you will carry forward.

Sunil Wadhwani

I will give you a counterintuitive answer. AI is making the world move faster and faster and faster. And all the traffic challenges we’ve had over here are teaching us patience. You will get there. Things will happen. Life will go on.

Shalini Kapoor

Thank you so much. Shikol, what’s the one feeling you’ll travel back with, back to Africa?

Shikoh Gitau

I think my best moment is, and I’m going to pick and be selfish, in the moment in Oberoi when I stood and saw this diverse sea of faces, I think about 300 people, and we’re all celebrating like, we can do this as the Global South. This is happening in our TAF, and this belief that the Global South has something to offer into this AI conversation. So it’s no longer a two -horse race, it’s a multiple -horse race. Thank you.

Lacina Kone

For me, it’s, you know, our vision is to transform Africa into a single digital market. Coming here this time, it shows me already our future. Having 1 .4 billion people on the one regulation. How does it feel like? So you know what I’m talking about. That’s one of our obstacles, says regulatory harmonization in India, you do not have that. multicultural, you have a multicultural multilingual, you have multicultural what does it feel like, including the traffic in the morning as well of course thank you

Shalini Kapoor

And my best moment for the summit was that back in Oberoi on 18th evening, several partners across Italy, Kenya Anthropic, Google Carnegie ORF, Gates Aikstep stood next to Nandini, Kenya and we all came together for 100 pathways till 2030 and we all were together and we were not doing non -collaboration pictures which is going on in Insta, we were all together so it was AI is about collaboration not competition, that’s the theme thank you thank you really enjoyed thank you thank you thank you you are the best man you are the best it’s a pleasure more photos more photos Thank you. Thank you.

Related ResourcesKnowledge base sources related to the discussion topics (38)
Factual NotesClaims verified against the Diplo knowledge base (4)
Confirmedhigh

“While serving on the Carnegie Mellon University board, he observed billions of dollars flowing into AI research.”

The knowledge base notes that, as a board member, he could see billions of dollars pouring into AI from companies like Google, confirming his observation [S15].

Additional Contexthigh

“The Ministry of Health identified TB as the country’s top infectious‑disease killer, accounting for nearly two million deaths globally and half a million in India.”

WHO data cited in the knowledge base confirms TB as the leading infectious-disease killer worldwide, though the exact death figures differ from the claim; the source provides the broader context of TB’s impact [S116].

Confirmedmedium

“The institute mapped the TB cascade and pinpointed three critical bottlenecks: lack of functional X‑ray equipment, slow sputum‑test turnaround across 64 government labs, and poor adherence to toxic medication regimens.”

The knowledge base lists the same three challenges-limited X-ray machines, costly and time-consuming sputum analysis, and toxic drug regimens leading to poor adherence-supporting the institute’s identified bottlenecks [S115].

Additional Contextmedium

“The TB AI tool plugs into the Nikshay case‑management platform, a Digital Public Infrastructure.”

A related source describes a government DPI called Nixia (likely referring to Nikshay) that serves as a patient-management system for TB data, providing context that such integration with a DPI exists [S5].

