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Hi, good morning everybody. I’ve got an incredible panel here this morning. The topic that we have is, I think, the most important topic at the summit. I think everywhere I’ve spoken or everywhere I’ve been, it all revolves around this critical topic around AI -driven strategies for workforce and economic growth. And I think the reason this topic is super important is the fact that if you are a government official anywhere in the world, I think this is their biggest concern, is that how is AI going to impact society? How is AI going to impact workforce? How is AI going to impact industries? So that’s going to be the most, you know, it’s the most important topic.

So I appreciate everybody who’s out here. My name is Tej Trikot Chopra, and I’m the founder and CEO of Industry .AI. So we are an AI company that focuses on driving productivity. productivity. passionate for me because I live and breathe this every day because we are trying to really do this is that how do you create the digital workforce or how do you empower the workforce across industries. A quick introduction of my colleagues on the call. We have Mr. Sathinder Singh from the ASEAN. Mr. Narendra Singh from Neve Cloud should be joining us any minute. Mr. Vinod Javar who is really the key in Nextra which is part of AIDA. Dr. Nihar Shah who is one of the best in the healthcare space and Dr.

Mahendra Karpan who is a presidential advisor to Guyana. Welcome everybody. Just one other key point. Just to put it in context. In India we have 70 million MSMEs. These MSMEs employ 230 million people. The MSME market in India produces 30 % of India’s GDP and 50 % of exports. The other big critical thing is how do we bring AI for all, how do we bring AI for all these companies that can’t afford what normally large companies do? And that’s the big challenge in front of us. And that’s what we’re going to talk about today. So in order to really kick this off, what I’d like to do is first really talk about three critical elements in today’s discussion, is that how do we redesign our workforce strategies given this new technology that’s coming up?

How do we build the digital and compute infrastructure? And I’ll request Vinod to talk about that. And how do we really ensure that economic growth driven by AI remains inclusive, responsible, and sustainable? So with that, I’m going to request Satvinder, if you don’t mind kicking it off, and it would be good to understand from your perspective, how is the Digital Economy Framework Agreement going to help governments around the world navigate the opportunities that exist? Over to you. Thank you.

Thank you, Mr. Chopra. Very good afternoon to all of you. Great to be here with all of you. I think all of us are enjoying this momentous impact event, and it’s a great place to be here sharing ideas. And I’m here specifically Mr. Chopra, if I don’t mind, I’m giving the perspective of the ASEAN some of you may not know ASEAN is next door to India today we are the 5th largest economic bloc, 700 million people, most of it middle and upper middle income economies who are part of ASEAN and with India of course we are deeply connected, we have a free trade agreement and we also have a very strong trade economic ties with India and we have a lot of cooperation going on with India including in the area of digital connectivity Mr.

Chopra talked about the digital economic framework agreement, let me just update you what is that, in short we call it DEFA DEFA is a digital agreement we are now negotiating in the midst of completing negotiations by March of this year last two years we have been negotiating it is the largest regional digital agreement in the world the only difference is also legally binding the only difference is also legally binding the only difference is also legally binding the only difference is also legally binding the only difference is also legally binding the only difference is also legally binding the only difference is also legally binding So we are actually negotiating with the 11 countries of ASEAN to come on board 700 million people to be digitally interconnected, interoperable So that we can do business better and so that we can grow our economies better Now the essence of DEFA came about post -COVID I think in COVID, like in India, in ASEAN too, it really changed us I think while COVID was not good for anyone, but COVID also had positive unintended consequence We saw the greatest transformation taking place in the way we live, work and play And that interpreted clearly into growth that took place post -COVID I think the leaders in my region saw the prospects And they also saw the numbers where huge chunk of economic growth is driven by digital And therefore there is a no regret move now to move the entire region into digital connectivity And that’s where the DEFA comes through I think the interesting thing in ASEAN I mean, well, India is one country in ASEAN Like I said, there are 11 countries.

We have LDCs there, least developed countries like Laos, Cambodia, Myanmar, and now Timor -Leste just joined us. And then we have advanced economies like Singapore, Malaysia, Thailand, Indonesia, who is like a middle -income economy. So it’s a mixed bag of economies, but the momentum of getting all of them together to do this, we were able to move the agenda because we were able to show very quickly through data that the biggest beneficiaries actually of the DEFA is not even advanced economies like Singapore, because they are already there, digitally connected, but actually are the LDCs. We were able to show that the impact of DEFA will be greatest in terms of jobs, prospects, economic growth, because they are really economies which are least developed, but they are going to be more developed.

We are moving into the latest of all kind of connectivity at the lowest cost. And they will be the ones who will be able to benefit on a per capita basis in a maximum way. So that is how we were able to get 700 million people from 11 countries. sitting on a common agenda of being integrated because you were able to show them the money. If you don’t show them the money, nobody is going to jump in and do any such agreement. Money here means jobs, economic growth, deeper depth in terms of growth of the people and communities. So DEFA, for example, already ASEAN is a very vibrant digital economy. Roughly it’s about 300 billion today and we are going to be moving to a trillion dollars in size by 2030 in the next couple of years.

But with DEFA, the numbers are showing that the region is going to double the size of digital economy. So I think this is where we are in terms of our ability to come together to be able to do business. And our idea is, of course, not to stop in ASEAN. The idea is that once DEFA is going to be in place, we want to be connecting to India. Economy to economy, I think this will really be fantastic. I think we can stop looking over the shoulder. I mean, basically global South India, Southeast Asia, there’s plenty of markets. demographics on our side. In fact, even the affinity of our people in wanting to embrace technology is on our side.

In fact, some of the studies that are showing that actually it’s economies like Southeast Asia and ASEAN as well as India is where people are seeing the translation of the use of AI in the most profitable way. The data is showing that it’s not in the West. It’s actually in our region where businesses are beginning to deploy small AI into their day -to -day business and making a big impact on productivity, on growth, and also in relevance. I’ll stop there. Maybe

Satyendra, you’re absolutely right because I think in our part of the world, I tell everybody that India is trying to lead and be the bridge between the advanced economies and the emerging economies. But I think the dynamics of technologies needed for our kind of part of the world is very very different from the West. So I think if we can get a good price point to provide these technologies, that will be great. Dr. Carpin, you’re an interventional cardiologist and you’re advising a lot of governments around the world. It would be great to get your perspective in terms of how you see AI and its impact in terms of transforming public health care. Now Dr. Carpin I was at a discussion two days ago with Vinod Khosla and he always says some really provocative things and one of the things he said was that in a few years from now AI, we won’t need doctors in the world and I know he said we won’t even need doctor surgeons in the world so you don’t need you’re the actual real person who does all this stuff so it would be good to get your perspective.

Thank you very much for having me here and I bring you greetings from our President Dr. Mohamed Irfan Ali and Vice President Dr. Bayard Jagdeo We are from a small country located in South America and we are here to help you just a population of 850 ,000 people. I believe on the way here I might have encountered 850 ,000 people on the road. So you can imagine the scale that we are dealing with. What is transformative about Guyana at this time in our history is that sometime 2015 we discovered oil offshore. And you know everything that comes with that transformative discovery. The oil and gas industry is now booming. And we are trying to learn all of the lessons from states that have walked this path before.

Those that have relied heavily only on oil and gas they have encountered tremendous difficulties that we are hoping to avoid. So one of the things, or a couple of the things that we are using these resources to do is to help us with our health care, our agriculture, and our digital transformation in the public service. One of the other important things about Guyana is that we have the majority of our population living on the coastal area, and most of the rest of the country is forest. So we actually are pioneers in selling carbon credits to the world. The sad and vulnerable part of this, however, is that the coastal area is on the sea level.

So using AI, predictive models, all of those things, it’s a survival tool for us, not just now, but for future generations. In recent times, we have, been fortunate to have visionary leadership to take us in the direction we’d like to go. and we have in our country several remote villages of indigenous populations and I’ll share an example, a personal example when I was more in hospital practice. There was an 18 -year -old boy who had to be flown out from an interior village by the military helicopter after a snake bite and when he came to the city it was the first time that he saw headlights on a car. That’s where we still are in some places.

So we have been able in recent times to use our resources to establish telemedicine in particular areas. We have now over 200 functional sites that can actually serve these remote communities. They can do simple things like this. EKGs, x -rays, blood pressure, blood sugar, all of the common things and respond. to trauma, etc. So a healthcare worker, not necessarily a doctor, a community health worker, somebody indigenous to that area, can assess these patients, they go on video conferencing and all 200 of these locations actually have Starlink or we’re trying to implement that now, so that they have connectivity. So the specialists on the coast and in larger centres actually can give real -time diagnosis, real -time advice.

I myself and the cardiac unit, my on -call team always will be able to review an EKG. Like in India, I suppose, our number one cause of mortality is still cardiovascular disease. Heart attack is a huge problem in our population. Historically, some of you may be familiar with the facts that most of our population at one time were indentured immigrants that left India. and the majority never returned. And they built homes and created generations of descendants from then in Guyana, Trinidad and Tobago, Suriname, in that entire region. So whatever is plaguing you in India from a healthcare perspective is the same thing that was transferred. Because we maintain the same lifestyle, we have the same foods, the same likes, the same dislikes, the same genetic predispositions.

So in our context, what we have been doing is to start at the basic primary level, because that’s where we are. We’re not yet at the Singapore level and the others, but we’re hoping to get there in a very rapid leapfrog type of strategy. So we’re using AI at this time to do primary healthcare, for inventory management, for surveillance. For surveillance. And we’re moving into areas like agriculture for soil management, food production, et cetera, and to help us in all aspects. So for those of you who are looking for opportunities where there are challenges, there’s always opportunities. So Guyana presents to you tremendous opportunity for investment, for development, and for long -term, multi -generational, sustainable involvement in our country.

And I am sure, and I bring you this message on behalf of the president, we welcome investors to Guyana.

Dr. Carpin, thanks so much for that. I completely agree because I think the world is going to face exactly the same challenges, whether it’s in health care, whether it’s in agriculture, and I think there’s a lot of cross -sharing that we can actually learn from. Niyar, I’m going to pull you in, right? So just for everybody’s benefit, Niyar is with the Lawrence Berkeley National Labs, which is really one of the leading public research institutes. It’s in the world. But Niyar, I just want to share with you about four or five weeks ago. there was a majlis in Adnok in Abu Dhabi and they had a hundred CEOs in a room on a Sunday and everybody in the world showed up and there were four groups of people every major CEO of every major oil and gas company in the world the CEOs of every major energy utility in the world the CEOs of every AI company in the world and the CEOs of every large capital provider in the world and I was trying to figure out myself what’s the connect and at the end of the day what came out was that the world will need four times more energy in the next 10 -12 years to support the growth of data centers and all the other things that are going to happen and that’s going to require four trillion dollars every year for the next 10 years so Nir with those numbers would love to get your perspective from a technology perspective and how should the world react to that kind of growth that’s needed thank you

yeah so as mentioned my name is Nihar Shahab and I work at Lawrence Berkeley National Lab it’s one of the 17 Department of Energy National Labs If you also are Oppenheimer, you might know where the National Labs came from. And, of course, we have a very distinguished history with a lot of Nobel Prizes, so I won’t bore you with all that. But I’m very grateful, first of all, to CII for this opportunity to speak. And with respect to the question, obviously energy is one of the things that I think in, you know, I go to bed every night, I wake up every morning thinking about energy, being one of the energy labs of the United States.

Now, one of the other things that is also probably not as well -known, I direct the global cooling program at Berkeley Lab. And another blind spot with respect to kind of, you know, you mentioned energy, you mentioned the huge growth, you mentioned, you know, the huge investment needed. Another thing that’s needed is going to be cooling. And that’s another blind spot that I think we don’t really pay attention to. So that gathering of CEOs, I hope that there’s also a gathering of HVAC and data centers and other CEOs. and then in addition I think one more thing that we would probably need to think through in countries like India is the water consumption so you think about these kinds of things you also so we need to really think about this in a holistic sense and then you know in the bigger picture thing I think some of these things of course AI we are at the intersection of so many different things but if somebody tells you that they know exactly what’s going to happen in three years or five years or seven years they’re selling you something so you might want to take a second look at that.

I’ll say a couple of other things right related to what you mentioned Vinod Khosla just a month ago I was in Silicon Valley Berkeley Lab is based in Silicon Valley and Mr. Khosla was giving a keynote there and he said as usual he said some very provocative things. One of the things he said was by 2030 everything that needs human expertise will be free or nearly free. Second thing he said was everything that needs you know labor is going to be and this is coming to our topic here is going to be also very nearly free. The thing that I disagree with Mr. Khosla about is that, again, I mentioned the energy blind spot and the cooling blind spot.

So really, I think some of these things are going to be infrastructure bottlenecks, which I think some of our co -panelists are going to be able to address. And then also, I think, along with that, there’s probably also going to be a talent bottleneck. And when I say talent bottleneck, I don’t mean talent across the board. I think it’s going to be particular kinds of talent that we’re going to need. And just today, you might have heard the U .S. and India signed or India formally joined this PAC -Silica initiative by the United States. PAC -Silica is an initiative about the whole AI supply chain. So now you’re talking about not just, you know, kind of compute and not just, you know, the infrastructure, but you’re also talking about the whole supply chain that will allow that to happen.

And that’s an initiative that the U .S. government has started. So, you know, there’s a range of different things we could talk about. The workforce dimension, of course, is super important. And, of course, I can. Come back to any of those things. I’ll mention one last thing. Berkeley Lab, the Energy Act of 2020. requires Berkeley Lab to essentially report to the U .S. Congress on data center growth. And they found that over the last decade, data center growth has tripled. So, again, some of these numbers bear out even if you look at history. And the forecast is that by 2028, triple again. So, you know, these things are, again, not very well known. But I do think that, you know, that these blind spots need to be addressed by all of us and all of you all.

And we’re at a very interesting point with the, you know, I would say industrial revolution. So let’s see what

Thanks for that, Nir. I think you’re absolutely right. I think the people are underestimating the challenges of developing all this infrastructure, whether it’s in terms of cooling, whether it’s in terms of power, whether it’s in terms of communication, fiber optics. So I think that’s going to be a huge challenge. And with that, I’m going to turn it over to Vinod. Vinod’s with Nextra. They’re building some of the largest data center networks in India. But just before this panel, I was actually talking to Vinod because I think there are going to be two parts of the world. There are going to be large cloud data centers, which Vinod is building, but I also think there’s going to be another parallel world that’s going to be on the edge.

We at Industry .ai this week launched the world’s first AI supercomputer for manufacturing, which can go on every factory floor of the world, especially at a price point for 70 million MSMEs to transform productivity. So two things, Vinod. One would be good to get your perspective on how the world’s going to pan out of cloud versus edge, number one. And number two, all the challenges or bottlenecks that Nir was talking about, whether it’s in terms of cooling, capital, technology, skill labor, would be good to get your perspective.

Sure. Thank you. Thank you very much. I represent Nextra. It’s a subsidy of Airtel. We are in this business of building infrastructure for data centers. So that’s it. That’s our bread and butter from Nextra point. So we’ve been doing this for the last 20 years. We’ve seen the evolution from a normal. server room racks to small enterprise customers to now hyperscalers. Now, we’ve got the expert also over here. We’ve got now the new, what do you say, elephant in the room called the AI requirement of data center. So much is the demand now which is coming through to build large infrastructure for data center that Nextra has decided to carve out a separate vertical called AI VC on that.

So that’s the vertical which I represent. We are here to develop large scale gigawatt kind of campuses to that to cater to the fast requirements of some of our customers to grow primarily in the Indian subcontinent areas here on that. Yeah, the right is said that the challenges are there. Power is the challenge. Land is a challenge and getting the right kind of skill set still remains a challenge. We come from 20 years of experience, so we have understood the ways to work on this, on that. So being one of the pioneers in the home ground industry in the data center here, so we have been doing that. Few of the challenges have been pushing us to go beyond certain areas, look at new areas to build data centers.

Some of them are very close to the coastal area so that it can also accommodate cable landing stations for us, so that takes care of a lot of data required requirements. Plus we are also putting sites which are close to national grids now. There were places where we used to source voltage at 33 kV, now we are looking at 700 kV volts and all. So this is the thing, thoughts, which has now evolved and all, and it requires a separate thought process and the… Obviously the large amount of capital is required to get into that. So we are in this and we are well prepared. The demand obviously is quite high. The expansions of Nextra is quite aggressive also.

And we got something going on in the south. We thought something the best going forward on that. So your second question of how do you do the power and sustainably portion of it? Obviously most of the power we are going to source from renewable energy. That’s the key strength in the Indian regions here. Luckily for some of the good policies which have been put across a few decades back by the government, we have got plenty of renewable energy generators here. The government is pushing for upgrading the infrastructure to evacuate this energy also. So once we are at this high voltage, we are also connected to the central grid. So it makes it very, very reliable for us.

We are aiming to be what we said net zero by 30, 20, 30, 32, something is what. And there is a big pool of renewable energy for us to tap into that. We are at present, we have contracted close to 400 odd megawatt of renewable energy. So no longer we are looking at just 50 % resource for energy. That percentage is going almost close to almost 100 % now. This is how the whole sustainably portion of data center. And India and Nextra are well positioned to tap into this. I think that’s the interest level in trying to do a green data center evolves for us on that. The other challenge which has been told about how do you do the skill set. Yeah, skill set is a challenge.

Probably it requires a lot of debate on that. It’s something which needs. To be handled both at the fundamental level at the schooling level. and at the university level and right at the immediate level of training the existing engineers to adapt. So by the time the next generation come in, we probably would have missed the bus. So there are three, four approaches we are looking at how we can build an immediate kind of skill upgrade to make them suitable to develop the data center on that. So this is some of the things which we are doing, and I think we are, as an extra, very, very well positioned to meet the demands of whatever the customer is looking at.

Thanks for that, Varun. I think one of the things that came out of that session in Abu Dhabi was the fact that the world that’s going to win the AI arms war is the country that has the cheapest energy. And I really do believe that in India we have an incredible opportunity. I come from a renewable space. My first solar farm eight years ago, my revenue was 18 rupees a kilowatt hour. Today we get 2 rupees 20. My first wind farm was 8 rupees 50. Today we are in 2 rupees again. So I think we have an incredible opportunity in India to really win this AI arms war because the cost of producing energy is quite cheap. So, Narendra, first of all, welcome.

Narendra is the MD of RackBank and NeveCloud. Narendra, you’ve heard all the challenges in terms of the cloud. It would be good to get your perspective in terms of, one, cost of compute in India. How do you really make it affordable for everybody? And two is, how do you ensure adoption across the country?

kilometers away from Earth. We partner with Agni Cool, which is a space tech company, and the space ecosystem has evolved in the country from the last seven years. And the government has given a lot of open up space for everyone, for the private player. The first mission we are sending before the end of this year, and we believe that this is for critical workload which can protect the borders, unmanned vehicles, and all those things. So we started exploring beyond Earth and that’s what it is needed. And we can lead as India because the ecosystem, today look at the cost of building data center in India is 4, 5, 6 million dollars per megawatt versus 12 million dollars in US, in Singapore and Dubai.

Any market you go, you get a cost of 12 million dollars. Why? Because the 80 to 90 percent product which required in supply chain is manufactured in India. And we have to strengthen that. As government announced the 200 billion, I believe this is only for data center infrastructure, not for chips. So chips cost is on top of it. It’s like 5x or 10x. It depends on what chips you are using. so opportunity is huge, it’s a trillion dollar opportunity for the country thank you

I met Narendra about 2021 at the JW Marriott Hotel in Mumbai and that time he was still putting together this whole strategy and I was thinking to myself in 2021 what’s going to happen about data centres and now he’s talking about data centres in space so it’s good to see the kind of progress that’s happening before I go, I have lots of questions to ask but any questions from the audience that they want to ask go ahead go ahead

Sir, I am Harsh Vartan basically from HDI industry but before that I was working as a research fellow in CSIRC so my question to Mr. Shah is we have seen hydrogen fuel cells being used at experimental level in railways and buses but it has not been implemented at a large scale neither in India nor abroad so what are the… Thanks.

Yeah, I have many colleagues at Berkeley Lab. Actually, they have been collaborating with India’s National Hydrogen Mission. So, you know, I think, and thanks for the question. You know, when you come to fuel cells, I think there are a few bottlenecks. You know, some of them have to do with also just even having the hydrogen infrastructure in the country, right? Right. So I’m not necessarily the right person to address like all of these issues in terms of why hydrogen fuels have not taken off. But I do think that some of these things are still, you know, kind of an R &D challenge. And I think many of these governments are looking at hydrogen to see whether or not you can actually, you know, eventually do the R &D to deploy it.

And there is collaboration going on. So, you know, stay tuned. I think it will obviously India is also doing a lot on that and other countries also.

I can add to this that. The bottleneck as an operator is the cost should not be higher than what we are getting today from the grid. innovation should like lower down the cost then the adoption will happen rapidly so that’s what we think and that’s why I believe the adoption is not happening because people don’t want to pay premium for that and I think India can take the lead in that in terms of cost adoption and price points the supercomputer for manufacturing we are seeing 6 .5 lakhs so I think that’s the kind of speed at which we are going to change the way things are going

so Tindra I want to pull you in and the ultimate question that everybody is asking impact on jobs at the ASEAN how are you thinking about it because huge concern for governments is AI going to replace jobs is it going to enhance jobs so it would be good to get your perspective and what you are going to say out here is going to drive policy all over the world

so I am also going to say it perspectively from also how data is being collected already on impact on jobs and actually I have taken this from the studies that actually were done by Entropic Entropic it’s a massive study on AI jobs and security and one thing is clear. I think right now, while there’s quite a major hype on AI, but when you actually study the impact that it has globally and even in Southeast Asia, in ASEAN, it’s really impacting certain segments of the economy. I think the biggest impact is actually more on white -collar jobs rather than blue -collar jobs. And it’s true because a lot of it, I think even in the white -collar jobs, a lot of it has got to do with collaborative augmentation rather than full automation and handing over to the AI to do everything.

So I think that’s at this stage where we are in terms of the technologies on AI that we have and how we’re deploying them. Of course, when you are watching Elon Musk and you watch all these technologies and what’s to come in two, three years’ time, they are saying, now this is going to move from collaboration to totally replacing the human factor. In fact, the takeover part is, I think, that’s what… scares most societies. And I must tell you this is now becoming front and centre of conversations in government, in policy makers. I’m actually quite certain that the governments are not going to hand over this ability of replacements of all important jobs at the high echelons of society to the machine.

That I can assure you is not going to happen. There will be a lot of effort and conversations going on and it’s happening in closed doors where policy will have to come in to determine what can or cannot happen. And those barometers are going to be there and I think that’s where you see this impact event. You saw the largest conglomerate of decision makers from the private sector sitting with governments in this one location. You can see that momentum is there. There will have to be an ability for us to differentiate and also collaborate with what the change is going to come. and otherwise I think you’re going to see societies breaking up, the contract of governments with people is going to break up, people won’t have jobs, and if we are saying that the impact is not so much on the blue collar I think then a lot of the farming community is probably sighing with relief but I think we all know that in the cities where there are millions of people, I think this is where it’s going to be quite critical for us to get this contract properly sorted out I think in the coming years you’re going to see a lot of ethical rules regulations set up in order to ensure that actually whatever change that we embrace coming from the latest of AI, it has to improve, not take away the quality of life I think collaboration is going to be the name of the game not displacement of the human the use of people and population and I think that is something in conversation it’s not something that in this room we can decide, but clearly you can see that the momentum is here for that kind of difficult conversations to take place

you’re right and I like what you just said and first of all I’m hearing that one collaboration not displacement because the word I’ve been using is enhancement not displacement but I think it’s going to be all of the above so I think you’re absolutely right Dr. Karpen it would be good to get your perspective especially of healthcare you talked about telemedicine right technically I guess a doctor in the United States or India could be providing advice to somebody sitting in Guyana so how are you seeing this whole world panning out in terms of the impact on healthcare jobs

thank you so I’m glad that you mentioned the collaboration of these services yes indeed we have in the telemedicine space we have doctors from India we have doctors from New York the Apollo hospitals here the Northwell group in New York they collaborate they are able to help us with patients in terms of displacement of human capital or human skill set though I think for most of the countries like ours, we’re starting out at a severe deficit. There is not a surplus of radiologists. There’s not a surplus of cancer diagnostic technicians. All of these skill sets are extremely limited. So AI actually comes in to help us with diagnosis, accuracy, speed of diagnosis, as well as the economic aspect of achieving all of those outcomes.

But I tell you one thing, as a physician, that we are not too concerned for some things. In the emergency room, when there’s a child who can’t breathe from asthma and they’re scared parents, an AI can make an accurate diagnosis. They can tell you exactly what to give, what mixtures to nebulize, but to comfort and reassure those parents. that’s a human function at difficult stages of life when you’re facing terminal situations end stage of cancer you want somebody with warmth to hold your hand that cannot and can never be replaced by AI so all of this we have to bear in mind the complementary aspect of this new era that we are entering into we rely on the AI to give us the accuracy of the diagnosis in fact in Guyana we just purchased software to help us with CT scan interpretations and the world is going towards more imaging earlier diagnosis and that will be used effectively to reduce cost to have better access to specialists there was a time when we could not even contemplate getting for the right treatment for the right treatment and we were just waiting and we were just waiting the top guys from Apollo to give us an opinion, or the top guys from Mount Sinai.

