Building Population-Scale Digital Public Infrastructure for AI

bought which farmers use and millions of farmers today, 2 .5 million farmers have downloaded this app. And this was built to make sure that farmers have access to the best information about access to prices, access to weather information and so on. And it’s very sophisticated. It took nine months to get this going in Maharashtra. But we learned a lot about how to do these things. And the next implementation was done in Ethiopia. So in Africa, and Ethiopia did the same thing in three months. So essentially what took us nine months the first time around took us three months. And recently, at the request of the Prime Minister, Amul implemented the whole thing. And Amul implemented it for cows and bought for dairy farmers to understand about the cows and whether they’re lactating or whether they’re, you know, milk and so on.

And that was done in three weeks. So I think you went from nine months to three months to three weeks. So what is the message in that is that if you get the lived experience of implementing these kind of systems for public good, you can actually dramatically reduce the time in which you can do that. And we call these ways of reaching the goal faster, we call them as pathways, because once you have a pathway, then you can get, somebody else can get to the same point quicker. And just like we had this notion that we’ll have 50 in five, 50 countries in five years, we are also now setting an ambitious goal for doing 100 diffusion pathways by 2030.

In other words, by 2030, all of us together across the world will develop these pathways to diffuse the use of AI in a positive way to help farmers, improve the life of young kids, allow people to get jobs through something called Blue Dot. There are so many things going on, but all of them are designed to be effective. to improve and make better people’s lives, can meet the aspirations in a very inclusive way so that everybody is in, nobody is left out. And so we announced a partnership. We announced a coalition of this, of 100 diffusion pathways by 2030. We announced that yesterday or day before yesterday. And we have a global coalition. Anthropic is there. Google is there.

Gates Foundation is there. UNDP is there. A whole host of people are there. And it’s a very open, it’s a big tent. Anybody can join the coalition. But our goal is all of us work together to very, in a focused manner, develop these pathways of diffusion of different kinds of positive AI use cases and then actually make it happen in countries around the world. So just like 15 .5 was a DPI goal, 100 diffusion pathways by 2030 is the AI goal we have. And we are confident that all of us collectively can get there. So I think this is important. I think it’s strategic for the world that we show the good use of AI, and it’s strategic that all of us work together to do that.

Thank you very much.

Thank you so much, Mr. Nandan. At this point, I would love to invite our panelists up to the stage. We’ll start by taking a quick group photograph together and then begin the discussion. So let me invite Minister Esther Dweck, Mr. Trevor Mundell, Ms. Reena Ghosh, and Mr. Shankar Maruwada, accompanied by Nandan, to be on the stage for a quick group photograph. Thank you. Let me now hand it over to Shankar Maruwala, who will moderate us to the next panel.

Good afternoon. We have an exciting panel discussion ahead. Let me start off with where Nandan stopped. Hundred pathways. What are these pathways? These are diffusion pathways to AI impact safely and at scale. Let me provide a bit of background. France invented better than Britain in the first industrial revolution yet Britain won that Britain in turn out invented US in steel, Germany out invented US in chemistry yet it’s the US that won the second industrial revolution what was the crucial thing it was not better invention or even innovation the missing ingredient was diffusion which the United States of America did much better diffusing the benefits and the impact of this technology throughout the economy and the society when we say diffusion we don’t mean awareness or access diffusion as Nandan described is the spread of know -how, trust and institutional capability that allows organizations to adopt AI safely and sustainably as he explained Maharashtra was the pioneer to do this in India it’s like Sir Edmund Hillary climbing Mount Everest for the first time he inspires he creates a pathway for others to follow and it would be rather stupid if after he came back he said I am not sharing this with others the pathway I created I have removed it so now you guys find your own pathway the societies that create such pathways allow a whole lot of others to prosper to make progress to create impact inclusively and equitably that is the when Nandan talked about diffusion hundred pathways these are the hundred diffusion pathways across sectors countries continents some are some may be led by proprietary models some may be led by sovereign efforts some may not be it may differ It’s the choice of the AI adopter to decide which pathway works best for them.

So the diffusion infrastructure we are talking about creating isn’t a platform app or model. It’s shared rails that compress learning curves, cost and risk. So that AI can be used by all of society for all of humanity. With that, I would like to begin the panel discussion. Irina, from the model builder’s perspective, what needs to be true for AI to be deployable at population scale? Not just impressive pilots, especially in high -stake public systems. What needs to happen?

