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 opened with Ankur Vora asking Sunil Wadhwani to describe how his Badwani Institute has used AI to address pressing social problems in India [1][2][7]. Wadhwani explained that after launching the institute in 2018-when AI was still nascent and most investment ignored societal needs-he and his brother shifted focus to partnering directly with government ministries to identify priority use-cases such as tuberculosis and early-grade reading proficiency [17-20][23-30][31-34][35-38][41-45].


By analysing the TB care cascade, the team created a smartphone-based cough-analysis tool that provides a probabilistic risk score and has become the national standard, an AI-driven sputum-analysis system that returns results within a day, and a predictive model that flags patients likely to abandon treatment, collectively raising detection rates by 25 % and reaching tens of millions [56-63][64-70][71-74]. In education, they built an AI suite that generates personalized reading exercises for each child, which was piloted in Rajasthan and subsequently mandated for all three million primary students in the state, demonstrating rapid scale [75-88][89-90].


Wadhwani highlighted three key lessons: impact requires early and deep government engagement, solutions must be designed for national-scale deployment from the outset, and leveraging existing digital public infrastructure such as Aadhaar, UPI, the TB case-management platform Nikshay, and the school platform Rakshak is essential for rapid rollout [91-100][101-108][109-118][119-124]. He also stressed that tools must ease frontline workers’ workflows, otherwise adoption will stall [125-127].


Ankur noted that moving from innovation to impact is not linear and praised the Gates Foundation’s new “Advantage India for AI” initiative that will support such government-led scaling [131-136]. Sunil reported that the institute now serves about 100 million Indians annually through more than 25 AI platforms, is fielding requests from other Global South nations, and has begun operations in Rwanda, Ethiopia and Kenya, aiming to reach 500 million people by 2040 [139-148][149-156][160-168].


Panelists Shalini Kapoor and Lacina Kone described AI diffusion as a “playbook” that can be shared across borders, emphasizing the role of Smart Africa’s AI Council and the need for coordinated public-private ecosystems to avoid reinventing solutions [179-188][189-197][198-226]. Kone argued that regulatory harmonisation and a “collaboration tax”-the resources needed to cooperate-must be addressed, with philanthropy acting as a de-risking layer for private investment [227-241].


Shikoh Gitau added that political goodwill and a shared sense of purpose are crucial for turning AI into a societal and economic lever across the Global South [259-272][273-277]. S. Krishnan reinforced the theme of democratizing AI, citing India’s frugal AI mission that offers compute at one-third global cost, sovereign models and datasets that can be shared, and the Gates Foundation’s partnership in showcasing thousands of AI-driven startups at the summit [289-319][320-328][329-342].


The participants concluded that the summit demonstrated tangible South-South collaboration, with attendees feeling inspired by the collective commitment to scale AI for people, planet and progress [382-400][401-408].


Keypoints

Major discussion points


AI-driven health and education solutions in India and how they were scaled – The Wadhwani Institute built AI tools for tuberculosis (cough-sound detection, automated sputum analysis, medication-adherence prediction) that are now national standards, and an AI-based reading-proficiency suite that has been mandated for millions of children in Rajasthan [41-70][74-90].


Key lessons for achieving impact at scale – Success required early and humble engagement with government ministries, designing for national-level rollout from day one, plugging solutions into existing digital public infrastructure (e.g., Nikshay, Rakshak), and ensuring the tools make frontline workers’ lives easier [91-127].


South-South collaboration and the diffusion of AI “playbooks” – Panelists highlighted the need to share pathways, leverage India’s digital public infrastructure experience, and create joint mechanisms (Smart Africa, Africa AI Council) so that innovations can move between India, Africa and other Global-South nations [177-194][198-226][259-270][363-381].


Strategic partnerships and the summit’s broader agenda – The Gates Foundation’s “Advantage India for AI” pledge, the India-Gates partnership, and the summit’s goal of democratizing AI, involving youth and emphasizing people-planet-progress, were repeatedly referenced as the framework enabling these collaborations [135-138][289-332].


Overall purpose / goal of the discussion


The conversation aimed to showcase how AI can be democratized and deployed at massive scale to solve pressing health and education challenges in India, extract the lessons learned, and position those experiences as a blueprint for South-South cooperation. By highlighting partnerships (especially with the Gates Foundation) and the summit’s vision, participants sought to catalyze cross-regional collaboration that accelerates AI diffusion across the Global South.


Overall tone and its evolution


– The dialogue began with an informative and enthusiastic tone as Sunil described concrete AI solutions and their impact [41-70].


– It shifted to a reflective, advisory tone when outlining the strategic lessons for scaling [91-127].


– The panel then moved to a collaborative and optimistic tone, emphasizing mutual learning, shared pathways, and collective ambition across continents [177-194][198-226][259-270][363-381].


– Throughout, there was a consistent undercurrent of positivity and forward-looking optimism, punctuated by brief acknowledgments of challenges (e.g., “working with government isn’t easy” [94-98]) but ultimately reaffirming confidence in partnership-driven AI democratization.


Speakers

Ankur Vora


Area of expertise: Global health and education initiatives, AI for social impact, philanthropy.


Role / Title: Chief Strategy Officer and President of the Africa and India Office, Gates Foundation[S13][S14]


Sunil Wadhwani


Area of expertise: Artificial intelligence research, AI for health and education, scaling AI solutions in low-resource settings.


Role / Title: Founder & Co-Chair (with brother Ramesh) of the Wadhwani Institute for Artificial Intelligence


Shalini Kapoor


Area of expertise: AI strategy, partnership building, ecosystem development.


Role / Title: Chief Strategist, XSTEP Foundation[S10]


Lacina Kone


Area of expertise: Continental AI policy, digital public infrastructure, public-private partnership in Africa.


Role / Title: Director General and CEO, Smart Africa[S1][S2][S3]


S. Krishnan


Area of expertise: National AI policy, digital public infrastructure, AI mission implementation.


Role / Title: Secretary, Ministry of Electronics and Information Technology (MeitY), Government of India[S4][S5][S6]


Shikoh Gitau


Area of expertise: AI product development, scaling AI in education and health, private-sector leadership in the Global South.


Role / Title: CEO, Kala[S7][S8][S9]


Additional speakers:


None (all speaking participants are covered in the list above).


Full session reportComprehensive analysis and detailed insights

The session opened with Ankur Vora asking Sunil Wadhwani to describe the work of the Badwani Institute in India, noting the organisation’s reputation for “democratising” AI and applying it to problems such as oral-reading fluency and tuberculosis screening [1-7].


Wadhwani explained that he and his brother founded the Badwani Institute for Artificial Intelligence in 2018, at a time 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 that none of this investment was directed toward societal challenges affecting three-four billion people lacking adequate health-care and education [24-26]. This realisation prompted the decision to launch a dedicated institute in India [27-30].


The early years were marked by technical development without scale. After a few years of “neat” prototypes that failed to reach users, the team reassessed its approach and concluded that having a good algorithm was insufficient; impact required a broader system of engagement [31-34]. The key shift was to work directly with government ministries, aligning AI projects with national priorities [35-38].


In health, the institute tackled tuberculosis, which the Ministry of Health identified as a top priority [41-44]. By mapping the TB care cascade they pinpointed three bottlenecks-lack of functional X-ray machines, slow sputum-lab turnaround, and poor medication adherence-and responded with a smartphone-based cough-analysis tool that delivers an instant probabilistic risk score, an AI-driven automated sputum-analysis pipeline that reduces results to one day, and a predictive model that flags patients likely to default on treatment, enabling 2 000 caseworkers to focus on the most at-risk individuals [56-60][63-66][68-70]. Together these interventions lifted TB detection by 25 % in the last year and now touch tens of millions of people [71-74].


In education, the institute addressed the high dropout rate among primary-school children by building an AI-powered suite that generates personalised reading exercises and stories for each child; after a successful pilot in a large Indian state, the Rajasthan government mandated the tool for all three million primary pupils in the target age group [75-84][86-90]. The design ensured that frontline teachers and workers found the tool easy to use, reinforcing the lesson that adoption stalls if a solution does not simplify users’ jobs [125-127].


From these experiences Wadhwani distilled three overarching lessons. First, scaling is impossible without early, humble partnership with senior civil servants; the government must be involved from day one and held accountable alongside the technical team [91-100][107-110]. Second, solutions must be engineered for national-scale deployment at the outset, with explicit plans for training, distribution and field use [101-108]. Third, leveraging existing digital public infrastructure-such as Aadhaar, UPI, the TB case-management platform Nikshay, the school platform Rakshak, and the broader DPI ecosystem-provides the data pipelines and user bases needed for rapid roll-out [111-124][311-314]. Finally, tools must make frontline workers’ jobs easier; otherwise adoption stalls [125-127].