Now they’re willing and they’re able to, despite the time difference. Actually, it’s like quarter to five in my morning time, so if I’m a little sleepy, please forgive me. But this is how we’re using it. But that human touch, I don’t believe it will be replaced at all.

Glad to hear that. And also, I sometimes think that when you go and search something on the internet, you get hallucinations, you get false answers, so the last thing I want is a doctor to be searching and getting the wrong answer and suggesting the wrong medicine. Any other questions? Otherwise, go ahead.

Hello, I’m the CTO at MindEquity .ai and I’m also the founder of AI Society. I have two questions, actually. My first question is that if I am starting an AI pilot company, and to have a full -time impact, what is the biggest challenge? technical barriers that I think have value in both that.

Do you want to take that? Do you want to go ahead?

So scale and AI. Today you spend $2 and you generate $1 because half of the 50 % goes to the AI chip company and this problem can be only solved through enabling indigenous AI chips which has a better performance and the lower cost and maybe in the big guys who is enabling the entire ecosystem, they have to reduce the cost. That’s the best way because once you build the agents, we have billion users. It’s the largest market in the world. Scaling, when you do the scale, people will not pay for the value. $20 is not enough or $10 is not enough for the subscription but your cost is higher so I think this will take some time. The new chips is coming and that’s where the job questions related to that, if I answer this.

The job is the AI voice is fairly usage happen in the country and we are losing even government is adopting AI. They are signing all the MU with foundation companies. What I believe that they should not remove the call center. They should come up with a policy because AI is costing 7 rupees per call versus call center is only 1 rupees per call. So adopting AI in this and you are firing millions of jobs and what happened after that. So I think in some area government has to restrict the AI and those are the challenges because we have to wait for some time to figure out what these people are going to do next. Upskill their thing.

Thank you. Can I give somebody else a chance? Thanks so much. Somebody at the back. Sorry. Just give me one minute. Go ahead.

Question to Mr. Sathinder. As you are taking the topic of job in securities, say upskilling or reskilling, how can these strategies can help preserve the jobs and still the human in the loop and in the end, the relationship between these two, more better, more human friendly.

So clearly the efforts in most countries in the world is to really start upskilling their populations. It’s really beginning. It’s starting from schools but it’s going out to the workforce because the workforce that’s actually today actively under siege with all this AI implementation. And obviously there are countries who can afford the upskilling. The more developed countries are quite generous in terms of capacity building and coming out with programs even empowering employers and workers to help themselves in order to do the upskilling. But I’m also worried sometimes, what are they upskilling with? what they sometimes upskill with may not be enough in two years time so I think this upskilling is going to be really an upskill task for all of us so ultimately I think you have to continue learning upskilling is the word but continuous learning to adapt is going to be the name of the game but you know it’s going to be harder for my generation and some of us in the panel, not all of you and for some of us in the audience but not for some of the younger ones and some of you who are just starting work right now and when I talk to some of them, the younger people they are less worried about this they are less worried because they have already grown up in the universe where things are moving in that speed and they are not talking about lifelong careers they are talking about lifelong skills that they will keep adapting to the new change so I think that is the name of the game to survive

go ahead

basically you told that the solar revolution came in India When it was A to be 30 kilowatt per hour, and now it’s to be 20. There were little, little catalysts which were involved to boost up the solar revolution in India. So, one of the revolution was subsidy and the information to people. Basically, I am in favor that AI should come and boost up because it won’t affect the jobs. It will fit the people at the particular level where they should be and it will increase the literacy. And are we also planning to give subsidy on various AI projects which are into development or subsidy level because it will play a catalyst to boost up the AI model as solar revolution came in India.

I think the government is doing a lot already. The government has already given 10 ,300 crores for the India AI mission for sovereign AI. They are giving GPUs available at 65 rupees per month. Yes. Right, per month. Not per year. Per hour. Per hour. So, we are already the cheapest in the world. So, the government has a whole slew of incentives and subsidies that they have announced and they keep on adding more. I don’t know if anybody else wants to add.

so there are quite a few no no it’s public it’s all public the event is all about India if you forget our global missions so the India is bring us together and build this entire ecosystem two years back Sam Altman was in India and talking about you can’t do this this is not part of now look at 12 foundation models country has launched so this is only possible when you democratize the AI access or GPU access to the innovators like you so that’s what government has already done you just request you will get the allocation of the GPU with one of the providers like us and you can get half of the prices paid by government by the way it’s not subsidy they are paying full price and subsidizing the end user like you the innovators thanks

So, I think when it comes to the higher education, the challenge is worldwide. So, I can tell you in dinner conversations of some of the most established people I’ve sat with, with their children who are all in higher education, you can see that the value system is changing. The focus today of some of the most influential people who can make a difference is to actually encourage their kids to become more enterprising. So, I think the culture of being enterprising has to be given prioritization in order for the ability for us to adapt to what’s coming. And I think if we change that, if we create that and inculcate that in the universities even more, and bring it up front, I think that that will be the way we can overcome some of the challenges you face.

At least, I’m trying to address this first part.

Yeah, I think I can adopt on top of it. Set up AI like you can get the GPUs from there. But now, you don’t need to learn code today. You can code through AI, right? So you can build this. We have a billion users, billion problems. So you can solve those problems. You can make them entrepreneurial, like solve one problem at home or a college related or school related. And that’s what we also encourage people should come. Our students should come and go to industry visit. Because if they see that, they will do that. And the physical world has a lot of opportunity than the digital world. Because digital world is now concentrated with, imagine how many apps you guys are using in your phone.

Now this will go to one app, which is open AI or cloud. So the money is going to one company. And that’s dangerous than anything else.

I’ll just add on the energy part, right? So you heard also, Narendra talk about putting data centers in space, right? So. And free cooling, free energy. So that’s one part of it. But the other part I think that’s interesting about this whole energy question is that you really, I think, with AI are able to imagine potential. I mean, we don’t know what we don’t know and we don’t know what we know even, right? So one of the examples I’ll give you on that is, you know, with respect to like designing better chips, right? It’s like when they gave the, you know, Google DeepMind, when they gave the problem to AI to actually design better chips, they found a 30 % improvement in the performance of chips because AI was able to design better chips.

And so you can think about AI designing better data centers, AI designing, you know, many different parts of the whole chain. And we don’t know all of the different things that AI, you know, even mathematical computational efficiency, these kinds of things. So there are many different domains that we haven’t even touched that potentially can also have a transformative impact. And so this is a very, like I said, a very exciting time in our lives where we get to really see what the impact is going to be. Thanks.

Vin, do you want to add? Do you want to add something? No, okay.

Just to add to that, what we expect AI tools probably to do is to give an opportunity to grassroots. so when these tools are being employed and they learn through that and now a lot of language barriers are also being broken up so English is no longer a barrier with all the AI tools so you will have some set of people who will be using that and qualification is not a driver for that so this is what AI will do and we will see a trend where you will have a lot of blue collar upskilling by themselves no need to link it to degrees it is the self learn module assisted by AI tools which will make them competent for the market they could be either a specialist advising or they could be entrepreneurs or they could be a coach on that need not be sitting in a desk and doing something which is written there I think this is how we feel the education system also will change with the tools being available there

Thank you. So obviously we are the newest, particularly in consideration of this room. We’re new to the AI game, but one of the things that we’ve been able to do in Guyana is to create a digital school at the primary level. And it started working. In fact, it’s now being requested by other countries in the region. That’s the Caribbean region. And part of our objective is to use this to get kids kind of hooked on technology, AI type of education. Hopefully it can be tailored to each individual child to identify strengths, weaknesses, to strengthen the areas that are weak, whether it’s literacy, numeracy, anything, and tailored to that particular child. And so that their interests can be peaked, their interests can be exploited and expanded.

and ultimately they may be able to condense eight hours of school time, maybe three hours, and then they can go outside and play like normal kids, the way we used to play kids as kids. So the digital schooling, the digital era, is not necessarily to take all their time behind a computer and a desk, but to give them more time and more freedom and to create a habit so that that could follow them, not just at the primary level, but when they get to university and all the way up to adulthood.

Thanks very much. I know we only have, we’re out of time right now, and I’ll quickly wrap this up in about 45 seconds just so that everybody, I think it’s been an incredible discussion. Boy, it’s going two times now, so I really have to wrap it up. But I think I just want to quickly wrap up. Six or seven key takeaways from today’s discussion that we all had. The first one is the fact that I think jobs are going to be key. It’s going to be collaboration, not replacement. I think the way we do our job, that’s critical. I think Dr. Carpin talked about agriculture and health and medicine. I think there’s going to be a huge transformation.

But the good thing is humans want touch. So that’s good. But, you know, there will be a lot of revolution in terms of telemedicine, et cetera. Nihar, you talked about cooling and bottlenecks. I think those are things that we all have to think about in our countries. How do we provide the infrastructure for cooling, et cetera, and something that you all can work on and let us know how to make it more efficient. I think that’s great. You talked about in terms of NEXTRA, in terms of the talent challenges we’re going to have and how we’re going to have to manage all these data centers. Somebody mentioned recently that some of the big data centers in India, for 30 percent of the time it’s down because of the lack of talent to maintain these data centers.

You talked about supply chain. It’s fantastic that India is also thinking about putting data centers in space, which is fascinating. There’s going to be a big debate about, you know, more data centers versus the edge. Like I mentioned to you, you know, there’s one school of thought that we can actually bring the big AI to every factory in India by bringing it on the edge. And the computing power that’s developing is going to make that happen. And the last point I want to say is the point that you mentioned. And I think this is going to be the key takeaway for all of us, is that the speed at which technology is changing is so rapid that we’re all going to require continuous learning going ahead.

And it doesn’t matter how old you are, but that’s going to be the biggest takeaway for me, that that continuous learning and upskilling is going to be the key for all of us. So with that, really, thank you very much to all my panelists. And to everybody. And hopefully we can all make an impact around the world. Thank you very much.

Thank you. Thank you. Thank you. Thank you. I’ll be here. Thank you. Thank you. Thank you. Mr. Cana Dipali will sit at the front please take a seat at the front Ambassador Garcia Ambassador Garcia and if anybody wants to Ibu Ayub okay, thank you very much everyone oh, do you want I’m getting my cues from the photographer it’s not my show yet until I start okay it’s the photographer Can we please, and you’d like us to stand up for a group photo? Okay. Thank you very much. It’s very Asia. It’s not an event unless there’s a photo, so thank you very much. All right. Thank you very much. Good afternoon, everyone, and welcome. It’s a real privilege for me to host and moderate this session, Trusted AI at Scale, a Global South Leadership Dialogue, here at the India AI Impact Summit.

Now, this session hits squarely within the summit’s trusted AI pillar, and deliberately so. Because trust is no longer a downstream concern, it is now the condition for scale. Across governments, enterprises, and societies, we are moving past the question of whether AI will be adopted. The real question is whether it will be trusted by citizens, by institutions, and across borders. So why this session, and why did ISA host this session? The framing for today’s conversation is very intentional. Much of the global AI governance debate is still shaped by frameworks emerging from the Global North, the US, Europe, and China. Those frameworks are important, but they are not sufficient for the lived realities of the Global South, where AI is often deployed at population scale, under real resource constraints.

and in context where the cost of failure is not abstract, it is social, economic, and political. This is precisely the gap that AI Safety Asia was created to address. I am one of the advisors of ISA, and our mandate is straightforward but ambitious to bridge the global north and the global south on AI governance, not by importing templates wholesale, but by co -designing governance approaches that are interoperable, pragmatic, and grounded in local institutional strengths. And we do this through the three pillars, collaboration, capacity building, and policy -relevant research. And what makes this session different? That brings me to expectations. This session is not about abstract principles or ideal end states. We are here to surface operational blueprints, how trust is built in practice, and we have an amazing panel that will hopefully be able to really bring that to the table.

Thank you. and how safety is governed under real constraints, how AI systems actually reach the people and states often struggle to serve. The speakers you will hear from today are not theorizing from a distance. They are governing, financing, regulating, and deploying AI in the real world, from small island states to large democracies, from welfare delivery to financial systems, from regional cooperation to enterprise risk management. So one final framing point before we begin. The goal of today’s dialogue is not to position the Global South as a passive recipient of AI governance norms, and we’ll hear definitely from Cambodia, from the Maldives, and Indonesia, and Brazil. It is to position the Global South as a co -author of those norms, contributing models of governance that are population -scale, institution -aware, and grounded in lived social reality.

That is the through line of this session, from why trusted AI matters to who it must reach to how it is enabled, governed, and ultimately operationalized. With that, I’m delighted to open this dialogue, and we’ll begin with opening remarks that set the stakes why trusted AI is existential and not abstract. And then we’ll move through discussion. I realize that time is very short, so I think one of the reasons Ed put me here is because I’m known to crack a whip a bit. So with all due respect, I know you’re all very important people, but I will let you know when the time is up. So with that, I would like to invite His Excellency Professor Mohamed Kinanath, Minister of State for Homeland Security and Technology from the Maldives.

Your Excellency. Your Excellencies, Distinguished Head Supporters. Delegations, Honorable Ministers. esteemed leaders. It is both a privilege and profound responsibility to stand here before not merely a representative from Republic of Maldives, but a voice for many CIDs, small island developing states. I extend my warmest gratitude to the organizations of this forum for creating a platform where the aspirations of nations regardless of their geographical size can be heard alongside the strategies of those leading the frontier of innovation. Ladies and gentlemen, if the global disclosure turns to AI, it is often centered on the ambition of large economies on the computing scale or on a trillion dollar geopolitical competition. And while these dimensions significant, this represents only part of the narrative for seeds like the Maldives.

Nations defined by geographical dispersal of small islands, 1 ,200 islands, narrow economy base, and acute exposure to climate change. AI is not a matter of competitive advantage alone. It is a matter of institutional resilience. It is a matter of sovereign capacity, and increasingly, it’s a matter of survival. The Maldives comprises nearly 1 ,200 remote islands, which is spread across 850 square kilometers. Our economy has been mainly based on tourism. Our exposure to sea -level rise remains among the highest of any nation on Earth. These realities do not diminish our ambitions, and they demand we adopt technologies that can deliver public services efficiently across vast distances, strengthening governance and diversifying the economic foundation. The government of the Maldives, under the leadership of our current president, launched a Digital Transformation Agenda, a comprehensive national vision to transform the Maldives into a digital first nation within the coming three years.

The technology vision is called Maldives 2 .0. It is not a technology initiative in isolation. It is a fundamental reimagination of how these states serve its people and how the economy grows and how opportunity reaches every citizen of the Maldives. We have already begun the implementation. Maldives have good technology infrastructure if you look at the region. We have the highest – we have one of the highest internet penetration networks in the region. We have the highest number of mobile subscribers in the region. Our population is half a million. We have mobile subscribers, 1 million. 4G coverage is 100%. 5G is 80%, one of the highest in the region. Six subsea cables. And also fiber to each household is 100%. So maybe some of the European countries have not even achieved these statistics.

So considering the delivery of the AI and also considering the geography of the Maldives, AI is very important for us when it comes to the health sector, education sector, since our islands are very remote. So AI intelligence also offers the Maldives a pathway to economic diversification, enabling us to develop a knowledge economy. To cultivate local technology enterprises and to position our youth. Thank you. digitally. The Maldives is not approaching AI without preparation. We are building governance structures to ensure that this technology serves our people ethically. In July 2025, Maldives launched the AI Readiness Assessment Methodology Report, which was developed with assistance from UNESCO. And this landmark assessment, the first of its kind in South Asia. So building on this assessment, the government is now advancing to develop a national AI master plan and also an AI Act, which is also underway.

So the UNESCO Readiness Assessment has further recommended the establishment of an independent AI governance body and multi -stakeholder advisory council. So these are some of the recommendations. The UNESCO Readiness Assessment has further recommended the establishment of an independent AI governance body and multi -stakeholder advisory council. So these are some of the of the report. So as I told you, Maldives is getting ready for the AI and also since we have this Maldives 2 .0 transformation mission, we are working very hard in the next three years to get digitalization complete in the Maldives. So excellencies, the Maldives may be small in land, but we are vast in determination. We are a nation that has built its identity upon resilience, resilience against the tides that shape our shores.

So Maldives 2 .0, I have said that the vision is our commitment to the future. AI deployed responsibilities governed ethically is central to this vision. We do not seek to replicate the digital trajectories of large nations. We seek to chart a course that is authentically ours. On that reflection, our values, address our vulnerabilities. As the world convinced to deliberate on the governance of AI, let us build on AI future, which is inclusive, intelligent, equitable, and as human as the technology is powerful. So thank you so much. Thank you

so much, Your Excellency, Minister Kananath. I’d now like to invite Dipali K hanna, Senior VP and Head of Asia for the Rockefeller Foundation for her remarks. Just before

I start, we were talking about global north, global south. What struck me in this panel is the women are at the periphery, right? So anything and everything that we’re going to do in this space, we’ll have to get women back in the center. But I was also excited that we have strong women who can manage these men. So anyway. Good afternoon, Ministers, Excellencies, colleagues, and partners. Let me begin by thanking AI Safety Asia for convening this dialogue and JPMorgan Chase for co -hosting. The fact that this conversation is happening here in this region with this leadership really matters. PM Modi in his keynote yesterday laid out the vision for Manav, building AI that is safe, ethical, and centered on people, ensuring technology serves humanity responsibly and benefits everyone, including women, right?

We’ve just heard powerful perspectives that bring the point to life. From the Maldives that AI is not abstract policy, it is a survival tool. I know a colleague from Togo couldn’t join, but I’m sure she would have mentioned that trusted AI can make the invisible visible. So the question before us is not why AI matters or who should benefit. It is how we build it responsibly, at scale, and with legitimacy. For over 100 years, the Rockefeller Foundation has leveraged, advanced technologies for betterment of society, and we believe that there are learnings from that work also to apply trusted AI. partnership, patient capital and institutional strength. What distinguishes success stories like Togo’s Novici and India’s Coven is not just technological sophistication, it is alignment.

Governments willing to move decisively, private sector actors willing to collaborate, technologists willing to design for public systems and catalytic capital willing to absorb early risk. Novici reached nearly a million informal workers, not in months, just in days. Coven delivered at population scale with transparency and interoperability built in. This was not mere luck. These were examples of ecosystems working together. That’s partnership. For adoption, users must trust both that AI will deliver the benefits without harm. Much like early vaccine development, we need to invest in both supporting users, to adopt the technology, as well as building robust evidence and systems that ensure safety. And scaling this trusted AI in the global south requires more than venture timelines.

It requires risk tolerance. It requires capital that understands that building sovereign AI capacity involves experimentation, regulatory iteration, and institutional learning. Philanthropy can truly play a catalytic role here, not by replacing markets, not by dictating governance, but by re -risking what some leaders have described as the smart adopter model. The smart adopter does not wait for perfect consensus. It adapts responsibly. It pilots with guardrails. It builds local institutional muscle alongside technical capability. Catalytic capital can support regulatory sandboxes, independent safety assessments, talent pipelines, and interoperable standards so that adoption is both fast, nimble, and short -lived. That’s the power of patient capital. And finally, institutional strength. Digital public infrastructure has shown us something profound. Trust must be designed from day one, not retrofitted after deployment.

Transparency, auditability, grievance redress, open architecture are not compliance burdens. They’re adoption accelerators. If our AI systems are to scale in health, climate resilience, food systems and financial inclusion, they must be built on institutional foundations that citizens recognize and most importantly trust. Businesses have a critical role here. Responsible innovation is not simply about internal governance frameworks. It is about long -term partnership with governments and societies. It is about seeing trust as a strategic infrastructure, not friction, because trusted systems scale, untrusted systems stall. The Global South is demonstrating that it does not need to choose between speed and safety. It can design both. The opportunity now is to align partnerships and patient capital behind that leadership. So that trusted AI at scale is not a slogan.

It is operational. The Rockefeller Foundation stands ready to continue playing a catalytic role in that journey because trusted AI is not simply a governance aspiration. It is a development imperative. Thank you

Thank you so much to both your Excellency Minister Kananath and Diwali for the… I thought, again, it’s a great way to start us off for the discussion today. You’re welcome to stay in front, sitting in front, but we’ll start the discussion. Actually, I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted.

I think it’s set the tone for what we really wanted. I think it’s set the tone for what we really wanted. so everyone that you see in front brings a very specific experience, skills and coming either from private sector or their government so I would love to in particular as Dipali mentioned, building trust starts from the beginning, it’s not an afterthought so I’d like to start with Under Secretary of State Your Excellency Sokeng I guess the question I’m going to have for everyone is what is the single biggest obstacle to operationalizing trust in your context based on your experience and what is it that this room that’s filled with quite a lot of people from different sectors what can we do about it as well and what you’ve heard as well in these past couple of days as you’ve been here in the summit

First, thank you Imah for for the very good set of tones of these discussions and I’d like to thank Asia AI Safety Asia for having me on these panel discussions from Cambodia perspective I would say a short answer to that is how to get the people familiar with that AI and that would start off with the people user the leader and the regulator and aside from that I can talk a little bit about the Cambodia experience on that similar to what Excellency Minister from ADEA mentioned Cambodia has began to back the journey of conducting the AI readiness assessment supported by UNESCO we completed last year in July 2025 and from that perspective we can understand rely on the recommendations and starting to think about what is the strategy for Cambodia to move forward in terms of AI adoption in Cambodia.

So based on the recommendation, the national AI strategy has been drafted and currently we are in the process of finalizing the national AI strategy. At the same time we are also drafting the national AI governance framework, keeping the national strategy in mind. And one of the key strategic priorities that we have in the national strategy is people, which is the first priority of our national strategy ranking from the user like I mentioned earlier, user the leader, regulator and also government official. The second priority is the infrastructure and data. The third and the first one is AI adoption in government and the private sector. The fifth strategic priority is the governance and the last one is cooperation and the research.

So based on this priority we can see that human is still the first and the key priority action for Cambodia. Building on that our draft AI governance framework also very much human centric so we believe that the governance should be aligned with the risk of AI. So context of our governance framework would be based on the risk assessment and to understand the risk people have to know the impact of the AI. So government has very clear intention is that we need to educate people, let people understand what is the AI tool and the implication of that. So since 2024 government introduced because Cambodia Digital Skills Roadmap, which we outline what is the plan for the next 10 years for Cambodia in terms of human development.

And our goal is that in the next 10 years, we will have 100 ,000 talents who are AI -ready. And in addition to that, we also introduced various programs to educate government officials. As of now, we have trained more than 10 ,000 government officials. Basic digital skills, and also part of it is the AI skill as well. And so based on what we have right now, if one thing that you ask me what we can do in this room is that I would like to say is to increase the capacity of humans to understand the risk of

Thank you so much. I have so many questions but I’m going to hold them for now I’m going to move to Ambassador Garcia as a tech ambassador for Brazil as a G20 country leaving BRICS and all of these along with Indonesia as well for G20 as I mentioned the global south must be architects and not observers and I believe Brazil is at the forefront of this can you say a little bit more about that and what obstacles do you find

capabilities to harness the power of the technology so somehow we need to enhance our one national capabilities but in cooperation with other partners overseas and finally we only had COP30 as you remember last November and we included digital technologies and climate change as a sustainability problem because now we have been discussing in terms of data centers energy efficiency so sustainability is key in a way that we are always trying to send this message in terms of AI development oriented strategy and I think for the global south it’s important that we engage in tech diplomacy because otherwise we will not get hurt to do what we are doing and we will not be able to do what we are doing to do what we are doing heard and we need to speak up and our voice be heard where it matters Thank you so much

So moving from one great nation in the south to another one, Ibu Ayu Ibu Ayu is the director of AI and Emerging Technology Ecosystems of the Ministry of Communications in Indonesia Ibu Ayu, can you tell us a little bit more about Indonesia’s national strategies, basically where do you find the obstacles as well and as Ambassador Garcia mentioned in the ecosystem of BRICS where does Indonesia or where is ASEAN sitting on this as well

Thank you Ima It’s an honor for me to sit here from Ministry of Communications and Digital Affairs I cannot say it is an obstacle, I say it’s challenging once you know mentioning about the challenging one Indonesia has many things to be resolved. One is the infrastructure, because you know that our penetration of broadband, especially for mobile broadband, even though it is above 95%, but it is still based on 4G coverage. You know that AI, we need more coverage for 5G. And then the penetration of fixed broadband and backbone is quite low, because you know Indonesia has hundreds of district area and 10 ,000 of sub -district area. Today, the penetration is still 70 % by sub -district area. That’s why we need to push the penetration of the backbone.

Regarding of the data center, today our providers of data center… We have many data centers, but the GPU basis is still limited. So I think we need to invest more regarding the processing for AI. And then this is relating to the infrastructure. And relating with this framework of regulation, right now in Indonesia we have set up the roadmap of AI, national roadmap AI, and also we are preparing the guideline ethical of AI. Talking about the national AI roadmap, we are sure that we need to have strategies that are real, not just theoretically. Because, you know, when we explain, execute our vision in national roadmap, We have four strategic directions. One is governance collaborative, and the second is encouraging innovation ecosystem.