External Sources (117)
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AI-Powered Chips and Skills Shaping Indias Next-Gen Workforce — -S. Krishnan- Role/Title: Secretary of METI (Ministry of Electronics and Information Technology)
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https://dig.watch/event/india-ai-impact-summit-2026/panel-discussion-ai-cybersecurity-_-india-ai-impact-summit — Sri S. Krishnan, Secretary, Ministry of Electronics and IT, my dear friend, Professor Ravindran, Excellencies, distingui…
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Panel Discussion AI & Cybersecurity _ India AI Impact Summit — Sorry, could I make a quick announcement to have all the panelists and the speakers on the stage for a quick photo? Mr. …
S4
Keynote-Vishal Sikka — -Honorable Ashwini Vasanthaji: Role/Title: Minister, Ministry of IT; Area of expertise: Information Technology -Sunil: …
S5
AI for Social Good Using Technology to Create Real-World Impact — – James Manyika- Sunil Wadhwani – Sangbu Kim- Sunil Wadhwani
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WS #214 AI Readiness in Africa in a Shifting Geopolitical Landscape — **Shikoh Gitau**, CEO of KALA, participated virtually and brought private sector perspectives. Her pointed question abou…
S7
IGF 2025: Africa charts a sovereign path for AI governance — African leaders at theInternet Governance Forum (IGF) 2025 in Oslocalled for urgent action to build sovereign and ethica…
S8
What is it about AI that we need to regulate? — InWS #214, Shikoh Gitau asked:”But who is drafting these policies? What agenda do they have? Do they have Africa at hear…
S9
Responsible AI for Shared Prosperity — -Ankur Vora- Chief Strategy Officer and President of the Africa and India Office at the Gates Foundation -Co-Moderator-…
S10
Keynote-Ankur Vora — “AI is not a leap into the unknown for India. It is the next chapter in a journey of building solutions that serve every…
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Open Forum #47 Demystifying WSis+20 — – **Lacina Kone** – CEO and Director General of Smart Africa, a Pan-African organization based in Kigali – **UNKNOWN** …
S12
WS #214 AI Readiness in Africa in a Shifting Geopolitical Landscape — **Lacina Kone**, Director General and CEO of Smart Africa, provided a continental perspective that proved influential th…
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What policy levers can bridge the AI divide? — – **Lacina Kone**: Director General and Chief Executive Officer, Smart Africa LJ Rich: H.E. Dr. Tatenda Anastasia Mavat…
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https://dig.watch/event/india-ai-impact-summit-2026/building-scalable-ai-through-global-south-partnerships — Thank you, Sunil. Are we I think we have a change of plans. Thank you so much. And Sunil, if you could please stay on st…
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Building Scalable AI Through Global South Partnerships — – Sunil Wadhwani- Shalini Kapoor
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Safe and Responsible AI at Scale Practical Pathways — – Shalini Kapoor- Ashish Srivastava
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Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — And accessibility has to be also broadened in terms of multi -modality and also, where necessary, include a human in the…
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Building Indias Digital and Industrial Future with AI — So I think we are in a very good place. We have got very robust infrastructure. And how do we now navigate this world of…
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Sovereign AI for India – Building Indigenous Capabilities for National and Global Impact — India possesses many essential ingredients for AI success: a robust software services industry, thriving startup ecosyst…
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Ad Hoc Consultation: Thursday 8th February, Morning session — Advocating for technology transfer positions India as a proponent for advancing Sustainable Development Goal 9, which em…
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Leading in the Digital Era: How can the Public Sector prepare for the AI age? — Tawfik Jelassi:Thank you, Pratik. Good morning, all excellencies, esteemed guests, vicious participants. I’m very please…
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FOREWORD — First, the public sector needs to be aware of the potential of AI and related emerging technologies to de…
S23
Empowering Civil Servants for Digital Transformation | IGF 2023 Open Forum #60 — Audience:Yeah, thank you. I’m Odas, CEO of Digital Muganda. So I’m coming more from a private sector perspective. I thin…
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Leaders TalkX: Securing the Digital Realm: Collaborative Strategies for Trust and Resilience — Marash Dukaj:Thank you for this exciting question. First of all, their colleagues, ministers, ladies and gentlemen, I am…
S25
The Global Power Shift India’s Rise in AI & Semiconductors — But also private capital, which within this week, the numbers that I’m hearing is more than 100 billion dollar plus comm…
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https://dig.watch/event/india-ai-impact-summit-2026/building-climate-resilient-systems-with-ai — And part of my work has been in energy. Part of my work has been in the built environment. Thank you. but I’m representi…
S27
Transforming Health Systems with AI From Lab to Last Mile — A major announcement during the session revealed a groundbreaking collaborative funding initiative between three organis…
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Open Forum #64 Local AI Policy Pathways for Sustainable Digital Economies — – **Infrastructure Sharing and Cooperative Models**: Multiple speakers advocated for shared computing infrastructure (re…
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Building Population-Scale Digital Public Infrastructure for AI — “These systems need to be auditable.”[58]. “there is this urgency to get things done and that might make one think very …
S30
Host Country Open Stage — High level of consensus on fundamental principles despite working in different domains. This suggests emerging best prac…
S31
Building a Global Partnership for Responsible Cyber Behavior | IGF 2023 Launch / Award Event #69 — This initiative aims to support these organisations in preventing and responding to cyber attacks effectively. Understan…
S32
Resilient infrastructure for a sustainable world — – **Importance of Partnerships and Open Collaboration**: All speakers stressed that no single organization can address t…
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AI as critical infrastructure for continuity in public services — Excellent question. Thank you so much for that. Good afternoon, everybody. Thank you for all the comments. So we’ve been…
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A digital public infrastructure strategy for sustainable development – Exploring effective possibilities for regional cooperation (University of Western Australia) — On a positive note, the potential for South-South cooperation and shared learning experiences in the field of DPI was hi…
S35
WS #82 A Global South perspective on AI governance — AUDIENCE: Thank you for the wonderful thought provoking conversation. I wanted to ask, I only attended half of the ses…
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Collaborative AI Network – Strengthening Skills Research and Innovation — Garg frames AI itself as a possible digital public infrastructure that must be trusted, interoperable and shareable, dra…
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AI for agriculture Scaling Intelegence for food and climate resiliance — It is being designed as a replicable public infrastructure model for India and the entire global south. In partnership w…
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AI Impact Summit 2026: Global Ministerial Discussions on Inclusive AI Development — The AI Impact Summit held in New Delhi brought together ministers and senior officials from multiple countries for discu…
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Leveraging AI to Support Gender Inclusivity | IGF 2023 WS #235 — By engaging users and technical communities, policymakers can gain valuable insights and perspectives, ultimately leadin…
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Democratizing AI: Open foundations and shared resources for global impact — Bernard Maissen: Yes, thank you. Hello, everybody, dear panelists. Nina, thank you for giving me the floor. In the globa…
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Transforming Agriculture_ AI for Resilient and Inclusive Food Systems — We are committed to work together on this through knowledge sharing, co -operation and collaboration. creation and capac…
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Policy Guidelines — line 1 , disseminating the fruits of its research and scholarship as widely as possible: The intention of the policy is …
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Meeting REPORT — The neutrality of the sentiment expressed in this context suggests an acceptance of the policy’s intent without explicit…
S45
Technology Rewiring Global Finance: A Panel Discussion Summary — In one day, there was maximum withdrawal of $7 billion equivalent of assets from Binance.com. No issues. In that week, t…
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Press Conference: Closing the AI Access Gap — Private sector can help deliver progress on sustainable development goals The goal is to move from a narrative to actio…
S47
Multistakeholder Partnerships for Thriving AI Ecosystems — The panel revealed sophisticated understanding of how different stakeholders must collaborate whilst maintaining distinc…
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Building Scalable AI Through Global South Partnerships — “One, the government needs to be creating a conducive environment for private sector to chip in”[33]. “We do believe tha…
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WS #288 An AI Policy Research Roadmap for Evidence-Based AI Policy — Virginia Dignam: Thank you very much, Isadora. No pressure, I see. You want me to say all kinds of things. I hope that i…
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AI for Democracy_ Reimagining Governance in the Age of Intelligence — “Global governance of AI is a precursor for a democratic development and evolution.”[1]. “So the way to democratize thes…
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Impact & the Role of AI How Artificial Intelligence Is Changing Everything — Power is accumulating rapidly in the hands of those at the forefront of AI development. A handful of technology corporat…
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Democratizing AI Building Trustworthy Systems for Everyone — And so there are different in quotes, markets here at UL. People who can pay at different levels. Even within a country …
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How the Global South Is Accelerating AI Adoption_ Finance Sector Insights — Compute infrastructure and research talent shortages present bigger obstacles than regulatory constraints Data residenc…
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Planetary Limits of AI: Governance for Just Digitalisation? | IGF 2023 Open Forum #37 — It is important to address this lack of diversity to ensure that AI systems are fair, inclusive, and do not perpetuate b…
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Governments and Technical Community: A Successful Model of Multistakeholder Collaboration for Achieving the SDGs — This comment articulated the bidirectional nature of learning required for effective collaboration, moving beyond the co…