Ankur reflected that the path from innovation to impact is “not a straight road” and praised the Gates Foundation’s new “Advantage India for AI” pledge, which aims to fund AI-for-social-good initiatives in the Global South [130-135].


Wadhwani then reported that the institute now reaches roughly 100 million Indians each year through more than 25 AI platforms, and that it has begun fielding requests from other Global-South governments [139-148]. A dedicated deployment team of about 100 staff supports these efforts [149-151]. In the past year the institute dispatched teams to Africa and launched operations in Rwanda, Ethiopia and Kenya [154-156], with the goal of impacting 500 million people worldwide by 2040 [160-168]. He linked this ambition to Prime Minister Modi’s vision of “design in India for the world, develop in India for the world and deliver these solutions to the world” [165-168].


The subsequent panel expanded the discussion to AI diffusion across borders. Shalini Kapoor described diffusion as the “rails” that must be laid for AI, likening it to the digital-public-infrastructure tracks that enabled earlier internet expansion, and argued that documented “playbooks” should be shared so that a solution built in Kenya can be reused in India and vice-versa [179-194]. Lacina Kone, representing Smart Africa, explained that the continent’s AI Council brings together governments, private firms and philanthropies to create a regulatory “cloud” that precedes financing; she stressed that finance is the “last thing” to consider once the policy environment is stable [198-226][231-238]. Kone also introduced the notion of a “collaboration tax” – the effort and resources required to coordinate multi-stakeholder projects – and suggested that philanthropy can act as a de-risking layer [236-238]; Shikoh Gitau echoed Kone’s point, noting that reducing the collaboration tax is essential for cross-regional partnerships [259-272][273-277].


S. Krishnan outlined India’s AI Mission, a frugal model that supplies compute at roughly one-third of global prices, builds sovereign AI models with taxpayer funding, and makes both the compute and models openly available for other Global-South nations [307-319][312-317][318-321]. He highlighted the “people, planet, progress” pillars that framed the summit [300-304], noted that close to 900 startups showcased AI applications across the halls [340-345], and described the “African village” set up to demonstrate solutions that work in different parts of the world [336-339]. The shared compute-and-model pool was referred to as the “AI Kosh” (also called the “AI treasury”) [322-327]. Krishnan also emphasized that DPI is the backbone for democratising AI [311-314]. He mentioned the partnership with the Gates Foundation in curating the summit, the establishment of a Centre for International Cooperation under the National Institute of Smart Governance, and the broader goal of opening the AI treasury to the world [329-342][332-337].


Across the discussion there was strong consensus that (i) government partnership and alignment with national priorities are indispensable for scaling AI; (ii) existing DPI is the backbone that enables rapid, cost-effective deployment; and (iii) South-South collaboration-through shared pathways, playbooks and mutual learning-offers the most efficient route to diffusion [91-100][111-124][179-194][198-226][259-272][307-319]. While the perspectives varied, Wadhwani stresses early government partnership, whereas Kone emphasizes that a predictable regulatory environment is the prerequisite for private-sector execution and that finance follows [92-100][231-238].


Key take-aways from the session include:


* AI-driven tools for TB (cough-sound detection, automated sputum analysis, adherence prediction) and for early-grade reading have demonstrably improved health and education outcomes at scale.


* Early, humble engagement with ministries, integration with national DPI, and user-centred design are essential for impact.


* South-South collaboration should be organised around documented “pathways” and playbooks, with bodies such as Smart Africa’s AI Council providing the regulatory “cloud” that enables private investment.


* India’s frugal AI Mission offers a replicable model of sovereign, open-source compute and datasets that can be shared internationally.


* Partnerships with foundations-particularly the Gates Foundation’s “Advantage India for AI” pledge-are viewed as critical bridges between innovation and impact.


Thought-provoking remarks that shaped the dialogue were:


* “The only way to scale is government… you have to work with government from day one” [92-94];


* “If the tool does not make the frontline worker’s life easier, it will not be adopted” [125-127];


* The diffusion metaphor of “rails” and “playbooks” for AI [179-184];


* “Finance is not the issue; the regulatory cloud is the rain that makes finance fall” [233-235];


* The introduction of the “collaboration tax” as a hidden cost of partnership [236-238];


* India’s compute being offered at a third of global cost, illustrating a frugal, open-source approach [307-319].


Action items emerging from the discussion include launching Badwani AI operations in Rwanda, Ethiopia and Kenya [154-156]; targeting 500 million people impacted globally by 2040 [160-168]; sharing India’s AI treasury (compute, models, datasets) with other Global-South nations once capacity thresholds are met [322-327]; establishing the Centre for International Cooperation to support DPI implementation abroad [332-337]; deepening the Gates Foundation partnership for funding and knowledge exchange [329-332]; and developing and disseminating “AI pathway” playbooks to lower the collaboration tax [179-194][236-238].


Unresolved issues highlighted were the lack of concrete mechanisms and timelines for transferring sovereign models and compute to partner countries, the challenge of harmonising regulatory frameworks across diverse African jurisdictions to create a continent-wide “cloud”, the precise financing models required for large-scale deployments beyond the statement that finance is a later concern, and the need for robust metrics to monitor the impact of exported AI solutions in new contexts.


Krishnan closed by celebrating India’s resilience and the collective spirit of the summit, underscoring the commitment to keep democratizing AI for the Global South [350-354].


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 (32)
Factual NotesClaims verified against the Diplo knowledge base (6)
Confirmedhigh

“Sunil Wadhwani and his brother founded the Badwani Institute for Artificial Intelligence in 2018.”

The knowledge base states that the founder set up Vadbani AI (the institute) back in 2018, confirming the founding year and founders’ involvement [S19].

Confirmedhigh

“Tuberculosis was identified by the Indian Ministry of Health as a top national priority.”

S17 explicitly notes that eliminating tuberculosis has been a national health priority for the Indian government for several years, confirming the claim.

Confirmedhigh

“Scaling AI solutions in India requires early, humble partnership with senior civil servants and government involvement from day one.”

Both S12 and S18 emphasize that government partnership from day one is essential for achieving scale in AI deployments in the Global South, supporting this lesson.

Additional Contextmedium

“Adoption of AI tools stalls if they do not make life easier for frontline health workers or teachers.”

S18 highlights that if a tool does not simplify the work of frontline health workers or teachers, it will not be used, adding nuance to the adoption challenge described in the report.

Additional Contextmedium

“The institute shifted its approach to work directly with government ministries, aligning AI projects with national priorities.”

S12 describes a systematic re‑evaluation that led to the insight that deep collaboration with government ministries is required for successful AI implementation, providing context for the reported strategic shift.

Additional Contextlow

“The institute’s work exemplifies “democratising” AI by applying it to oral‑reading fluency and tuberculosis screening.”

S15 discusses AI’s role in addressing hard problems in education, such as assessing each child’s learning journey, while S17 covers TB as a health priority; together they provide supporting context for the institute’s focus areas, though they do not directly use the term “democratising”.