Thank you so much, Shankar. And absolutely a pleasure and honor to be here with all of you. Thank you so much. The way I think about it is AI deployment would seldom, if ever, have any roadblocks because of a complexity in the model or the performance. The only reason it fails to gain scale is because the perception in our mind about the complexity. And one of the things that we really feel is that you have to be all in, first yourself, diffuse it to people around you to make it happen. Now, if you think about it, in a pilot, you’ve got experts doing it, you’ve got guardrails, you’ve got the intensity of people, and you’ve got a select group.

Now, when that kind of goes and spreads out, you’ve got a teacher in Bihar kind of implementing it, you’ve got a health worker in Coimbatore, you’ve got a small business leader in Indore doing it, who are not into ML, but for them, AI will start having significance when it stops being a scientific tool to something which is as intuitive for them. So three things which come into play. The first one is that for diffusion, it needs to be contextual to the local language that you speak. Second, it needs to be in the workflow of what you’re doing every day and you don’t need to do net new things. And the third is to be, you have to be iterative and be at it to make it happen.

And I’ll give you a small example as to how diffusion is happening. First of all, Shankar, really honored to have worked with X -Step to make it diffused across so many realms of life. And at Anthropic also we said that it’s not a technology for the sake of the technology only in the hands of developers and builders. We found that in India, India happens to be the second largest user base of cloud outside the US. So a big round of applause to all of us out here for making that happen. And what we also felt is that when we are building tools, one of the tools you might have heard is co -work, which earlier used to be done a lot by developers.

But now, people who are information workers or who are just thinking as to how to solve things. The idea is that you do not have to develop code, read a lot of intense things. You can make the tool work for itself. So in my mind, diffusion really means, first, how do I think that everything that I do, I have to be AI first. Second, the ecosystem being in India around myself, I enthuse everybody. And third, how am I giving back to everybody in the last mile to make it happen?

Fantastic. One of the things I liked about what Anthropic CEO Dario Amadei said is very soon, imagine a country with a whole bunch of geniuses living in data centers. What will that country do? Think about it. But till we reach there, and Dario says in two, three years, but till we reach there, Trevor, as president of Gates Foundation looking at global health, you are dealing with a situation where you’ve seen a whole bunch of, you’ve seen a whole bunch of AI pilots. not too many of them have scaled. From your experience, what separates pilots from systems that have scaled and become institutional? What separates an experiment from a scaled, institutional, sustainable impact?

Thank you, Shanka. And thank you for the invitation to be on this good panel. And also for the overview you gave me a few days ago of the very good work you’re doing at XSTEP. I learned about Open AgriNet and where that has made progress. But on this issue of scaling of AI, I had an opportunity to, this morning, sit down with the heads of entities which we call scaling hubs. There are two of them here in India, and there are three, soon to be four, in Africa. And there’s also a pan -African venture called Smart Africa. And you might say, well, what are these scaling hubs? So the idea is that we would support a partnership with the governments now in Rwanda, Nigeria, Senegal, and soon to be Kenya, wherein we place funding that the government can use to take the pilots that are out there and to really push them to large scale.

And why would we need a hub like this to do that? Well, one of the big barriers that we are currently seeing is the fragmentation that is occurring out there in terms of many, many ventures, some that we fund, other funders, everything with very good intent. Let’s do a small pilot. Let’s quickly do something over here. Thousands of them occurring out there. You take it at a government level. They have people approaching the Ministry of Agriculture, the Ministry of Education, the Ministry of Health, Ministry of Finance. all of them with different groups and on the DPI front, all of them trying to put in place the necessary DPI infrastructure to support their pilots. And now this fragmentation which is occurring over there, which I think is a big inhibitor of scaling to real population scale that we need.

So we are going to invest in these hubs that can be points of aggregation. We don’t want to inhibit diffusion. People have the idea of diffusion as a more random process which goes anywhere, and there’s something good about that. But if we can channel the diffusion into these centers of excellence, I think at the country level, the feedback that we’ve had from the governments is that that is a way that we are really going to get to scale more rapidly. Thank you.

Excellent point. Excellent point, Trevor. And I think you brought out the inherent stress in the phrase diffusion pathways. Diffusion pathways. Definition is everywhere, right? Pathways by definition is fixed. So it’s how do you spread. a technology in certain fixed pathways towards certain impact. It is indeed a stress. I believe that stress needs to be there because we are talking of the stress of safe AI impact at scale. But it is indeed a challenge, and together we have to solve it very quickly. I want to talk a bit about Minister Esther Dweck’s ministry, MGI, or the Ministry of Management and Innovation. Isn’t that a cool concept? The government of Brazil has a minister and a ministry looking after the idea of innovation and management.