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Accelerating Structural Transformation and Industrialization in Developing Countries: Navigating the Future with Advanced ICTs and Industry 4.0 — This comment introduces a critical geopolitical dimension often overlooked in technical discussions. It highlights how d…
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Empowering Inclusive and Sustainable Trade in Asia-Pacific: Perspectives on the WTO E-commerce Moratorium — To ensure successful integration, bridging the gap between academia and industry is essential. Due to the rapid advancem…
S58
Ad Hoc Consultation: Thursday 8th February, Morning session — Egypt aligns with South Africa and Peru on technology transfer inclusion. Egypt aligns with statements by South Africa …
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Democratizing AI: Open foundations and shared resources for global impact — The ‘sovereign-able’ approach allows countries and organizations to build upon open foundation models while maintaining …
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Global AI Policy Framework: International Cooperation and Historical Perspectives — -Sovereignty vs. Openness in AI Development: The concept of “open sovereignty” emerged as a key theme – the idea that co…
S61
Discussion Report: Sovereign AI in Defence and National Security — Faisal responds to concerns about competing global AI policies by arguing that the sovereign AI framework is adaptable t…
S62
Taxing Tech Titans: Policy Options for the Global South | IGF 2023 WS #443 — Overall, the analysis highlights the multifaceted nature of international taxation and the complex considerations involv…
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TAX COOPERATION POLICY BRIEF — On March 2019, the PCT launched the First Global Conference on the Platform for Collaboration on Tax Report. 10 The Con…
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Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — I mean, access to compute is what makes or breaks a startup. So the way in India, the way I see it, the way we have star…
S65
AI as critical infrastructure for continuity in public services — Data is siloed, data is not ready for AI scale. There’s no governance built around data. And that’s why POCs, you use a …
S66
Building Population-Scale Digital Public Infrastructure for AI — Combine urgency of deployment with systematic safety frameworks, using DPI infrastructure as foundation for safe scaling
S67
Building Scalable AI Through Global South Partnerships — And we make government accountable for a lot of it. Just as we’re accountable for the technical side. The other really k…
S68
https://dig.watch/event/india-ai-impact-summit-2026/building-scalable-ai-through-global-south-partnerships — And we make government accountable for a lot of it. Just as we’re accountable for the technical side. The other really k…
S69
AI for Social Good Using Technology to Create Real-World Impact — Sunil Wadhwani shared concrete examples from Wadhwani AI’s work, including AI systems that diagnose tuberculosis from co…
S70
AI health tools need clinicians to prevent serious risks, Oxford study warns — The University of Oxfordhas warnedthat AI in healthcare, primarily through chatbots, should not operate without human ov…
S71
Effective Governance for Open Digital Ecosystems | IGF 2023 Open Forum #65 — Cooperation, sharing of technology, and learning are important for effective implementation at scale. Changing mindsets …
S72
WS #271 Data Agency Scaling Next Gen Digital Economy Infrastructure — This challenges traditional hierarchies of expertise in technology development and advocates for genuine inclusion of di…
S73
AI for agriculture Scaling Intelegence for food and climate resiliance — “We will move from pilots to platforms, from fragmented data to interoperable systems, from experimentation to execution…
S74
Resilient infrastructure for a sustainable world — – **Importance of Partnerships and Open Collaboration**: All speakers stressed that no single organization can address t…
S75
A digital public infrastructure strategy for sustainable development – Exploring effective possibilities for regional cooperation (University of Western Australia) — On a positive note, the potential for South-South cooperation and shared learning experiences in the field of DPI was hi…
S76
Collaborative Innovation Ecosystem and Digital Transformation: Accelerating the Achievement of Global Sustainable Development Goals (SDGs) — John OMO: Thank you very much, Mohamed. I really appreciate being here. Context. Africa has SMEs contributing conservati…
S77
Open Forum #82 Catalyzing Equitable AI Impact the Role of International Cooperation — Abhishek Agarwal: Yeah, like what we need to do, like a lot has already been spoken, and I would say that if I have to l…
S78
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — And accessibility has to be also broadened in terms of multi -modality and also, where necessary, include a human in the…
S79
Building Indias Digital and Industrial Future with AI — -India’s Global DPI Model and Knowledge Transfer: The discussion highlighted India’s role in sharing its DPI framework g…
S80
Open Forum #30 High Level Review of AI Governance Including the Discussion — Abhishek Singh, Under-Secretary from the Indian Ministry of Electronics and Information Technology, emphasised that oper…
S81
WS #43 States and Digital Sovereignty: Infrastructural Challenges — The speaker mentions India’s national ID system (Aadhaar) and payment system (UPI) as examples of DPI enhancing sovereig…
S82
AI Impact Summit 2026: Global Ministerial Discussions on Inclusive AI Development — The AI Impact Summit held in New Delhi brought together ministers and senior officials from multiple countries for discu…
S83
Democratizing AI: Open foundations and shared resources for global impact — Bernard Maissen: Yes, thank you. Hello, everybody, dear panelists. Nina, thank you for giving me the floor. In the globa…
S84
Leveraging AI to Support Gender Inclusivity | IGF 2023 WS #235 — There is an acknowledgement of the need for more alignment and coordination in the field of AI regulation. Efforts are b…
S85
Transforming Agriculture_ AI for Resilient and Inclusive Food Systems — We are committed to work together on this through knowledge sharing, co -operation and collaboration. creation and capac…
S86
Democratizing AI Building Trustworthy Systems for Everyone — Financial mechanisms | Artificial intelligence | Capacity development Role of Philanthropy and Public‑Private Partnersh…
S87
AI Innovation in India — The tone was consistently celebratory, inspirational, and optimistic throughout the discussion. Speakers expressed pride…
S88
AI for food systems — The tone throughout the discussion was consistently formal, optimistic, and collaborative. It maintained a ceremonial qu…
S89
Partnering on American AI Exports Powering the Future India AI Impact Summit 2026 — The tone is consistently optimistic, collaborative, and forward-looking throughout the discussion. Speakers emphasize “l…
S90
Panel Discussion AI & Cybersecurity _ India AI Impact Summit — The discussion maintained a consistently optimistic and collaborative tone throughout. Speakers expressed enthusiasm and…
S91
AI for Good Innovation Factory Grand Finale 2025 — The discussion maintained a consistently positive, encouraging, and professional tone throughout. It began with exciteme…
S92
https://dig.watch/event/india-ai-impact-summit-2026/building-trustworthy-ai-foundations-and-practical-pathways — I give this example because I’m fairly confident that when you look it up and when you try it yourself it will work. And…
S93
https://dig.watch/event/india-ai-impact-summit-2026/how-nonprofits-are-using-ai-based-innovations-to-scale-their-impact — There is a mentor talking to them. So we thought we’ll improve the student report first, but that didn’t work out so wel…
S94
https://dig.watch/event/india-ai-impact-summit-2026/keynote-ankur-vora — complex ones are referred appropriately and millions of lives are saved. AI will not just speed up innovation. It can he…
S95
https://dig.watch/event/india-ai-impact-summit-2026/leveraging-ai4all_-pathways-to-inclusion — And so we thought the biggest use case, the biggest investment would be on making the speakers better for Ray -Ban Meta …
S96
Keeping up with Smart Factories / DAVOS 2025 — The overall tone was optimistic and forward-looking. Panelists were enthusiastic about the potential of smart factory te…
S97
WS #283 AI Agents: Ensuring Responsible Deployment — The discussion maintained a balanced, thoughtful tone throughout, combining cautious optimism with realistic concern. Pa…
S98
Indias Roadmap to an AGI-Enabled Future — The discussion maintained an optimistic and ambitious tone throughout, with speakers expressing confidence in India’s ab…
S99
Regional perspectives on digital governance | IGF 2023 Open Forum #138 — There has been successful collaboration with stakeholder in Africa and other sub-regional activities
S100
Launch / Award Event #78 Digital Governance in Africa: Post-Summit of the Future — The tone was largely informative and collaborative, with panelists sharing research findings, policy perspectives, and r…
S101
World Economic Forum Annual Meeting Closing Remarks: Summary — These key comments transformed what could have been a standard ceremonial closing into a meaningful reflection on the ph…
S102
Closing remarks — The tone is consistently celebratory, optimistic, and forward-looking throughout the discussion. It maintains an enthusi…
S103
Closing Session  — Distinguished colleagues, as we come to the close of the summit, I want to particularly say how grateful I am for the op…
S104
Closing Ceremony — As the host country representative, Aukrust highlighted Norway’s commitment to digital inclusion through “investing in d…
S105
Powering AI Global Leaders Session AI Impact Summit India — -Prime Minister: (mentioned as having spoken the day before, but did not speak in this transcript) -Speaker: Role/title…
S106
Leaders’ Plenary | Global Vision for AI Impact and Governance- Afternoon Session — And just this week, you know, the first two -nanometer chip in India has been designed by our team. I thank Mr. Vesnav t…
S107
ChatGPT: A year in review — As ChatGPT turns one, the significance of its impact cannot be overstated. What started as a pioneering step in AI has s…
S108
https://dig.watch/event/india-ai-impact-summit-2026/ai-2-0-reimagining-indian-education-system — If we take AI out of Western knowledge, if we promote it in Indian knowledge, Indian context, Indian languages, then we …
S109
https://dig.watch/event/india-ai-impact-summit-2026/ai-2-0-the-future-of-learning-in-india — If we take AI out of Western knowledge, if we promote AI in Indian knowledge, Indian context, Indian languages, then we …
S110
AI in Africa: Beyond the algorithm — Global connectivity divide Kate highlights the fundamental infrastructure gap where close to half of the world’s popula…
S111
Global Digital Governance & Multistakeholder Cooperation for WSIS+20 — – Gitanjali Sah- Thibaut Kleiner- Participant Provides specific statistic of 2.6 billion people without internet access…
S112
Opening Ceremony — Chami highlighted the gap between basic Internet access statistics and meaningful connectivity, arguing that while 68% o…
S113
GPAI: A Multistakeholder Initiative on Trustworthy AI | IGF 2023 Open Forum #111 — Alan Paic:Thank you very much, Inma. And it is my pleasure to address you today. I will give an introduction to GPAI as …
S114