And the third is to strengthen capabilities and capacities, including infrastructure. And the last is mitigating risk. You know that this national roadmap is important for us because we need clarity for five years ahead. Regarding to the issue that come from AI. Regarding to the ethical AI guideline, we set up the rule and clarity of responsibility of AI actors. And also we preparing the instrument for monitoring and evaluation. Because you know, ethic not just ethic, we have to monitor it one. And the last is we have… We have to put the safeguard for the people, how they using and develop AI. That’s the main thing that we preparing.

Thank you, Ibu Ayu. I think it’s been mentioned already, and I’m glad Ambassador Garcia mentioned it. And, of course, in his address, Minister Kinanath mentioned, and I’m going to turn to Dr. Parakana. You did mention something about, in our, sorry, I’m going to bring the discussion that we had in the green room. I’m calling it the green room. It’s not green. It’s green, about how you, as a private company, utilize and how that could be beneficial for climate resilience and development, especially in vulnerable countries. So can you tell a little bit about that? Dr. Parakana, of course, is the founder and CEO of AlphaGeo.

Thank you. Thank you so much, Ina. Thank you all for being here. Well, AI as a concept evokes this notion of leapfrogging. Do you remember when we used to use that? We used to use that term all the time, leapfrogging. And, of course, it applied very appropriately to mobile telephony, to fintech, to renewable energy, solar panels. So, you know, faster, better, cheaper. and inherent in that concept, which is very important now when we talk about AI, is the notion of second mover advantage. That’s what leapfrogging was fundamentally about, having second mover advantage. Now, why that’s relevant right now is because, of course, developed countries in particular, particularly the United States and others, have invested enormous amounts of capital in the capex requirements of AI.

You know, some significant percentage of U .S. GDP growth, for example, is attributable right now to that AI capex -related infrastructure investment. But that is not something, of course, that nations of the global south, so to speak, developing countries can afford. And so the question becomes, is there an advantage to late development when it comes to AI that can save developing countries of the south a lot of money while still enjoying the fruits of that innovation? So it’s important to not, especially in the context where you’re making trade -offs, between electricity, food, water, the basics for your population, while, of course, there’s now this almost emotional and hype pressure to invest, you know, to clear land, to build data centers, to divert energy, we have to ask ourselves, is that the right way to allocate capital?

Or should one be taking advantage of cloud computing, edge computing, sovereign cloud solutions that can generate the same or better output, bang for your buck, with less CapEx expenditure? And that’s the moment that we’re at. And it’s important to remind that we’re having this conversation in India. And one of the virtues of India hosting this event is precisely that India’s rise as an AI superpower, you know, breaks the narrative out of this conventional wisdom that it’s a two -horse race between the U .S. and China, and that you’re doomed in some way to choose between your data being, you know, hoovered up by one or the other player. what India is offering, at least more than in theory, is rapid diffusion of the latest technologies, cloud -based models and solutions, through the tools of digital public infrastructure, DPI, which has been one of the benefits of this event now over the course of the years.

People have learned this all -important acronym, DPI, which I’ve adhered to or believed in, been a supporter of for quite some time, because it does hold the promise for neutrality, for a menu of options, for being delivered in a way that protects sovereignty of data, but in a very affordable way. And India has done it, been a pioneer of it, obviously domestically, with Adar and so forth. And for those, if it hasn’t been disclosed enough this week, more than 50 countries are building payment systems and identity systems on that stack. So that’s a great example of DPI. So think of AI in that mold of second -mover advantage, leapfrogging, following the mold of cloud -based sort of solutions that can be low -cost.

Now let me just quickly talk about two areas where there are huge gaps in public sector access to data or ownership or simply knowledge of solutions that AI, and particularly the AI that we, the way in which we apply data science, is to geospatial data. And these are also two areas that are of critical, fundamental, if not existential importance to developing countries. One is sustainable urbanization, and the second is climate adaptation. Anywhere you go in the world, actually rich or poor countries, if you survey the average person on the street and ask them, what is the biggest problem plaguing your society, nine out of ten people that I speak to in dozens of countries around the world is affordable housing.

And I think that’s a really important part of the problem. just sustained urbanization that is so organic, so rapid, so unplanned accelerating around the world but now finally governments have the tools again, geospatial tools, mapping tools understanding which districts, which settlements are expanding and why where are people coming from what kinds of housing need to be built where governments have always been fighting backwards or if not given up, quite frankly, on grappling with these issues but now we have foresight AI -powered geospatial tools that can look decades ahead and say this has been your time series urban expansion this is how you map it out this is where you should be building what and when and so bringing together demographics bringing together infrastructure bringing together migration, fiscal spending and directed targeting in a way that is a great use case for AI that almost the entire development developing world could use and has barely begun to use and is very cost -effective, right?

So that’s number one. The second, equally if not more important, is climate adaptation. Climate risk is both acute and chronic. We’re talking about monsoons, floods, fires that are becoming more frequent and devastating. And, of course, complex climate modeling is not the kind of thing that any individual country can or should finance. We have global climate models that are AI -powered, that are developed with the world’s best institutions, that are publicly financed, that are now available to be downscaled for your country. And this is something that, again, especially developing countries, especially countries of the south, that are most affected by climate dislocation, by climate risk, can and should take advantage of. But, again, to be clear, they have barely begun to do so yet.

So targeting infrastructure investments, targeting your infrastructure to adapt to climate risk, where do you need to build? Seawalls, flood barriers, flood control measures. irrigation systems for drought, all of that, we are again, just like urbanization, are years and years and years behind, and there’s almost no country on this panel, almost no country in the world, even wealthy countries, that are ahead of the curve on this. The entire planet is behind, as we know from COP summits, but it is countries of the south that are going to be the worst affected on the fastest timeline. If you’re not using the tools that are available right now, AI -powered climate modeling, scaling, adaptation scoring, in order to plan your national infrastructure, and then putting together the public -private partnerships to get it done, you’re behind.

So this is about global public goods, right? Affordable housing, a manageable urbanization at a global scale, climate adaptation for people everywhere, but it has to be delivered locally. And that’s why it’s incumbent on each nation represented here, each nation of the global south to really harness and take advantage of these tools.

Thank you so much, Dr. Parag. Again, lots of questions in my head. Kip Wainscott, Executive Director of Global AI Policy from JPMorgan Chase, one of our biggest supporters as well. Thank you so much for supporting us for this event. You are not here just because of that, I can assure you. But I’d love to hear what you have to say, particularly the question that I brought forward earlier about the obstacles, in particular in where you see it. But again, financial services, model risk management, and all of that in the safety architecture of AI. Yeah. Go ahead.

Thank you. A lot to unpack there. This is a great panel, by the way. So many esteemed panelists. I really feel privileged to share the microphone with all of you. You know, it’s interesting thinking about these things from the vantage of JPMorgan Chase because we’re really kind of interrogating the questions from sort of three different perspectives, right? One is one of the world’s largest financial houses that’s deeply invested in artificial intelligence. We have an acute interest in unlocking the value of this technology and seeing the growth potential of this technology. But there’s a simple truth that we recognize, and that is that AI is only valuable if it is deployed, and deployment depends on trust.

And so really building out, you know, we have an interest in this sort of multi -stakeholder dialogue about what that trust model that is going to unlock diffusion, you know, not just across enterprises, but in the public sphere across the global south, and really putting this technology into organizations that are impacting people’s real lives. The second perspective from which we’re looking at all of this is as a deployer of the technology ourselves. We are one of the world’s largest deployers of AI. And what’s interesting, we’re also one of the most regulated industries in the financial sector, and yet financial services have been the earliest adopters of AI. We’ve been using artificial intelligence through this sort of evolutionary ramp of really more than a decade.

To combat fraud, to protect consumers, to create just more efficient personalization of financial services. And I think one reason why you see financial services companies so ready and eager to adopt is because we have that existing trust architecture. Trust isn’t just a feature of financial services. It’s the core business model. And so we have… Thank you. We have these, you mentioned, you know, model risk management. We have these rigorous practices of evaluating models, of documenting governance and oversight, of really ensuring that there’s ongoing monitoring across all of our technology deployments in a way that just lends itself to what I would call a comfort in sort of building the trust ecosystem for responsible deployment. And then sort of the third prism that we look at this issue from is as a purchaser of these technologies and like kind of almost a procurement lens here.

We spend $20 billion annually on our technology budget. That puts us in this really sizable position in the innovation ecosystem of, you know, startups and scale -ups that they want to sell their products. They’re building innovative new artificial intelligence applications with the hope that they’re going to be able to sell their products to the world. Selling it to JPMorgan Chase. And we see a real innovation. inefficiency right now in the fact that there isn’t a shared sort of set of expectations for trust, for, you know, what these products should be benchmarked against in order for us to ingest them, you know, in a way that, you know, we have the confidence is going to serve our customers well, is going to reflect our, you know, our responsibilities, our duty of care as a, you know, a regulated industry.

And so, you know, it speaks to, I mean, I think one, the need to bring these diverse perspectives to this conversation around governance so that we can really kind of get past the sort of compartmentalization of like AI safety as sort of a siloed conversation and accelerated AI adoption as a different conversation. This is the same conversation. And, you know, what we really need to, I think the purpose of both of those conversations is to really get past the sort of compartmentalization of AI safety as sort of conversations is to really align on this trust model that is going to ensure that we can deploy these technologies, you know, in a very broad and impactful way across the economy.

I’m going to put you on the spot while you have your microphone a little bit you’ve been here throughout the week and you’ve heard the panelists just now speak from what you’ve been hearing throughout the week how optimistic are you in terms of I think His Excellency Sokeng mentioned about collaboration how confident are you in building these collaborations to build trust in AI just from the conversations that you’ve had this week?

Yeah, no I’m optimistic I think just the fact that we’re here in New Delhi and having this summit in this environment this is a much bigger summit, I think it’s a more inclusive cross section of voices and so I think that that reflects that this conversation is getting bigger that we’ve moved past this focus on that technical capabilities, which is kind of where we have been, to now I think capability has really been almost commoditized and legitimacy has not. And we’re in this phase now where we really need to establish the legitimacy of these technologies and that they are fit for purpose, that they can be trusted and deployed across these different societal sectors. And so I think that I am optimistic.

It requires intention. And I’m seeing the intentionality, I think, around the curation of these conversations. I think there’s a lot to carry forward here. Also, some of you may have seen we’re very near the end of this summit. And I think before we were even halfway through, excuse me, I’m running on fumes at this point, but we were more than halfway through the week and people were already writing up the assessment. Of what, you know, what the themes were, what the takeaways were. and people were saying, you know, oh, this summit is no longer about, you know, responsibility or safety or, and I just, that isn’t my perception of these conversations. It really is that, you know, we’re just talking about them in a different way.

We’re talking about them in how they are going to impact real lives, how we can take this technology into the economy in real valuable ways. And in order to do that, we have to include that sort of trust dimension.

Okay. Thank you so much. We have eight minutes left, and I’ve been told that we have to finish on time, but I really want to get this question in and hopefully be able to hear from everyone. So Ambassador Garcia and Your Excellency Sokeng and Ibu Ayu, what are you taking away? What are you taking home from this panel, first of all? Or from the week that you’ve been here, reflecting on what Kip just mentioned?

Yes, thank you. First, I think India was very successful bringing this summit to the Global South for the very first time. But this was the Bletchley process that began in the UK in 2023. We have Seoul, Paris. So this, what we have been discussing here, is something that is more inclusive. And some new concerns were added to the agenda, sustainability, not that it was not discussed before, but with this perspective coming from the Global South, which is important. So I would conclude with three recommendations, because we need to be practical. We are thinking mostly we agree on high -level principles in terms of AI governance. But when we think of countries lacking resources, or having other competing priorities, so they need to decide what to do and prioritize in many cases.

So I think they should start small and have a few small scales. quick impact projects so that they can build on proven success so let’s say focus on some education, healthcare, agriculture then focus on some specific projects and then build to reach the next level second is that we need to seek they need to seek international partners sometimes it’s it’s useful and needed to enhance national capabilities it’s difficult for a single country alone to do this investing in infrastructure and do something that’s expensive so seek international cooperation and third, as I said before engage in these discussions at an international level engage in tech diplomacy and send more people to discuss where I think it’s important including the United Nations thank you so much

Thank you. Having seen the time, I’ll just go very quickly on the last sentence. Coming to this summit, I agree with our parents that it’s very inclusive and we can see perspective from all the stakeholders, from the government, the industry, academia, and even the startup. So learning from this, I have just one wish, which is that we have to be honest with each other, the industry, the government, and bear in mind that we are here to protect people for the people. So whatever we do, we need to think about people first. With that, please consider that when we think of governance frameworks, the regulation of the law that the government might put should be the mechanism to promote innovation.

It’s not an obstacle. It’s not an obstacle for the innovation. So in order to do that, we need to build trust also, and we need to be honest with each other. Thank you.

Ibu Ayu, quickly.

Thank you. Actually, I’m very impressed with the spirit of Prime Minister Modi yesterday. I think every country has the same spirit regarding to the AI. So the three points that I’m taking from this summit, one is collaboration, indeed, and then inclusive, because if we consider about inclusive, we need intention from government, from industry, from the people. And then the last is investment, because investment, you know that AI needs more and more investment. This is a collaboration come. But the issue will come is how we define the sovereign, because sovereign is based on the… the needs of the country. how we define, is it equal or not? And still, it’s an under question of me also.

Thank you so much, Iwayu. Dr. Parag, I’m going to have you bring it home in one minute. What you’re taking home, in particular in your conversations with some of the different governments here.

Well, the first thing is I actually want to echo Kip’s point is that we’re at an inflection point where we can’t, we’ve been, in phase one, let’s say, there was a lot of harping about trust. Can we trust? Can we not trust? And I think it’s a good thing that that pressure was there, but now that pressure to have transparency in models has delivered to some degree. And that it’s been done in a way where public and private have not been on opposite sides of the discussion, but have really partnered. So I think we’re really beyond that. And now we can move from models and theory into action and application. And that’s the part of the stack that we want to be on.

The infrastructure build -out is there. It’s being provided. The apps are being developed. They’re being deployed. I have seen a little bit of but would want to see a lot more in subsequent editions of this, especially as this summit, you know, migrates around the world now and remains perhaps in the hands of developing countries on the application side as much as possible and that we think not just about very specific verticals as we have been here and elsewhere, sort of your health care, education, I’ve emphasized climate and others, but probably something more societal and around resilience. You know, resilience is a term that comes up a lot but doesn’t really get quantified enough. And if we can push for that, that’s going to help us to establish performance benchmarks, not just of models but in applications.

And that’s really what I think everyone wants to see to make sure that AI doesn’t become something of not just a financial bubble but something almost of a policy bubble as well.

Yes, you would think it will create access, it will democratize access to healthcare. Yes. Both in terms of price, in terms of availability. Yes. That’s great. Kritika, over to you. Tell us about yourself, the organization, and your take on AI for Good.

Sure. Thanks. Thanks, Kavi. Really happy to be here and privileged to share the stage with each of you. I’m Kritika Sangani, and I work as Chief of Staff at Indus Action. I’ve been in the development sector for about 10 years, started in the corporate sector. Serving investment banks in another lifetime. I’m also a Teach for India fellow. So I joined Teach for India and decided to not look back and continue with this sector. And, in fact, have spent, have been associated with Indus Action for almost 10 years now. Indus Action, as Kavi also alluded to, we work with governments on making social protection accessible to vulnerable citizens. The end goal is that we ensure that. welfare is delivered to them during critical life moments for the household such that they are able to actually tide over moments of crisis and make the most of moments of opportunities like education or healthcare to ensure that they’re trying to codify pathways out of poverty for themselves and their families.

The government is the protagonist in our work because, of course, they are the biggest implementers of social protection. We are the enablers. We come in with tech solutions, policy redesign solutions, capacity building solutions as a team. Our model is to embed these solutions within existing systems instead of creating parallel systems, and that is what we are about. We work with about 18 state governments, actively working with national ministries like Labor and Ministry of Social Justice and Employment. We work with the Department of Health and Human Services, And yeah, very excited to be here. I think when I listened, I heard about the prompt, Kavi, as what AI for good, what does it mean to us really?

It actually is about enabling equitable access for vulnerable citizens to social protection. Right now, they take about 10 steps, 10 burdensome steps to access a single entitlement. How do we bring that down to a single touch process? That is what AI for good stands for us.

Great. I heard access. You’re talking about simplicity and speed. I’m really curious to find out more themes as we keep talking. Thank you, Kritika. Himanshuji, over to you.

Yeah. Thanks. Thanks, Kavi. And thanks to, I think, the government of India for managing such a humongous event. I know as a matter of fact because we’ve been part of the planning team, almost a year’s preparation has gone into it. So kudos to everybody who’s sort of pitching in their own respective roles. So I’m Imanshu. I come from an organization called Atli Innovation Mission, which is the federal body that manages innovation for the country. We are housed under NITIO, which is the public policy think tank of the government of India. Atli Innovation Mission was the brainchild of the current Prime Minister, Honorable Narendra Modi ji. 2016, when we felt as a country that you need a body for innovation that cuts across all ministries, all life cycles.

So essentially it works from school to startups. And as a startup, even the high -tech startups like space. So we’re going to celebrate 10 years next week. The new title that we have sort of decided for ourselves is School to Space. Right. covering almost everything. I’ll not drain by talking too much about the programs that we do. Maybe we’ll talk it in a bit. I think for me and for the organization as a whole, because you’re also housed within a government institution, so social good means that whatever AI is trying to enable, is it having some impact at the grassroots level? Whether we work with incubators like T -Hub or startups directly or the state government so I also lead a program called Setting Up a State Innovation Mission, for example, where the idea itself is that how do we move beyond the current level of innovation that is happening?

How do we bring every part of the ecosystem together and put a layer of AI? Not only AI, I would say all frontier technologies. For example, when you talk to mature states in the southern and the western part, there’s this huge conversation on quantum. There’s huge conversation around even AI for health, for example. There are conversations where Can we enable AI to improve the public service delivery, for example, right? Where what we say we are not building unicorns, but we are building or we are saying not unicorn in terms of valuation, but in terms of social capital, right? Where we say that can it start impacting a billion lives, right? Yeah. So I’ll pause here.

No, this is great, Himanshuji. Thank you. We heard access. We heard simplicity and speed. You talked about scale and infrastructure because that’s the mandate with which organizations like AIM come from. I’ll talk about one bit on what we see at T -Hub. We have understood this very clearly that as T -Hub, as organizations, even like AIM, the goal is not for startups to be verticals or horizontals. Startups, MSMEs, nonprofits, I think we’re all here as vehicles to implement all this. Of economic growth. If you talk to startups, they don’t talk about policy. they don’t talk about infrastructure, they’re talking about building when they’re talking about building they just want to solve problems, they want to create value and I think AI has become a supercharged tool for startups to do that so the point I was trying to bring out at least from our experience with startups is that at least in the last 15 years, we see it all as a wave but it is probably the strongest tool that startups have ever had access to and a lot of social impact startups, even if you look at the Swiggy’s of the world and Zomato’s and if you take a step back and if you look at what they’re talking about on social media in the last let’s say a few months, both positively and negatively, you will see the major uproar is about gig economy and I was reading an article on The Economist which said that we are one of the only countries or economies in the world where the gig economy has become a true form of employment right so people are talking about minimum wages they are talking about you know labor treatment they are talking about human rights talking about all of that now who would have thought that a food delivery company right food delivery multiple food delivery and grocery delivery companies will actually create an organized labor market in our country so that’s the lens that we take for startups I will hand it back to you I have a question for some of you based on what you said but I will also drop in one theme here now when I said AI for good I think it obviously means AI for good impact you know for economic growth but it also means AI for good which is the other way to you know interpret the English of saying that it’s here to stay it’s here forever and it is our job to now figuring out what to do with it right and I’ll quote I think Nandan Nilkeniji yesterday said something about you know either it is a race to the top or race to the bottom with AI.

And I think the only way to go to the top is to focus on impact. So, Kritika, I want to pick this up with you. We know that intersection, when you talked about equitable rights, you talked about social protection, you talked about welfare and access to welfare and reducing 10 steps. Tell us a little bit of the work that you’ve done in RTE. I don’t think the audience is aware. Talk about admissions. And then tell us, where do you see the biggest opportunity for reducing those steps in that example with AI?

Absolutely. Thanks, Kavi, for setting that up. So, intersection started with the Right to Education Act. There is a specific clause under it. It’s called Section 12 .1c. Most of you would have watched this movie called Hindi Medium or English Medium, both versions. So, it essentially mandates that 25 % seats in private, unedited schools be reserved for children from economically weaker sections and disadvantaged groups. Now, we picked this sliver of a large constitutional mandate, which is Section 12 .1c, and with a fundamental belief in the power of choice for parents from vulnerable sections of the society to put their children in a school of their choice and not whether it’s public or private. And with that spirit, which is also part of the letter and spirit of Section 12 .1c, we started working on this particular right.

So we started our work in Delhi. I think we started with running on -ground awareness campaigns. And very early we realized that we were able to mobilize about 19 ,000 applications in 2013 -2014 in Delhi, of which only 196 students got a call for admissions. And this is a constitutional right. This is their right to get into the school. that was a huge eye opener for us and we realized that only by working with citizens and parents we are not going to be able to solve this problem because I think the government system also needs streamlining and support and they were really trying to also there was willingness to execute but the government is also really constrained for resources tech capacity and so on so we went there and fortunately the government showed willingness to actually experiment with a full online approach Rajasthan government had already done it so some of the peer effect also worked in our favor and that’s when we introduced what we call the RTE MIS that was our first solution it’s now evolved into an education digital public good it’s an open source modular product that we’ve launched for any government to adopt but what that did was what parents were going through was they had to it was it was this particular act it actually works on a lottery mechanism for selection So the draw of lots the school has to do, and the parent has to go to 10 schools that they have applied to, to actually see which child has, if the child has been selected in A, B, C, or D.

We cut that entire physical transaction and we got a digital lottery integrated, which is actually our secret sauce. Yeah. And that particular module has now was now adopted in some shape or form across 18 states that we’ve sort of worked with.

How many applications in total and children in schools now?

Children in schools now are about 900 ,000, 9 ,000, 9 ,000 children from one 96, 10 years ago, 10 years ago. And states, we actually when we started, we made an exit from our first state. Which is Delhi and then Uttarakhand. Uttarakhand adopted our end to end system. We took about seven to 10 years. Now we’ve shortened that entire cycle to three

And tell me, Kritika, to prevent, what is AI giving you as a tool to simplify and scale what you just described?

I think what we’ve actually now moved it to the next level by saying we are now focusing on who are these children who are applying to this right? Are they the most vulnerable? So it’s the challenge of discovery that we are using AI to for improved targeting for the state.

And how are you using targeting, AI in targeting?

So AI in targeting, what we are currently experimenting with is we have a WhatsApp chatbot. It’s a multilingual chatbot, which serves as a first interface for the students or the parents to apply to. And also using frontline, building frontline worker capacity. What that does is it actually reduces the load of the overburdened frontline worker with respect to reaching out to these students as well. and also being able to target the most vulnerable with respect to just having that multilingual advantage, having a physical touch point if I’m confused to actually reach out to somebody to navigate the system and then apply to the…

So the path from 196 to 9 lakhs was about 10 years, but I’m understanding that from 900 ,000 to 9 million will be much shorter. It wouldn’t be 10 years.

Absolutely. So there are 20 lakh seats available under this particular right.

Every year?

Every year.

Annually 20 lakh children?

Annually there are 20 lakh children. Currently there is about 60 % coverage. When we started it was at the 30 % mark. So we definitely…

So AI will scale that. And I think you didn’t talk about this, but I know from our other conversations that while AI will help you drive this deeper and scale, what you’re also doing is horizontally the DPG, the Digital Public Good they have built in education, the stack that they have built is now being universally applied to other entitlements or constitutional rights. So we heard the example of education. Absolutely. The dream, I know it’s called project name is UEI. They want to build a UPI like so instead of United Payments Interface, Intersection wants to build a United Entitlements Interface. Think of a DigiLocker meets a DigiYatra, meets the constitution. You log in, you check your own eligibility and you can tap into a constitutional right and apply for it and actually get access to something that is already rightfully yours.

Absolutely. This is phenomenal. Thank you so much for that, Kritika. Himanshuji will take a step back. I love depth, especially when people who are in action talk about it. Tell me where are you seeing, pick a specific example and where are you seeing AI truly unfold the real impact of what the work that you guys do. I have the same question for Rajesh Babuji after this.

Okay. I’ll probably step maybe two steps even further back, right? When the cabinet approved our extension in 2024, one of the programs that was really high on priority was setting up a state innovation mission, right? We did a couple of workshops and we realized that there’s a huge disparity between the western and the southern parts at one end of the spectrum and the northeast, eastern and northern part of the country, right? So Telangana, Andhra, Karnataka, Maharashtra are already talking in a level which is more towards the evolved countries as a whole, right? Let’s not say that these are parts of India in terms of technology enablement and even understanding of how this can be leveraged by a startup.

And when you take a diametrically opposite view to even the startups in northeast and even eastern part of the country, right? They are really, really not even at the basic level, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right? Even in the eastern part of the country, right?