They are collaborating very closely with India on a range of issues, and it’s my honor, Your Excellency, to have you here. Minister, I want to ask you a question. Scale efforts, diffusion. A lot of times fail inside government, not because of technology. But because of procurement process change and accountability, what has to change inside the state for AI to move from pilots to durable public services?

Thank you, Shankar. Thank you for inviting me and also for the partnership that we have with India. And Brazil is looking for this partnership with India because of scale. If anything can be scaled up in India, it can be in Brazil because compared to India, we are not such a big country. But compared to many other countries, very large. So for us, very important, this partnership. But when you talk about the problem inside the state, our ministry was created. The whole name is Ministry of Management and Innovation in Public Service. So we are focusing on innovation inside the public services. And we created a special secretary for state transformation because we saw that the state had to be transformed in order to actually be able to have innovation.

Because if we stand with the same way of doing procurement, actually we won’t be. We won’t be able to. do it. So we think that we need, in terms of AI, we need to transform the state in three main areas. The first one is procurement, for sure. And any kind of innovation procurement needs to be changed. So also the infrastructure, especially the digital infrastructure, and of course the governments. And when I talk about the procurement process, usually people are looking for the lowest price, lowest risk, and usually civil servants are very afraid of doing procurement because the auditing bodies are trying to look if they’re doing something wrong. So they usually try to go for the lowest risk possible.

And this is what prevents innovation inside the government, especially because innovation comes with errors. We know that any innovation might come to error. And if the civil servant cannot make any mistakes, then we never innovate. So one of the things that we found out when we’re trying to ask for how to do innovation procurement in the government, the first thing people say, I’m afraid of doing any mistakes, then the auditing body will come after me and then I won’t be able to be a civil servant. So what have we done is to change the mindset of the procurement process. Instead of more process -oriented, we are looking for a more policy -oriented and looking at the outcomes and not only the lowest price thing.

And with many other ministries, we are discussing how to actually build that culture of innovation procurement with this idea that it must fail. And you can also interact with the one you’re buying off. Because, of course, you’re buying something that doesn’t exist. How do you explain to them what you need? So there are a lot of things that you have to change in terms of procurement in order to actually be able to do AI. And, of course, the second thing is the digital infrastructure. As, of course, as Nandan has said before, Brazil, since 2023, when we came here for the G20 in India, we brought this idea of DPI to Brazil very… as something very strong.

Thank you. and we already know that we had something that could be called the DPI, but we didn’t know the concept before. And one of the things that was very important for us was our digital ID and our platform for services, a digital platform for service, which both called gov .br. And based on this platform, you were able to, what we are discussing now in terms of optimizing, but also in having more personalized services, knowing the people, if you know the citizen, we will be able to provide them specialized service, and we’re doing AI to do this, how to actually specialize service, what the people actually need. So I think using this, having a good DPI infrastructure, especially in terms of identification, and be able to also, of course, to have a better data governance.

That’s the third thing I would like to say is the governance inside the state. When we launched our plan for AI, and this morning, today, we had a session on the Brazilian AI plan. And the first thing the president said is that we need our database. He said we need the Brazilian database. We cannot have silos anymore. We cannot have this minister saying, no, this is my data. No one can access this data. So we have to do it, of course, in a private, preserving privacy in a security way. So we discussed all the data governance. We’re about to launch a new decree on data governance. Having every minister to have a chief data officer, someone who actually knows the data, knows how to use the data.

So we are actually looking at these things in order to access from the state to be able to innovate into this AI. Thank you. That’s it. Thank you.

Wonderful. Thank you. Irina, you’ve been in the IT space for three decades. You’ve seen the Internet thing boom, bust, and now you’re seeing AI. From your vast experience, what is the most common failure mode when AI moves from pilots to everyday workforce, everyday? And what kind of safety infrastructure actually prevent?