UNSC meeting: UNSC Conflict prevention: A New Agenda for Peace — In this speech, India’s representative addresses the complex nature of conflict prevention and peacekeeping in today’s w…
S115
https://dig.watch/event/india-ai-impact-summit-2026/ai-for-social-good-using-technology-to-create-real-world-impact — First one is diagnosis and diagnosing TB in economically vulnerable communities isn’t easy. X -ray machines, sputum anal…
S116
The Sustainable Development Goals Report 2019 — Tuberculosis remains a leading cause of poor health and death worldwide. An estimated 10 million people fell ill with th…
S117
The Millennium Development Goals Report 2015 — The tuberculosis (TB) incidence rate has been falling in all regions since 2000, declining by about 1.5 per cent per yea…
Speakers Analysis
Detailed breakdown of each speaker’s arguments and positions
S
Sunil Wadhwani
7 arguments162 words per minute2517 words932 seconds
Argument 1
TB detection and treatment cascade AI
EXPLANATION
Sunil describes how AI is used to improve tuberculosis detection and management in India, covering diagnosis, lab processing, and treatment adherence. The AI tools provide rapid, probabilistic assessments and automate laboratory workflows to speed up care.
EVIDENCE
He explains that the AI can detect TB from the sound of a cough using a smartphone, delivering instant risk probabilities and becoming the national standard, with the World Health Organization calling it a potential game-changer [56-63]. He also notes that an AI model now automates sputum analysis in 64 government labs, delivering results within a day [64-66]. Finally, AI algorithms predict which patients will drop off medication, allowing 2,000 caseworkers to focus on high-risk individuals, impacting tens of millions and raising detection rates by 25% in the last year [68-73].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
AI-driven TB detection from cough sounds and lab automation are documented in AI for Social Good case studies and in the Global South partnerships report, confirming rapid, probabilistic assessments and lab workflow automation [S5] [S15].
MAJOR DISCUSSION POINT
Health AI scaling
Argument 2
AI‑based early reading proficiency tools to reduce school dropout
EXPLANATION
Sunil outlines an AI‑driven suite that assesses early reading skills and delivers personalized exercises to young learners, aiming to curb high dropout rates in primary school. The solution is being scaled across millions of children after successful pilots.
EVIDENCE
He identifies the inability of children aged 7-9 to read as the main cause of dropout and describes an AI-based tool that creates personalized reading exercises for each child, delivering stories they can practice at home [80-86]. After a pilot with a state, the program was made mandatory for 3 million school-age children in Rajasthan, demonstrating large-scale adoption [87-90].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The reading-improvement system scaling to tens of millions of children and its impact on dropout rates are described in the AI for Social Good overview and the Global South partnerships analysis [S5] [S15].
MAJOR DISCUSSION POINT
Education AI intervention
Argument 3
Early government engagement, scalability planning, integration with Nikshay and Rakshak platforms
EXPLANATION
Sunil emphasizes that successful AI impact requires partnering with government from the outset, aligning with national priorities, and embedding solutions into existing public digital platforms. This approach ensures rapid scaling and accountability.
EVIDENCE
He recounts working directly with the health and education ministries to identify priority use cases such as TB and early reading, and then integrating AI algorithms into the government’s Nikshay TB case-management system and the Rakshak education platform used by 70 000 schools and 8 million students in Rajasthan [37-41][118-124].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The integration of AI models into India’s Nikshay TB case-management system and the Rakshak education platform is highlighted in the Global South partnerships briefing [S15].
MAJOR DISCUSSION POINT
Government partnership for scaling
AGREED WITH
S. Krishnan, Lacina Kone, Ankur Vora
Argument 4
India’s digital ID, UPI, and other public infrastructure enable AI at scale
EXPLANATION
Sunil points to India’s established digital public goods—Aadhaar and UPI—as foundational infrastructure that facilitates the deployment of AI solutions across sectors. These systems provide identity verification and payment mechanisms essential for large‑scale AI services.
EVIDENCE
He cites Aadhaar as a great example of digital ID and UPI as an incredible digital payments infrastructure, highlighting their role in enabling AI applications in health, education, and agriculture [111-114].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Aadhaar’s digital ID role and UPI’s payments infrastructure as foundations for large-scale AI services are discussed in the digital public infrastructure commentary [S14] and the partnerships report [S15].
MAJOR DISCUSSION POINT
Digital public infrastructure
AGREED WITH
Lacina Kone, S. Krishnan
Argument 5
Mutual learning, not one‑way technology transfer, leveraging similar challenges
EXPLANATION
Sunil argues that AI challenges in the Global South are shared, and collaboration should be a two‑way exchange of ideas rather than a simple export of Indian solutions. Both regions can learn from each other’s experiences.
EVIDENCE
He notes that the challenges and values across Africa, India, and other countries are similar, emphasizing mutual learning and shared ideas rather than one-way technology transfer [363-370].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The report on building scalable AI emphasizes a two-way learning approach among Global South nations and cites technology-transfer frameworks aligned with SDG 9 and 17 [S15] [S20].
MAJOR DISCUSSION POINT
South‑South knowledge exchange
AGREED WITH
Shalini Kapoor, Lacina Kone, S. Krishnan, Shikoh Gitau
Argument 6
Capacity‑building for civil servants and large deployment teams to ensure rollout
EXPLANATION
Sunil describes how his organization trains senior government officials on AI use, develops data‑governance standards, and maintains a sizable deployment team to operationalize solutions at scale. This capacity work underpins successful implementation.
EVIDENCE
He mentions training senior civil servants on AI capabilities and limitations, helping ministries develop data-governance standards, and maintaining a deployment team of close to 100 people to ensure rollout [145-151].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Capacity-building for public-sector staff and the need for civil-service AI readiness are outlined in the public-sector AI preparation brief and the capacity-building foreword [S21] [S22].
MAJOR DISCUSSION POINT
Institutional capacity building
Argument 7
Patience and resilience amid rapid AI pace and logistical challenges
EXPLANATION
Sunil reflects on the fast‑moving nature of AI development and the practical challenges (such as traffic) faced during the summit, suggesting that patience is essential for progress. He frames these obstacles as teaching moments.
EVIDENCE
He states that AI is making the world move faster and that traffic challenges in India are teaching patience, assuring that things will happen despite logistical hurdles [391-395].
MAJOR DISCUSSION POINT
Personal resilience in AI deployment
AGREED WITH
S. Krishnan, Shalini Kapoor, Ankur Vora
A
Ankur Vora
2 arguments92 words per minute584 words377 seconds
Argument 1
Innovation‑to‑impact gap highlighted
EXPLANATION
Ankur points out that moving from an innovative AI prototype to real‑world impact is not a straight line; it requires deliberate effort and partnership. He praises Sunil’s learnings as examples of bridging this gap.
EVIDENCE
He remarks that the journey between innovation and impact is not straight, noting that while it is possible, it is not guaranteed and requires hard work, referencing Sunil’s lessons as illustrative [131-134].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The gap between AI prototypes and real-world impact, and the need for deliberate partnership, is a central theme of the Global South partnerships analysis [S15].
MAJOR DISCUSSION POINT
Bridging innovation and impact
AGREED WITH
Sunil Wadhwani, S. Krishnan, Shalini Kapoor
Argument 2
Gates Foundation partnership fuels AI for impact initiatives
EXPLANATION
Ankur highlights the long‑standing partnership with the Gates Foundation and a new initiative, Advantage India for AI, which will channel investments into AI solutions for the Global South. This collaboration is positioned as a catalyst for scaling impact.
EVIDENCE
He mentions the seven-year partnership with the Gates Foundation and the newly announced Advantage India for AI pledge, which aims to invest in AI for the Global South and anticipates further collaboration with Sunil’s team [135-137].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Ankur Vora’s role at the Gates Foundation and the foundation’s AI for impact collaborations are documented in the summit keynote and profile summaries [S10] [S9].
MAJOR DISCUSSION POINT
Philanthropic partnership
S
S. Krishnan
2 arguments170 words per minute1499 words526 seconds
Argument 1
India AI Mission model: subsidised compute, sovereign models, open‑source data for the Global South
EXPLANATION
Krishnan outlines India’s AI Mission, which provides low‑cost compute, sovereign AI models, and open data to enable other countries to adopt AI. The model is framed as a frugal, publicly‑subsidised approach that can be shared globally.
EVIDENCE
He describes a model where AI compute in India is offered at a third of the global price, sovereign models built with taxpayer resources, and an open-source data framework that can be shared with the UN and other Global South nations, emphasizing the AI Kosh model and willingness to share it now [305-319][320-328][329-344].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
India’s AI Mission offering low-cost compute, sovereign models, and open-data frameworks for sharing with other countries is described in the scalable AI partnerships report and the shared infrastructure discussion [S15] [S28].
MAJOR DISCUSSION POINT
National AI infrastructure for global sharing
AGREED WITH
Sunil Wadhwani, Lacina Kone
Argument 2
Democratizing AI through inclusive participation, youth engagement, and open access
EXPLANATION
Krishnan stresses that true AI democratization means opening the rooms and halls of AI events to a broad audience, especially youth, so they can see and understand AI applications firsthand. He frames this as a core achievement of the summit.
EVIDENCE
He notes that the summit allowed people, especially youth, to enter rooms and listen to leading AI minds, emphasizing inclusive participation, and declares that this reflects the democratization of AI [289-298][327-329].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Inclusive AI events that welcome youth and broaden participation are highlighted in the Global South partnerships briefing and the open-forum on AI policy pathways [S15] [S28].
MAJOR DISCUSSION POINT
Inclusive AI engagement
AGREED WITH
Sunil Wadhwani, Shalini Kapoor, Ankur Vora
S
Shalini Kapoor
2 arguments113 words per minute923 words488 seconds
Argument 1
AI diffusion as shared playbooks and cross‑country learning
EXPLANATION
Shalini describes AI diffusion as the creation of reusable playbooks and digital rails that allow solutions to move between countries, similar to how electricity spread historically. She argues that shared knowledge accelerates adoption across the Global South.
EVIDENCE