External Sources (114)
S1
Open Forum #47 Demystifying WSis+20 — – **Lacina Kone** – CEO and Director General of Smart Africa, a Pan-African organization based in Kigali Lacina Kone pr…
S2
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…
S3
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…
S4
AI-Powered Chips and Skills Shaping Indias Next-Gen Workforce — -S. Krishnan- Role/Title: Secretary of METI (Ministry of Electronics and Information Technology)
S5
Empowering India & the Global South Through AI Literacy — -Shri S. Krishnan: Secretary, Ministry of Electronics and Information Technology (MeitY), Government of India
S7
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…
S8
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…
S9
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…
S10
https://app.faicon.ai/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…
S11
Safe and Responsible AI at Scale Practical Pathways — – Ashish Srivastava- Prem Ramaswami- Shalini Kapoor – Rohit Bardawaj- Shalini Kapoor
S12
Building Scalable AI Through Global South Partnerships — – Sunil Wadhwani- Shalini Kapoor
S13
Keynote-Ankur Vora — – Ankur Vora: Works at the Gates Foundation, overseeing the foundation’s work across Africa and India offices. Previousl…
S14
Responsible AI for Shared Prosperity — -Ankur Vora- Chief Strategy Officer and President of the Africa and India Office at the Gates Foundation
S15
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 everyo…
S16
Capacity Building in Digital Health — Well, so here is the, there’s a, that’s a spicy question, but let me, let me, let me handle it. Well, this is in the U ….
S17
AI for Social Good Using Technology to Create Real-World Impact — – James Manyika- Sunil Wadhwani – Sangbu Kim- Sunil Wadhwani
S18
Building Scalable AI Through Global South Partnerships — Evidence:The solution is now rolling out nationally, TB detection rates have increased by 25% in the last year, and the …
S19
AI for Social Good Using Technology to Create Real-World Impact — 1463 words | 166 words per minute | Duration: 528 secondss Thanks, James. Good morning. Just so we’re all clear, there’…
S21
https://app.faicon.ai/ai-impact-summit-2026/building-trusted-ai-at-scale-cities-startups-digital-sovereignty-panel-discussion-moderator-amitabh-kant-niti — Absolutely. I think there’s already work going on. Specifically, there are three big areas where there’s thinking going …
S22
Building Indias Digital and Industrial Future with AI — Good morning, everyone. Warm welcome, distinguished guests, colleagues and partners and speakers who have joined us toda…
S23
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — So I think, firstly, India’s journey in DPIs has been a fascinating one. It makes me immensely proud that whichever coun…
S24
Building Indias Digital and Industrial Future with AI — Thanks, Rahul. Those were very key messages which you gave in which the network is being used for citizen -centric servi…
S25
https://app.faicon.ai/ai-impact-summit-2026/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…
S26
Artificial Intelligence & Emerging Tech — Victor Lopez Cabrera:Thanks so much. I really appreciate the invitation and I thank IGF Secretary for inviting us to be …
S27
AI for Democracy_ Reimagining Governance in the Age of Intelligence — Lord Rawal emphasizes that one of the core tenets of the Gayatri Parivar organization is adaptability to change, which h…
S28
Effective Governance for Open Digital Ecosystems | IGF 2023 Open Forum #65 — Changing mindsets while implementing at scale is crucial for effective implementation. Existing initiatives like the Gl…
S29
Donor roundtable: Enabling impact at scale in supporting inclusive and sustainable digital economies — Collaboration and partnerships are necessary to overcome the obstacles associated with such initiatives. There is an arg…
S30
Advancing Scientific AI with Safety Ethics and Responsibility — Global South Perspectives and Adaptation: A significant focus was placed on how emerging scientific powers can shape AI …
S31
Democratizing AI Building Trustworthy Systems for Everyone — -Participant- Works with the Gates Foundation in India, focuses on strategic partnerships between Indian researchers and…
S32
Partnering on American AI Exports Powering the Future India AI Impact Summit 2026 — Balancing national champion support with American technology foundation to enable local innovation while maintaining str…
S33
Democratizing AI Building Trustworthy Systems for Everyone — The Gates Foundation representative focused on ground-level challenges, particularly around sustainability and accessibi…
S34
Keynote Adresses at India AI Impact Summit 2026 — Strategic partnership between democracies: Multiple speakers emphasized the alliance between the world’s oldest and larg…
S35
Impact & the Role of AI How Artificial Intelligence Is Changing Everything — The discussion maintained a cautiously optimistic tone throughout, balancing enthusiasm for AI’s potential with realisti…
S36
Transforming Agriculture_ AI for Resilient and Inclusive Food Systems — The tone was consistently optimistic yet pragmatic throughout the conversation. Speakers maintained an encouraging outlo…
S37
Driving Indias AI Future Growth Innovation and Impact — The discussion maintained an optimistic and forward-looking tone throughout, characterized by enthusiasm for India’s AI …
S38
Inclusive AI Starts with People Not Just Algorithms — The tone was consistently optimistic and empowering throughout the discussion. Speakers maintained an enthusiastic, forw…
S39
Collaborative AI Network – Strengthening Skills Research and Innovation — “We’re talking of AI being a possible DPI, a digital public infrastructure.”[1]. “I think those are aspects which a DPI …
S40
A digital public infrastructure strategy for sustainable development – Exploring effective possibilities for regional cooperation (University of Western Australia) — However, there are concerns that need to be addressed when implementing DPI. One major concern is the risk of exclusion …
S41
Panel Discussion AI in Digital Public Infrastructure (DPI) India AI Impact Summit — Thanks for the question. You’re right, I think those three words are very key. When you’re talking from a government per…
S42
Building Scalable AI Through Global South Partnerships — The institute’s breakthrough came through systematic re-evaluation, leading to three critical insights. First, governmen…
S43
Building Scalable AI Through Global South Partnerships — Wadhwani learned that working with government from the beginning is the only way to achieve scale with AI solutions. He …
S44
Multistakeholder Partnerships for Thriving AI Ecosystems — Jain outlines the critical success factors for AI deployment based on practical experience. He emphasizes that governmen…
S45
Artificial Intelligence & Emerging Tech — Jörn Erbguth:Thank you very much. So I’m EuroDIG subject matter expert for human rights and privacy and also affiliated …
S46
WS #288 An AI Policy Research Roadmap for Evidence-Based AI Policy — Alex Moltzau: Yes, so one thing that I didn’t mention that we are working on currently is also these AI regulatory sandb…
S47
AI Collaboration Across Borders_ India–Israel Innovation Roundtable — This addresses the need for systematic innovation pathways that can serve both sectors effectively
S48
How the Global South Is Accelerating AI Adoption_ Finance Sector Insights — Compute infrastructure and research talent shortages present bigger obstacles than regulatory constraints Sharma identi…
S49
How the Global South Is Accelerating AI Adoption_ Finance Sector Insights — Summary:Sharma identifies compute resources and research talent as the main barriers, suggesting regulatory issues are l…
S50
Secure Finance Risk-Based AI Policy for the Banking Sector — Compliance functions increasingly rely on automated pattern recognition, while adaptive cybersecurity models respond to …
S51
WS #82 A Global South perspective on AI governance — Lufuno T Tshikalange: Thank you, Dr. Melody, and thank you for having us here today. In Africa, we do now have a reg…
S52
From India to the Global South_ Advancing Social Impact with AI — Low level of disagreement with high convergence on AI’s transformative potential. Differences are primarily tactical rat…
S53
From India to the Global South_ Advancing Social Impact with AI — Disagreement level:Low level of disagreement with high convergence on AI’s transformative potential. Differences are pri…
S54
WS #484 Innovative Regulatory Strategies to Digital Inclusion — Carlos Rey-Moreno: So someone that was said at a session yesterday was in relation to to fair trade. Right. Was in relat…
S55
High-Level sessions: Setting the Scene – Global Supply Chain Challenges and Solutions — By aligning their financial services and efforts, these institutions aim to avoid confusion and conflicting initiatives …
S56
WS #262 Innovative Financing Mechanisms to Bridge the Digital Divide — – The need for innovative financing mechanisms and enabling policy/regulatory environments
S57
Democratizing AI Building Trustworthy Systems for Everyone — Evidence:Example of pregnancy risk stratification tools needing to work differently in Uttar Pradesh versus Telangana du…
S58
Open Forum #64 Local AI Policy Pathways for Sustainable Digital Economies — Rather than following historical patterns of automation that replace workers, AI development should prioritize applicati…
S59
AI Impact Summit 2026: Global Ministerial Discussions on Inclusive AI Development — And this requires proactive and coherent policy responses. First, people must be at the center of AI strategy, as we hea…
S60
Collaborative Innovation Ecosystem and Digital Transformation: Accelerating the Achievement of Global Sustainable Development Goals (SDGs) — While participants agreed on core objectives, they differed on implementation approaches and priorities. Some speakers e…
S61
WS #231 Address Digital Funding Gaps in the Developing World — The discussion revealed relatively low levels of fundamental disagreement among speakers, with most conflicts arising ar…
S62
NATIONAL INFORMATION AND COMMUNICATION TECHNOLOGY POLICY — A challenge has now arisen for the country to implement this policy and I therefore call upon all stakehold…
S63
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…
S64
AI for Social Good Using Technology to Create Real-World Impact — This discussion at the India AI Impact Summit focused on how open networks and digital public infrastructure (DPI) can e…
S65