Even in the eastern part of the country, right? new to all of these states. So one of the first few state innovation missions that we plan to set up is in the northeast, right? And the eastern part of the country. The first launch will be next week. I don’t want to take the thunder away. I know what’s coming up. We’re talking to them about it. You’re launching a state innovation mission next week. I won’t name the state. Yes, correct. And some of the conversation that we’re having with the state, right? And it’s a very progressive, evolved state. Not too much in terms of manpower or resources because of the simple reason that all the good people migrate to Hyderabad and Bangalore of the country and don’t remain in that state, right?

But those who have remained back, right? They’re actually building something really incredible for the state itself, right? They’re trying to solve some grassroot problems. I’ll talk something that is more relevant. And this is a conversation that happened about two months back when almost everything was set and the idea was how do we sort of launch a hackathon which is a ministry -backed hackathon. Now the state of, the state, let me not name the state, the water in the state has high content of iron, right? The idea was can we convert this into a hackathon and say that leverage AI, right, to first identify the content of water in different parts of even the district. So it’s not talking at the state level, going one level below district and even one level below district to a sub -district level.

Identify what is the different level of iron or different types of iron and then build a hackathon where a startup can leverage this data that has got collected, right? And build solutions two ways. One, a low cost diagnostic, right? Can we build a 100 rupee kit that can sort of diagnose this water, right? And second, maybe build a low cost solution to solve this particular problem. Other conversation that started happening. Now the state has a lot of bamboo, right? In fact, any of you. You would have been to the newer terminal at Bangalore airport. If you see all those beautiful bamboo things happening, it’s come from the state, right? The quality of bamboo is better than China and Vietnam, but they only get about one -tenth or one -twentieth the price, right?

One, because they don’t have access to market. Two, they are not able to even identify what bamboo will sell where at what price, right? Can we link it to the global market, identify who needs what? So they are trying to create a dashboard for it, right? And that thought came because when they realized that people have appreciated the bamboo that has gone into the terminal 2 of Bangalore airport, there could be a lot of use cases, right? Third element, and this was fascinating, right? And this is something which is sort of not something to be proud of in government. We have a lot of data, right? Everybody knows we have a lot of data in the government, right?

You are just trying to build a small funnel, right? That what’s the total? What’s the total number of innovations? who convert to a startup and create the jobs, right? Now, from startup to job is a slightly easier sort of, maybe multiplied by 10, 15, or even you have the data for these 100, 200 startups that are there in this district -registered DPIT startup. The first thought that came to my mind while I was talking to the secretary there, he said, let’s look at innovations that are happening in the state. Now, imagine the state has 120, 130 startups, but has 1 ,100 documented innovations which are grassroot innovations, right? And these are validated innovations because the overall innovation is 3 ,000, which is better than a lot of states when we took it in terms of per capita than even Karnataka and Maharashtra.

I come from a consulting background. The first thing I did was just divide it by the population, right? Look at a per capita thing. It’s twice the number for Maharashtra, right? And they are trying to solve problems which are very basic. For example, they are saying that, hey, if we mix this plant and this plant, that can lower your… blood sugar. I don’t know. Some of them may be patentable. Some of them may not be patentable. But the idea is that even if 10 % is good enough, right? So 10 % of that 1000 is about 100. It is just double the number of startups. Yeah. Where the innovation is already existing. Right? Somebody has to commercialize that innovation. Right? Right.

And this number sits on the National Innovation Foundation dashboard. Right? Which is, which was, and the state government had no clue about it. Yeah. That, oh, these many innovations. And then

And a lot of this you can now supercharge with AI. Yes. Which was slower before. And now we have multiple, let me just say, infrastructure layers, right? You have everything from 5G smartphone usage to the largest user base for chat GPT to, you know, I don’t know if you know. This number, but 22GB is the average usage of 5G internet. mobile data in India, 22 GB per month.

Correct. And the other thing that we are talking to this, how can we enable AI to improve the public service delivery when I was talking about that, right? And there are some broad use cases, right? Simple cameras on the traffic lights which monitors what is the flow of traffic at each hour or each minute, right? And LinkIt can be sort of automate that to reduce the consumption of the petrol, diesel, whatever it is, right? And then show some savings and fairly earn carbon credits. One very simple use case. Can we utilize, for example, satellite imagery to identify how lakes are drying up, right? Or what is the water? Just basic imagery. Everything is available. It’s not that we have to create newer thing.

And this is available with the government, right? One of the startups that we have funded at Artle Innovation Mission is a startup that has created small sensors that are embedded in the pipeline that tell you where, and measuring the flow of water at each level, maybe at every 10, 20, 30 feet, that where exactly is the leakage. So that people don’t have to go to that pipe and then see, oh, where is it going, let me just fix it up. You directly go to that. The next level that the startup is building is, can we also look at where the leakage is happening for the multiple times and look at what is the material, right? Is there some more stress at that particular point of the pipeline to ensure that next time that you’re building or repairing it, the leakage doesn’t come from this particular part of the pipeline.

No, this is great, Himanshuji. What I’m hearing from you is that, first of all, I can feel your sense of energy and excitement for the region. Upcoming state innovation mission launch as well as the beautiful vest you’re wearing today. This is not. This is not the state that. Yeah, we’re not giving any hints. And I was just going to say that what you’re saying is in like the pipe. example that you talked about is that there are existing innovations, there are existing latent energy that startups have, there are real problems that can be solved and AI is helping, you know, as an example, tie all of this together. It’s not just supercharging or fueling this, but it’s the glue that’s bringing all this together.

And the best part is that in truest sense, this is democratization because AI will enable that Assam and Manipur or Meghalaya or Sikkim are at the same level as maybe Karnataka because if you have that AI level on a data, right, and you want to take any data back decision for public good, right, it doesn’t stop Sikkim to take that decision which Karnataka can take today.

And the other very important thing that we are trying to build within this state innovation mission is to create a peer -to -peer learning network, right. There could be something fascinating that Sikkim can offer in terms of organic vegetation or agriculture which can be picked up from the other agrarian states. Yeah.

Yeah. This is great. I’ll, we’ll go over to Rajiv. Rajiv Babuji and then we’ll break for questions. You know, we’ve heard the building in public perspective from a non -profit as well as a federal organization. I want to see the private slash corporate, you know, and the work you guys do in the foundation perspective, Rajababuji. You already spoke about, you know, access. You spoke about availability in healthcare. Pick an example that you’re already building towards and tell us what are you most excited about, about how the products and services that you are building will truly unlock value for social good and economic growth.

Always. Yes. So I’ll first touch upon one point. See, there is a lot of concern and fear about AI coming and taking away the jobs, right? I think that’s partially true, but in the big picture, it is not true. See, what’s going to happen every time technology comes, for example, when humanity started, as a civilization we started, there was only one industry, which was agriculture. Yeah Pre -industrialization Exactly And then after that When industrialization happened More opened up It could have been seen Then with the power it came It saw very disruptive From horse power to steam power That could have been seen very disruptive To many people who Livelihood was depending on horse power Or bull power or whatever power it was Same way from steam When it moved to electricity It could have been very disruptive People whose business was all in steam Coal and all that stuff They would have thought Oh this is not necessarily a good thing It’s going to put all of us And even yesterday somebody was referring In early late 1800 They said we’ll close the patent office Because everything needs to be Innovated has been already innovated There is not much to do But then here we are Almost 130 -140 years later Of making the patent And we are still in the process Of making the patent And we are still in the process And we are still in the process Of making the patent And we are still in the process new things are unfolding.

So I think, you know, like in the, you know, many in the forum leaders spoke, A is like another energy, right, like electrification. It’s going to do a big transformative change, and there is not a thing it won’t touch. It will touch everything. And it’s going to make everything intelligent. And when everything becomes intelligent, largely, very good things will happen. Very good things, very positive things. It’s going to enhance the livelihood or quality of life for many people, and it’s going to create a lot more opportunities. With that being said, in the industry we are focused in, right, for example, I will tell one example which we tried, Agilesium. One of the things we wanted to do is that, so we work with pharma company, biotech company.

One of the problems we were trying to solve I will just give few problems Which we were trying to solve Was not unsolvable At that time because of the technology As recent as 4 -5 years

Tell us more about this example

For example in 2018 We invested in a project Our customers were Pharma companies It was reps So the reps basically are going And meeting the doctors They are supposed to know which doctor They are meeting What was the last time the conversation They had with the rep And doctor said I wanted to know About this medicine What kind of adverse impact it could have For the patients Side effects it may have if I give it to the patients Some technical details the doctor would have requested So they have to go back And maybe talk about it Give those details and all that stuff But between two appointments Sometimes it’s three months to six months So they don’t remember what happened So what we wanted is Last time conversation recorded Of course they have it in paper or sales force And all that stuff They may put it But then you are going today from the previous meeting You don’t remember that You are not accessible You are in the car driving and all that stuff So what we wanted to do is We wanted to do an AI Where in the phone app it’s implemented And you Previous day Based on it will look at your calendar It will look at who are all the doctors you are visiting And then it will go to the CRM What was the conversation Last two three conversations happened with the doctor You take that And what homework you did on that It will take all that Then it will take as a voice memo Morning 7am when you are going to meet the doctors You play that voice memo It says hey today you are meeting so and so doctors This is what was the conversation last three This is what you are supposed to tell It’s

almost like a morning presidential briefing Exactly,

morning presidential briefing And I got this idea based from that only Amazing then at that time The technology available for me Was Lex Lex is a Offering tool AWS had released from Alexa So Alexa, whatever they used to do Build Alexa They gave that tool as Alexa And then there was another technology called Poly, which also from AWS Technology So we took these two technologies For our customers from the AWS platform We were trying to build it It sucked At that time The experience was really bad Because They could not understand the keywords The medical keyword It would not understand People’s accent it would not understand And reading structured and unstructured data Because you have to read the unstructured data Structured data and all that stuff And you have to do it And you have to do it It was not so great Plunky now, so we have to shelve that project.

We invested almost a million and a half, two million dollars at that time, multiple customers of ours. And the experience when we took it to the rep, it was not so great. They did not If it’s not easy and usable, nobody adapts. It would not understand the question. Can you tell, repeat it again, repeat it again, three times, they would throw the phone. Right? It was like that. And when it got it also, it did not understand. It was bad. Because of the accent, various things, medicine, technical. Now, that has been implemented a year back with the AI super seamless.

And where do you see the impact of adoption of this technology?

Across the board, everywhere. Because everywhere if you think about it now, right, it can take every individual, every one of our individual, let’s say our calendar we can take. We can create a personal agent for it. and it will look at the task, it will look at our email, it will look at our calendar and it will tell this is what you are supposed to do. I

think a lot of people have begun to use that. Yes,

and then now not only self, an organization can create an agent for each and every buddy. Instead of manager going and telling each and every person, hey, you are supposed to complete this, we can have their personal agent who is their agent buddy can tell them, which is a little bit private and more comfortable to hear. And

I think in healthcare and the service that you are in, a continuous flow of information, high quality information, makes a huge difference in availability of the work that you do. Exactly,

this is simple as accessibility information like you said. And scale. But then I will take another complex situation where we are working with another research institute in San Diego. Yeah. Where… every individual, every one of our individual, let’s say our calendar we can take, we can create a personal agent for us. And it will look at the task, it will look at our email, it will look at our calendar, and it will tell, this is what you are supposed to do. I think a lot of people have begun to use that.

Yes.

And then, now, not only self, an organization can create an agent for each and everybody. Instead of manager going and telling each and every person, hey, you are supposed to complete this, we can have their personal agent, who is their agent buddy, can tell them, which is a little bit private and more comfortable to hear.

And I think in healthcare and the service that you are in, a continuous flow of information, high quality information, makes a huge difference in availability of the work that you do.

Exactly.

Which is access to medicine. This is simple as accessibility, information like you said. And scale.

But then, I will take another complex situation where we are working with another research institute in San Diego.

Yeah.

Where basically, this is in the liver transplant. Where the patient basically is waiting there Who is in the waiting list For a longer time The donor comes in, they would be matched Now what we are doing is That is not the way Because you know in the liver transplant Any organ transplant Absorption of that organ is very difficult Because it is seen as a foreign body

Yes, the foreign body

And the body will Try to reject it Making it accept is a very very big problem So then You need to look at the biologics Of both the patients You see who is most conducive From a biological point of view To receive this organ So there are parameters, biological parameters And physiological parameters that now you can do a match And there are too many parameters to do In a simple Old algorithm It is very difficult Now what we do, and then there is multiple research papers

How far along are you on this matching For organs

Now we are helping Institute One of the top institute in San Diego Scripps And lot of Nobel laureates from medical are there And we are helping One of the researchers actually from India It’s published so I can quote His name is Sunil Kurian He has published and he has implemented this with this and with the AI Now he has told based on the patients, donor and the patient The AI can tell who is the best recipient for this match And their livelihood, the organ, after that they will have a better living condition It can predict based on various parameters which is not very easy to do So from a simple use case as a

No it’s a phenomenal To very deep science You went from morning presidential briefings to almost Tinder for organs in one shot No this is great I want to ensure Rajeshji we have enough time for questions We have a small audience here I wish we could take live questions online too But we don’t have that facility yet But any questions here, small audience Any questions that we have Very happy to take them You can point it to a certain panelist if you like Yes please we will pass the microphones right behind you please tell us your name and you can point a question to any of us thank you very much

I have two questions one is for Rajesh ji and one is for Kritika ji so Rajesh ji what medical breakthrough do you believe will come up in the market in next 3 -4 years after this integration of medical science and AI is my question crystal ball question on healthcare what is the breakthrough on the healthcare medical side that you see it’s a crystal ball question that might happen in AI that you are excited about second question for Kritika your initiative is very impressive what I want to know is the average poor person somewhere in the rural area is he aware of your initiatives or if not then in what time frame do you believe that you will be able to reach that awareness question

I will add a flavor to Raj’s question of saying can that awareness be unlocked for further media We will first start with Rajababuji Please go ahead

Thank you, very good question, appreciated See, I think definitely Lots Lots, right, it is going to be very Transformative, it is going to be Both in the healthcare side First the healthcare, right, I think the Primary healthcare Lot of it will move to AI, right, I think Through our AI Basically doctors will Start creating the doctor agent And first your Questions, your personal doctor agent will Address it, basic, and then Based on that, your personal doctor agent Your doctor’s agent Will now next contact the doctor If there is a need

I have a feeling that the doctor’s agent will talk to your patient’s agent

Yes, yes

Before it comes to the two humans

Exactly

So what is the breakthrough for you in that

See, that Basically the access, no waiting Especially, you know, in the western countries, the waiting, some of these specialists is unbelievable, we would not have heard in India, they wait for six months to one year.

Right, that’s the waiting time for surgeries too.

Yes, so I think that will definitely transform and then also you said like, talked about one of the things, sir talked about the bamboo situation, why not a global healthcare? Like why can’t there be a marketplace immediately, they can reach and there is a doctor, of course already that is happening but that can happen in a much much greater level because these agents of AI could be sitting at hospital level, doctor level, patient level, as a patient, I may not be AI savvy to reach various things but my AI agent avatar which I have created, if we can create in some easy way that would reach it and definitely address it.

No, I think you brought up a very important point and we will go to Kritika right after is that, what you have said is that in highly skilled professions, where human capacity is at a very significant scarcity that capacity can be unlocked to a further level simply because agentic will come in. No that’s great I hope Yuvrajji the first question is answered I just add on to what you are saying right I think we were doing a joint program with Swissnext which is the Swiss arm of the embassy of Switzerland here and this was like a exchange kind of a program right and one of the Indian startups that actually spent a week in Switzerland what they are trying to create is look at all the profile of cancer patients right and start mapping that to the DNA and start predicting what is the probability right so they have created a test it seems still at a validation state what is the probability of you getting cancer looking at all the data patterns that they have in the database right and they feel that this will sort of enable a much faster cure for cancer because you’ll be able to predict it much better.

Yeah. Krithika, over to you. I’ll expand Ugra’s question again. I’ll recap. I think he’s asked something very interesting and important is can in a country of a billion plus people despite the availability of technology awareness of important, interesting impactful things continues to be a challenge. Given your experience at Indus Action, do you feel there is an awareness problem based on the work you’ve done with now 9 lakh students and can that awareness challenge be leapfrogged using AI? Over to you.

Absolutely. Absolutely. No, this is a really relevant question and something that we are very actively working on. So what we’re saying is we want to flip this question. Whoever the citizen is, citizen discover. That the citizen has to discover the scheme. Can we use AI? And tech to flip this and say can the state discover the citizen? and building in those layers of AI ML into existing data which is exhaustive and it is with the state. To say that what if I layer your VBG Ramji or erstwhile M .G. Narega data with PDS repository with say aspirational district data and say in this district almost 95 % citizens are eligible for this particular entitlement. And this currently happens for demographic longitudinal research.

Correct. Absolutely. It is not used for actual access. Absolutely. And actually there is an organization. So Educate Girls has a model wherein they have been able to use ML to be able to identify and pinpoint to say these are the districts which need an intervention. These are the districts where we have the most number of out of school girls. So awareness will flip to actual targeted outreach. That is exactly what we are now trying to attempt with the state. The problems of validation, verification of citizens, are they eligible, not. Can we use? Yes. Can we use AI? Yes. ML, tech to be able to flip this and say, let tech enable the stage to discover the fascinating.

And that will also reduce the 10 steps in one, in one single shot.

What an answer. Love this. I think we can take one more question. We have a few minutes left. We have to, we’ll take to keep your question short and pointed to, you know, uh, whoever you want to ask, please go ahead.

Thank you for a valuable session. Uh, I just wanted to ask that, uh, there are a lot of startups in India, but in which sector you think there should be more startups, but, uh, but aren’t currently.

Great question. Great question. Do you want to answer it? You and I can pick this up. You go first and I’ll follow which sector lose largely defined, loosely defined. Do you think there’s a huge volume opportunity for founders?

Okay. Uh, I think that it is a two level. One is if I define sector by, uh, what we traditionally call a sector, let’s say ed tech or a health tech, uh, or the second would be. Is it like a frontier AI, non AI kind of a thing, right? Uh, So, if you look at the current sectors, right, what most startups are doing, that’s my personal belief, not the organizational belief, right? What most startups are doing is they’re trying to, or they were trying to chase venture capital money. So, if I felt that EdTech is hot today, let me go EdTech. Today, that same thing is for deep tech, right? Just enable some element of AI or some element of deep tech and say, I am a deep tech startup, right?

The point is not that. So, I think one, founders should look at some problem to solve. I’m not saying a social problem. Some problem, some gap that they feel. With the current ecosystem or the current startups are not fulfilling. Or create your own niche. That’s one. Second, what some of our programs also do, and I did talk briefly about it, is the social kind of a thing, right? Do we create solutions that can sort of enable or solve for problems in India? Because a lot of problems that we solve for in India or in Indian context. Are replicable to the global south. Because similar per capita GDP, similar environment, similar diversity, similar price point.

you want to know that is great very very interesting perspective it’s a fantastic question i’ll just answer it with one simple perspective foundership is a long journey even if you look at the current ipos they were all been building for 10 -15 years ai will not shorten that journey but will make impact faster i believe so i think given the world that we are in right now and this massive phenomenal super tool that we are getting i think founders should build things that take time because that can be accelerated now these are essential problems if i have to pick sectors i would say healthcare and education and not because we have them here but i think even if 10 percent of the talent that’s currently in fintech or is currently in consumer tech i think if they move to healthcare it could be engineers founders investors it will fundamentally change this you know what this country can do and needs and it’s the same for education i don’t want to say unemployment but we have a massive massive young talent pool the largest in the world if we can change you know as founders and as startups we can build more tools for education i think we will unlock a superpower you know for our country so i think that’s where founders should go thank you one last question you had one to ask us

hello i’m yashi i wanted to ask one question anybody from the panelists can answer my question is how can we ensure that ai systems deployed for public welfare do not deepen digital divides especially for rural and marginalized communities

what an amazing question ai can deepen divides i will use the i’ll take the generosity to not just say digital it could be economic social uh you know cultural there are many divides that exist in the country how can it ensure uh kritika do you want to take a shot at it given that you’re in the welfare space i’ll ask maybe all of us can give a quick one line 10 second rapid fire answer on what can we do ahead proactively for the reducing a potential digital divide thanks to ai

yeah i think uh two perspectives uh One is in our solutioning, when we, and I’m going to take a live example, when we actually embed, say, a lottery algorithm within a state, what we tend to do is we also have this equity algorithm to say that the gender ratios are going to be balanced. It’s going to be the girl and the boy are going to have a 50 -50 application rate there, right? Or to say that this is going to be the percentage of, you know, children with special needs or children from, say, socioeconomically weaker backgrounds or like SESD, OBC. So one is, like, how do we proactively embed these algorithms to be able to address what you’ve just suggested, which is also a problem that we actively work on.

I think other is I definitely feel. I feel that we cannot discard the human in the loop. I feel like AI has to make. their jobs easier, give them my Anganwadi worker or my Asha worker. They need to have access just the way Rajbabuji was referring. They have information which is so simple and so easy to disseminate that they don’t have to spend hours and hours parsing through these complex eligibility criteria for welfare schemes and so on. Those are the two perspectives that I have.

Fantastic. You are basically saying build a pro -social equity bias into AI.

Absolutely.

Rajbabuji, do you have a quick rapid fire answer to this?

Thank you. I want to also answer the previous question because that was a very good question. I will quickly touch on that and come to this. See, I think like Kavi told, don’t chase the VC money. I mean, it should be always see the monetization should follow the value. What you should be focusing is Are you creating a value And what is happening is Many are chasing the monetization And missing the value Are you making a difference If you are making a difference And wherever it is, whatever small it is Like he said, health tech Especially a lot of potential is there We need to be making disruption there So focus on that But there are others also On digital divide and social equity On the digital divide I think AI with the phones already Everybody has a phone now Almost everybody is going to have a smart phone If not, already they have it Once they have a smart phone, they have AI So AI is not going to divide If anything, it’s flattening everybody out For example, the people with computer engineering They were on the pedestal Programmers Now it has brought innovation Not only for a programming person It has brought down to everything If anything, AI is the biggest equality It is not a divider

Fantastic Himanshuji, any view on that? What can we do proactively?

Yeah, I think I think sort of agree to what everybody has talked about. I think with AI and the smartphone and we are one of the largest consumers of Internet. Right. It has sort of democratized the divide between rural urban. I think there’s one more divide which we generally don’t talk about is a language divide. Right. There are 22 scheduled languages. No other country in the world has 22 scheduled languages. Right. And this is just scheduled languages. In a state like UP, it looks like Hindi, but you travel from the western part of the eastern part, the dialect changes. Right. So that’s another divide that probably is being sort of democratized by AI, for example. And everybody’s building solutions around it.

Right. All the LLM models that are being developed, both by the government and even some of these larger companies that want to look at our data. Right. So in the future, I think one thing that we need to be very, very cognizant about is which we I started with. Right. The divide between the western. And the southern states at one end of the. spectrum, eastern and the northern states at the other end of the spectrum, right? The challenge is that a lot of mentorship, VC money, all that has not reached it. So they are still trying to build those native solutions, which is not bad, right? But for them to really equalize with some of the other states, that’s where AI is going to enable.

And some of the state governments are building and all of us are playing our own little role in that.

This is great. You brought it all together Himanshree. We’ll wrap this panel now. Now, I think AI is here for good and it’s our duty to build AI for good. And what an interesting conversation. The divide is a matter of what we do. And it’s great to have all your perspective. Thank you everyone for joining us and for this conversation. Thank you so much. Thank you. Thank you. Thank you. This is the photo of the marathon. That’s why they take it at the start. You can check it out there. Please, thank you. Thank you. Thank you.

Today’s session begins from a simple but powerful premise. We cannot govern what we do not understand. It is my honor to open this session with a special address by the Secretary General of the United Nations, whose leadership has placed science and multilateral cooperation at the forefront of global AI governance. So please join me in welcoming His Excellency Antonio Guterres.

Thank you very much. There is a computer here. I don’t know to whom it belongs. Excellencies, ladies and gentlemen. Thank you for joining this discussion on the role of science in international AI governance. We are barreling into the unknown. AI innovation is moving at the speed of light, outpacing our collective ability to fully understand it, let alone govern it. AI does not stop at borders, and no nation can fully grasp its implications on its own. If we want AI to serve humanity, policy cannot be built on guesswork. It cannot be built on hype or disinformation. We need facts we can trust and share across countries and across sectors. Less noise, more knowledge. That is why the United Nations is building a practical architecture that puts science at the center of international cooperation on AI.

Thank you for watching. and it starts with the Independent International Scientific Panel on Artificial Intelligence. This panel is designed to help close the AI knowledge gap and assess the real impacts of AI across economies and societies so countries at every level of AI capacity can act with the same clarity. It is fully independent, it is globally diverse, and it is multidisciplinary because AI touches every area of every society. And I’m delighted that the General Assembly of the United Nations confirmed the 40 experts I proposed to member states. Now the real work begins on a fast track to deliver a first report ahead of the Global Summit. The Global Dialogue on AI Governance in July. The panel will provide a shared baseline of analysis.

helping member states move from philosophical debates to technical coordination, and anchor choices in evidence so policy is neither a blunt instrument that stifles progress nor a bystander to harm. That is how science transcends decision -making. When we understand what systems can do and what they cannot, we can move from rough measures to smarter, risk -based guardrails. Guardrails that protect people, uphold human rights, and preserve human agency. Guardrails that build confidence and give business clarity so innovation can move faster in the right direction. Science -led governance. Governance is not a brake on progress. It is an accelerator for solutions. A way to make progress safer, fairer, and more widely shared. It helps us identify where AI can do the most good the fastest.