She explains that AI diffusion involves laying routes and rails, creating playbooks that can be shared, referencing Geoffrey Hinton’s analogy to electricity’s spread from Germany to India and the USA, and stresses that this enables cross-country learning [179-190].
MAJOR DISCUSSION POINT
Knowledge sharing mechanisms
AGREED WITH
Sunil Wadhwani, Lacina Kone, S. Krishnan, Shikoh Gitau
Argument 2
Emphasis on collaboration over competition as the summit’s key takeaway
EXPLANATION
Shalini highlights that the summit demonstrated AI as a collaborative effort rather than a competitive race, citing the joint announcement of 100 pathways to 2030 and the collective presence of partners from various regions. She frames this collaborative spirit as the main lesson.
EVIDENCE
She recounts that partners from Italy, Kenya, Anthropic, Google, Carnegie, Gates, and others gathered at the Oberoi event to launch 100 pathways to 2030, emphasizing that AI is about collaboration not competition [408-410].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The summit’s collaborative spirit, including the joint launch of 100 pathways to 2030, is emphasized in the partnership report and the collaborative strategies forum summary [S15] [S24].
MAJOR DISCUSSION POINT
Collaboration as core message
AGREED WITH
Sunil Wadhwani, S. Krishnan, Ankur Vora
L
Lacina Kone
3 arguments159 words per minute807 words303 seconds
Argument 1
Smart Africa AI Council: multi‑nation coordination, private‑sector‑led ecosystem
EXPLANATION
Lacina outlines the formation of the Africa AI Council, a multi‑government and private‑sector body that coordinates AI policy, investment, and thematic groups across the continent. The council is designed to foster private‑sector execution within a supportive regulatory environment.
EVIDENCE
She details that 49 African countries signed a declaration in April 2025, the AI Council was launched in November 2025 with 15 members (seven ministers and eight private sector representatives), and that six thematic groups address computing, data, skills, regulation, market, and investment, emphasizing private-sector leadership [213-224][225-236].
MAJOR DISCUSSION POINT
Continental AI governance structure
AGREED WITH
Sunil Wadhwani, Shalini Kapoor, S. Krishnan, Shikoh Gitau
Argument 2
Private sector execution supported by conducive regulatory environment; philanthropy as de‑risking layer
EXPLANATION
Lacina argues that a favorable regulatory climate enables private‑sector AI execution, while philanthropy can act as a de‑risking mechanism to accelerate investment. She stresses that finance is secondary to having the right regulatory clouds.
EVIDENCE
She states that the government must create a conducive environment for private sector, that finance is the last consideration, likening financing to rain that requires clouds (regulatory environment) to fall, and that philanthropy should serve as a de-risking layer for projects where governments are hesitant to invest [231-240].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Balancing regulation with private-sector AI execution and the role of philanthropy as a de-risking mechanism are discussed in the private-sector innovation commentary and the private-capital investment overview [S23] [S25].
MAJOR DISCUSSION POINT
Enabling private sector and philanthropic support
Argument 3
Vision of a single African digital market and regulatory harmonisation
EXPLANATION
Lacina presents a vision of unifying Africa’s 1.4 billion people into a single digital market with harmonised regulations, drawing parallels to India’s digital public infrastructure successes. She sees regulatory harmonisation as a key obstacle to overcome.
EVIDENCE
She notes that Africa’s 1.4 billion population can be leveraged through collaboration, cites India’s digital ID and election management as examples, and emphasizes the need for regulatory harmonisation across the continent, referencing her own view of transforming Africa into a single digital market [202-207][402-406].
MAJOR DISCUSSION POINT
Continental digital integration
S
Shikoh Gitau
2 arguments152 words per minute394 words155 seconds
Argument 1
Need for political goodwill and reducing “collaboration tax” to enable partnership
EXPLANATION
Shikoh stresses that political goodwill is essential for AI collaboration and that the effort, resources, and coordination required—termed the ‘collaboration tax’—must be minimized to make partnerships feasible. She calls for mechanisms that lower these barriers.
EVIDENCE
She defines the ‘collaboration tax’ as the effort and resources needed to collaborate, argues that governments should handle this to reduce pain, and notes that while people, builders, researchers, and policymakers are present, political goodwill is needed to bring them together [266-273].
MAJOR DISCUSSION POINT
Reducing barriers to cross‑border AI work
Argument 2
Celebration of Global South unity and multi‑horse race narrative
EXPLANATION
Shikoh shares a personal moment of seeing a diverse audience of about 300 people, interpreting it as evidence that the Global South is collectively advancing AI, moving from a two‑horse race to a multi‑horse race. This reflects a sense of shared purpose.
EVIDENCE
She recounts standing at the Oberoi venue, seeing a diverse sea of roughly 300 faces, and feeling that the Global South can collectively drive AI forward, describing the shift from a two-horse race to a multiple-horse race [398-401].
MAJOR DISCUSSION POINT
Collective Global South momentum
Agreements
Agreement Points
Scaling AI solutions requires early and deep partnership with government and integration into existing public digital platforms.
Speakers: Sunil Wadhwani, S. Krishnan, Lacina Kone, Ankur Vora
Early government engagement, scalability planning, integration with Nikshay and Rakshak platforms India AI Mission model: subsidised compute, sovereign models, open‑source data for the Global South India’s digital ID, UPI, and other public infrastructure enable AI at scale Innovation‑to‑impact gap highlighted
All speakers stress that working with government from the outset and embedding AI tools into national platforms such as Nikshay, Rakshak, Aadhaar, and UPI is essential for rapid, large-scale impact, and that public support (subsidised compute, open models) underpins this scaling [37-41][118-124][111-114][305-319][131-134].
POLICY CONTEXT (KNOWLEDGE BASE)
Multistakeholder frameworks call for governments to create enabling environments and embed AI in existing digital public infrastructure, as highlighted in discussions on DPI and government-private collaboration [S42][S47][S64][S66].
South‑South collaboration should be a two‑way exchange of knowledge and playbooks rather than a one‑way technology transfer.
Speakers: Sunil Wadhwani, Shalini Kapoor, Lacina Kone, S. Krishnan, Shikoh Gitau
Mutual learning, not one‑way technology transfer, leveraging similar challenges AI diffusion as shared playbooks and cross‑country learning Smart Africa AI Council: multi‑nation coordination, private‑sector‑led ecosystem Democratizing AI through inclusive participation, youth engagement, and open access Need for political goodwill and reducing ‘collaboration tax’ to enable partnership
Speakers agree that countries in the Global South face similar AI challenges and should share solutions, playbooks, and regulatory lessons, fostering mutual learning and joint pathways such as the 100 pathways to 2030 initiative [363-370][179-190][198-224][289-298][266-273].
POLICY CONTEXT (KNOWLEDGE BASE)
Policy briefs stress bidirectional learning between governments and technical communities and note recent South-South statements on technology-transfer inclusion [S55][S56][S58].
Democratizing AI and ensuring inclusive participation are central to achieving impact.
Speakers: Sunil Wadhwani, S. Krishnan, Shalini Kapoor, Ankur Vora
Patience and resilience amid rapid AI pace and logistical challenges Democratizing AI through inclusive participation, youth engagement, and open access Emphasis on collaboration over competition as the summit’s key takeaway Innovation‑to‑impact gap highlighted
All speakers highlight that AI must be made accessible to frontline workers, youth, and broader society, with inclusive events and tools that lower barriers, thereby democratizing the technology [125-127][327-329][408-410][131-134].
POLICY CONTEXT (KNOWLEDGE BASE)
Inclusive AI governance is a core element of AI-for-democracy agendas and open-foundation initiatives, emphasizing participation beyond the Global North [S50][S52][S59].
Robust digital public infrastructure (e.g., Aadhaar, UPI, Nikshay, Rakshak) is the foundation for large‑scale AI deployment.
Speakers: Sunil Wadhwani, Lacina Kone, S. Krishnan
India’s digital ID, UPI, and other public infrastructure enable AI at scale India’s digital ID, UPI, and other public infrastructure enable AI at scale India AI Mission model: subsidised compute, sovereign models, open‑source data for the Global South
The panel repeatedly points to India’s existing digital public goods-Aadhaar, UPI, and sector-specific platforms like Nikshay and Rakshak-as critical enablers for AI solutions, and notes that the AI Mission extends this model with low-cost compute and open data for sharing globally [111-124][204-206][305-319].
POLICY CONTEXT (KNOWLEDGE BASE)
Digital public infrastructure is repeatedly cited as the backbone for scalable AI, with examples from India’s DPI and calls to treat AI as shared public infrastructure [S64][S65][S66][S47].
Similar Viewpoints
Both emphasize building capacity within government—through training civil servants, deploying dedicated teams, and engaging youth—to ensure AI tools are adopted and scaled effectively [145-151][327-329].
Speakers: Sunil Wadhwani, S. Krishnan
Capacity‑building for civil servants and large deployment teams to ensure rollout Democratizing AI through inclusive participation, youth engagement, and open access
Both stress that moving from prototype to impact requires deliberate partnership and collaborative spirit rather than isolated competition [131-134][408-410].
Speakers: Ankur Vora, Shalini Kapoor
Innovation‑to‑impact gap highlighted Emphasis on collaboration over competition as the summit’s key takeaway
Unexpected Consensus
Role of the private sector in scaling AI alongside government
Speakers: Lacina Kone, Sunil Wadhwani
Private sector execution supported by conducive regulatory environment; philanthropy as de‑risking layer Capacity‑building for civil servants and large deployment teams to ensure rollout
While Lacina foregrounds private-sector-led execution within a supportive regulatory cloud, Sunil, a primarily government-partnered actor, also highlights a sizable deployment team and the need for private-sector-friendly environments, revealing an unexpected alignment on the necessity of private sector involvement for scale [231-236][145-151].
POLICY CONTEXT (KNOWLEDGE BASE)
Guidelines highlight the private sector’s execution capacity when governments provide conducive frameworks, underscoring private contributions to SDG-aligned AI deployment [S42][S46][S47][S48].
Open‑source sovereign AI models as a shared global resource
Speakers: Lacina Kone, S. Krishnan
Vision of a single African digital market and regulatory harmonisation India AI Mission model: subsidised compute, sovereign models, open‑source data for the Global South
Lacina’s vision of a harmonised African digital market aligns unexpectedly with Krishnan’s description of India’s sovereign AI models and open data that can be shared globally, indicating converging views on open, reusable AI assets across continents [402-406][305-319].
POLICY CONTEXT (KNOWLEDGE BASE)
Emerging policy concepts promote open-foundation, sovereign-able AI models that can be customized locally while remaining globally shared [S59][S60].
Overall Assessment