Building Scalable AI Through Global South Partnerships — It’s like a case management system for tuberculosis patients. We’ve integrated everything. We developed algorithms into …
S66
Donor roundtable: Enabling impact at scale in supporting inclusive and sustainable digital economies — To accelerate impact in key sectors such as agriculture, manufacturing, and services, a systems approach to digitalizati…
S67
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 …
S68
Building Scalable AI Through Global South Partnerships — India’s AI mission offers several innovations for global sharing. The country has created compute infrastructure availab…
S69
India’s AI Future Sovereign Infrastructure and Innovation at Scale — Ganesh describes successful collaboration through a consortium of 9 academic institutions working via a Section 8 not-fo…
S70
AI Meets Agriculture Building Food Security and Climate Resilien — This discussion focused on using artificial intelligence to enhance food security and climate resilience in agriculture,…
S71
Democratizing AI Building Trustworthy Systems for Everyone — -Participant- Works with the Gates Foundation in India, focuses on strategic partnerships between Indian researchers and…
S72
Partnering on American AI Exports Powering the Future India AI Impact Summit 2026 — Balancing national champion support with American technology foundation to enable local innovation while maintaining str…
S73
Keynote Adresses at India AI Impact Summit 2026 — Strategic partnership between democracies: Multiple speakers emphasized the alliance between the world’s oldest and larg…
S74
Partnering on American AI Exports Powering the Future India AI Impact Summit 2026 — Balancing national champion support with American technology foundation to enable local innovation while maintaining str…
S75
Keynote Adresses at India AI Impact Summit 2026 — -Strategic partnership between democracies: Multiple speakers emphasized the alliance between the world’s oldest and lar…
S76
The Innovation Beneath AI: The US-India Partnership powering the AI Era — The tone was consistently optimistic and forward-looking throughout, with panelists expressing excitement about opportun…
S77
Driving Indias AI Future Growth Innovation and Impact — The discussion maintained an optimistic and forward-looking tone throughout, characterized by enthusiasm for India’s AI …
S78
Impact & the Role of AI How Artificial Intelligence Is Changing Everything — The discussion maintained a cautiously optimistic tone throughout, balancing enthusiasm for AI’s potential with realisti…
S79
Transforming Agriculture_ AI for Resilient and Inclusive Food Systems — The tone was consistently optimistic yet pragmatic throughout the conversation. Speakers maintained an encouraging outlo…
S80
Inclusive AI Starts with People Not Just Algorithms — The tone was consistently optimistic and empowering throughout the discussion. Speakers maintained an enthusiastic, forw…
S81
WS #302 Upgrading Digital Governance at the Local Level — The discussion maintained a consistently professional and collaborative tone throughout. It began with formal introducti…
S82
WS #236 Ensuring Human Rights and Inclusion: An Algorithmic Strategy — The tone of the discussion was largely serious and concerned, given the gravity of the issues being discussed. However, …
S83
Safeguarding Children with Responsible AI — The discussion maintained a tone of “measured optimism” throughout. It began with urgency and concern (particularly in B…
S84
AI Algorithms and the Future of Global Diplomacy — These key comments collectively transformed what could have been a technical discussion about AI tools into a sophistica…
S85
The Geopolitics of Materials: Critical Mineral Supply Chains and Global Competition — The tone was professional and analytical, with participants generally optimistic about technological solutions while ack…
S86
Open Forum #18 World Economic Forum – Building Trustworthy Governance — The tone was largely collaborative and optimistic, with panelists from different sectors sharing perspectives on how to …
S87
GermanAsian AI Partnerships Driving Talent Innovation the Future — The discussion maintained a consistently optimistic and collaborative tone throughout. Speakers demonstrated mutual resp…
S88
Science as a Growth Engine: Navigating the Funding and Translation Challenge — The discussion maintained a consistently thoughtful and collaborative tone throughout. While panelists acknowledged seri…
S89
Dynamic Coalition Collaborative Session — The discussion began with an optimistic, collaborative tone as panelists shared their expertise and perspectives. Howeve…
S90
Powering the Technology Revolution / Davos 2025 — The tone was generally optimistic and forward-looking, with panelists highlighting opportunities for innovation and prog…
S91
Global AI Policy Framework: International Cooperation and Historical Perspectives — The discussion maintained a constructive and optimistic tone throughout, despite acknowledging significant challenges. S…
S92
The Purpose of Science / DAVOS 2025 — The tone was largely optimistic and excited about AI’s potential to accelerate scientific progress. Speakers emphasized …
S93
Welfare for All Ensuring Equitable AI in the Worlds Democracies — Despite the optimistic tone, participants acknowledged persistent challenges. An audience member, Rita Soni from the Dig…
S94
Inclusive AI Starts with People Not Just Algorithms — The tone was consistently optimistic and empowering throughout the discussion. Speakers maintained an enthusiastic, forw…
S95
OpenAI is set to launch GPT-5 this summer — OpenAI, the renowned AI research lab and owner of ChatGPT, is poised tounveilits latest breakthrough in AI with the immi…
S96
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…
S97
Building Sovereign and Responsible AI Beyond Proof of Concepts — yeah I think you’re right and I think you have your own kind of description of this problem but I was in the US a few mo…
S98
GPAI: A Multistakeholder Initiative on Trustworthy AI | IGF 2023 Open Forum #111 — Alan Paic:Yes, it was not about further countries joining. Well, I can also mention that. So we do have a membership pro…
S99
Keynote-Martin Schroeter — Despite significant financial investments in AI technology by most organizations globally, there is a substantial discon…
S100
Open Forum: A Primer on AI — He believes the future of work should be designed around a better society rather than greed The investment for artifici…
S101
Agenda item 5: discussions on substantive issues contained inparagraph 1 of General Assembly resolution 75/240 (continued)/ part 4 — The most contentious issue emerged around the structural organisation of the future permanent mechanism, particularly th…
S102
India accelerates semiconductor ambitions with launch of India Semiconductor Research Centre (ISRC) — The Indian government is making significant advancements in the semiconductor sector, with the approval of two major inv…
S103
https://dig.watch/event/india-ai-impact-summit-2026/from-india-to-the-global-south_-advancing-social-impact-with-ai — In a few months. We have a few innovators present here today. In fact, three of the inspiring young innovators who are h…
S104
Global Governance of Digital Technologies: A Contemporary Diplomacy Challenge — Adesina OS (2017) Foreign policy in an era of digital diplomacy Summers J [ed]. Cogent Social Sciences , 1 January 3(1),…
S105
Economic and Commercial Diplomacy in Micro-states: A case study of the Maldives and Mauritius — The first tourist resort in the Maldives opened in the country in 1972 with minimal facilities. During the early year…
S106
From concept to cornerstone, Ethereum turns ten — Ethereum has officially turned ten, marking a decade since the launch of its mainnet, Frontier, on 30 July 2015. Conceiv…
S107
https://app.faicon.ai/ai-impact-summit-2026/keynote-bejul-somaia — You are the protagonists for it. And that is not a comfortable position because protagonists carry weight. They make dec…
S108
Re-evaluating the scaling hypothesis: The AI industry’s shift towards innovative strategies — In recent years, the AI industry has heavilyinvestedin the ‘scaling hypothesis,’ which posited that by expanding data se…
S109
How nonprofits are using AI-based innovations to scale their impact — And we had a certain approach in mind. But through the cohort, we realized that people were trying to solve similar thin…
S110
https://dig.watch/event/india-ai-impact-summit-2026/inclusive-ai_-why-linguistic-diversity-matters — And then obviously, we started growing up as a team looking at various use cases. People started initially looking at th…
S111
Multistakeholder Partnerships for Thriving AI Ecosystems — I think there is the role of governments coming in. because, yes, there are tremendous advantages of artificial intellig…
S112
Agents of Change AI for Government Services & Climate Resilience — So I’m probably going to jump on the train here. You know, what we were seeing last year was narrow agents able to solve…
S113
The Millennium Development Goals Report 2015 — Also in 2013, 6.1 million people diagnosed with TB were officially reported to public health authorities. Of these, 5.7 …
S114
https://dig.watch/event/india-ai-impact-summit-2026/ai-for-social-good-using-technology-to-create-real-world-impact — So fortunately, the government has a DPI called Nixia. It’s a very large data platform. It’s a patient management system…
Speakers Analysis
Detailed breakdown of each speaker’s arguments and positions
S
Sunil Wadhwani
10 arguments162 words per minute2517 words932 seconds
Argument 1
AI‑based cough analysis for rapid TB screening, increasing detection rates by 25% (Sunil Wadhwani)
EXPLANATION
Sunil describes an AI model that analyses the sound of a cough captured on a smartphone to detect tuberculosis instantly, providing a probability score rather than a simple yes/no. This tool has been rolled out nationally and has contributed to a 25% rise in TB detection rates.
EVIDENCE
He explains that the AI system converts cough sounds into a risk probability for TB, works instantly on a smartphone, and has become the national standard, with the World Health Organization calling it a potential global game-changer [56-62]. He also notes that the rate of TB detection has increased by 25% in the last year due to this cough-based tool, allowing more patients to receive treatment [71-73].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
External evidence shows the AI cough analysis tool has been rolled out nationally, raising TB detection by 25% and being highlighted by WHO as a game-changer [S12][S18].
MAJOR DISCUSSION POINT
TB cough AI
Argument 2