And it helps us anticipate impacts early, from risks for children, to labor markets, to manipulation at scale. So countries can prepare, protect, and invest in people. Today, international cooperation is difficult. Trust is strained, and technological rivalry is growing. Without a common baseline, fragmentation wins, with different regions and different countries operating under incompatible policies and technical standards. A patchwork of rules will raise costs, weaken safety, and widen divides. Science is a universal language. Guided by the independent panel and the global dialogue on AI governance, we can align with the world. We can align our technical baselines. When we agree on how to test systems and measure risk, we create interoperability. So a start -up in New Delhi can scale globally with confidence because the benchmarks are shared, and safety can travel with the technology.

Finally, let us be clear. Science informs, but humans decide. Our goal is to make human control a technical reality, not a slogan. And that requires meaningful human oversight in every high -stakes decision, injustice, health care, credit. And it requires clear accountability so responsibility is never outsourced to an algorithm. People must understand how decisions are made, challenge them, and get answers. Excellent. Thank you, ladies and gentlemen. The message is simple. Less hype, less fear. More facts and evidence. Guided by science, we can transform AI from a source of uncertainty into a reliable engine for the sustainable development goals. Let us build a future where policy is as smart as the technology it seeks to guide. Thank you.

Thank you, Secretary General, for those inspiring opening remarks. Ladies and gentlemen, we were going to have Mr. Brad Smith. Vice Chair and President of Microsoft Corporation as our next speaker, but he’s running a bit late, so we will move to the next item in the agenda. I would like to welcome Professor Yashwa Bengio to the stage, Scientific Director of MILA and one of the world’s leading AI researchers. He and I will be in a fireside chat and we’re hoping that Mr. Brad Smith will be able to join us very soon. Thank you. So, welcome Professor Bengio.

Thank you for having me.

Our pleasure. So, you are the most cited computer scientist And I looked it up. You’re actually the most cited living scientist today and have played a unique role at the global science policy interface, including through the UN Scientific Advisory Board and your leadership of the International AI Safety Report. So from your perspective, how do these science policy interfaces actually work in practice and where do they add the most value?

So it’s tricky, right, because there are many different views, especially different interests in business, in different governments. And the role of science, the role of a kind of synthesis of science that we want for the UN panel, that we have seeked for the… AI Safety Report. is to try to make it, to provide a shared understanding as a basis for those political discussions and not be influenced by as much as is humanly possible by those tensions that exist in our societies. And I think it’s particularly important because maybe unlike in the case of climate, the scientists themselves don’t always agree on what to expect for the future or even how to interpret the science that exists.

I just want to add something. So something that’s a little bit subtle about this kind of exercise is that to be able to recognize the uncertainty and the divergences that exist, and where is it that scientists agree, where is it that the evidence is strong, where is it that we have clues that matter. Even if we’re not certain about a particular risk, we might have clues about it. But if the risk has huge severity, in other words, if it does unfold, then it could be catastrophic, then policymakers need to make attention. And it’s always difficult when we don’t have proof that something terrible is going to happen. Maybe a good analogy is tipping points in climate, right?

Because there’s not enough past evidence to be sure that a particular tipping point is going to happen. So the situation is similar in AI in the sense that we don’t have the experience of, say, machines that are really smart and can change society, and be even potentially smarter than us. So how can we deal with the right policy decisions? but that’s why it is so important to have as neutral and as fact -based evaluation of what is going on available to everyone and in a language that is accessible to everyone and of course for policy makers which by the way is difficult for scientists to achieve they need help, they need iterations they need feedback from people who are used to the interface between science

Is there anything in particular about the highly technical nature of AI and also the pace of change that makes this interface particularly difficult?

Yes, yes The facts shown in the scientific benchmarks across labs, companies and academia on AI show very rapid growth of the capabilities of these systems and the capabilities of AI and the capabilities of AI and the capabilities of AI and the capabilities of AI and the capabilities of AI and that growth is uneven so we see AIs even surpassing most people on some measurements of capability and being kind of stupid or like a six -year -old on some other things so it’s very difficult to grasp what that means but because it’s moving so fast there’s always going to be a lag between even like the scientific papers take time to be written if there are studies so think about studies that involve people they’re going to take months and so by the time we start seeing clues that there’s a potential problem so you can think of something recent that was not expected like the psychological effects on people of these chatbots we now have lots of anecdotal evidence and we’re only starting to see the scientific studies and of course on the policy side it’s going to be even more difficult even later because those discussions are going to happen after we see scientific evidence so there is going to be a lag and that’s a real problem because things could move

So maybe that leads well into our next question. We often hear that AI governance is moving too slowly and from your experience, what kinds of scientific assessments or benchmarks could realistically keep pace with this rapid change?

Yeah, that’s a great question. My opinion on this is that we should be thinking about not just policy and the usual sense of coming up with principles, but we should try to strive for high -level principles that can be applied without having to go into the details because the details are going to change. And the second thing is I think we should strive for technology that are going to help to implement those guardrails in the field, in the deployment of AI, because otherwise there’s not enough time to

Well, thank you for those insights. And also congratulations on your recent appointment to the Independent International Scientific Panel on AI. In a few words, how do you see this new panel helping to strengthen the link between science and global AI policymaking?

Well, I think there’s something really important about this panel, and it’s global aspect and being rooted in the UN. And the reason I’m saying this is that AI is going to be transforming our world very clearly, and it’s going to have global effects, whether it is on the good side, the benefits are on the risks, but also the kind of power relationships that are going to be changing in the future. And I’m personally very concerned about how this will unfold for developing countries in the global south. And we need to work. Thank you. in a multidisciplinary array so that we can foresee those effects and we can start discussions to make sure that everyone is at the table and no one is on the menu.

Well said, Professor Bengio. Well, thank you very much for kick -starting our discussion. We will now turn to our panel. So, ladies and gentlemen, it is essential that discussions about AI policy include the voices of key industry actors, and I am pleased to invite Mr. Brad Smith, Vice Chair and President, Microsoft Corporation, for his keynote address.

Well, good morning, everyone. It’s a pleasure to be here. My apologies for being a few minutes late. I want to offer a couple of thoughts this morning. The first thing I think we should come together to think about is that, in my opinion, this is a moment in time when we need to reflect on and reinvest in the importance of the United Nations. There is a well -known economic theory that says that humanity is, in many ways, almost destined to repeat its great economic mistakes every 80 years. The reason it’s 80 years is because that is basically the lifespan of human beings. And so every 80 years, almost everyone who had any living memory of a prior financial calamity has left the planet.

If you look at the Great Recession that started in 2008, what you realize is that it happened 79 years after the stock market crash that led to the Great Depression in 1929. And you can follow this series of financial mistakes all the way back to the bursting of the tulip bubble in the Netherlands hundreds of years ago. I think there is a corollary worth thinking about. Just as there is a risk that humanity forgets the mistakes it made 80 years ago, humanity runs the risk of forgetting the great successes. it created 80 years ago. It was just over 80 years ago that the world came together to create the United Nations. It was, in my opinion, one of humanity’s greatest accomplishments of the 20th century.

It is a unique organization in a very imperfect world. And so, of course, on any day and any year, it is possible for anyone to blame the United Nations for the imperfections that we see all around us. But the truth is this. Those imperfections are fewer, and their consequences are less disastrous, in my view, because of the United Nations. And one of the great things about working at Microsoft in a job like Microsoft, in my opinion, is that I get to work in a global organization. We have subsidiaries in 120 countries. We do work in 190 countries. We see the world. It turns out that everywhere we go, we see the United Nations. Sometimes it’s the United Nations Development Program, working to foster economic development.

Sometimes it is UNHCR, helping refugees. Sometimes it is the UN Office of Human Rights, seeking to protect human rights. But the truth is, if there’s a problem, the United Nations is almost always part of the solution. We need to remember this. And we need to remember that however challenging the last 80 years have been, we have managed, as humanity, as a species, to live. with the ever -constant presence of nuclear weapons without using them or destroying ourselves. The United Nations has, in fact, in my view, been indispensable to not just the protection of people, but the preservation of our species. Why does that matter now? Why should we talk about it today and this week in Delhi?

Well, because here we are on the cusp of the future. A technology that we all know will likely change the future. Here we are in the second month of the second quarter of the 21st century, and we need to focus on how we bring the institutions on which we rely into that future. So then let me talk about a second aspect that I think is so important to think about this month. One of the things I’m constantly struck by… leading a global organization is how often everyone disagrees with each other about almost everything. But one of the things I’ve learned along the way is that I think one of the reasons people so quickly disagree is that we rush so quickly to debate competing solutions.

This happens in domestic politics. It happens in international diplomacy. It, frankly, happens in a global company. It actually happens everywhere, even in families. As soon as there’s a problem, people want to talk about the solution. And then people have different solutions, and then they debate, and they disagree, and they argue, and sometimes it’s even worse than that. One of the things I’ve learned is the reason people so often disagree about the solution is they don’t have a common understanding of the problem. They don’t spend enough time talking about the problem. They don’t have a shared contextual understanding. of the problem they’re trying to solve. They’re too quick to want to blame someone for the problem, and then that spirals into a discussion that becomes completely unconstructive.

Why does that matter today? Because what we’re here to talk about today is all about creating a more common understanding together based on science of where artificial intelligence is going. This is an indispensable tool. Indeed, it’s a critical service for humanity so we can all learn together, we can all think together, we can all understand together what is going on in the world. I think it’s especially critical, to be honest, when it comes to artificial intelligence because I think if you even communicate, consider most of the conversations you have about this technology. I would argue that it has two flaws. The first flaw is it usually involves people making very grandiose predictions about the future.

You know what? I’ve worked in the tech sector for 32 years. I have listened for more than three decades to my colleagues in my industry around the world make bold predictions about the future. No one ever holds them accountable a decade later for whether they were right or wrong. I used the researcher agent in Microsoft Copilot a couple weekends ago, and I loaded a lot of names. I won’t say whom, but you can guess. And I said, look at all the predictions they made about all the technologies, and look at the predictions they made about when these technologies would come to do something or another, and give them a grade. The average grade was 25%. You couldn’t even get close to the top.

You were at the bottom. So let’s just understand one thing together. There is no such thing as a crystal ball. No one has one. But what we do have is the ability to understand where we are today. And what we do have is a better understanding to just appreciate what is happening each and every year. There is a second flaw, in my view, in many of the conversations that take place, including at this AI summit. Everybody wants to talk about how they’re going to make machines smarter. That’s interesting. I think it’s interesting to imagine living in a world where a data center is like a country of geniuses. But as I mentioned yesterday, compared to the people who lived in the Bronze Age, we’re all geniuses.

We’re all geniuses already. What that should remind us… is that human capability is neither fixed nor finite. And so what really matters, in my opinion, is not whether we are going to build machines that are smarter than humans. Yes, in some ways we will. But how will we use those machines to make people smarter, to help us do what we need to do? That is what this effort is all about. Wow. Let’s harness the power of science to build a common understanding of what is changing each year, and then let’s connect it with the global dialogue on governance so we can pursue policies that will ensure that this technology serves people. There’s no better place to get started than here.

There’s no better time than now. And let’s face it, there is no better institution on the planet that can do more to serve humanity and protect the world. than the United Nations. And on behalf of Microsoft, I just want you to know we are putting our full energy and resources to do everything that we can to help. Thank you very much.

Thank you. Thank you, Mr. Smith, for those insights on responsibility, accountability, and the role of industry. We now turn to our panel. Our panel brings together scientific leadership, public policy expertise, and international coordination. Please welcome to the stage our speakers, Professor Balaraman Ravindran, IIT Madras, Swaminathan, former Chief Scientist, WHO, Ajay Kumar Sood, Principal Scientific Advisor to the Government of India, and Anne Bouveraud, France’s Special Envoy for AI. I am also pleased to introduce our moderator, Amandeep Singh Gill, Undersecretary General and Special Envoy for Digital and Emerging Technologies. I invite him to guide the discussion. Thank you very much.

Thank you very much. Thank you, Anil, for leading us and for those who have not read his book, The Elegant Math Behind Machine Learning, please do have a go at it. We cannot govern something that is not possible. Something that we don’t understand. So something as simple as, like, if 90 % of AI is matrix multiplication, a 0 .01 % as he was explaining, improvement in efficiency of matrix multiplication has huge energy implications. So I want to welcome our esteemed panelists. The stage has been set by very inspiring keynotes and a fireside chat. So we will dive straight in. And since we are running a little short of time, I’m going to compress the two rounds into one rapid -fire round.

So all of you have worked on or are working on the science policy interface. And my sense is that there is a loop here, that there is a loop between science and evidence, and evidence and… and policy. And we want to explore that loop today in the context of the significant development of the setting up of the International Independent Scientific Panel at the United Nations. So I want to start with you, Soumya. You were the first chief scientist, first woman chief scientist at the WHO and worked at a very difficult time during the COVID when evidence, trusted evidence was so critical. So in your view, what makes this evidence that comes from science trusted and actionable for policymakers?

The evidence is very rapid. The field is moving so rapidly. In COVID, we had to review a couple of hundred publications every day to understand what was happening on different aspects, on the virus, on the immunology, on how vaccines were working and drugs, and we had to make recommendations based on the best available evidence that day. I think we may be in a similar situation with AI, and it’s wonderful that the UN has now set up this body, which I see as something like the IPCC. I think we do need global governance. We need something like, you know, we’re talking now about preventing future pandemics by sharing data on pathogens, making sure that we have protocols in place where countries are willing to share that data, and also, of course, to share the tools, the vaccines or drugs when they become available, when or in case there is another pandemic.

Similarly, I hope that this scientific body that’s been set up by the UN would also establish systems that would, would link to national bodies and systems, and that would ensure the voices of all are heard. So one of the things during COVID was some of our recommendations were relevant. in high -income countries but not in low -income countries because the context is very different. And the WHO was criticized for this, I think rightfully so, and we need to learn from those mistakes. So it’s the voices, for example, of women, a low -income woman, a farmer in a remote place, is going to use technology very differently from a large farmer with access to lots of machines in Europe or North America.

So if AI has to work for everyone, then we need to make sure that those voices are heard. And ultimately, I think that loop you talked about, sometimes policy is made in advance of evidence. You have to. You can’t wait. But the policy must change. It must ask for the relevant evidence and be able to adapt when that is clear.

Thank you very much, Soumya. I’m going to come to you, Ravi, Professor Balaraman Rabindran. Now, as AI policies begin to take shape and you’ve been involved in some policymaking yourself, what signals from… regulators or public sector users should most urgently guide future AI research priorities? So in a sense, you know, the loop coming back into research.

so thank you for that question so I mean AI right now especially in the global south so we don’t completely understand the implications of adopting AI and how is it going to affect the society, the people livelihood and everything in fact I also feel that we don’t have enough evidence about how AI is even affecting the social fabric how are children getting increasingly isolated with the adoption of AI and whether the effect is uniform between cities and rural India because the cultural setup is very different and so on and so forth so if the government as we heard our honourable prime minister say yesterday should focus more on youth and the impact of AI on youth what is the evidence do we have about what is happening in India so we hear stories about you know how there is dependence of on AI models of children and also people who are mentally challenged and so on and so forth who are under stress but all of these stories are coming to us from the west so what is it that’s happening in India so when we have these kinds of policy decisions that have to be made the government says that AI should be pushing efficiency in agriculture so do we have a benchmark in India that can evaluate the efficiency of effectiveness of these AI models in agriculture what are the kinds of flaws that happens when I for example build a bot that can act as a co -pilot for a farmer so these are bigger challenges so we have a lot of questions

if I can quickly follow up where do you actually see evidence for impact in the sustainable development goals space just a quick example or two

so I I That was not in the notes he gave us earlier, so I have to think on my feet here. So let me take one thing that we are very familiar with, we are working on right now, is on the education space, right? So, for example, we don’t know, we don’t have evidence of AI interventions. How likely is it to change student learning behavior? So we have done some preliminary studies. So the author of the study is somewhere in the audience, because he has been sending me pictures of the stage. So what we have found out is the effectiveness of AI adoption is a direct function of habit. So if the students are using AI more, then they tend to…

But now I don’t know what is the causal factor there. I don’t know if the causal factor is whether they are using AI more, therefore they get better effect, or do they use AI more because they are getting better effect. So these are questions that we have to ask. Even in something as simple as education. I am saying simple because there is a lot of positive buzz around using AI in education. But even there, we need a lot more evidence to come.

Thank you, Ravi, and we’re honored to have you on the new International Independent Scientific Panel. So if I may jump to you, Anne, and you’re an AI scientist yourself. You know, all of us know you as a special envoy of President Macron, who made the February summit happen last year in Paris, but you’re also an AI scientist. So from your perspective, you kind of lived in these two worlds. So what works best for the interface? What kind of scientific evidence would you take to President Macron if you were to convince him to change the policy?

Well, thank you for the question. I studied AI a long time ago, but I’m not really a scientist. But I try to understand, of course. Understanding, I think, is probably the very first thing. And before we move to policymakers, I think it’s for citizens, for us as human beings. The things that we don’t understand… We tend to be more afraid of. I often quote scientist Marie Curie. She wasn’t an AI scientist, but she’s one of the brightest scientists that we’ve had, two times Nobel laureate. And there’s a wonderful quote by her. She says, nothing in life is to be feared. Everything is to be understood. And now is the time to understand more because, of course, there are more things we can be afraid of at the time when she was living and now as well.

So trying to understand things, having scientific panels is definitely the right thing to do. And we’re fully supportive in France of the scientific panel. We’re very proud that Joëlle Barral is our nominee. She’s a scientist in AI and health and a member of the panel. This is absolutely excellent. So, yes, understanding things. is absolutely key. And then maybe just a second point to give an example of how understanding something or not can lead to very different policy decisions in the field of AI and work. We’ve had predictions. I remember in 2013, that was the previous AI revolution, but scientists, I believe, at Oxford said within 10 years, half of the jobs will disappear. We haven’t seen that.

At the AI summit in Bletchley Park, for very good reasons, we had frontier AI leaders in particular, Elon Musk saying within two years, half of the jobs will disappear. So, of course, the fact that this didn’t happen doesn’t mean that there isn’t a risk for work. Of course, there’s a risk for work. But if your potential or probable outcome is the end of jobs, then you need to think about universal basicism. Basic income, what are we going to do with all the people who don’t have jobs? If what economists are saying is that 80 % of the jobs will be transformed, then the policy outcome is training, skilling, reskilling, and helping to educate people. That’s why listening to economists and having the International Labor Organization and other institutions really follow closely what is happening in which countries for younger people, for older people, for women, for men, for different types of jobs, that’s super

Merci beaucoup, Anne. Merci. And I’m going to turn to you, Professor Sood. You occupy an important position within the Indian system, and you look at science broadly. And India has deployed some of these technologies at societal scale. India stack the digital public infrastructure. So how do you look at the AI opportunity, and importantly, how do you look at AI risks? And how are you prioritizing R &D allocations to harness the opportunities, manage the risks?

Thank you very much for having me on the panel. As you know that all the aspects which you asked, we have had very extensive consultations across all stakeholders. And we came out with the National AI Governance Framework, not the regulatory framework, but how do we really handle AI, all aspects. And there we have looked at how do we enable the compute facility, compute resources to our people. Because we are not at the scale when a few trillions of dollars are being invested. So we came out with some framework which we think with public -private partnership we could enable it. And we could see the results of that within a year as demonstrated in AI Summit.

Summit, the release of AI, so on, models and so on. Other aspect which is very important, as you rightly said, the risk assessment. So this is where, as has been mentioned, our experience with the digital public infrastructure, which has been rolled on a very public scale with the safety and security, which is as difficult as in AI. AI, of course, is more difficult. We still do not know the risks. But when we were dealing with the digital public infrastructure, either for the financial transactions or for identity, identity verification and so on, it was a challenge. And that was done by embedding governance through technical design. And this is what we call techno legal, which Honorable Prime Minister said in the Paris summit.

And also mentioned here. So this is where we are suggesting that this could be one way to look at. It’s not that everything is laid out. We will need framework for that. We will need technologies for that. But this is one way which will have a smooth. interaction if we can bring this technological framework.

Thank you so much for those insights. And now that since we are running out of time and I’m going to discriminate against the men on the panel so my apologies in advance. So I’m going to turn back to you Soumya and Aan for like 40 second, 30 second reflection. What do you think in terms of the pace and direction of technology opportunities including for accelerating scientific discovery and risk. What would be your advice for the international independent scientific panel maybe Anne you can go first 40 seconds.

Yes I think AI has a strong potential for helping science we’ve seen that with the two Nobel prizes in physics and chemistry a year back. There’s many more areas in science where AI can help. It can only be possible if we have databases of scientific data that are available to the world and that are constructed by scientists and funded by governments and international institutions around the world. So this is a very important topic for research.

Thank you, Anne. Soumya, you have the last one.

Yes, I agree very much with Anne. And I think that the scientific panel could actually help network many more groups of scientists from around the world, perhaps sectorally, for example, what’s happening in health, what’s happening in education, what’s happening in agriculture, looking at the evidences as they emerge, encouraging research, setting priorities, but also looking at safety and risks, because I think that’s going to be very important. There may be unanticipated risks and harms that we have not considered. And, of course, equity, being a UN -led panel, ensuring that equity is at the heart of AI and it’s being done for public good.

fantastic thank you that’s a great closing ladies and gentlemen please join me in thanking our outstanding panel and we are going to move straight to the closing over to you Anil

thank you to the panel for a rich and forward looking discussion to close this session it is my honor to invite Josephine Teal minister for digital development and information of Singapore to deliver the closing remarks minister Josephine Teal

good morning everyone first allow me to thank the secretary general for his remarks and it serves as a very useful guidance to all of us working in this important technology for the closing this morning I thought that it would perhaps be useful to offer a perspective from a small state Singapore has a population of just 6 million people and more than 30 years ago at the UN we became the convener of the Forum of Small States which still has about 108 members I will just make three points on how we look at developments on this front The first point is that we believe in AI being used as a force for the public good but to do so, it is important that we continue to invest in the science that underpins it and ground trust in evidence This certainly requires sustained investment in research and is also the reason why we set aside a billion dollars in a national AI R &D plan which will include foundational and applied research into responsible AI We believe in it and we have to put money behind this effort There are of course other investments such as in building up a digital trust center.

It’s our designated AI safety institute that has been participating in important conversations on this topic, as well as setting up a center for advanced technologies in online safety. So those are just some of the efforts that we can dedicate resources to doing as a small state. The second point I want to make is that there is almost always going to be a tension between moving quickly, given the pace of AI development, and moving carefully, giving the latest evidence that presents themselves on what we should be paying attention to. Both impulses are necessary, and we believe it is not impossible to try and balance them through integration of science and policy. It is not easy, but it is not an effort that we must give up on.

I should just add that on this score, it will be much better if we can cooperate internationally to develop sound approaches that can also be interoperable across different jurisdictions. And this is one effort that we believe underpins the work that is being carried out by the UN. And this brings me to my third point. I want to highlight the important role that an organisation like the United Nations plays in facilitating global discourse to bridge science and policy. I cannot overemphasise the importance of this effort. We must recognise that global AI governance landscape is becoming increasingly fragmented. There are multiple initiatives, frameworks and institutions. The UN’s unique value lies in your legitimacy and inclusiveness to encourage interoperability across efforts.

The Secretary -General talked about this too. We therefore welcome… We welcome the establishment of the… independent international scientific panel on AI, building on the work of the UN High -Level Advisory Body on AI, which published its report on governing AI for humanity at the end of 2024. We note that the panel’s multidisciplinary approach, covering machine learning, applied AI, social science, ethics, all of these are necessary to address the complexity of AI governance challenges. Finally, I would just like to acknowledge that we now have substantial convergence on the high -level AI principles. Yoshua talked about this. Transparency, accountability, fairness, safety. But the challenge is in operationalizing them. We need to find standardized evaluation methodologies that work across different regulatory contexts.

We need capacity building so that all countries can meaningfully engage with the technical and the technical challenges. We need to work with the technical evidence and not just with the large AI research ecosystems. I would encourage all stakeholders to view scientific input not as a constraint on policy flexibility, but as a constraint on policy flexibility. as a foundation for more durable, effective governance that can maintain public trust. We need to keep the conversations going, one where science informs governance, and governance sharpens science. I would just perhaps end by highlighting Singapore’s continued commitment to contribute to advancing these discussions. We were very fortunate to host the International Scientific Exchange on AI Safety and to bring about the Singapore Consensus on Global AI Safety Research Priorities.