yeah I think one of the things that we have to remember is that the failure never happens with a big bang it just slowly dies because people just stop reducing the level of interaction they have gradually and you suddenly realize that it’s not relevant anymore so what really needs to happen that you need to keep it in a way that people use it daily and use it in the way that is contextual for each of them. For example one of the reasons why it might fail is because the data sets are speaking across to a country of a different nature which is kind of setting benchmarks in banking and financial systems which is not the same way where in agriculture is the biggest thing that we require hence collecting data for Indian languages nuancing it by say legal, by agriculture by what people are speaking across in that dialect in that language, this is very critical so if I want to look at three things that needs to happen, first of all keep it contextual to the domain, micro domain in which it is required at Anthropic we have kind of worked closely to ensure that we now have Indic language availability for 10 Indian languages from Hindi to Malayalam to Gujarati to Urdu and it’s available in the latest models and it is incrementally improving day by day and the last part I would say is that ensuring that whatever you are doing the ROI that we look at should be if I invest in a language say Bengali how many net new use cases have been opened up because of that and how many more people have got the benefit of that and I think the work that say we are doing with Aikstep and thanks to the fields employed education, healthcare, everything that’s the litmus test that we should be measuring ourselves on

I want to ask a question to the audience by raising hands how many of you use UPI keep your hands up if you know how UPI works, what’s the protocol behind it what’s the technology behind it hands are steadily coming down this is my point, we don’t care about technology as long as it works, for something to work at population scale technology has to be boring technology has to be invisible till the time it is not, it has not diffused, it is just some magic mystery thing that we all are stuck with figuring out what to do it’s a long journey from technology as magic to technology as normal boring in fact this wise old man once told me when you stop thinking of something as technology that’s when it has diffused 500 years ago this was magical ocular technology.

It allowed someone to see. Now we don’t think of it as technology. A day will come when we don’t think of AI as technology. That is the day we can say that AI has diffused through all of society. We have some way to go for that. Trevor, when you hear of things like Open AgriNet, some exciting work happening, what makes you think that fees like infrastructure versus yet another project that is going to the path of pilotitis, death by pilots?

Well, I do look a little bit with envy at Open AgriNet. Having looked across the work that the foundation does in agriculture and in health, traditionally the narrative has been how fortunate those health folks are because there’s such huge funding into the health areas, such huge investment in research, in genomics, in human health. and much less on plant genomics, which admittedly is potentially more complex, the clinical trial infrastructures for developing new products on the human health side versus on the agriculture side. But now we come to AI, and I have to say I look at OpenAgronet, and I think that the agriculture community is ahead of human health in terms of the implementation of a system which is personally useful to a farmer smallholder farmer, for instance, being able to get the information they need, being able to determine what crop disease they have to deal with or a disease in their cattle and what the weather is going to be and how they can maximize the finances in their small farm.

All of these types of things I would love to see in the health space, a personal health assistant. In low – and middle -income countries, so many people are not very close to a tertiary hospital. And they may be 10, 20 miles even from a primary health care clinic. Can we not provide for them with a system that can personally provide them with the information that they need in a safe way? And I think Open AgriNet really puts those components of infrastructure together. The way that it’s modular, the way that you can adapt it to the local circumstances, it’s in many ways exactly what we need in that personal health side of the picture. So I only have some envy, but I hope we can duplicate that on the health side.

Thank you.

Thank you, Trevor. Open AgriNet is just a group of organizations coming together, collaborating, as Trevor said, each bringing in one piece of the puzzle so that together we can create those diffusion pathways. And as Nandan said, that is what allows us to take something from Maharashtra, which took nine months, to Ethiopia in three months. Back to India in three weeks. from agriculture to livestock, from India to Ethiopia, from Asia to Africa and back. That is the exciting possibility that India has been in the journey of for the last 15 years, what we call as DPI. The thing about DPI is when you start with a strong use case in mind, as Arina and others have said, you harness technology, so technology becomes a good slave to a very powerful cause.

Then you take advantage of rapidly evolving technology. Minister Dweck, if you designed a national diffusion pathway for one public service, what would you prioritize first, institutions, incentives, data readiness or governance?

Well, it’s difficult to choose only one thing, I guess. Maybe this perspective from management, you’re always looking for some kind of a systemic approach, trying to look at all these things. Together. Together. And actually, we recently have launched a program, an R &D for AI in Brazil. It’s called INSPIRE in English, but in Portuguese means BREATHE, INSPIRE, but the same acronym, which is AI for Public Service with Innovation, Responsibility, and Ethics. And it has this systemic approach inside of it. Because the first thing, we create this new institutional arrangement. It’s not new, but we had in this R &D project, we have the government, of course, we have some state -owned companies, we have some private companies, and our innovation ecosystem in Brazil, all of them bringing together in order to help the government to have new AI platforms.