Automated sputum‑analysis AI reduces lab turnaround to one day (Sunil Wadhwani)
EXPLANATION
Sunil reports that an AI model has been integrated into India’s 64 government TB labs to fully automate sputum analysis, cutting the result turnaround time to a single day and enabling faster treatment initiation.
EVIDENCE
He states that the AI model now automates sputum analysis across all 64 government labs, delivering results within a day and sending them back to patients for prompt treatment [63-66].
MAJOR DISCUSSION POINT
Sputum AI automation
Argument 3
Predictive AI alerts caseworkers to patients likely to abandon TB medication (Sunil Wadhwani)
EXPLANATION
Sunil outlines an AI algorithm that predicts which TB patients are at risk of dropping out of their medication regimen, allowing a limited pool of caseworkers to focus their outreach on those high‑risk individuals.
EVIDENCE
He notes that the predictive AI identifies patients likely to fall off medication, enabling 2,000 TB caseworkers to target the right people among 4 million patients, thereby improving adherence [68-70].
MAJOR DISCUSSION POINT
Medication adherence prediction
Argument 4
Personalized AI reading‑proficiency tools lower primary‑school dropout rates (Sunil Wadhwani)
EXPLANATION
Sunil describes an AI‑driven suite that creates personalized reading exercises for young children, addressing the main cause of early school dropout—lack of reading ability. The pilot was adopted statewide, reaching millions of students.
EVIDENCE
He explains that the biggest reason for dropout is inability to read, and the AI suite generates personalized stories and exercises for each child; after a successful pilot, the state of Rajasthan made it mandatory for 3 million school-age children [80-88].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
The reading-proficiency AI has been scaled to tens of millions of children, providing personalized exercises that address early-grade dropout, as documented in the partnership reports [S18][S17].
MAJOR DISCUSSION POINT
Reading proficiency AI
Argument 5
Early engagement with ministries and alignment to national priorities is essential for scale (Sunil Wadhwani)
EXPLANATION
Sunil emphasizes that working directly with government ministries to identify national priorities ensures that AI solutions are relevant and can be scaled effectively from the outset.
EVIDENCE
He recounts that they identify challenges by speaking directly with the health and education ministries, learning that tuberculosis is a top health priority, which guided their AI focus [37-41].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Collaboration with health and education ministries from the problem-identification stage is emphasized as a key lesson for scaling AI solutions [S12].
MAJOR DISCUSSION POINT
Government‑first approach
AGREED WITH
S. Krishnan, Ankur Vora, Lacuna Kone, Shikoh Gitau
DISAGREED WITH
Lacuna Kone
Argument 6
Integration with existing government platforms (Nikshay for TB, Rakshak for education) enables nationwide rollout (Sunil Wadhwani)
EXPLANATION
Sunil points out that embedding AI algorithms into established public platforms such as the TB case‑management system Nikshay and the education platform Rakshak allows rapid, country‑wide deployment.
EVIDENCE
He details that the TB solution was integrated into Nikshay, a national case-management system, and the education AI was plugged into Rajasthan’s Rakshak platform covering 70 000 schools, 400 000 teachers and 8 million students, which would have been impossible without these digital public infrastructures [118-124].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
India’s digital public infrastructure and platforms such as Nikshay and Rakshak are referenced as examples of embedding AI into existing systems for rapid country-wide deployment [S23][S24].
MAJOR DISCUSSION POINT
Platform integration
Argument 7
Leveraging India’s digital public infrastructure (Aadhaar, UPI) provides data pipelines and identity for AI services (Sunil Wadhwani)
EXPLANATION
Sunil highlights that India’s existing digital public goods—such as the biometric ID system Aadhaar and the digital payments network UPI—serve as essential data and identity backbones for scaling AI applications.
EVIDENCE
He cites Aadhaar and UPI as prime examples of digital public infrastructure that underpin health, education and agriculture AI solutions, noting that these platforms were critical for scaling [111-115].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
Aadhaar and UPI are cited as core digital public infrastructure that underpins data and identity pipelines for health, education and agriculture AI services [S12].
MAJOR DISCUSSION POINT
Digital public infrastructure
AGREED WITH
S. Krishnan, Shalini Kapoor
Argument 8
Solutions must simplify frontline workers’ workflows to ensure adoption (Sunil Wadhwani)
EXPLANATION
Sunil stresses that AI tools must make life easier for health workers and teachers; otherwise, even top‑down mandates will not lead to real usage.
EVIDENCE
He explains that if a tool does not ease the frontline worker’s job, adoption will fail, emphasizing the need for a pull rather than a push from the top [125-127].
EXTERNAL EVIDENCE (KNOWLEDGE BASE)
User-centric design that eases frontline health workers’ and teachers’ tasks is highlighted as essential for organic adoption of AI tools [S18].
MAJOR DISCUSSION POINT
User‑centric design
Argument 9
Mutual learning model: India exports know‑how while also absorbing African innovations (Sunil Wadhwani)
EXPLANATION
Sunil notes that while the Wadhwani AI initiatives began in India, they are now engaging with governments across the Global South, sharing expertise and also learning from African innovations, creating a two‑way knowledge flow.
EVIDENCE
He describes how, after scaling to 100 million Indians, they received inquiries from Kenya, Rwanda, Indonesia, Egypt and Mexico, leading to the establishment of operations in Rwanda, Ethiopia and Kenya and a commitment to mutual learning [139-168].
MAJOR DISCUSSION POINT
Two‑way South‑South learning
AGREED WITH
Shikoh Gitau, Lacuna Kone, Shalini Kapoor, S. Krishnan, Ankur Vora
Argument 10
Patience amid rapid AI change and logistical challenges is a key personal takeaway (Sunil Wadhwani)
EXPLANATION
Sunil reflects that AI is accelerating change, and the logistical challenges of the summit (traffic, etc.) taught him patience, a quality needed to navigate fast‑moving AI environments.
EVIDENCE
He offers a counter-intuitive answer, saying AI makes the world move faster and the traffic challenges taught patience, assuring that progress will happen despite obstacles [391-395].
MAJOR DISCUSSION POINT
Patience in AI era
S
Shalini Kapoor
2 arguments113 words per minute923 words488 seconds
Argument 1
AI diffusion relies on shared “pathways” and playbooks so innovations can move across borders (Shalini Kapoor)
EXPLANATION
Shalini argues that AI diffusion requires documented pathways and playbooks, similar to digital rails, which allow solutions developed in one country to be replicated elsewhere, fostering South‑South knowledge transfer.
EVIDENCE
She describes AI diffusion as the “routes and rails” that need to be laid, likening it to digital public infrastructure, and says these playbooks enable sharing of AI innovations across nations, referencing the concept’s origin with Geoffrey Hinton [179-194].
MAJOR DISCUSSION POINT
AI pathways and playbooks
AGREED WITH
Lacuna Kone
Argument 2
Celebrating collective collaboration (100 pathways to 2030) highlights that AI progress stems from joint effort rather than rivalry (Shalini Kapoor)
EXPLANATION
Shalini celebrates the “100 pathways to 2030” initiative as a clarion call for shared learning, comparing it to mountaineering routes that guide future travelers, emphasizing that AI should be collaborative, not competitive.
EVIDENCE
She references the 100 pathways call, likening it to Edmund Hillary’s route sharing, and notes that panelists agreed AI progress comes from collaboration, not competition [245-256].
MAJOR DISCUSSION POINT
100 pathways initiative
L
Lacina Kone
2 arguments159 words per minute807 words303 seconds
Argument 1
Smart Africa’s AI Council unites governments, private sector, and philanthropies; a clear regulatory “cloud” is needed before financing can flow (Lacina Kone)
EXPLANATION
Lacina explains that the AI Council, comprising governments, private actors and philanthropies, provides a governance structure, but financing only follows once a stable regulatory environment—metaphorically a “cloud”—is in place.
EVIDENCE
She details the AI Council’s formation with 49 countries signing a declaration, its governance composition of ministers and private members, and stresses that finance depends on a predictable regulatory “cloud” before private sector investment can occur [198-240].
MAJOR DISCUSSION POINT
Regulatory cloud for finance
Argument 2
Vision of a single African digital market illustrates the power of harmonized regulation and shared infrastructure (Lacina Kone)
EXPLANATION
Lacina shares a vision of integrating Africa’s 1.4 billion people into a unified digital market, arguing that harmonized regulation and shared digital infrastructure can unlock continent‑wide scale.
EVIDENCE
She states that Africa’s 1.4 billion population can be leveraged through collaboration, cites the AI Council and the need for regulatory harmonization, and highlights the ambition to create a single digital market with common regulations [401-406].
MAJOR DISCUSSION POINT
Pan‑African digital market
S
S. Krishnan
3 arguments170 words per minute1499 words526 seconds
Argument 1
The India AI Mission delivers frugal, sovereign compute and model infrastructure at one‑third global cost (S. Krishnan)
EXPLANATION
S. Krishnan outlines that India’s AI Mission provides low‑cost, sovereign compute and model resources, making AI infrastructure about one‑third as expensive as comparable global offerings.
EVIDENCE
He notes that AI compute in India is now available at a third of the price of the rest of the world, reflecting a frugal approach to building AI infrastructure [315-319].
MAJOR DISCUSSION POINT
Low‑cost sovereign AI infrastructure
Argument 2
Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan)
EXPLANATION
He states that the AI models and platforms built under the mission are government‑subsidized, open‑source, and the Indian government is willing to share them with other Global South countries.
EVIDENCE
He mentions commitments to share the AI Kosh model, sovereign models, and other resources with the UN and other nations, emphasizing that they are taxpayer-funded and open for export [320-327].