Joshua was in Singapore for this very momentous event. We will continue to participate in joint testing efforts of the International Network for Advanced AI Measurement, Evaluation and Science. We have organized two editions of the Singapore AI Safety Red Teaming Challenge, the first multicultural and multilingual AI safety red teaming exercise focused on the Asia -Pacific region. And as chair of the ASEAN Work Group on AI Governance, we have actively spearheaded efforts to foster a trusted environment in ASEAN by adapting global norms and best practices for ASEAN and in bringing about regional harmonization through the ASEAN Guide on AI Governance and Ethics, as well as expanding it to address the risk in generative AI. We are now working within ASEAN to explore practical tools for AI safety testing and aim to collectively develop a set of AI safety benchmarks that reflect our region’s concerns.

And finally, I’d like to welcome all colleagues to join us in Singapore for the second edition of the International Scientific Exchange, which we expect to take place on the 17th and 18th of May, and we look forward to furthering

Thank you very much once again. Thank you, Mr. Teo, for your closing remarks. This session is now concluded. Thank you very much. Thank you.

And this is what we will explore at this event. To introduce the topic, we will first have two distinguished speakers. First, I have the honor to welcome Mrs. Anne Le Henanf, France Minister Delegate for AI and Digitalization Affairs. Welcome, Madam Minister.

Excellencies, distinguished guests, ladies and gentlemen, it’s an honor to address you at Smaller Footprints, Bigger Impact, co -organized by France, UNESCO, and the Sustainable AI Coalition. This event is a continuation of the work co -chaired by India and France in preparation of this AI Impact Summit. putting resiliency, sustainability and efficiency at the heart of the global agenda. The question we face is no longer how can AI work for us, but how can we ensure AI works efficiently, responsibly and fairly for people and for our planet. Resilient and sustainable is the key to unlocking digital transformation, environmental protection and inclusive development. Sustainable AI is not an option, it’s an imperative. First, it’s an energy and environment imperative as governments decarbonize.

AI’s energy demands. Threaten to outpace green energy progress. Model providers face a stark reality. AI’s energy needs are growing faster than supply. Second, it’s a fairness crisis. Massive AI models without sustainability create new divides and can exclude regions and communities lacking resources. That is why France, at the AI Action Summit, made sustainable AI a priority through the Sustainable AI Coalition, launched with UNEP, ITU and India as founding members. Our goal? Leverage AI to solve environmental challenges without exceeding planetary boundaries. From 90 initial partners, we have grown to over 220 million people. We are the first to have a sustainable AI. including tech firms, startups, utilities, NGOs, and research institutions backed by eight international organizations and 15 countries with the Netherlands joining this year.

Sustainable AI is now a global priority. Embedded in the UN Global Digital Compact and a UN Environment Assembly resolution. To turn vision to action, we focus on three pillars. First, research. In 2026, the coalition will launch AI research pitch sessions to connect university projects with funding and industry partners. Second, measurement. You can’t improve what you can’t measure. Today, I’m proud to announce on behalf of the coalition ITU, the Institute of Electrical and Electronics Engineers and ESO that we published the second version of the global approach on standardization for AI environmental sustainability to promote consistency in AI environmental sustainability standardization and third, action. France is implementing policies for low carbon efficient AI, powered by renewable energy hosted in green data centers and designed to be leaner and smarter this approach boosts competitiveness and discovery with minimal environmental costs that’s why as an AI Impact Summit outcome, India, France and UNESCO launched the Resilient AI Challenge, a global challenge to advance compressed, more energy -efficient AI models.

This initiative supports innovation aligned with our shared goals. Sustainable and resilient AI must be the global baseline. The only path to equitable development that services people and the planet. France and India have led this effort from Paris to New Delhi by focusing on people, planets and progress. Now we must deliver together. I look forward to our panelists’ insights and now invite to continue. Thank you.

Thank you. Many thanks, Madam Minister, for this insightful introduction and the pioneering role of France in Sociable AI. I have now the pleasure to welcome Dr. Tafik Delassie, Assistant Director General for Communication and Technology Sector at UNESCO, whose landmark report on smaller models was published in July last year. Thank you.

Madame la Ministre de l ‘IA du Numérique, Madame l ‘Envoyé Spécial pour l ‘IA, distinguished participants, esteemed colleagues, dear partners and ladies and gentlemen. I’m very pleased on behalf of UNESCO to be with you this afternoon for this important session. But allow me first to raise a question. What if the next breakthrough in AI is a breakthrough in AI? is not about building other larger models, but about building leaner, more resilient systems, systems that can solve whole world problems and real world constraints, including in low resource environments. Before turning to the resilient AI challenge, I would like to warmly thank the government of India for its leadership in convening this timely, strategic, and important forward -looking summit.

I also would like to acknowledge the co -chairs of the Working Group on Resilience, Innovation, and Efficiency, the Ministry of Power of India, and the Ministry of Ecological Transition of France for their strong commitment, engagement, and stewardship. My sincere thanks also go to our technical and ecosystem partners, including Mistral, Google, Hugging Face, Alkosh, Sarvam AI, and the broader Sustainable AI Coalition. alongside many academic experts who have contributed to this collective effort. UNESCO is proud to serve as a key knowledge partner for this initiative and to support the vision of India regarding AI that truly serves the people, the planet and prosperity. I would like to convey briefly three messages. First, the future of AI will not be defined by scale alone, but rather by resilience.

Second, resource -efficient AI is not a trade -off. It is a path to inclusion and access. Thirdly, delivering impact at scale requires global collaboration that is truly grounded in real -world validation. We are at a critical inflection point. Generative AI tools are now used by more than 1 billion people on a daily basis. Yet, behind every prompt lies a growing energy and resource footprint. Inference already amounts to hundreds of gigawatt hours per year, and this is comparable to the annual electricity use of millions of people in low -income countries. Training frontier models is even more energy intensive. A single large AI model can consume over 1 ,000 megawatt hours of electricity, enough to power villages across India for a whole year, placing increasing pressure on energy systems and reinforcing inequalities in access to compute and infrastructure.

These challenges are not theoretical. They are real. They directly affect whether AI can be deployed. In public services, also by small, medium -sized enterprises, the technology is used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a system that can be used to build a in rural health systems and low connectivity environments, both in developing countries but also in advanced economies facing growing energy constraints.

This is why the next breakthrough in AI will not come from building ever -larger models. It will come from building smarter, leaner, and more resilient systems that can deliver impact under energy constraints rather than exacerbate them. A proverb says, a good life is for everyone. It captures the spirit of living well together, in community, inclusively, and in harmony with our planet. In the same spirit, AI must be designed not only for those with the greatest computing power, but for all communities. It is everywhere around the world. The work of UNESCO shows that small but conscious design choices, such as model compression, task -specific architectures, and optimized inference can reduce AI energy consumption by up to 90 % without compromising performance.

Resilient AI is therefore not only greener, it is more inclusive, more affordable, and more adaptable. It lowers barriers for researchers, empowers local ecosystems, and enables AI solutions to reach communities too often left at the margins of the digital transformation. This brings me to why we are here today. It is my pleasure to officially announce the launch of the Resilient AI Challenge, which is a flagship initiative under the India AI Impact Summit Working Group on Resilience, innovation, and efficiency. This challenge moves us decisively from principles to action. It brings together model providers, researchers, startups, and academic teams to demonstrate how open -source AI models can be optimized, compressed, and deployed to achieve strong performance while significantly reducing the use of energy.

Rather than comparing entirely different models, the challenge focuses on improving one base model per task, ensuring transparency, fairness, and rigorous benchmarking. Submissions will be evaluated on shared infrastructure and ranked on both accuracy and energy efficiency, generating clear and actionable evidence. The winners of the challenge will be announced at the AI for Good Summit this coming July in Geneva, but the real success will be, of course, much broader than that.

Thank you. before we delve into the panel I will invite the keynote speaker and the panelists to go up front for a picture now that we have the final line up and then we start the panel thank you Thank you. Thank you very much. Thank you very much. So now let me welcome our distinguished panelists and Mrs. Anne Bouvreau, Special Envoy on AI for France, moderator of this panel, to discuss how to make these models work and deploy in real life to the benefit of all. Thank you so much.

Thank you very much, Hélène. Thanks to the… the two keynote speeches that we just had first. Without further ado, I think what we want is to head into the discussion, so I will not make long introductions. I’m delighted to welcome our distinguished guests, James Manika, Senior Vice President, Google Alphabet, Arthur Mensch, CEO of Mistral AI, Abhishek Singh, lead organizer of this summit. A round of applause for him, please. Thank you. And Ambassador Philip Tigo, Ambassador and Tech Envoy for Kenya. Thank you. So the AI industry, according to the International Energy Agency, will probably consume 3 % of worldwide electricity production by 2030. This is not the end of the world, but this is a huge expansion.

The world’s largest energy source is the United States. The world’s largest energy source is the United States. The world’s largest energy source is the United States. The world’s largest energy source is the United States. And therefore, there are environmental costs and impacts that we need to mitigate. AI, of course, at the same time also creates opportunity to optimize resources, including energy. So how can we ensure that AI’s development, in particular in developing countries but everywhere as well, is something that comes together with a focus on the planet? I’ll start with a question for Ambassador Philip Tigo. Let me turn to you first. You’re an attractive proponent of, active proponent of a more efficient and sustainable AI.

Africa is one of the most energy -constrained regions. It’s also a continent where adoption is becoming very frequent. We saw that with mobile phone payment. We saw that with other technologies. technologies. How is Kenya approaching efficient AI? What can you share with

Thank you so much. And I’ll be very quick because I can see the ticker. There are a couple of things. One is that we’re very lucky as a country that our energy mix is already 95 percent. And we keep on investing into that. So we have geothermal, we have wind, we have water, we have solar, and we have hydro. So that’s the first kind of framework that we have, that it really must be green by design. The second part, of course, is that where the green comes in, it’s always not necessarily on the efficient data centers or how they’re energy efficient, but also on the use of it. So part of our green by design is also kind of wide scale of education around how people use these resources.

For example, you shouldn’t be looking for the next Starbucks, for example, when you’re using AI. You should really be using Google as an option. So people need to have those choices in their heads by design. The third part, of course, is protecting Kenya alone is not enough. You can put a green shield around the country. but AI is global. So the third part quickly is working in the international framework. So as you know, we worked with the Coalition for Sustainable AI to champion the first ever AI resolution environmental sustainability, and part of it had the four parts, right? The energy, the life cycle, the sustainability piece, but also the improving the set of the science to continue to understand the energy efficiency component of AI.

Excellent. Thank you so much. And we’ll try to keep this lively. My next question will be for James, for James Manika. Google is one of the key players, of course Mistral as well and Hugging Face, but you’re a key player in publishing transparent data on environmental impact of AI. And you develop both very large frontier models and also smaller, very efficient models. It’s the Gemini and the Gemma. Thank you. So I’ll start with the Gemini family. From a business and an engineering standpoint, I think it’s a very interesting family. Where is the real frontier? Is it scaling up or scaling down?

Well, thank you. Pleasure to be here at the summit with you, Anne. I think just to get to the question, we’re actually looking at this on multiple fronts. On the one hand, if you look at, for example, our Gemini models, it’s not one model. We have a whole model family, which starts with the Gemini Pro, goes to the Gemini Flash models, which are some of the most efficient models. So we’re trying to make sure with our models, our Gemini family, we cover the performance efficiency frontier of these models. You may have noticed that recently no one really talks a lot about model size. Remember, two, three years ago… It used to be the big craze.

It used to be the big question, how many are, how many parameters. And that’s because even with our Pro models, we’re now pursuing this mixture of experts’ architectures, where the activation of the model doesn’t activate. The entire model. No one activates the dense models anymore. People are activating and reactivating our mixture of experts. So on the Gemini models, we’re trying to cover the performance. performance and efficiency frontier. Then we also have our GEMA models. Our GEMA models are our most efficient open source, open weights models. In fact, here in India, on AI Kosh, which is the platform in India, we actually have on there 23 GEMA models. And that’s because we’ve optimized them for different sizes.

Some of them are efficient and run on a single GPU because we know that the needs on the edge, people want a variety of model choices. To make sure we drive efficiency. I’ll say two more quick things very quickly. Every year we focus on efficiency because it’s both from an energy point of view, from a computer efficiency point of view, even from a business standpoint, it’s the right thing to do. Because as you start to serve many more people, you want the most efficient systems. I’ll say one last thing finally, which is we are making probably extraordinary, probably the most investments of any… anybody into using green energy, clean energy for our energy, for our compute.

In fact, we’ve made this audacious goal that some point in the 2020, 2030, 2035 era, we want to be 24 -7 carbon free. So we’ve made investments in nuclear, in geothermal, we actually have several operational data centers in geothermal. We’re using hydro, we’re using wind and solar. So we’re making, we’re trying to get to a point where all our energy uses for our compute is carbon free. That’s our kind of our moonshot goal.

Excellent, thank you so much. I’d like to move to Archer Mensch, to Archer. Mistral is developing very large models, but really also being very good at high performance compact models. And I know your engineers and you as a co -founder and CEO also strongly believe in the environmental impact of AI and what can be done there. So what can you share with us on that and with your both business and engineering experience, where does model efficiency have the highest return? So I would say the second one. You can. Thank you. Wonderful. Tim Mark.

So I would start with a couple of technical aspects. So to James’ point, the model size is indeed not only the only thing that we should be looking at. Effectively, we are using sparse mixture of experts because those are models which have a lot of parameters to store knowledge, but where you only activate 5 % of them. So that has been a key way of reducing the number of flops you do to generate one token, which is the one thing that matters for energy and therefore for carbon intensity. It’s one of the multipliers, actually. So the sparse… city matters and then you the other thing that matter is the systems on top I would say the the caching systems that you can put the way you’re managing the context so that you’re not reprocessing information and beyond just releasing the model weights that is something that we’ve always done we’re also heavy contributors to inference frameworks that are doing more and more advanced that are using more and more advanced technology to handle the caching systems in a way that where we are actually removing the wasteful computations that we used to do so it’s a it’s an algorithmic problem it’s actually very interesting it’s also a machine learning problem because depending on the request that you’re getting you can actually route the request to a small model or to a large model and so to James point it’s actually very important for any company doing models to actually have small models all the way to large models in particular because the large ones can be used to make specialized models after that so very important that’s an important point But I would say if you look at the carbon footprint today of artificial intelligence, because most of the GPUs are currently being used for training, I would say most of the weight comes from the fact that you have around 10 labs in the world that are training models that at the end look very similar.

And so for us, if I look at our biggest leverage there, the fact that we’ve been open sourcing models that are very large and we’ve been open sourcing our best models really, has been a major way of reducing the externality cost that you’re producing. Because we’re investing and it costs a lot of carbon to actually train a model, but then we give it for free to everyone else. And what that means is that people can build on top. And that’s amortized costs. Suddenly you don’t have 10 companies doing and training the same kind of models, but this thing is out there and you don’t need to reinvest. So I think that’s the big part. So that’s really on the training front.

And today training is the thing that takes most of the cost. when it comes to training. Now, when it comes to our own approach to sustainability, and I think I agree with James, one of the multipliers is the carbon intensity of your energy. And so there is a locality aspect to it, and we’ve been building our data centers and training our models recently. We’ve been training our models recently on our own hardware, which sits in France, which France is heavily nuclear, so the carbon intensity is low. Also 95%. Yes. Philippe, sorry. And in Sweden, it’s not 95%. Still very good, still very good. But in Sweden, and in Sweden where you have hydro. So choosing the locality is important because it’s one of the multipliers that you want to optimize for.

And finally, the one thing to worry about is, I mean, model size is one thing, carbon intensity is one thing, and then chips are also another thing. So being able to use the diversity of chips is huge. It’s super important. And we are in the… on using new kind of chips that are much more efficient from an energy perspective. Now to James’ point I would like to add the good thing about AI is that we are energy constrained and so suddenly it means that efficiency is actually driven by business. So I mean I would say transparency is super important for us and matters for our customers so we give, we’ve done like a very deep study on how that works and the carbon intensity of our training, we’ve done it with Mistral Large too with third party auditors etc.

But the business is also driving the, it’s also a reason why we’re going toward more efficient models because we don’t have enough energy, we need to have things that run on smaller hardware and it depends on the countries as well. Like there’s actually in the US the constraint is higher than in Europe and I think it’s going to be very high as well in Africa and in India and down the line. So it’s always good when business aligns Yes. Of course you can. And I think it would be valuable for public procurement in particular to put more pressure on sustainability as a way to accelerate the industry because that raises the stake and so that also pushes us toward more efficiency.

Wonderful. Thank you so much. I think that was really… Do you want to react quickly, James? No, no. Before we go to Abhishek?

I was going to agree with Arthur, but I’ll maybe add a couple more components. One of the things that is also important in this conversation is what you actually apply AI to. So there’s a whole range of applications of AI that actually are helpful for sustainability, grid management, managing with the adaptation and effects of climate change. And we’re seeing a lot of those kinds of applications at scale in ways that make an enormous difference to the sustainability question.

So adding to that, you have agriculture as well where you have a lot of leverage. You have material science and chemistry. So we work with vertical AI companies to try and make that happen.

Great to see this. Thank you. I think we have a very high -quality exchange in this panel. Abhishek, I’d like to move to you and, yeah, and the microphone as well. And Archer actually introduced the fact that energy constraints are real, and they’re real in India, of course, and you have such a high population and wide market and also, of course, infrastructure constraints. How do you approach this? How does the AI mission in India approach this? And what are you doing on this front?

the AI factories, with the hope that ultimately this investment will pay out. But when we ultimately look at how it will pay out, it will come out through inferencing. And we are doing inferencing at scale, ultimately users will have to pay. So until and unless you have focused on efficiency and sustainability, actual ROI on the investments will not work out. So it will be in the interest of everyone and only those players will survive who actually ensure that per token energy use is the minimal. So it will require innovation at multiple levels. It will require innovation at how do you do the algorithms, how do you do the inferencing, how do you use it. And therein, the value of small language models will come in.

While it’s fashionable to go for a trillion parameter model and more, but ultimately if you are building use cases in key sectors like healthcare or education or agriculture, you’ll need to go through smaller models which will be consuming less energy and which will be able to cost less. So sustainability is something that is given. So what we are doing, of course, in India… mission and in India is, number one, we are not chasing the trillion parameter models. We are not in the parameter game, number one. Number two, we are not even right now at the stage in which our companies are. I don’t think anyone of us is chasing AGI, which is like glamorized by some of the frontier AI models.

We are trying to think of what are the solutions which can be built by using current level of models which are available, which can solve societal problems in various sectors. To have real impact. Real impact. It’s a plug for you. Yeah, exactly. And when we do that, the cost per inference, the cost per query is something that becomes material because many of the public sector applications, especially in sectors like agriculture or healthcare, education, for some time will have to be funded by government, which will mean the taxpayers’ money. So we cannot be extravagant in doing that. So ensuring that the PUEs or data centers are lesser, ensuring that grid efficiency, we have, in fact, we are doing a project with the Ministry of Power, which I think finds a mention in the resilient inter…

committee’s report also, wherein we are using AI for improving grid efficiency, reducing the transmission distribution losses and what we have felt is that doing it smartly and using technology for doing that brings down the T &D losses by almost 10 to 15%. That’s again a big, big gain. So we’ll have to look at the entire ecosystem right from what kind of chips you are using for what, if you are doing inferencing do you need the high -end chip for doing that. So classifying it, having a very sector -specific application, specific use case basis approach for designing your systems will ultimately be where the game is and those who are able to do that will be able to build more sustainable systems their cost per query will be lesser and they will be able to survive.

So we, as government we are trying to enable this but ultimately I feel that business sense will ensure that sustainability comes in. We cannot be, it cannot be like that we can consume as much energy as we want unmindful of the ramifications. We have the funds and the VCs will pay only till a particular time. It cannot be forever.

Excellent. Thank you. We’re unpacking a number of things and we’re unpacking training from inference and utilization. We’re unpacking large models with smaller models and actually you need to get the larger models ideally through open source to be able to do the smaller ones. We’re looking at how AI can further then loop back and help optimize. We’ve heard a number of super interesting things. We started, you started a little bit on this Artur, but let me ask this question of everyone quickly. What, first of all, we also heard that business interests and commercial interests are aligned with the desire to make AI more sustainable which is a very hopeful message but what can governments and institutions do to further help improve this?

Artur, you hinted at public procurement. Do you want to say a few more words on this?

Yes, it’s one of the ways in which we can build and make sure that efficiency is favored. Again, I think the market can solve it, but it can be accelerated, and the faster we can go, the better, because effectively we’re really building a lot of electricity at the moment for AI, and so if we can just make sure that efficiency is part of the consign, that’s good. It’s worth noting that for better or worse, artificial intelligence, generatively, is turning into being a utility company. Being an AI company is turning into being a utility company, in that you’re basically turning electricity into tokens. It’s highly competitive, so that means the margins are getting, I would say, thinner, and which means that things are also getting price sensitive, and so when it comes to being price, when things get price sensitive, efficiency really matters.

So that’s going to be partially solved, and that’s what we’re going to do. the market, but can be accelerated. And I’d say the way it can also lead the way is probably by sustaining open source projects that actually go beyond the models. The inference path, what we call agent harnessing, is also something that will eventually become common goods and can be used everywhere. And so good practices, incentivizing research as well, because the domain of routing, picking the right models, the domain of distillation, those models do not require you to have thousands of GPUs. And so you can do efficient research, so public research on that domain is very much possible, and we’d love to see more of it.

So I guess that’s the three things that I can mention.

Wonderful. Thank you. James, do you want to add a few words on that?

Yeah, first of all, I agree with the three things that Arthur mentioned. I would add a couple more. One of the things that’s actually quite interesting is the more government can actually incentivize and encourage… Come on. to use off -grid solutions is super important because that takes the burden off the public infrastructure that affects citizens. And so, for example, we’re spending a lot of time thinking about off -grid solar, off -grid wind, and we’re thinking about geothermal. We’ve even built in our own small modular reactors. And we’re also investing, to Arthur’s point, in breakthrough research. One of the most exciting areas, by the way, which is not as far away as people think it is, is actually fusion energy.

So we’ve made some of the biggest investments in fusion energy. And, by the way, AI is actually helping us make that progress because one of the things you worry about with fusion energy is how do you do what’s called plasma containment, where you can actually hold these high -energy particles and contain them. And AI has actually helped us do that. So even the use of AI in breakthrough research like that is pretty important. I’ll say one other quick thing very quickly because it reinforces, I think, something that Arthur and actually the minister said, which is… Inference is going to turn… to be the most important thing in many respects, far more than the training part of this.

And we’ve actually started to invest in that. So, for example, we’ve actually built, you know, we have our own chips, TPUs. We use TPUs and GPUs. In TPUs, we’ve actually built some inference -specific TPUs just for inference, to be able to do inference even more efficiently than what you would typically do with a general kind of GPU.

Wonderful. Thank you. Ambassador Philip Tigo, what can you… Maybe you can take the microphone from a neighbor, and then I’ll ask Abhishek to conclude.

No, very quick, because a lot of the solutions are for developed economies. I think we have to be a little bit realistic in terms of where emerging economies… I think, one, there’s a bigger question of sovereignty, right? And there are conversations around that. And there has to be trade -offs. Like, every country wants to have the entire stack in their country. So I think governments need to be very realistic around which parts of the stack they really want to keep in their country, especially if you have this… AI for green and green AI… conversation. I think the second part again is to look at, especially in emerging economies, is to look at sustainability across the stack.

So we may not have compute necessarily, but we have other parts of the stack. So how do you ensure that part of the training gets that done? The third part I think is to expand this definition of safety, because AI safety is very much around the models and not necessarily around the use and potential harms of the environment. I’ve not seen that research. So there could be an expansion of research around looking at AI safety, including environmental concerns. The other quick one, of course, is you can only know the environmental footprint from use cases, and it has to be specific. And these are deep dives, and I have a sense people need to invest in deep dives.

When I look at food systems, that’s an entire food system, so there’s potentially problems there if we do not necessarily have, and to my last point around the standards, we really have to invest in the standards. We’ve seen that in other electronics, right? So we need to see that. So everybody, everybody knows the kind of environmental standards that you do that, and that’s needs to be done at scale. Thank you so much.

Abhishek, what can governments do? You represent a government. You want the… It works?

Governments are doing… Every government is conscious of this. In India, in fact, recently we did kind of focus on the small model reactors, which James mentioned, is that we came out with a new policy under which the sector has been opened up for the private sector also to invest. What we do believe is that as inferencing needs go up and India, when we are talking inferencing, we are talking inferencing at scale. Say if 100 billion or 200 billion in the first phase and up to ultimately 500 million and more, people start using these services and the kind of back -end infrastructure that we need will be huge, which will consume a lot of energy. So to reduce the load on the existing grid, we will need to think of off -grid solutions.

We will need to think of dedicated small modular reactors, which can power the air applications. the world over what we are seeing is the more and more AI adoption is going up, energy costs go up. And if energy cost goes up, ultimately for elected governments it doesn’t be so well. So it has to be thought of, the entire strategy has to be thought of, how do we balance the needs between having more efficient and more intense AI solutions with the needs for sustainability, with the needs of reducing the carbon footprint, because we are also a few years away from 2030 SDG, Sustainable Development Goals. So ultimately we need to balance the both, the need for having more efficient AI and the need for reducing the impact on environment.

Otherwise we can’t solve one problem and create another. So that’s again something the governments are concerned of and I think augmenting the renewable energy sources, solar, wind and nuclear, the fusion thing will be the way to go forward.