The discussion shows strong convergence on four pillars: (1) government partnership and integration with public digital platforms; (2) South‑South mutual learning and shared playbooks; (3) democratization and inclusive participation; (4) reliance on robust digital public infrastructure. These shared positions span AI, ICT‑for‑development, capacity building, and enabling environments, indicating a cohesive vision for scaling AI impact across the Global South.

High consensus – most speakers reinforce each other’s points, creating a unified narrative that AI impact will be driven by public‑private collaboration, open infrastructure, and cross‑regional learning. This alignment suggests that future initiatives are likely to be coordinated, leveraging government platforms, shared resources, and South‑South partnerships to accelerate AI‑enabled development.

Differences
Different Viewpoints
Approach to scaling AI solutions – government‑led versus private‑sector‑led
Speakers: Sunil Wadhwani, Lacina Kone
Sunil: “the only way to scale is government” and stresses working with ministries from day one, making government accountable, and integrating AI into public platforms [92-94][118-124] Lacina: emphasizes that private-sector execution is primary, with government only creating a conducive regulatory environment; finance is deemed not the issue, likening it to rain needing clouds (regulation) [231-236]
Sunil argues that scaling AI impact requires direct government partnership and integration, while Lacina contends that scaling should be driven by the private sector once the regulatory environment is set, downplaying the role of government in execution and stating finance is not the main barrier. Their views diverge on who should lead and what factors are decisive for scaling.
POLICY CONTEXT (KNOWLEDGE BASE)
Debates reference multistakeholder partnership models that balance government leadership with private-sector scaling capabilities [S47][S46].
What is the primary constraint for AI deployment – financing versus regulatory/environmental readiness
Speakers: Lacina Kone, Sunil Wadhwani
Lacina: claims that finance is the last consideration and that the regulatory ‘clouds’ are what enable investment, using the metaphor that financing is like rain needing clouds [233-236] Sunil: focuses on building a large deployment team, training civil servants, and developing data-governance standards, implying substantial resource and funding needs for rollout [145-151]
Lacina downplays financing as a barrier, emphasizing regulatory conditions, whereas Sunil highlights the need for significant human and financial resources to operationalise AI solutions, indicating a disagreement on which factor is most critical.
POLICY CONTEXT (KNOWLEDGE BASE)
Analyses from the Global South identify compute and talent shortages as larger barriers than finance or regulation, while also noting environmental and regulatory challenges [S53][S54][S56].
Unexpected Differences
Finance portrayed as non‑issue
Speakers: Lacina Kone
Lacina: states that “finance is not the issue” and that the regulatory environment is the real prerequisite for investment [233-236]
While most participants discuss funding, capacity building, and deployment resources, Lacina’s outright dismissal of finance as a barrier is unexpected and not directly contested by other speakers, highlighting a divergent perception of financing importance.
POLICY CONTEXT (KNOWLEDGE BASE)
Some assessments argue that financing is not the main bottleneck for AI rollout, emphasizing technical constraints such as compute infrastructure instead [S53].
Introduction of a ‘collaboration tax’ concept
Speakers: Shikoh Gitau
Shikoh: defines the collaboration tax as the effort and resources required to collaborate, urging reduction of this burden for effective partnerships [266-273]
The notion of a collaboration tax is not addressed by any other participant, making it an unexpected point of contention regarding how cross‑border AI work should be facilitated.
POLICY CONTEXT (KNOWLEDGE BASE)
Policy discussions on taxing digital platforms and tech giants explore mechanisms that could be adapted as a ‘collaboration tax’ to fund joint AI initiatives [S62].
Overall Assessment