MAJOR DISCUSSION POINT
Open‑source AI sharing
AGREED WITH
Sunil Wadhwani, Shikoh Gitau, Lacuna Kone, Shalini Kapoor, Ankur Vora
DISAGREED WITH
Lacuna Kone, Other speakers (Sunil Wadhwani, S. Krishnan)
Argument 3
Partnership with the Gates Foundation underpins the mission’s focus on people, planet, and progress (S. Krishnan)
EXPLANATION
Krishnan highlights that the Gates Foundation has been a key partner from the planning stage, aligning the AI Mission with the broader agenda of people, planet and progress.
EVIDENCE
He acknowledges the Gates Foundation as a “very key partner” throughout the mission’s design and implementation, and notes joint sessions and collaborations that reinforce this focus [329-332].
MAJOR DISCUSSION POINT
Gates Foundation partnership
A
Ankur Vora
1 argument92 words per minute584 words377 seconds
Argument 1
The innovation‑to‑impact journey is non‑linear; sustained partnership (e.g., Gates “Advantage India for AI”) is vital (Ankur Vora)
EXPLANATION
Ankur reflects that moving from innovation to real‑world impact is not a straight path and requires continuous effort and partnerships, such as the Gates Foundation’s new “Advantage India for AI” initiative.
EVIDENCE
He remarks that the road from innovation to impact is not guaranteed, cites the seven-year partnership with Gates and the newly announced AI pledge, and calls for continued collaboration [131-137].
MAJOR DISCUSSION POINT
Non‑linear impact pathway
AGREED WITH
Sunil Wadhwani, S. Krishnan, Lacuna Kone, Shikoh Gitau
S
Shikoh Gitau
2 arguments152 words per minute394 words155 seconds
Argument 1
Framing AI as a political and economic issue and addressing the “collaboration tax” are critical for cross‑regional cooperation (Shikoh Gitau)
EXPLANATION
Shikoh argues that AI must be treated not only as a technology but also as a political and economic matter, and that the “collaboration tax” – the effort and resources needed to coordinate across borders – must be acknowledged and mitigated.
EVIDENCE
She discusses making AI a political/economic issue, defines the collaboration tax as the effort and resources required for cross-regional partnership, and calls for mechanisms to reduce this burden [260-273].
MAJOR DISCUSSION POINT
Collaboration tax
Argument 2
The summit showcased Global South unity, reinforcing that AI development is a collaborative race, not a competition (Shikoh Gitau)
EXPLANATION
Shikoh describes the summit’s atmosphere of collective celebration, emphasizing that the Global South can work together on AI, turning it into a multi‑horse race rather than a binary competition.
EVIDENCE
She recounts standing before a diverse audience of about 300 people, feeling that the Global South is united in AI, and that the landscape has shifted from a two-horse race to a multiple-horse race [398-401].
MAJOR DISCUSSION POINT
Global South unity
Agreements
Agreement Points
Government partnership and alignment with national priorities is essential for scaling AI solutions.
Speakers: Sunil Wadhwani, S. Krishnan, Ankur Vora, Lacuna Kone, Shikoh Gitau
Early engagement with ministries and alignment to national priorities is essential for scale (Sunil Wadhwani) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan) The innovation‑to‑impact journey is non‑linear; sustained partnership (e.g., Gates “Advantage India for AI”) is vital (Ankur Vora) Smart Africa’s AI Council … a clear regulatory “cloud” is needed before financing can flow (Lacuna Kone) Framing AI as a political and economic issue and the need for political goodwill (Shikoh Gitau)
All speakers emphasized that working closely with governments-through ministries, regulatory frameworks, and sustained partnerships-is the cornerstone for deploying AI at scale, whether in health, education, or broader AI initiatives [37-41][92-100][111-118][320-327][131-137][231-238][266-271].
POLICY CONTEXT (KNOWLEDGE BASE)
The Building Scalable AI reports stress that government partnership from day one is essential for achieving scale in the Global South, emphasizing deep collaboration with senior civil servants and alignment with national priorities [S42][S43][S44].
Digital public infrastructure (DPI) is a critical backbone for AI deployment and scaling.
Speakers: Sunil Wadhwani, S. Krishnan, Shalini Kapoor
Leveraging India’s digital public infrastructure (Aadhaar, UPI) provides data pipelines and identity for AI services (Sunil Wadhwani) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan) AI diffusion relies on shared “pathways” and playbooks, analogous to digital rails (Shalini Kapoor)
The panelists agreed that existing digital platforms such as Aadhaar, UPI, Nikshay, Rakshak, and the broader AI mission infrastructure enable rapid, nationwide AI roll-outs and can be leveraged by other countries through open-source sharing and documented pathways [111-124][307-319][179-194].
POLICY CONTEXT (KNOWLEDGE BASE)
Multiple panels describe AI as a potential component of DPI, highlighting inclusion, integrity, and safety as core pillars and noting that AI is still in early-days of being integrated into DPI frameworks like Aadhaar or UPI [S39][S40][S41].
South‑South collaboration and mutual learning are vital for AI diffusion across the Global South.
Speakers: Sunil Wadhwani, Shikoh Gitau, Lacuna Kone, Shalini Kapoor, S. Krishnan, Ankur Vora
Mutual learning model: India exports know‑how while also absorbing African innovations (Sunil Wadhwani) Framing AI as a political and economic issue and addressing the “collaboration tax” (Shikoh Gitau) Smart Africa’s AI Council unites governments, private sector, and philanthropies; a clear regulatory “cloud” is needed before financing can flow (Lacuna Kone) AI diffusion relies on shared “pathways” and playbooks … collaboration not competition (Shalini Kapoor) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan) The innovation‑to‑impact journey is non‑linear; sustained partnership … for Global South (Ankur Vora)
All speakers highlighted the importance of two-way knowledge exchange, shared playbooks, and coordinated institutional mechanisms (AI Council, Gates partnership) to spread AI solutions across Africa, Asia and beyond, stressing that collaboration, not competition, drives impact [139-168][260-273][203-210][245-256][320-327][131-137].
POLICY CONTEXT (KNOWLEDGE BASE)
The importance of South-South partnerships is highlighted in the Building Scalable AI study and reinforced by the African Union’s 2024 AI strategy, which calls for regional cooperation and knowledge sharing across Global South nations [S42][S51][S52].
Documented pathways/playbooks are needed to replicate AI innovations across borders.
Speakers: Shalini Kapoor, Lacuna Kone
AI diffusion relies on shared “pathways” and playbooks so innovations can move across borders (Shalini Kapoor) The AI Council’s thematic groups address computing, data, skills, regulation, market and investment – forming collaborative pathways (Lacuna Kone)
Both panelists argued that creating clear, documented routes-whether called “digital rails” or thematic groups-facilitates the transfer of AI solutions from one country to another, enabling scalable South-South learning [179-194][221-226].
POLICY CONTEXT (KNOWLEDGE BASE)
The India-Israel Innovation Roundtable identified systematic innovation pathways and playbooks as a key requirement for effective cross-border replication of AI solutions [S47].
Similar Viewpoints
Both stress that government‑backed, open‑source AI resources aligned with national priorities enable rapid, large‑scale deployment and can be exported to other countries [37-41][92-100][111-118][320-327].
Speakers: Sunil Wadhwani, S. Krishnan
Early engagement with ministries and alignment to national priorities is essential for scale (Sunil Wadhwani) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan)
Both highlight that a predictable regulatory environment created with government involvement is a prerequisite for financing and scaling AI initiatives [92-100][231-238].
Speakers: Sunil Wadhwani, Lacuna Kone
Early engagement with ministries and alignment to national priorities is essential for scale (Sunil Wadhwani) Smart Africa’s AI Council … a clear regulatory “cloud” is needed before financing can flow (Lacuna Kone)
Both see structured, thematic pathways (playbooks, councils) as essential mechanisms for AI diffusion across nations [179-194][221-226].
Speakers: Shalini Kapoor, Lacuna Kone
AI diffusion relies on shared “pathways” and playbooks … (Shalini Kapoor) The AI Council’s thematic groups … provide collaborative pathways (Lacuna Kone)
Both underscore that long‑term partnerships (e.g., with the Gates Foundation) and open‑source sharing are key to moving from innovation to impact at scale [131-137][320-327].
Speakers: Ankur Vora, S. Krishnan
The innovation‑to‑impact journey is non‑linear; sustained partnership … is vital (Ankur Vora) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan)
Unexpected Consensus
Finance is not the primary barrier to AI scaling; regulatory certainty is.
Speakers: Lacuna Kone, Sunil Wadhwani
Finance is not the issue; regulatory “cloud” must exist first (Lacuna Kone) Early engagement with ministries … ensures scaling; focus is on government alignment rather than financing (Sunil Wadhwani)
While many discussions assume funding constraints limit AI deployment, both Lacuna and Sunil converge on the view that a stable regulatory environment and government partnership are the real prerequisites, making the finance-vs-regulation emphasis an unexpected point of agreement [233-238][92-100].
POLICY CONTEXT (KNOWLEDGE BASE)
Analyses of AI adoption in the Global South note that compute and talent shortages outweigh financing constraints, while regulatory sandboxes are promoted to provide certainty, suggesting regulatory frameworks are more decisive than finance for scaling [S46][S48][S49].
AI tools must be designed to make frontline workers’ lives easier to ensure adoption.
Speakers: Sunil Wadhwani, Shikoh Gitau
Solutions must simplify frontline workers’ workflows to ensure adoption (Sunil Wadhwani) Political goodwill and a “pull” approach are needed for AI adoption (Shikoh Gitau)
Sunil’s user‑centric design point and Shikoh’s emphasis on political goodwill both converge on the idea that AI adoption hinges on making tools attractive and easy for end‑users, a nuance not explicitly stated elsewhere.
POLICY CONTEXT (KNOWLEDGE BASE)
Evidence from health and agriculture pilots shows that AI systems need to work on the edge and be tailored to local policies, emphasizing usability for frontline workers as a prerequisite for adoption [S57][S58][S59].
Overall Assessment