Yeah, thank you very much. I think this has been a fascinating discussion. The we can we heard from all of the panelists that the environmental impact of AI is not an afterthought. It’s actually front and center. It’s part of the competitive advantage. It’s part of what companies and governments think about. This is a very strong and positive message that I think we can all be reassured with. Let me just close by mentioning the Resilient AI Challenge that was mentioned at the beginning. Registrations close on March 15th. So please submit your solution. Please join me in thanking this wonderful panel. Thank you, everyone, for joining us today and really hoping to see you engage into this Resilient AI Challenge.

This is first at the international level working on improving research on compressed models. So one of the… solution and tool that was presented in the panel so we really encourage you to register so thank you so much to our panelists another round of applause thank you Thank you.

The context is, have you overdone it? Right? When we talk about AI and cybersecurity, these two areas, how do they come together? There’s AI for cybersecurity, and there is cybersecurity for AI. Right? So what we’re going to do is, we’re going to discuss both aspects. We’re going to at least try. So, you know, the first question, and I’d like to actually point it to Ms. Zazie, you know, what has changed, you know, if you were to look at the larger picture, the big picture, you know, in terms of AI coming into cybersecurity? What has changed?

I think as what happens with all technologies, and AI is no different in that sense. It is, of course, as we’ve been hearing over the last few days, a technology that will redefine humanity and how we live, work, play, all of that. But one thing that it hasn’t come, and with all of the other technologies, that have come before it is that it’s both an opportunity and a challenge. And it’s particularly true when it comes to the security space. So on one side, there is the promise that, you know, for some time now, with the advent of technologies, number of things getting connected, all of our lives going fidgetal, cyber threats have become, the landscape has, of course, expanded, and threats have become more and more complex and complicated.

And for some time now, we’ve not been able to manage cybersecurity at human scale. So machine scale was, you know, a lot of tooling was already in that space. So there is the promise with AI that you can manage security better. So there is definitely that opportunity. But at the same time, there is the recognition, like Dario Amadai said on the main stage yesterday, that his biggest concern and all of our concerns is that AI brings a set of risks, which not all of us have. And there are a lot of them that we know of at this point in time today. So both of these, so it’s also, like I said, that commonality is there with all technologies that came before it.

It is both a challenge and an opportunity and a challenge. Because we’ve got to protect models from being jailbroke. We’ve got to make sure that the models don’t leak our confidential information or poison our data. We’ve got to make sure that most of these are open source models, that they come with inherent vulnerabilities, so how do we detect them? So we’ve got to think about securing AI as well.

Absolutely, and very rightly said. So it’s becoming a fundamental part of the infrastructure that is being then used to build applications. So earlier I think the perspective that changed was that we were looking at AI just at the application layer, but it’s gone much below in the infrastructure. It’s got embedded into the kind of systems which are now getting created and deployed. So we’re looking at AI as a way to make sure that we’re not just building a system that is just going to be running on AI. We’re also looking at AI as a way to make sure that we’re not just building a system that is just going to be running on AI. So we’re looking at AI as a way to make sure that we’re not just building a system that is going to be running on AI.

So we’re looking at AI as a way to make sure that we’re not just building a system that is going to be running on AI. So, and that is where I’d like to bring in, Narendra, you, your perspectives on what are you seeing in terms of national security? You know, is it something which is giving us a spike, a blip, something which you can discuss, disclose here?

Yeah, I mean, it’s required to be discussed. That’s one thing that’s definite. No, one, you know, I take the points that you’ve said. One thing about all the other technological revolutions, as you said, is that, you know, there was a time frame over which that seeped into the system. Okay, and then we had time to look at how do I use it beneficially and also to look at the adversarial effects of it and how do I mitigate those things. Case of AI is that it’s really happening at a breakneck speed. And there’s also an adoption, a willingness to adopt into enterprises of the different AI tools that are there. So that is where the scary part is there.

And the other is the adversarial part of the AI is that. though you use AI for cyber security but the issue is that there are nation states or big enterprises which are adversarial enterprises which would be using AI as a tool for doing it and they have got a lot of motivation to put in effort and thought process into how do I use it more effectively. Then the persons who are actually using AI for their own benefit, in terms of they are looking at how do I improve my productivity, how do I improve my efficiency, that’s the focus area that they are in. So this is where there is a disconnect and this has to be really bridged and that’s where the problem is.

The summit actually in one way it’s helping people become conscious about some of the measures that have to be taken. That is one part. The other is the difference between other systems and this is a little technical in the sense that in the other systems we have a separate control plane and a separate data plane. There we could actually control they provide access limits to the control plane. but here the data itself is the control so you have that poisoning of models happening through the inputs that are there so you could have a drift and over a period of time you will find that the model will not be behaving as you would expect it to behave and it’s not also very deterministic so there are challenges in how do I protect it now there’s AI systems to see that it gives me the consistent results after a period of time then there is also lack of clarity about what is the cyber security issue there and what is the issue of malfunctioning or a poor design of an AI system that lack of clarity also results in the challenges that are there.

I think these are the preliminary thoughts that I have. So at the national scale the issue is that when you have multiple entities at the enterprise scale and financial sector, the telecom sector and all of them and the power sector adopting AI the effect on compromises and the critical information machine infrastructure is something that would actually make us wake up and then see that what could be done. Those are issues that are there.

Excellent pointers, sir. Excellent pointers. And I think since you brought in the private sector and the way they’ve evolved and they’re also subjected to these risks which are evolving in nature. I’d like to bring in Lakshmi, sir, from Tata here. So, sir, a lot of infrastructure is being built, connected, communicated using what you’re building for the nation. So, how are you seeing the paradigm shift from let’s say how it used to be before AI was commoditized and everyday technology. It used to be the labs. Now it’s out in everybody’s hands. So what is the change that you are seeing and the impact you’re seeing on critical infrastructure?

I don’t think people have woken up to the fact that they are fast running towards the cliff. Because I genuinely think that that the digital infrastructure in enterprises today are already fragile. And we know that from an enterprise security point of view, there are so many attacks that are happening. And we know that there are huge issues when it comes to, for example, now we more talk about IT, OT security, the operational technology in factories were never in the purview of IT security. And there are, you know, security today and digital infrastructure in general is still very fragile. It’s islands of different OEM technologies and many, many things. And, you know, I don’t want to, you know, it is a major issue.

Now, on top of this fragility, you add AI. And this fragility is going to be multiplied 100 times. It comes over, right, on many, many kinds of platforms. because AI is going to increase the network traffic, especially the east -west traffic, by, again, multifold. The number of API calls that somebody… And we all are saying, oh, I’ll embed AI at the edge of the device, and if I have a banking application, I’ll do that, but nobody has thought through. If I put an inference there, or if you put an inferencing at the edge, the number of API calls these have to do is tremendous, and these API calls are long -lived sessions. They’re not traditional API calls.

So the edge infrastructure is going to come under tremendous strain. So that’s why I’m saying that in all our excitement of AI, I’m very passionate and excited about AI, but I genuinely feel that people are not looking at the foundations and… …properly. So that is very fragile, and that is one point I want to make. The second point about this is I would like to expand the scope of this discussion. It’s not about AI and cybersecurity alone. It’s also about a broader trust question. I think we all know, you know, whether fake, the messages, you don’t know. Apply that in the enterprise context. And there was a talk about, you know, model drifts and so on and so forth.

So what we at Tadacom are doing, one is to protect the digital infrastructure through many, many things that we can do. And the unfortunate part is I don’t think enterprises have woken up to the fact that they have to do it. So I tell them that you can’t build a skyscraper with a foundation of a bungalow, which is what they’re trying to do. But when it comes to the drift and the trust part of it, I do believe that enterprises require, require an AI operating system. and what we mean by that AI operating system is something that brings the context together because LLMs will provide the knowledge. To make that knowledge into actionable intelligence, you need the context layer, you need the agentic layer, and more importantly, you need to have a trust and governance layer which will control what an agent will do or will not do.

And if I take that control in my hands, and say that I will configure and ensure this agent will do something or not do something, I can make use of the models underneath a lot more intelligently. So I think rather than focusing on whether this LLM is good or that LLM is good and so on, this AI operating system is what is required for people to build an application which will ensure that all of these are governed properly.

Sir, that’s a great point. In fact, I was having a conversation a few days back, and I was saying that that from the time of corporate social responsibility, it’s time to evolve to corporate AI responsibility, where corporates start talking about how they’re controlling and owning the actions of the AI that they’re building and deploying. Great perspective, sir. Thank you very much. At this point, I’d like to bring in Richard to sort of continue the talk about digital infrastructure and resilience. So how has resilience in your perspective evolved when we talk about AI risks to cybersecurity and vice versa?

Well, the question of resilience is a complex question. So I will bring a few aspects that I think are very important. So it is well understood that there are a lot of people who are in the industry and that people are typically the weakest link in cybersecurity. the reason is that we as human beings we were not evolved to deal with machines, computers and so on and most of us don’t have really deep technical knowledge about how systems work and so on so we are to a big extent dependent on relatively superficial understanding and so we are more easy to be tricked by different social engineering tricks and so on. Now with AI this is becoming a big issue because how you can distinguish a scam from a real communication when the scam communication looks exactly like the real communication I’m talking about deep fakes and so on so this is one aspect of the risk connected directly with people.

The other aspect is that we want AI to empower people to do things, more things and make them in an easier way so we have those agents and we give them some or we want to give them some commands like do this for me or that for me but we don’t understand all the steps that the agent will take on our behalf when performing those tasks and each of those tasks can be there can be a risk factor involved without us knowing like if you want to perform this action you will need to have those additional tools to achieve that and where you get those additional tools if AI decides on your behalf these are the tools you need software packages, whatever it is and they get to your computer without this being supervised then this is a problem so and we have to be very careful and we have to be very careful where I’m heading, like resilience here is really protecting or paying attention to details.

What is actually happening? What is running in the background? How are your commands transferred to the agents? Is there a possibility for them to be intercepted, to be modified? So it’s even it was difficult and complex even before advent of the new AI agentic approach. Now it’s becoming even more important to really go into all the details and we just heard from Lakshmi that he sees that we are moving towards a cliff. Well, depends on us of course. We want to go fast. We want to employ. We are all excited about AI but we maybe sometimes we need to slow down a little bit and make sure that the pieces are in the place and cyber security is not overlooked.

Excellent, excellent perspectives. And I think an offshoot to that question can be to Ms. Daisy, which is what are you seeing as changing when you’re talking about digital infrastructure and especially the connectivity which it needs because you’re at Cisco, right? And here is something which is connecting a lot of things to a lot of other things. So how are you seeing changes happening, especially when you talk about resilience and what’s going on inside digital infrastructure?

So I think Lakshmi touched on a very important point of the underlying, the fragility of the underlying infrastructure. And that is something that I want to reiterate. For the past few years, we’ve been publishing an AI readiness index. And the good news is that we are as ready as everybody else. The bad news is maybe we’re not as ready as we think we are, which is the point Lakshmi is making, right? 90 % of… just under 1 ,000 large enterprises that we spoke to in India want to deploy agents this year. Forty percent want that agent to work alongside a human being, but only about two -thirds of those enterprises really have a data layer, data strategy, a data platform, and a data governance strategy.

Only about one -fourth have the compute capacity they need. Only about one -third are able to understand AI threats and deal with them. And less than one -fifth have the innovation engine to think about building and scaling and maintaining AI applications and use cases. So clearly there is this ambition versus reality gap which we have to solve for. That’s not a problem as long as we all know that that’s where it is, and they were acutely aware of this issue. Thank you. the other thing is AI is essentially leading to what this means is that we are rewiring and restacking the enterprise it’s not just networks it’s compute it’s silicon I know you know at the national level silicon security is a conversation so all this resiliency which we used to build almost like a bolt on at the top and particularly we used to think of it only as cyber resiliency it’s a system resilience which is built into all layers of the infrastructure stack all layers of the AI stack and that’s why at Cisco since you asked me a network specific question we used to have we used to deal with connectivity largely as connectivity and now we know the persona of that end port that connects to an end device that might be doing an inferencing or it’s in the data center that persona has to be that it will be on one side it will be a switch or a router but on the other side it will also be a security defense point.

So this ability of building special grade of security appliances and putting them in various parts of the network is fast becoming an outdated idea. And the point we’ve got to do is we’ve got to break it into a number of virtual instances that can go wherever you want the security policy to be. So it becomes a very virtual distributed mesh rather than hardware. Yes, there will be hardware. I’m not saying it will go away. But this ability to infuse it into the fabric and networks tend to be the all pervasive fabrics. That’s the way at least at Cisco we’re thinking about it. So this domains of networking and security are crashing together. So secure networking is like the conversation in the network space particularly.

The other part about this is the performance requirement which also Lakshmi alluded to. AI will put pressure on the underlying infrastructure. In a way it’s an exponential technology. The demands it will create on its underlying layers is also exponential. So we’ve got to almost build a new category of technology. Silicon systems, applications, everything. A new category has to be built and we have to build it in new ways. You cannot build it in the ways of how we built it in the past. Applications is an interesting one. We used to give an input, expect the same output on the other side. But now if you are going to deploy AI models, this thing is probabilistic.

And I refer to it. So you want to get it to a degree of assistance so that you cannot expect in a financial application or a very important citizen service application, you give an input and the output has to be deterministic. But you’re using at the core of it a probabilistic technology. So that refinement also takes a whole lot of work. So it’s rethinking in ways in all layers. of the, from silicon to software to systems, you have to rethink everything. Every rule we have to rethink.

Excellent, excellent. And since you brought in that perspective of rethinking, reimagining, and how we’re using AI in the operating system of the company, I’d like to bring in Darshan here. So Darshan, you do a great work, you do a lot of great work in creating thought leadership content as well as doing consulting work for very large companies. Of course, there are CXOs and a very highly ranked official of the government sitting here, but then what are the other six CXOs thinking about when it comes to AI? Is it still a compliance thing, or has it percolated into strategy?

First of all, thank you. I’ll probably add some context to whatever I’ve heard so far. So first of all, my views is any technology disruption brings in two emotions, right? So hope as well as fear. And I’m sure all the other panelists have rightfully covered the fear construct of AI in cyber safety. And rightly so. No disputing that truth. But there is a huge hope component from a cybersecurity company like for us because we are a hardcore deep tech cybersecurity company. I see a lot of opportunities. And we as a country, India, can also be, we are at the sweet spot between intersection between AI and cybersecurity. And this topic is very aptly crafted because I think it’s a huge opportunity for us to also utilize.

And I’ll tell you why. Cybersecurity has so far been a very asymmetric equation. The intruders have always had an advantage over the defenders or anyone who’s actually defending a network because they just need to get one thing right and we need to get everything right. So it’s always been asymmetric. But with AI, now all of a sudden we are at the level playing field from a technology standpoint to identify a needle in the haystack. For example, one classic use case. Can be an agent. security operations center. Because at the end of the day, if you have ever visited a security operations center, it is a 24 bar 7 someone, analyst looking at a screen and almost an inhuman job, so to speak.

But today, with AI, now you’ve got a level playing field because we’ve seen those kinds of use cases being deployed at our SOC, where even a shift handover is done by an agent. So a lot of real use cases. So I’m on the hope side. There’s a lot of opportunities that today we have. And second, in terms of talent, I mean, we have a lot of youngsters sitting in this room who are looking to grow. We have spoken so much about other services, other areas evaporating in terms of job opportunities. I think we can create the world’s cyber security talent combination with AI because cyber security and AI are not two different fields. They actually, cyber security needs AI and AI needs cyber security.

So I think we are at a very, very opportunistic opportune time for us. to really ride this wave and create world -class talent which can address. So now on the second part which you just spoke about, that’s what we are hearing at the CXOs globally since we deal with a lot of people in the payment ecosystem. CXOs obviously have the same construct of hope versus fear, right? So some are obviously being a CISO or a CIO. There is amount of fear that is also coming in because these are real problems, right? For example, deep fakes or spear phishing attacks have become more robust with AI. But one of the key things that we are trying to explain is that, yes, those are things that you need to address, no doubt, but can you also look at how you can take advantage of those AI?

And Lakshmi rightly pointed out, how do you have an AI operating system? Similarly, we talk about how you can have an AI security operating system, right? Which you should have a playbook on how to leverage AI rather than being on the defense player. So those are the… Those are my views. Samarath.

Excellent, excellent views and thank you very much for those perspectives and I’m glad that I still see people coming in, you know, this is an interesting session and some people standing as well. So I would like to bring in Pradeep now. Pradeep, you know, as a follow on to what I just asked Darshan, here is something which is, you know, at the top and we’re saying, you know, while it is percolating into strategy a bit, do you think that we should have a dedicated function within an organization and what are you seeing currently not just in India but elsewhere as well?

Yeah, thank you for that. So probably adding on to what Dharshan said, right, I don’t mind the hope and the fear thing because being in cyber security space, both of them do add more to what we can do, right, for the industry as a whole, for the country as a whole, if you would. When we look at strategically, when we talk to, let’s say, leaders and boards at companies in India across the world, predominantly when the conversation is about AI, the topic goes towards innovation, competitiveness, and ability to bring in, let’s say, productivity gains, right? What often gets missed is that AI is quietly reshaping the risk equation within the enterprise, right? Now, cybersecurity, right, so can no longer be just about protecting systems and the data.

Now, don’t get me wrong, right? Cybersecurity is still needed to be able to identify all the systems within your enterprise, enterprise beyond the extended enterprise, as well as be able to protect the data that is on all of these systems. But it needs to evolve into something more, given the AI landscape, which is, I love how Lakshmi put it, right? It’s going to be about trust. So going forward, can cybersecurity, how can it evolve to start protecting decision -making and trust? Because trust is starting to become measurable, right, through provenance, through authenticity, as well as verification. Now, all of these mechanisms are going to come in, in a way. that we are able to identify, measure, rate, risk, rank, and call out whether this particular transaction that you’re doing, whether it’s a payment approval or it’s an executive communication, is trustworthy or not.

And then accordingly, the agent of the system that’s allowing the transaction to go through allows it or not, right? So that’s something that we’re seeing, and AI in this context is a force multiplier, right, on both sides. For us as defenders, we are seeing, like Darshan said, how we are able to detect, identify threats at scale and speed that we have never seen before, right? And definitely, right, bringing in, again, it’s not going to, so if you ask, okay, is it going to completely revamp how we do and run SOCs? A little yes. It’s not going to replace all the analysts, but definitely in terms of certain tasks that we are doing, we already started seeing Microsoft with its security co -pilot, how it can automate tasks, right?

Like different agents doing different tasks, so we’re already starting to see that. Now, but in addition to that, it’s also helping attackers on the other side of the equation, which is it is industrializing disruption at scale. Think fishing. Think social engineering. Now, all of these manipulation, now it’s happening at an unprecedented scale. That’s going to continue, and you’re going to see it continue for, let’s say, the next few years because that’s where we’re headed in terms of air -aided phishing. I would say, yeah, definitely manipulation and how this is going to impact the industry as a whole. Now, I would say that’s pretty much how all of these, the shift is, the tectonic shift is happening, right, across.

So as I would say working with leaders and board members, we are looking at how to look at these risks and how to frame these risks, and here usually we see three lenses. So one is the compliance risk, which is am I complying with the EU AI Act, right? Am I complying with the TDPDP or other sectoral guidance? So it’s more of a check -in -the -box approach. Maybe helps with me in protecting against regulatory exposure, but not with systemic risk. like what Ms. Daisy was saying. The second angle which some companies have started to move towards is the operational risk, right? Where the boards are starting to ask, the models am I using? Is it reliable?

Is it safe? Is it trustworthy? And what is the risk if this particular model, a service provider who’s providing that model goes down? So that’s the operational risk angle that we’re seeing more of. The third angle which I think it’s very few companies doing today is probably the strategic risk angle, right? Where in being able to call out if I’m using this particular, if there’s an AI -driven attack, identity attack, right? That is reducing or impacting the reputation of my organization with my customers, what is my exposure in financial terms? Now these are questions that boards would start to need to ask because we are starting to ask those questions and get those questions from leaders in order to how are we able to measure those and how do you quantify risk in financial terms?

And be able to convey that to the board as well because that’s what at the end of the day boards are concerned in being able to. the stakeholders and shareholders.

That’s great and those are some interesting lenses that you put to the whole conversation. Sir, I’d like to bring you in now from your vantage point. When we talk about India’s DPI, we are implementing AI into systems which cater to healthcare, which cater to telecom, across the citizen supply chain, if you will. So how do we make sure that the AI deployments that we’re doing and what capabilities do we have to make sure that these deployments are secure and they’re taking care of the risks that the fellow panelists highlighted?

The financial sector, for example, is mature. But let’s say, take the health sector. It’s not as mature as others. But if you look at the enthusiasm, for example, of the health sector to adopt AI, you’ll find that the level of enthusiasm is similar to what is there in the other sectors. So that is a big challenge. We’ve been engaging with the health sector, for example. We’ve had recent meetings also to say that, how do I improve the cybersecurity posture of that sector? So that’s a big challenge, actually. So we had this digital divide. We have a cybersecurity divide that’s there. And now we are going to have this AI divide that’s going to be there across enterprises in different sectors.

So that is a challenge that is required to be addressed. That, I think, is the capacity building part. And also coming up with frameworks where people have access to that framework and understand what is really required to be done. And you talked of assessment. When an enterprise is coming with the AI system, is it secure? Is it doing the work? Is it doing the work it’s supposed to do? So we don’t have those assessment frameworks now. So if you’re aware, you know, the testing and assessment part is an important part, and creating that infrastructure so that people could go and then test and assess, that is an important part. The department of DRD has come up with an ETI framework, if you’re aware of it.

Similarly, from our office also, we funded a project, and still it’s around a year back. It started in November of 2024, we funded the project for coming with an assessment framework for AI systems. So that one is the security aspect of that, and the other is, of course, the functional aspect, you know, that also. In the sense that somebody claims that this AI system does something. How do you actually assess that? So I think one is the capacity building part, and the other is the, you know, having the frameworks in place is good. One thing good about this country is that we have an institutional framework that’s been established, especially because of cybersecurity over the period of time, like we have got the CERT India and CIPC, or their institutional framework.

and also the sectoral regulators also come up with sandboxing regulations in the sense that if you want to try out something new, you have these regulations that helps you to try out something new. So I think these like in the financial sector, you have the RBI sandboxing, the telecom sector also this mechanism. So I think people start using these sandboxes to prove technologies, to prove applications, to prove use cases and that will help them to actually understand how it really works before they deploy in production. That I think would help going forward.

Awesome. Thank you, sir. And I think it’s enlightening and enriching for all of us here to know your perspective especially what the government is doing towards it. I’d like to bring in Lakshmi, sir from Tata here for the next question. So, sir, if we reconvene five years from now here, what are we going to be talking about? What did we do? What did we get right?

I think this discussions are very healthy. whether AI with a positive lens or with a fear lens. I think we need to – I – on two comments. One is, you know, the question on assessment. We ourselves in TataCom, when we asked ourselves the question, where do you want to be five years from now? And I made a statement that the next five years will determine the health of the company for the next 50 years because the technologies are moving very fast. So for an assessment framework, we developed a framework ourselves. We studied a lot of material. We didn’t find something good. So we developed an assessment framework where on one axis we plotted the capability. You know, it includes talent.

It includes the platform, which is when I said, look, no point in doing individual use cases in an organization. How many use cases will you do? You need a platform approach, which is where we said AI operating system is required. So that is maturing. So one – on one axis. On one axis, we are going to plot the capability. So it’s talent, even culture. AI, I don’t know whether people have appreciated this is a very different paradigm even now in the discussions I see people talking about how AI can help automate things and do things faster no, that’s not what AI will do AI, you know while the previous technologies of cloud and internet have helped companies to scale transactions AI is going to scale decisions and when you’re scaling decisions you need to think of a different paradigm all together, and we are still talking in the old paradigm of what tasks can be automated and how it can be done so this is a new paradigm, so in the capability axis the culture dimensions would have to be thought through carefully and talent appropriate, I find some of the younger talent are more easy to train on AI than some of the older, unfortunately so I think the whole talent and capability equation is one axis, and we’re going to plot ourselves and the other axis is on the outcomes what outcomes do you really want to deliver with AI?

And there, you know, outcomes could be more on efficiency. Outcomes could be more on the revenue enhancement. Outcomes could be more on the trust and the customer satisfaction. All those outcomes need to be plotted. I must admit, we ourselves are somewhere in the lower quadrant, and I hope we as a company will move to the top quadrant. And that needs to be defined, and that needs to be visualized. And only then you can move towards that. And that’s what we’re driving the company towards. And all the platform development that we’re doing, strengthening our infrastructure for enterprises, and we’ve shared some of these assessments to our customers as well. So that is one. So I hope that most people would see themselves moving towards the top quadrant in five years’ time.

The second thing that I worried about in the context of strategy is, again, you know, when people talk about AI and strategy, and I think that’s something that’s been really important to me, and I think that’s something that’s been really important to me, And I believe that, like in the previous technology when we had Internet and cloud, there were new business models that came about. So we had intermediaries coming, the booking .coms and others who disintermediated many, many people, or fintechs who came and did things better than the larger banks. And only when the larger entities woke up to the fact that these people are going to eat their lunch. And that’s what happened in the previous wave of technology.