Because when we, although we’re already using AI in Brazil, we saw that we have a lot of lack of technological expertise and lack of financial support as well. So we’re trying to create this platform where we can actually offer many bodies of the government different solutions that can be used in many different areas, as you said. As I was saying before. So this idea, well, first thing we are discussing to have the data more sovereignty on the data and how to actually use better, but also for the data to be ready to be used. So one thing I was explaining before. So using AI to help to improve our data set. So it’s going both ways.

Another thing is also in the governance perspective, of course, we’re creating, as I mentioned, this shared tools and common practices and trying to share how, and specifically in this project, we’re creating this generative AI platform, and we’re trying to apply to different solutions. So recently, at the end of last year, we have this university enrollment exam for people finishing high school. So we created this whole complete, for them to know when they’re finishing school, what they’re going to do. Are they going to the job market? Are they going? Enroll school to enroll university? How to apply? What’s the best thing for them? So using AI to help them to actually decide this. And they’re doing the same thing for health care, for.

agriculture sector as well. So we’re looking at all these things. And, of course, in capacity building. So we are a lot training civil servants. We have four trails, actually, for people who actually are the managers, the top managers, for IT experts, for people controlling data, and for regular civil servants. Because one thing, when we’re talking about state transformation, we thought the one thing you have to train and to change, of course, is the civil servants. And nowadays they have to have a digital mind. And some of them have been there for many years. They didn’t have the digital capabilities. So we’re training all of them in digital capabilities and specifically on AI as well in order to think how to use this new technology in their regular life in order to improve civil service.

So I think it’s a more systemic approach there.

Pathways are like digital rails. What should model developers focus on so that AI can plug into these pathways safely across sectors and countries?

Very interesting. And I’ll just try to kind of paint the picture by giving a context. Now, think about it. We’re talking a lot about agriculture. It has the last mile. Now, if you were to solve for that farmer day in and day out, there’ll be various kinds of work that they have to do. Look at what is the weather conditions, one source of data. Look at how the crop yield, et cetera, is performing in other source of data. The market prices in other source of data. Whatever has to be done across for reaping and sowing. So these kinds of data, if they want to infuse, anybody wants to infuse AI on top of that, and if you build it every time, it is so cumbersome.

Now, if you kind of do the same thing that, Nandan, you’ve been talking about, at one point of time, all of us are different. We’re different. We’re different. We’re different. We’re different. We’re different. We’re different. We’re different. universal adapter came, it took it away. We all use UPI for digital payments. Do we know anything to do with the technology behind it? Whether it’s earned, whatever is coming across as the small micropayment, we have no idea. So one of the things here to be done is have a universal language which accesses the tools as well as the data. So we came out with this concept in Anthropic in 2024 called the model context protocol. And very simplistically put, I think of MCP as to AI was say what UPI was to payments.

And in effect, what it really does is you develop things once and you make it MCP ready. And anything else that you want to do it further, you do not have to keep on writing again and again. So all the cases of agriculture, healthcare, anything else put together, that can happen seamlessly. Why does it matter for India? There’s a lot of data which already exists in hell. in education, in various ways that citizen services are going across, and that is a rich level of data. So if we kind of make this data AI ready, use the tools which are going across, then the case of diffusion and that accountability of everybody coming together will be that much more quicker.

Excellent. A lot of people who deploy AI, they have an old notion that it’s like normal software. You buy great software, it is perfected, and you deploy, and you can close that and go away. In AI, that is just the start, because as you use it, data comes in. The data gets better, the models get better. With better models, you provide better services, usage increases, more usage, more data. This cycle, and while it is happening, the models improve, the data improve, so for a lot of adopters, once they go beyond procurement how do you continuously invest to upgrade and evolve? That’s again a very important question. So when we talk of 100 diffusion pathways these are 100 diffusion pathways to safe AI impact at scale which creates a second stress and I’ll come to you on that Trevor.

When lives are at stake where do you draw the line between speed 100 pathways to 2030 and safety and coming from health safety means literally lives, right?

Yes Shankar and there are a lot of lives at stake and I feel the urgency. Every year we don’t have the next generation of malaria vaccines we’re seeing hundreds of thousands of young children dying. Every year we don’t have a personalized education coach for every child no matter where they are. we see a tremendous amount of human potential wasted. So there is this urgency to get things done and that might make one think very carefully on the safety front and it is that safety issue where people are in the health area saying we need to take a step back, we need to look carefully at the frameworks before we just jump in with like the application I talked about, the self -application, how would that be gated, how would that be guarded.