The discussion shows modest disagreement centered on the preferred scaling model (government versus private sector) and the perceived primary constraints (finance versus regulatory environment). Participants share a common goal of expanding AI impact in the Global South, but differ on the mechanisms and priorities to achieve it. Unexpected divergences arise around the role of financing and the concept of a collaboration tax.

Low to moderate – disagreements are largely about emphasis and strategy rather than fundamental opposition, suggesting that consensus can be built through coordinated policy design that balances government leadership, private‑sector dynamism, regulatory readiness, and adequate financing.

Partial Agreements
Both aim to foster South‑South collaboration on AI, but Sunil focuses on mutual technical learning, while Shikoh highlights political facilitation and lowering collaboration costs as the key to making such partnerships work [363-370][266-273]
Speakers: Sunil Wadhwani, Shikoh Gitau
Sunil: stresses mutual learning and two-way knowledge exchange between India and other Global South countries [363-370] Shikoh: emphasizes the need for political goodwill and reducing the ‘collaboration tax’ to enable cross-border partnerships [266-273]
Both seek widespread AI adoption across the Global South, yet Shalini proposes structured knowledge‑sharing mechanisms, whereas Krishnan stresses inclusive participation and experiential learning as the path to democratization [179-190][289-298]
Speakers: Shalini Kapoor, S. Krishnan
Shalini: describes AI diffusion as creating shared playbooks and digital rails that allow solutions to move between countries [179-190] Krishnan: frames democratizing AI as opening rooms and halls to youth and broader audiences, enabling people to see and engage with AI directly [289-298]
Takeaways
Key takeaways
AI can be leveraged for high‑impact health and education outcomes in the Global South, exemplified by TB detection via cough analysis and AI‑driven early‑reading tools in India. Successful scaling requires early and deep partnership with government, integration with existing digital public infrastructure (e.g., Nikshay, Rakshak, Aadhaar, UPI) and planning for deployment at scale from day one. India’s AI Mission model—subsidised compute, sovereign open‑source models, and shared data pipelines—offers a template that can be exported to other low‑ and middle‑income countries. South‑South collaboration is essential; the focus should be on sharing playbooks, mutual learning, and reducing the “collaboration tax” rather than one‑way technology transfer. A conducive regulatory environment, private‑sector execution, and philanthropic de‑risking together create an ecosystem that can move AI from prototype to production. Democratizing AI means inclusive participation (youth, civil servants, NGOs), open access to resources, and keeping humans at the centre of AI deployments. Collaboration over competition was repeatedly highlighted as the overarching spirit of the summit.
Resolutions and action items
Wadhwani Institute to launch operations in Rwanda, Ethiopia and Kenya and to continue expanding AI solutions globally. Commitment to impact 500 million people by 2040 through AI‑driven health and education platforms. India’s AI Mission to share its compute‑as‑a‑service model, sovereign AI models (AI Kosh), and open‑source datasets with other Global South nations. Smart Africa AI Council established to coordinate AI policy, investment, data, skills, and regulation across 49 African countries. Gates Foundation partnership to fund and support AI‑for‑impact initiatives in India and other Global South countries. Capacity‑building programmes for senior civil servants on AI governance, data standards and use‑case development to be replicated abroad. Development of the “100 Pathways to 2030” playbook to document successful AI deployment routes for reuse across regions.
Unresolved issues
Specific mechanisms for operationalising South‑South knowledge transfer and joint implementation (e.g., joint funding models, joint governance structures) remain undefined. How to quantitatively reduce the “collaboration tax” – the time, resources and coordination overhead – was discussed but no concrete framework was agreed upon. Financing models for large‑scale AI rollout beyond initial government subsidies were mentioned as a challenge, with no clear solution presented. Regulatory harmonisation across diverse legal and linguistic contexts (e.g., between India’s multilingual environment and African nations) needs further work. Ensuring sustained frontline adoption (health workers, teachers) beyond initial rollout – how to monitor and iterate on usability – was highlighted but not resolved.
Suggested compromises
Balancing private‑sector execution with public‑sector regulation: governments provide an enabling environment while private firms handle implementation, with philanthropy acting as a de‑risking layer. Integrating AI tools into existing government platforms (Nikshay, Rakshak) rather than building parallel systems, to minimise duplication and ease adoption. Designing AI solutions that both improve outcomes for end‑users and simplify the workflow of frontline workers, thereby aligning technical goals with user convenience.
Thought Provoking Comments
We realized we were not approaching it quite right. Having a nice technical solution is not enough; you need to work with government from day one, think about scale from the start, integrate with existing digital public infrastructure, and make sure the tool makes life easier for frontline workers.
This reframes the common tech‑first mindset by highlighting that impact depends on policy, scale‑thinking, and user‑centric design, not just algorithmic brilliance.
It shifted the conversation from describing AI products to discussing systemic factors for scaling. It prompted Ankur to reflect on the innovation‑to‑impact gap and opened the floor for the panel to explore government partnerships and South‑South knowledge transfer.
Speaker: Sunil Wadhwani
AI‑based cough detection for TB is now rolling out nationally in India, becoming the national standard and recognized by WHO as a potential game‑changer globally.
Provides a concrete, high‑impact example of AI moving from prototype to nationwide deployment, illustrating the earlier point about scaling through government channels.
Anchored the abstract discussion in a real‑world success story, giving other speakers (e.g., Shalini and Lacina) a tangible reference when talking about diffusion and replication in other countries.
Speaker: Sunil Wadhwani
The only way to scale is government. You have to approach senior civil servants with humility, plan deployment at scale before you even build the solution, and hold the government accountable alongside the technical team.
Emphasizes partnership dynamics and accountability, challenging the notion that private innovators can scale alone.
Led Ankur to acknowledge the non‑linear path from innovation to impact and set the stage for the panel’s focus on public‑private ecosystems and South‑South collaboration.
Speaker: Sunil Wadhwani
AI diffusion is about the routes and rails that need to be laid, just like digital rails were laid for DPI. Playbooks can be shared so that a solution built in Kenya can be used in India and vice‑versa.
Introduces the metaphor of diffusion infrastructure, moving the dialogue from isolated projects to a systematic, replicable framework.
Prompted Lacina to discuss the role of regulatory and financial ecosystems, and Shikoh to bring up the concept of a ‘collaboration tax,’ deepening the conversation about what enables diffusion.
Speaker: Shalini Kapoor
Finance is not the issue; the regulatory environment is the cloud that creates rain. Private sector needs a predictable, conducive environment to invest, and philanthropy should act as a de‑risking layer.
Reframes the common belief that lack of capital blocks AI adoption, instead highlighting policy and risk mitigation as the true bottlenecks.
Shifted the panel’s focus from funding to governance, influencing Shikoh’s point about making AI a political and economic issue and reinforcing Sunil’s earlier remarks about government’s role.
Speaker: Lacina Kone
We need to start talking about the ‘collaboration tax’ – the effort, resources, and coordination required to make cross‑country AI projects work, and how governments should shoulder that burden.
Coins a new term that captures the hidden costs of partnership, prompting a nuanced discussion about who should bear those costs and how to streamline collaboration.
Added a layer of complexity to the South‑South dialogue, leading participants to consider practical mechanisms for joint work rather than just high‑level aspirations.
Speaker: Shikoh Gitau
Democratizing AI means letting people into the rooms, making compute available at a third of global prices, building sovereign models, and sharing them openly with the Global South.
Articulates a comprehensive, frugal model for AI democratization that combines infrastructure, policy, and open‑source ethos, moving beyond isolated pilots.
Provided a macro‑level vision that resonated with all panelists, reinforcing Sunil’s points about government support and prompting the final reflections on how the summit itself embodied democratic AI.
Speaker: S. Krishnan
Overall Assessment