The panel displayed strong consensus around three pillars: (1) government partnership and regulatory alignment as the foundation for scaling AI; (2) leveraging existing digital public infrastructure and open‑source models to enable rapid, cost‑effective deployment; (3) fostering South‑South collaboration through shared pathways, playbooks, and mutual learning. These agreements signal a coordinated vision that AI for development can be accelerated by aligning policy, infrastructure, and cross‑regional cooperation.

High consensus – the speakers repeatedly reinforced the same themes across multiple statements, indicating a unified strategic direction that could drive coordinated actions among governments, donors, and private actors in the Global South.

Differences
Different Viewpoints
Scaling approach: government‑first versus private‑sector‑led execution
Speakers: Sunil Wadhwani, Lacuna Kone
Early engagement with ministries and alignment to national priorities is essential for scale (Sunil Wadhwani) Smart Africa’s AI Council unites governments, private sector, and philanthropies; a clear regulatory “cloud” is needed before financing can flow (Lacuna Kone)
Sunil stresses that AI solutions must be co-designed with ministries from day one, with government accountability and integration into public platforms to achieve scale [92-100][107-110][111-118]. Lacuna argues that while governments set the environment, the private sector must execute and that financing only follows once a predictable regulatory “cloud” exists, downplaying finance as a barrier [231-235][236-238].
POLICY CONTEXT (KNOWLEDGE BASE)
Forum discussions reveal a split between government-led and market-driven scaling models, with policy papers calling for clear delineation of public and private roles in AI ecosystems [S60][S61][S62].
Importance of financing for AI scaling
Speakers: Lacuna Kone, Other speakers (Sunil Wadhwani, S. Krishnan)
Smart Africa’s AI Council … finance is not the issue (Lacuna Kone) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan)
Lacuna claims that finance is the last concern and that the regulatory environment is the primary prerequisite for private investment [233-235]. In contrast, Krishnan and Sunil discuss the need for subsidized resources, partnerships (e.g., with the Gates Foundation), and sharing models, implying that financing mechanisms are central to scaling AI across countries [320-327][139-168].
POLICY CONTEXT (KNOWLEDGE BASE)
While some studies downplay finance as the main obstacle, other reports emphasize the need for innovative financing mechanisms and coordinated financial services to bridge digital gaps, underscoring ongoing debate on financing’s role [S48][S55][S56].
Unexpected Differences
Finance as a non‑issue versus need for financing mechanisms
Speakers: Lacuna Kone, Other participants (Sunil Wadhwani, S. Krishnan)
Smart Africa’s AI Council … finance is not the issue (Lacuna Kone) Government‑subsidized AI resources are open‑source and can be shared … (S. Krishnan)
Lacuna’s claim that finance is merely the last hurdle contrasts with Krishnan’s and Sunil’s emphasis on subsidized resources, partnerships, and funding models to enable large-scale AI deployment, revealing an unexpected divergence on the role of financing [233-235][320-327][139-168].
POLICY CONTEXT (KNOWLEDGE BASE)
Policy recommendations advocate for innovative financing instruments to address the digital divide, indicating that even if finance is not the top technical barrier, dedicated mechanisms are still considered essential [S56][S55].
AI as a political/economic issue versus a technical solution
Speakers: Shikoh Gitau, Sunil Wadhwani
Framing AI as a political and economical issue and addressing the “collaboration tax” (Shikoh Gitau) We have a government that is very pro‑technology … clear regulatory framework … openness in government (Sunil Wadhwani)
Shikoh argues that AI must be treated as a political/economic matter and that the collaboration tax must be mitigated, while Sunil focuses on technical solutions, government openness, and regulatory frameworks without explicitly addressing the political-economy dimension, indicating an unexpected mismatch in framing [265-271][260-273][139-168].
Overall Assessment