In the AI, I think similar disruption is waiting to happen. Don’t know where and when and what. But if a strategy does not think about that, as to what disruptions are going to happen, we would have missed the bus. So five years from now, I would expect a new class of companies who are AI native, who are out there going to disrupt the existing business model. So those are the two things I would expect in five years to happen.

Fabulous. And sir, one last question to you. If you were to give me a call five years from now and say, Samrat, this is how… nation states have changed what would that be?

See one is AI and I have talked somewhere else also, adoption of AI is a competitive advantage so that’s why you have to adopt AI and you don’t have any other because there are other nations who are going to adopt, there are other enterprises going to adopt AI and they are going to try to look at how do I do business better so that way going down you will find that we would adopt AI and this conference is very good for that five is down the line the other is protecting yourself from the adverse effects of AI because it’s a very powerful tool and then it’s just a thought process but I think as pointed out just one year we have found that such a lot of development has happened we do not know where this is really going to lead us so the thing is for us to be on our toes and to actually look through that how is this technology going to affect the way we do business and how we run our countries and then also and then you know this development of capacity capability and identify the dependencies that we have when this technology is adopted and try to see that how do I mitigate the dangers of those dependencies this is where I think the thought process would be and this is where I think the road map for the next five years for us.

Thank you, thank you very much sir and thank you all the panelists for taking time out and agreeing to do this for the audience I see the room is full and a lot of people waiting on the sides as well thank you all for paying attention please put your hands together for the esteemed panel that we have here together we have to conclude this panel only for the paucity of time otherwise we could have gone on thank you very much Thank you.

of these agents work with each other smoothly. And protocols are so important because that’s how builders interact with these products and how we make them interoperable and open to the rest of the world to sort of build on that for your own businesses, for your own benefits. And so we have an amazing panel here today. We have, so first of all, I’m Sihao Huang. I’m Senior Policy Advisor for AI at Emerging Tech at the White House. We’re joined with Austin Marin, who’s the Director for the Center for AI Standards and Innovation at the Department of Commerce, which really is the center for a lot of AI activity within the U .S. government, setting standards, driving innovation, measuring AI systems, improving metrology, and a lot of the smartest people in the U .S.

government are within Austin’s organization. And then we have the four frontier AI companies from the United States. So we’re very happy to be joined by Mike Salito, who is the Head of Global Affairs at Anthropic. We have Owen Lauder at Google DeepMind, who’s the Senior, Director and Head of Frontier Policy and Public Affairs. We have Mike Brown, who is head of growth and operations for OpenAI for our countries. And, of course, we have Weefi Fernandez, who is the director for global government affairs at XAI. So really the amazing lineup of U .S. industry. I said this in a previous panel, but American companies are spending $700 billion into infrastructure this year, just this year alone. And they probably won’t like it that I say this, but they’re competing very hard against each other to make AI models cheaper and more powerful for you guys to build on and to drive those applications.

And so this is going to be a panel on how we make that happen, how we standardize interfaces with those AI systems. And so first I’m just going to ask a question to the AI companies that are sat here. So over the past few months, I think, we’ve seen the emergence of an ecosystem of standards that move. To support the deployment of AI agents. I think one of the most notable ones is Anthropix Model Context Protocol, which a lot of other companies are building off of right now and is sort of becoming the industry standards. Of course, you have Google DeepMind’s A2A Agent -to -Agent Protocol, OpenAI’s Agentic Commerce Protocol, and then XAI, of course, has been working on its highly secretive and famous MacroHearts agent project.

And so all the companies here are very much involved in sort of this agent discussion. And so maybe open it up to the companies here to tell us a little bit about what these agent protocols actually do and what they have unlocked. And that’s sort of the builders who are sat here, the audience. What do they enable a software engineer or an AI engineer at India or other countries to create?

Okay. Well, first I want to start off by thanking Suhao and OSTP for organizing this panel and all the people who are here. Thank you. So it’s great to be here with Austin. I think Anthropic has really had a really strong partnership with the Trump administration and appreciated the leadership of Secretary Lutnick in expanding and enhancing the Center for AI Standards and Innovation, which is really critical to making this technology work for everybody in a manner that’s safe, responsible, and open. MCP is a universal open standard for connecting AI systems to the tools and data sources that people already use. So imagine the knowledge bases inside of an enterprise. You can imagine government data sources.

The Indian government, of course, is a real leader in, why am I forgetting the acronym right now, DPI, sorry, and just has massive amounts of data that are already digitized. And so MCP is a way that you can connect your AI models and agents to those data sets and also tools. And it really, you know, simple. intuitive way. You just need to give the model a rough description of what’s in the data source and what kind of tools or how can it access it. And then the model will intuitively know how it can use those data sources the same way that somebody in your enterprise or your organization would know if I want to get payroll data, I need to go to this human resources system.

If I want to get data about, you know, our revenue, I need to go into HEX or whatever your particular tools are. You know, before MCP, you really had to build all these systems in a very bespoke manner, which meant that if you built them with one model or one vendor, you were kind of stuck because you’d have to rewrite everything if you wanted to switch. MCP being this open source protocol that’s supported by all of the major AI companies means that you really have this degree of interoperability, which just enables the whole system to be much more open and competitive. We also recently built SKILLZ, which is a software that’s been around for a long time.

It’s a software that’s been around for a long time. It’s a set of instructions that teach agents how to perform. specific tasks. The way that I describe this or think about it is, you know, imagine a new person joins your team. You spend a little bit of time teaching them, you know, how to do work the way that your organization does it. And then you expect them to just be able to follow those instructions all the time. So you kind of teach once and then they’re able to do that. It’s the same thing with skills, which also is another open protocol where you can build these skills. And then if you decide that, you know, you want to switch from Anthropic to any of the other fine companies here on the panel, you can move those skills over.

And so that interoperability and data portability is really a critical piece of making this an open and competitive environment.

Amazing. Thank you, Mike. And, yeah, thank you to Sehow. Thank you to OSTP and the U .S. government for the event and all the partnership. And a big thank you and congrats to our Indian hosts on a fantastic summit week. If you take a step back, it has been, I think, a really exciting week, a demonstration of how advanced AI is now being used around the world to do incredible things. It’s been really exciting. I think seeing the way that people are using Gemini right across India, really exciting to see the way that everyone in India from world -class scientists using AlphaFold to teachers and students using AI in the classroom. And I think with all of the progress that we’ve seen in the last few years, it’s easy to forget sometimes that this is still relatively new technology.

We’re still in the relatively early innings of working out how to develop this technology and use it for good. And one of the things that we need to do, I think Seahaw covered this very well in his opening gambit, is build out this ecosystem of technical standards to make sure that we can continue using this technology in the right ways. There’s a couple of ways that we’re thinking about these standards. One is technical standards, interoperable standards, and then also standards for testing these systems, making sure that we can use them in a reliable and secure way. We really want to contribute right across the piece here, so we’re excited. We have various standards that we have contributed to the ecosystem.

Our agent -to -agent standard that Seahaw mentioned. This is basically a standard for how… agentic systems talk to each other. At the moment, it’s a little bit tricky for agents to converse with each other. You have to often write bits of bespoke code for an agent to talk to an agent, or they have to be running on the same walled garden code base. So what we do with agent -to -agent is essentially have a sort of digitized clipboard of information that an agent will share with another agent. What’s my ID as an agent? What are my capabilities? What am I trying to do? How do I take data? What are my security requirements? This is going to be absolutely fundamental to sort of greasing the wheels of the agentic economy.

UCP, another standard that we’re working on, so we have our universal commerce protocol at Google. This essentially does the same thing, but it’s for how agents talk to websites and payment systems. This is going to be transformative for business. It’s great to be able to partner with companies right around the world, whether it’s Walmart and Target in the U .S. or Flipkart and Infosys in India that we’re working with across these agents. Excited to see what… everyone is going to do with the technology that we can enable with this.

Thanks for the tip. Hi, everyone. My name is Michael Brown. My name placard says George Osborne, who’s a colleague. He got tied up in another panel, so I’m here. George and I work extremely closely together, but he has a much nicer accent because he’s from the U .K. I’m doing my best here. You’re doing very well, I might say, very well. For me, this is a fun panel because it feels like a very collaborative and cooperative opportunity to grow the pie, and the companies that are on either of our side are extraordinary companies with extraordinary humans, and it’s fun to just work with them in some of these areas. If I were going to kind of explain, why we’re here in this particular panel to my kids who are 9 -11, I would sort of say, look, are there countries out there in the world where when you get to a stoplight, red means go?

I don’t think so. I think mostly red means stop and green means go. I mean, if I’m wrong, I apologize. I’m not an expert. But, you know, having sort of shared understanding in countries, rich and poor, advanced and still developing around how things work, I think grows the pie because it allows builders to build in a way that everyone can kind of know that what they’re building is going to be both secure and is going to be accessible and hopefully enjoyable or useful to people anywhere in the world. And I think each of the companies up here is contributing something great to that. You know, I’ve joined OpenAthens. I relatively recently, but like MCP to me is something like I just knew it’s like that’s really important.

And like, well, Anthropic introduced it. Hopefully, Anthropic would agree with this, that now it’s just like the thing, right? And I think that’s terrific that it’s the thing. You know, Owen also mentioned in commerce, I don’t know if these standards compete or if it’s cooperative, but at OpenAI, we have a commerce protocol as well for the same thing, because there’s a world where these agents are going to be out shopping for us, which is kind of fun, right? So, you know, if the agent knows that you’re planning on taking a family vacation and it knows that you want to visit Goa and the agent can go actually secure your travel flights and your hotel, these commerce protocols can do that.

So agents of different companies, potentially in different countries, can all partner and work well together because they understand how they’re supposed to be looking for shared information and how that information should be shared. There’s kind of a shared understanding there. And so I think all of us are working to build these protocols to grow the pie, to create more democratization, more commerce, more benefit for everyone by having these common protocols in place.

Thank you, Sihal. Great to be with you all here, and thank you to the government for having us. What an exciting week, frenetic and kinetic and chaotic, as I was saying earlier. So it’s just an honor to be here and to feel the energy and all the innovation and to meet a bunch of different builders across India. So Wefredo Fernandez, folks call me Weefy for short. It’s a nickname I got in the 90s before wireless Internet was a thing, so my name became relevant later. But, yeah, this is certainly a topic that brings us all together, which is wonderful. You know, XAI is only two and a half years old. So we’re all in this together.

So we’re all in this together. So we’re all in this together. So the foundational work done by these peer companies have enabled us to accelerate our development. We’re better because of those, and we’re better because we can all build on top of those. And these standards and protocols that folks have built and that we sort of lay out and sort of agree to as an industry and as governments really make sure that not just us four compete, right? This enables a ton of innovation. So, you know, on the X side, and, you know, XAI and X sort of operate in tandem, it’s been really neat to see the AI community sort of build and test and discuss and debate in public.

So, like, when Malt Book was taking off, I think you likely found out about it on X. And so it’s just neat to see the ecosystem sort of converge in that discussion space. And just in thinking about this panel and thinking about MoldBook in particular, it’s like, well, do we regulate social media platforms that are agent driven? Just it brings like all these really novel questions about about how we regulate. But I think at the end of the day, we all agree that these open standards that are creating sort of this call it a layer, call it a new ecosystem, call it a parallel Internet. I just really crucial for for our development of the Internet writ large.

And so, yeah, excited about the panel and the discussion here today.

Thank you so much. Your name is formalized in the 802 dot 11 protocol, which is what allows my phone to connect to the Internet in D .C. and here in India. So it’s extremely relevant. I’m going to use that. That’s awesome. So I think we’ve heard a little bit from our companies who are engaging a lot of dynamic activity, pushing out agent protocols of all kinds. And I think. There’s a lot of industry excitement over agents right now. One of the big announcements that we’re here to make, also Director Carrazio’s made early on the main stage, is the Agent Standards Initiative, and that is something that is let out of Casey in NIST. So I’ll turn to Austin to introduce that.

Absolutely, and thanks, Hal, and thank you to OSTP for convening this event and to my fellow panelists. I’ll start with a brief introduction of my organization. So I am the Acting Director for the U .S. Center for AI Standards and Innovation. Our background, we were founded about two years ago as the U .S. AI Safety Institute. In June of last year, Commerce Secretary Howard Lutnick refounded us as the Center for AI Standards and Innovation, which signaled a shift from sort of safety concepts to standards and innovation. And our remit is to be the front door to industry to working with the U .S. government. There’s, I think, two aspects of our organization I think that bear note is, first, that we’re located within the Department of Commerce.

We are commerce -focused. We are industry -focused. We work. We work with all of the companies on this panel. Some of them we have formal research. or pre -deployment evaluation agreements with so that we can work with them on their models and the research questions they’re tackling. We also do take seriously our role trying to serve as a front door to the U .S. government for industry. We want to make sure that when industry is trying to navigate government that they’re speaking to the right people, that the people in government they’re speaking to have advisors who understand frontier AI and agentic AI, and also that the industry isn’t being overwhelmed by duplicative requests from different aspects of government.

You don’t want 10 different agencies asking the same company basically the same thing and creating unnecessary work, and so we try to act in sort of a coordinating role to make sure that industry is being heard and they’re navigating U .S. government. The other aspect of our organization that bears note is we’re located within NIST, the National Institute of Standards and Technology, and NIST has an over -century -long track record of not regulating but helping industry through, consensus, develop voluntary standards and best practices. Acting Director of NIST, Craig Burkhart, he likes to talk about taillights, brake lights on the back of a car. I’m sure you all see them in India. It’s the same color red as it is in the U .S.

That’s because it was a NIST standard of what exactly color red is going to be on the taillights. But another important aspect of that anecdote is it wasn’t government that said this is the color red that you all must use. It was industry came together, and with the help of NIST experts through a convening, they agreed on what the color should be. And so now when we look at it, what does the future bring and where can NIST bring its industry -driven, consensus -based voluntary standards work into the new AI world, we’re looking to AI agent standards. So as to how said, we announced this week an AI agent standards initiative, which is looking at all facets of AI and AI agents.

There’s a couple aspects of it that have already been announced that we’re working on, and I’ll tick through those relatively quickly. The first is we have a request for information. I’m going to go ahead and get this. So we have a request for information. We have a request for information. in the field. It closes in March and we encourage you to engage with us and provide comments on AI agent security. AI agents obviously bring a whole host of new security challenges and we’d love to hear from you and your organizations about what challenges you are facing. Learning and identifying those challenges is a first step. Once we identify those challenges we can then take the next step of seeing where can NIST’s approach of voluntary standards and best practices documents, how can they help address and mitigate those those challenges.

Another aspect, our colleagues at NIST, the Information Technology Laboratory or ITL, they have a draft out for comment on AI agent identity and authorization. Again, encourage you to engage and interact with them. A third initiative that we recently announced is we’re going to hold sector specific listening sessions hopefully in April in the sectors of education, healthcare, and finance where we’re going to convene various members of industry and say to them look there’s this great technology out there called AI have you heard of it, AI agents, why aren’t you adopting it? it? What challenges are you facing? And we may not be able to solve those challenges, but maybe we can. And so one example I give, and I don’t know that it’s going to be something we find out, but for instance, in the education and healthcare sector, there’s business concerns and existing regulatory concerns about PII, personally identifiable information.

And perhaps what we’ll learn through these listening sessions is that hospitals or schools aren’t deploying AI because they can’t reliably evaluate how AI agents are handling the PII. And so that’s something that KC, my organization, could develop metrology, benchmarks, evaluations, best practices, documents that could give confidence to those types of institutions that the agents are performing as desired. And maybe that’s a step that we could take through voluntary consensus driven best practices and standards that unlocks adoption. So we’re very focused on that. We’re looking forward to learning what those challenges are. I don’t know if the challenge I mentioned is actually a challenge facing industry. And that’s part of NIST’s approach, which is we … In D .C., we only see a small slice of what’s going on in industry.

We only have a tiny window into the world. And so it comes from a place of humility. We don’t know what the challenges people are facing. The companies that are on this panel, they’re doing an incredible job coming up with protocols for some of the challenges that they’re facing. We talked about agent -to -agent for how agents communicate. We talked about MCP for how agents navigate databases. We talked about UCP and OpenAI’s commerce protocol for engaging in e -commerce. And I’m sure through these conversations, we’re going to identify other areas where open source protocols, where standards, best practices could help unlock adoption and implementation. And we’re really excited to work with both you and all your institutions and companies on stage to identify those opportunities and see how we can leverage NIST’s convening authority to help.

Thank you so much for that, Austin. I think to reemphasize, this standards initiative is really wanting to make sure that the products that we build, on top of it, are able to connect with each other into our… such that if there’s a builder in India, a builder in Kenya, building on top of our AI products, American companies can use them as well. American companies can buy from them as well. And similarly, if you want to switch to a different model, nothing is sort of locked in. And I think this really ties back to a perspective that we sort of, as U .S. government, in particular the Trump administration, has about AI and AI products. We think back a lot on the history of the Internet and what that enabled for the world, but also what that enabled for America.

I think there was a perspective in the U .S. from a previous administration that technology had to be strictly locked down, and we think that’s a mistake. We want to share the best AI technologies with the rest of the world, and that’s also a sort of leading message that our delegation has here at the India AI Summit. And when we think back at the success of the Internet, what enabled that? There’s actually a number of companies and countries that tried to create their own closed version of the Internet that were centralized, that were tied to particular nations, at their own telecom networks. and they saw a little bit of success. A lot of them were state -subsidized, but none of them really scaled to the global level of the World Wide Web.

And the World Wide Web became so successful precisely because of the protocols that the U .S. government had supported. The U .S. government had made a very intentional effort to make sure that the Internet was a decentralized system, created protocols like TCPIP, HTTPS, the sort of Internet suite that was actually funded by the U .S. government back then to create independent development of these protocols that enabled the rest of the world to build on that. And what you had is really this win -win situation where the entire world now benefits from sort of the access of the Internet, the ability to build applications, companies on top of that that’s driven so much prosperity for countries around the world, but also made Silicon Valley one of the most wealthy places in human history.

And it is because of this open commerce. And that’s what we really want to create with a world of AI in the future as well. I think just to add a bit on to what Austin had said, sort of the agent security. piece. Why is agent security so important to us? It’s precisely because of adoption. You need security -driven adoption. If you look back again also at the history of the internet, the development of the secure sockets layer, SSL, and then eventually HTTPS, was what enabled e -commerce. And so, again, I think it’s a lot about the efforts that we’re going to be working with industry together to make sure that there is this standards ecosystem, that there are these interoperable interfaces that everyone can build on and trust to create the AI economy that we’re all looking forward to.

So I’ll stop ranting, but I’ll turn to the companies here. And I guess I’ll ask you all, how do you see sort of the future of AI standards and agent development? And how can AI agent standards really reflect the same principles that enable the open internet, including interoperability and including security?

I feel like I need to somehow fix this. an automobile analogy in here since there’s been a setting. Maybe I’ll use my favorite one, which is right now if you go to buy a car and you go down to the car dealership, those cars are going to have a bunch of metrics that you can use that have been independently determined to understand the characteristics of that vehicle. So it will tell you what the fuel economy is, how far can you drive on a gallon or liter of gas, how does it perform in various types of crash tests. These are all metrics that are done in a standardized way that are oftentimes done by third parties, and so you can have sort of trust and confidence in them, and you can know what kind of car you want to buy.

Maybe I’m a single person and I like to drive fast, and so I’m just worried about head -on collisions because I’m going to be going as fast as the car can. I’m going to be driving as fast as the car can possibly go, and that’s the biggest danger for me. Or maybe I have a family and I’m worried about you know, what happens if we get hit from the side and I’ve got kids in the back seats. You know, a piece that this standardization can help us get to is having that same kind of confidence in knowing what you’re purchasing that, you know, customers and governments and the public, you know, can have. I think another real benefit, and it’s really aligned with, I think, some things that Michael Kratzios, the OSTP director, talked about today and also in an op -ed that he had in the Financial Times around exporting the American AI stack, right?

There’s a lot of concerns today about sovereignty, about having control over systems in your data and so on. And a way that I think you can both use the best technology in the world, which sometimes comes from American companies, but also have confidence that there’s resilience in the system, is really having things be built to open. Open standards, right? And that gives you the ability to… to decide to make changes. If today Anthropic is producing the best technology and tomorrow it’s X or it’s OpenAI or someone else, you can change. Or maybe an open source model gets good enough at the use case that you want and you want to switch over from a proprietary model to an open source model.

So I think that’s what this can enable. I think that’s sort of the opportunity that we have ahead of us. And I think that the vision of the AI security standards work that Casey’s going to be working on is, if you’re going to entrust these systems with access to your personal data or your financial data or the ability to do things in the real world on behalf of your enterprise or what have you, you need to have some sense that there’s security, there’s authentication for things, that there’s an ability to come back and check with the user before making certain significant decisions or taking certain decisions. Certain significant actions. And that’s… You can test and evaluate and report that information in a way that is intelligible to the customer, that they know what they’re buying, and they know when to trust, and they know when not to trust.

What’s up there?

Yeah, well said, and I endorse a lot of what Mike mentioned there and Austin and Sihau as well. I do think there’s a lot you can learn from the history of standards in various different industries that we can apply to AI. Sihau mentioned some of the early Internet standards. I mean, I’m just about old enough to remember people in the early 90s talking about how they would never, ever, ever put credit card information on the Internet. That would be absolutely insane. And it sort of was when you had information being shared in plain text in a totally unencrypted way. Then you have the secure layer that Sihau mentioned, HTTPS, and it’s completely unlocked the modern Internet economy as we know it to be.

History of electrical standards as well. Actually, this was something that drove the adoption of electrical products in the late 20th and early 21st century. You had a scientific approach to standardizing units of. measurement like ohms and volts and amperes, which allowed power supplies to connect their energy to the grid. It also meant that you could invent things like fuses, which could be set to a certain amperage, and if you had an electrical current above that, it would shut itself off. So I think we need to continue learning from history. I think there are a few principles that we should take forward as we do that. I think open standards, as we’ve been discussing, is the right way to go.

You need technically robust standards that are really informed by an understanding of the technology and how they work, and we should be looking to prioritize interoperability as well. Maybe a final thought for this piece is also learning from standards that are not done well. There are many industries that have not quite gotten this right. A lot of us have traveled here from around the world having to bring adapters with us because our electrical products won’t plug into the wall. It’s really, really annoying. It’s actually also a massive hindrance on commerce as well, because it means if you’re producing a computer or another electronic application, you have to have a different plug socket in every single country around the world that you’re developing your product for.

So things to avoid. as well we need to be mindful of.

automobile industry or something, two humongous but separate industries, and how they’re going to have to come together to set up norms for how agentic systems work and how data is shared, I think government can probably play an important role in bringing together industries to establish those dialogues. But the industries certainly still need to be front and center in establishing what works for them because they are the practitioners and the experts on what their customers need, what their colleagues need. And so I think we’re all going to have to kind of navigate that world together and figure out what is the role for the research labs, how does government support, and then how does industry play a leadership role in both governing and building for itself industry -specific standards for the future of AI.

Yeah, I think this conversation has been a bit of a history lesson. I appreciate that. Thank you. And it made me think about how I used to get music when I was a kid, and some of the panelists may appreciate. You know, there were these music catalogs that would come to your house. You’d select however many compact discs you wanted, CDs. You’d put cash or a check in an envelope and send it away. And some weeks later, magically, some CDs would appear on your doorstep. So when I think about, you know, instructing an Asian to go download music or acquire music on my behalf, like, I’d much rather have that than I don’t know how we used to put so much trust in a system without standards or, you know, a process that could not be audited.

So I think sort of the guiding principles that have developed the Internet still apply. We want privacy -preserving technology. We want technology that allows us to audit. We want technology that considers authenticity. We want technology that considers means of consent. And to Michael’s point, I think ultimately agents serve the user and agents serve organizations. And so if we view it through that lens, it should guide us right. They don’t serve us as the model developers.

Great. Thank you all so much for that. So that was a bit of a nerdy discussion on standards, a bit of a history lesson. I love that. But we’re also here right now at the India AI Impact Summit talking to a country of builders, talking to the developing worlds, which are some of the most dynamic AI markets in the world. And so I think it will also be amazing to hear from the panelists here, including Austin, how you all are engaging with the rest of the world on these standards, how your organizations are engaging with other countries on AI. And one of the most exciting applications you’ve seen develop on top of your standards and products.

I guess I’ll lead off. One of the main forums that Casey engages internationally is through the International Network for Advanced AI Measurement, Evaluation, and Science. It’s a bit of a mouthful of a name, but it’s ten countries that have established AI security institutes or, like we do, the Center for AI Standards and Innovation, and we meet a couple times a year. We also engage in informal technical and scientific exchanges and we share best practices in measurement and evaluation science. In December, we met in San Diego on the sidelines of the NURFS conference and we sat down and discussed sort of open questions in measurement science and the challenges that we’re facing, and we published a blog post, I think, about a week ago that summarizes some of the periods of consensus and the open questions.

And there, the work we’re doing, I think, is very important because when we talk about the evaluation, of AI systems, particular capabilities, particular security vulnerabilities, etc. It’s important for us to have consensus on the methodologies.