I do think that because of the excellence of the DPI stack here in India and because of the thousands of application efforts I see, you are going to probe those frameworks for the safe introduction probably first in a context which is, as Nandan was mentioning, the frugal innovation that will be relevant across lower middle income countries and actually beyond. So I do think that we are very much looking at India as a safe introduction. The foundry of AI application. and we want to see those frameworks whereby we can safely introduce the technology. In terms of the technology itself, just having a type of black box system that gives a health recommendation is almost never adequate, almost never satisfactory.

These systems need to be auditable. And I have to say that Anthropic has made quite a lot of progress in their research on how are these concepts, how are these recommendations actually represented in the model. People want to be able to audit that. They don’t just want something that comes out of nowhere. If you have a human clinician that makes an error, you can talk to that person. You can say, well, where did this, why did you think this was the case when you made a misdiagnosis over here? Was it because you didn’t elicit the right question from the patient or you transcribed incorrectly? And that is the kind of transparency that we actually demand of the AI systems at the end of the day.

So I think that… But between the work going on here in India and some of that transparency research, we can get there.

Thank you, Trevor. Minister Dweck, as you’re thinking of implementing AI solutions at scale, what is the hardest political or economic challenge, and what are some tips on how one should deal with it?

Okay. I think it’s kind of a political economy issue now, I think, in Brazil we are looking for. Of course, one thing is the workforce problem, because we may be going to this utopia that no human need to work anymore, and the machines work for us. So how actually create, how divide this wealth in order to come from these machines working? But there’s one point. But more concerning in our current period now in Brazil is about digital sovereignty. Of course, very few countries, maybe only two countries in the world, will be totally digital sovereign right now. But I think we have to. We have to increase our digital sovereignty in terms of being able to.

have our services and be able to operate it, be able to know where our data is, to know how we’ll be able to continue with our services to our populations. So we are discussing a lot of this in Brazil, how to increase our level of digital sovereignty. Of course, we know we’re probably not in a very, in a few years, be totally digital sovereign, but at least we’re to increase. And we’re actually working with our suppliers in order for them to offer us more sovereignty or at least some security that we not have any discontinuity. So I think using the state capacity and using the state procurement purchasing power, it’s very important to do this.

And we’re actually using it in order to talk to our buyers. And we discussed this sovereignty in three levels, in the data level. And for this, we’re bringing back the data to Brazil. We’re trying to have… We have two, as I mentioned before, two federal, state -owned companies that are actually having resident clouds within our companies to know where the data is, but only know where the data is not enough. So we are increasing our operational access to the data. And also, I think the third level is why you’re using technology, something that we’ve been discussing a lot here. And it’s not directly related to AI, but it’s related to digital services. I think one thing that we’re doing together here in India, using a technology that was developed here, a verifiable convention, which was very important for us, we are using right now in two pilot projects yet, but we want to scale it up.

One is related to rural credit, but the second one is related to something that I think the whole world is discussing, how to protect child online. So now in Brazil, we passed a law last year, which is a very important law. It was very quick to pass. After one of the digital influencers showed what was happening to children in the Internet, especially on social media, and we passed the bill and it said by 17 March, you have to know what age the person who’s accessing the Internet is. So how to do this in a way that you protect the privacy? We don’t actually know what people are using. So a lot of things are discussed and we’re trying to do this verified recognition in order to have this age verification in a very simple way, very easy for people and for people not to be afraid that the government is actually looking at the Internet.

So I think this is the way to make things that are actually useful and important to protect our citizens but also to provide them with very good services.

Thank you. Today’s topic was building publish and scale digital public infrastructure for AI. By 2030, when we would have made a lot of progress on that, we would stop calling DPI digital public infrastructure and we’ll start calling it digital public. intelligence. With that, a big thank you to all my panelists and to the audience. Thank you.

Thank you. Shankar, if I can just request you to send a token of appreciation to the panel. Thank you. Now the next session is about to start on a very unique topic, AI for Democracy. So we request all the audience here to remain seated. A very wonderful topic, AI for Democracy, and we are very blessed that today we have with us Honorable Chief Guest, Mr. Om Birlaji, Speaker of Parliament of India, Mr. Martin Chongungji, Secretary General, IPU, Mr. Laszlo Z, Deputy Speaker, Parliament of Hungary, Dr. Chinmay Pandya from All World Gayatri Parivar, Ms. Jimena.