The discussion pivoted around a few core insights: the necessity of government partnership and scale‑by‑design, the power of concrete, nationally‑backed AI deployments, and the need for systematic diffusion pathways supported by regulatory and political frameworks. Sunil’s early remarks about moving beyond pure technology set the tone, which was expanded by Shalini’s diffusion metaphor, Lacina’s regulatory focus, and Shikoh’s ‘collaboration tax’ concept. Krishnan’s framing of democratic AI tied these strands together, positioning the summit itself as a model of inclusive, frugal AI. Collectively, these comments shifted the conversation from showcasing isolated innovations to constructing a replicable, cross‑regional ecosystem for AI impact in the Global South.

Follow-up Questions
What are the specific steps and challenges in adapting India’s AI solutions (e.g., TB cough detection, sputum analysis) for deployment in African countries like Rwanda, Ethiopia, and Kenya?
Sunil mentioned starting operations in these countries but did not detail the localization process, indicating a need for deeper insight into transferability and implementation hurdles.
Speaker: Sunil Wadhwani
How can the ‘collaboration tax’—the resources and effort required for cross‑country AI collaborations—be measured and reduced?
Shikoh introduced the concept of collaboration tax but did not explore metrics or mitigation strategies, highlighting an area for further study.
Speaker: Shikoh Gitau
What financing models best unlock private‑sector investment for AI deployment in the Global South, given that regulatory environment is the primary enabler?
Lacina argued finance is not the core issue and emphasized regulatory conditions, suggesting research into financing mechanisms linked to policy frameworks.
Speaker: Lacina Kone
How can India’s AI Kosh (AI treasury) model be evaluated and adapted for other nations seeking sovereign AI infrastructure?
Krishnan described the AI Kosh model and its willingness to share it, but did not provide evaluation criteria or adaptation pathways, indicating a research gap.
Speaker: S. Krishnan
What are the cost‑effective AI compute strategies that allow India to offer compute at one‑third of global prices, and how can these be replicated elsewhere?
Krishnan highlighted frugal compute as a key achievement but did not detail the technical or policy levers, warranting further investigation.
Speaker: S. Krishnan
How can the ‘100 Pathways to 2030’ framework be operationalized to guide AI pilots through to production across diverse contexts?
Shalini referenced the 100 Pathways initiative but did not outline implementation steps, indicating a need for concrete methodology.
Speaker: Shalini Kapoor
What are the best practices and challenges for integrating AI algorithms into existing government digital public‑infrastructure platforms (e.g., Nikshay, Rakshak)?
Sunil noted successful integrations but did not discuss technical, governance, or user‑adoption challenges, suggesting further research.
Speaker: Sunil Wadhwani
What metrics and impact‑evaluation methods should be used for large‑scale AI interventions in health (TB) and education (early reading) to inform replication in other countries?
While Sunil shared impact numbers, a systematic evaluation framework was not described, pointing to a research need.
Speaker: Sunil Wadhwani
What governance and data‑standard frameworks are being developed for AI use in Indian ministries, and how can these be adapted for other Global South governments?
Sunil mentioned capacity‑building on data governance but did not detail the standards, indicating an area for further study.
Speaker: Sunil Wadhwani
How can digital public infrastructure (e.g., Aadhaar, UPI) be leveraged or replicated to enable AI scaling in other countries?
Sunil highlighted India’s digital infrastructure as a scaling enabler but did not explore replication models for other contexts.
Speaker: Sunil Wadhwani
What mechanisms can align private‑sector, philanthropic, and government actors to de‑risk AI projects in the Global South?
Lacina discussed the role of philanthropy as a de‑risking layer but did not specify coordination mechanisms, suggesting further inquiry.
Speaker: Lacina Kone
How can AI tools be designed to genuinely reduce workload and improve acceptance among frontline health workers and teachers?
Sunil emphasized making tools easier for frontline users but did not provide design guidelines or evidence of impact, indicating a research opportunity.
Speaker: Sunil Wadhwani

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