The discussion showed broad consensus on AI’s potential for social impact and the need for South‑South collaboration, but clear disagreements emerged around the primary scaling mechanism (government‑first vs private‑sector‑led), the role of financing, and how AI should be framed (technical tool vs political/economic issue).

Moderate – while participants share common goals, divergent views on implementation pathways could affect coordination and resource allocation, requiring deliberate alignment to avoid fragmented efforts.

Partial Agreements
All three agree that AI can generate large‑scale social impact, but Sunil focuses on technical deployment through government platforms, Ankur stresses the need for continuous partnership to bridge innovation and impact, and Krishnan highlights open‑source, subsidized resources as the means to achieve that impact [56-62][71-73][131-137][320-327].
Speakers: Sunil Wadhwani, Ankur Vora, S. Krishnan
AI‑based cough analysis for rapid TB screening, increasing detection rates by 25% (Sunil Wadhwani) The innovation‑to‑impact journey is non‑linear; sustained partnership is vital (Ankur Vora) Government‑subsidized AI resources are open‑source and can be shared with other Global South nations (S. Krishnan)
All endorse South‑South collaboration, yet Shalini emphasizes documented pathways, Shikoh stresses reducing the collaboration tax and political goodwill, while Sunil describes a two‑way learning model, showing different preferred mechanisms for knowledge transfer [179-194][260-273][139-168].
Speakers: Shalini Kapoor, Shikoh Gitau, Sunil Wadhwani
AI diffusion relies on shared “pathways” and playbooks so innovations can move across borders (Shalini Kapoor) Framing AI as a political and economic issue and addressing the “collaboration tax” are critical for cross‑regional cooperation (Shikoh Gitau) Mutual learning model: India exports know‑how while also absorbing African innovations (Sunil Wadhwani)
Takeaways
Key takeaways
AI‑driven tools in India have demonstrably improved health outcomes (cough‑based TB screening, automated sputum analysis, medication‑adherence prediction) and education outcomes (personalized reading‑proficiency platform). Scaling AI solutions requires early, humble partnership with government ministries, alignment to national priorities, and integration with existing digital public infrastructure (e.g., Nikshay, Rakshak, Aadhaar, UPI). Front‑line user experience is critical; tools must simplify workflows for health workers and teachers to achieve adoption. South‑South collaboration is framed as sharing “pathways” and playbooks so that innovations can be diffused across borders without each country reinventing the wheel. Smart Africa’s AI Council illustrates a multi‑stakeholder model (government, private sector, philanthropies) where a clear regulatory “cloud” precedes financing. India’s AI Mission provides frugal, sovereign compute and model infrastructure at roughly one‑third global cost, with open‑source, government‑subsidized resources intended for sharing with other Global South nations. Partnerships such as the Gates Foundation’s “Advantage India for AI” are seen as essential to bridge innovation and impact and to support the 100‑Pathways‑to‑2030 agenda. The summit highlighted the non‑linear journey from innovation to impact, the need for patience amid rapid AI change, and the collective spirit of the Global South in AI development.
Resolutions and action items
Launch operations of the Wadhwani AI Institute in Rwanda, Ethiopia, and Kenya (team dispatched this month). Commit to impact 500 million people globally by 2040, building on the current 100 million annual impact in India. Share India’s AI treasury (compute, models, datasets) with other Global South countries once capacity thresholds are met. Establish a Center for International Cooperation under India’s National Institute of Smart Governance to support DPI implementation abroad. Continue and deepen partnership with the Gates Foundation for funding, knowledge‑exchange, and scaling of AI solutions. Integrate AI algorithms into existing government platforms in partner countries (e.g., analogous to Nikshay and Rakshak). Develop and disseminate “AI pathway” playbooks to facilitate South‑South knowledge transfer.
Unresolved issues
Specific mechanisms and timelines for transferring India’s AI models and compute infrastructure to other countries remain undefined. Details on how to harmonize regulatory frameworks across diverse African nations to create the required “regulatory cloud” are not settled. The financing model for large‑scale deployments in partner countries (beyond the statement that finance is “the last thing”) lacks concrete plans. Operationalization of the “collaboration tax” – how to reduce the effort and resources needed for cross‑regional partnerships – was raised but not resolved. Metrics and monitoring frameworks for evaluating impact of exported AI solutions in new contexts were not detailed.
Suggested compromises
Prioritize creation of a stable regulatory environment (“cloud”) before seeking large private‑sector financing, acknowledging that finance follows regulation. Adopt a humility‑first approach when engaging with government officials, positioning AI providers as partners seeking to understand problems rather than imposing solutions. Balance private‑sector execution with public‑sector facilitation: government provides open digital infrastructure and subsidies, private firms deliver technology, philanthropies de‑risk early pilots. Design AI tools to make frontline workers’ lives easier, thereby generating pull‑based adoption rather than top‑down mandates.
Thought Provoking Comments
The only way to scale is government. You have to work with government from day one, think about scale from day one, and integrate your AI solutions into existing digital public infrastructure like Nikshay for TB and Rakshak for education.
Highlights that technical brilliance alone is insufficient; sustainable impact requires institutional partnership, early planning for scale, and leveraging existing public platforms—a perspective that reframes how AI projects should be designed.
Shifted the conversation from describing AI solutions to discussing the structural requirements for nationwide deployment. It prompted follow‑up remarks about digital public infrastructure from other panelists and set the stage for the discussion on South‑South knowledge transfer.
Speaker: Sunil Wadhwani
AI solutions are great at a macro level, but if the frontline health worker or teacher doesn’t find the tool makes their life easier, it won’t be adopted.
Emphasizes the human‑centered design principle that adoption hinges on usability for end‑users, not just on algorithmic performance, adding depth to the scaling conversation.
Led participants to consider user experience and incentive structures, reinforcing the earlier point about government partnership and influencing later remarks about political goodwill and collaboration tax.
Speaker: Sunil Wadhwani
AI diffusion is about the routes and rails that need to be laid, just like electricity was diffused across continents; we need playbooks that can be shared so that a solution built in Kenya can be used in India and vice‑versa.
Introduces the metaphor of diffusion infrastructure, framing AI adoption as a systematic, transport‑like process rather than isolated projects, and calls for reusable playbooks.
Opened a new thematic thread on “pathways to scale” and prompted Lacina Kone and Shikoh Gitau to discuss regulatory and collaboration mechanisms, moving the dialogue toward concrete mechanisms for South‑South exchange.
Speaker: Shalini Kapoor
Finance is not the issue; finance is the last thing you should think about. The real prerequisite is the regulatory environment – the ‘cloud’ that creates the rain for investment.
Challenges the common assumption that lack of capital blocks AI projects, redirecting focus to policy and regulatory stability as the primary enabler.
Reoriented the discussion from funding scarcity to the need for conducive policy frameworks, influencing Shikoh’s point about political goodwill and reinforcing Sunil’s earlier emphasis on government partnership.
Speaker: Lacina Kone
We need to start talking about the ‘collaboration tax’ – the effort, resources, and pain required to bring different stakeholders together, and how governments should help lower that tax.
Coins a new term that captures the hidden costs of cross‑border and cross‑sector collaboration, highlighting a practical barrier often overlooked in high‑level AI talks.
Prompted the panel to consider concrete steps to reduce collaboration friction, linking back to earlier points about regulatory clouds and government facilitation, and deepening the conversation about operationalizing South‑South partnerships.
Speaker: Shikoh Gitau
India’s AI mission model provides compute at a third of the global price, builds sovereign models, and is designed to be shared with the Global South – a frugal, open‑source approach to AI infrastructure.
Offers a concrete, scalable model for democratizing AI resources, moving the dialogue from abstract principles to a tangible example of how a nation can enable widespread AI access.
Validated Sunil’s and Shalini’s earlier points about public infrastructure, and gave the audience a real‑world template for replication, steering the conversation toward actionable sharing of resources.
Speaker: S. Krishnan
The journey from innovation to impact is not a straight road; we often focus on the innovation part and assume impact will follow, but it requires deliberate work and partnership.
Summarizes a central theme of the session, reminding participants that breakthroughs need systematic pathways to translate into societal benefit.
Served as a reflective pivot that reinforced earlier insights about scaling, government involvement, and South‑South collaboration, tying together the various strands of the discussion.
Speaker: Ankur Vora
Overall Assessment

The discussion was shaped by a series of pivotal insights that moved it from showcasing impressive AI applications to dissecting the systemic foundations needed for real impact. Sunil’s early revelation that government partnership and integration with digital public infrastructure are essential reframed the conversation around scalability. This was deepened by Shalini’s diffusion metaphor, Lacina’s regulatory‑vs‑finance argument, and Shikoh’s ‘collaboration tax’ concept, each adding layers of complexity about policy, economics, and operational friction. S. Krishnan’s concrete example of India’s frugal AI mission provided a tangible model for replication, turning abstract ideas into actionable pathways. Collectively, these comments redirected the dialogue toward practical, collaborative, and inclusive strategies for South‑South knowledge transfer, emphasizing that technology alone is insufficient without the right institutional, regulatory, and human‑centered frameworks.

Follow-up Questions
How can the learnings from India’s AI initiatives be effectively transferred and adapted to other Global South contexts?
Ankur asked Sunil to discuss South‑South partnership and the transfer of learnings, indicating a need for concrete strategies for cross‑regional adaptation.
Speaker: Ankur Vora
What are the concrete opportunities for South‑South collaboration in building pathways to scale AI solutions?
Shalini sought Lacina’s view on collaborative pathways, highlighting a need to identify specific mechanisms for joint scaling across countries.
Speaker: Shalini Kapoor (addressed to Lacina Kone)
How can AI diffusion help move use cases from pilot to production across the Global South?
She asked how diffusion can be operationalized, pointing to a gap in understanding the transition from experimental pilots to large‑scale deployment.
Speaker: Shalini Kapoor (addressed to Shikoh Gitau)
What is the ‘collaboration tax’ and how can it be reduced to facilitate smoother partnerships?
Shikoh introduced the concept of collaboration tax, suggesting further investigation into the resources and effort required for cross‑country AI collaborations.
Speaker: Shikoh Gitau
Why is the investment cycle perceived as too slow for AI, and what financing models could accelerate AI deployment in the Global South?
Lacina highlighted the sluggish investment pace, indicating a need for research into more agile funding mechanisms for AI projects.
Speaker: Lacina Kone
How can frugal AI compute models and sovereign AI infrastructure be shared internationally while ensuring security and sustainability?
Krishnan discussed India’s low‑cost AI compute and sovereign models, prompting further study on scalable, secure sharing of such resources with other nations.
Speaker: S. Krishnan
What are the best practices for integrating AI solutions with existing government digital public infrastructure (e.g., Nikshay, Rakshak) to achieve scale?
Sunil emphasized the importance of leveraging government platforms, suggesting research into integration frameworks and interoperability standards.
Speaker: Sunil Wadhwani
What is the measurable impact of the AI‑based cough detection tool on TB treatment outcomes beyond detection rates?
While detection rates increased, the transcript notes a need to assess downstream effects on treatment success and mortality.
Speaker: Sunil Wadhwani
How effective are AI‑driven personalized reading tools in improving literacy outcomes at scale, and what metrics should be used?
Sunil described the reading proficiency initiative, indicating a need for rigorous evaluation of its educational impact across millions of students.
Speaker: Sunil Wadhwani
How can AI tools be designed to make frontline health workers’ and teachers’ lives easier, ensuring adoption and sustained use?
Sunil highlighted that tools must ease frontline workers’ tasks, pointing to research on user‑centered design and adoption incentives.
Speaker: Sunil Wadhwani
What mechanisms can ensure that AI solutions remain open‑source yet protected from cyber threats in large‑scale deployments?
Krishnan mentioned open‑source AI with security safeguards, suggesting further study on balancing openness with cybersecurity.
Speaker: S. Krishnan

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