Uncategorized

…infrastructure in the country. He’s a prominent expert on open source, scalable digital systems and decentralized networks. It is now my honor to call upon Pramod to take the stage to give his keynote address. Thank you.

Friday evening can be really hard. It’s tiring right after a long week. So thank you for having me here and I don’t want to take up too much of your time. First of all, I want to congratulate Government of India, METI, MEA. What a fantastic week. And compared to last time in Paris, we heard actually from many people who attended that last time it was elite, exclusive people attending it. This is true democratization. You can see that number of students, children, entrepreneurs, young entrepreneurs walking in. It just tells you that… India can definitely demonstrate what it means to democratize and diffuse AI. And our prime minister is, I think, a mastermind at it. So he’s a great supporter of it.

But what I wanted to give you about five minutes or so is that why India is peculiarly in advantage of diffusing AI. Now, we have two arguments we can make. Our own LLM. I think much of our discussions and today AI discussions are all about sovereign LLM, big LLM. How are we going to build our own LLM? LLM is only one part of it. There’s so much more there to AI, especially for the people who have lived. My master’s was in AI. I was in 89. So. AI has been there for a while. I think now it’s all coming together. But AI spans much beyond LLMs and why India is peculiarly set up to succeed is because of the serendipity, but it is because of the investment we made in the last decade, digital investment.

And people who have not looked at the macro picture, it’s very important to understand India over the last decade brought a billion people from invisible to the system. They were invisible to the system to being visible to the system. And we formalized a billion people by giving everyone an identity, everyone a bank account, everyone can transact. Make payments, paperless signature. So we built Aadhaar, begin with. Of course we built in 2000, I remember 2014 was seminal for us because I was actually architecting eSign, DigiLocker and UPI at the same time. And who knew they were all going to play out. But I think brave people are also lucky. I think when we attempt something bold and audacious, sometimes luck comes in the way and Indians have truly embraced all this into actually at population scale, in one sense going beyond what we can.

And it did not stop there though. We actually digitized businesses through GST. India is the only country where we have billions of invoices, actual proof of purchase in machine -readable, cryptographically protected, digitally designed fashion. That’s like a goldmine. That’s each of those steps we made. Or fast tag. When fast tag gets done in the road, there’s a proof of transport, an eBay bill. Each of them is again machine -readable, cryptographically signed and usable by the next layer of innovation. So what we did with GPI by formalizing is one inclusion story. It was a brilliant inclusion story to get everyone into the formal system. but it also said you know serendipity set up the most powerful two ingredients for AI data and programmability every one of our infrastructure components DPI components are API based every one of them this is why we have fun pay is why we have the road and grow and everyone else building applications and workflows using this underlying digital public infrastructure API’s identity API’s verification digital occur verification a document verification API’s he signed for paperless signature UPI and mandates for recurring payments and other collections or payments each of them is programmable combining that with data that gets in later a billion people billion plus people you in India generate verifiable data trail.

And that’s beautiful. But even more beautiful when it is controlled and owned by the individuals, which is our DPDP Act actually giving you. Our privacy bill is giving us the right to control our own data. And India has truly demonstrated that the data belongs to the people, data belongs to the small businesses, using which now they can create a virtual cycle. So I think AI’s two biggest ingredients, programmability and composability, combined with data, verifiable data trail, allows India, and this is a bold prediction I’m making, 10 years later, when you compare countries’ economic progress and growth, countries who have invested in DPI and combined, AI on top of DPI, would have done 10x or 50x better than countries who have no underlying infrastructure.

So I think India is lucky, right place, right political will, right regulatory push, right infrastructure readiness, all in the last decade, all in one decade. But for my favorite part of all that thing is that India is also blessed with young adventurous entrepreneurs. Entrepreneurs who have no inhibition at all. At least a few of you came to meet me outside saying I’m starting a company. It’s just music to our ears because India’s problems are a plethora. As you know, we are a country of problems. So we have anywhere you look, we see problems. Energy sector, agriculture, agriculture. We have a lot of problems. We have a lot of problems. We have a lot of problems.

We have a lot of problems. We have a lot of problems. a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of problems. We have a lot of We have a lot of problems. We have a lot of problems. We have a lot of problems. We have access to capital, access to investment, access to the right products, not solved.

We have much to solve. And if you combine our infrastructure and diffuse AI, but diffuse AI through entrepreneurship. The way we diffuse DPI through entrepreneurship, we went from 1 ,000 companies in 2016 to 100 ,000 startups today. And the prediction is that we’ll get 1 million startups by 2035. It’s a very high chance we’ll get. Doesn’t mean all of them will succeed. But attempting matters. I think young people have to attempt, audacious attempt, bold attempt to solve problems. And India has beautifully set up. And we have a wonderful panel. I don’t want to take up too much of time. Wonderful panel talking about the combinatorial power of DPI and AI. Combining both what can be really an exponential power and why countries who are investing, and they’re all global, and they’re all global, experts in deeply investing into DPI.

So I give my floor to them. Thank you. Thank you to all of you too, even if so many people coming and sitting, really appreciate it, much appreciate and a wonderful weekend and keep imagining and keep building and keep solving. Thank you so much.

Thank you so much for setting that context. Now we will have the panel on AI and digital public infrastructure. The session will explore how integrating AI into DPI can unlock new benefits at scale while also discussing the challenges and risks of such an integration. How can DPI architecture mitigate new risks and emerge as AI becomes embedded in foundational digital systems? What are the opportunities and risks that emerge as a result of integrating AI into DPI? And could integrating AI into DPI enable the development of new products, services and market ecosystems?

that can take you farther and farther ahead in the world economy. So I see it not as a job reduction. I see it as opportunity for India to create a global AI -dominant nation. And that does not mean that that is easy to do. But at the same point of time, most of us have to take a will, and we should be taking the commitment towards leveraging AI first. For example, like if you have a smartphone now, even a small shopkeeper does not believe he does not have a smartphone. They get payment from Paytm and work a lot of life. They assume that there is a smartphone, which is fair. Now, if you believe that you can build a business model, or entrepreneurs will build a business model as a customer, you use AI first products, the productivity will be dramatically higher.

So a person who was running a shop can run multiple shops. So GDP growth will automatically come in a different size because the productivity per person will be higher. And that is the lacking thing of India. the problem will be that will India be that bigger market or not and I want to tell you this is a very good lucky moment that we all are sitting it is tough to grow from 20 billion to 25 billion anyway that is 50 % growth and 25 % growth is very tough but luckily India is right now living in 2 .5, 3, 3 .5 trillion economy and I am comfortably going to say that 2 trillion dollar can be compoundingly growing in next 7, 10 years so if you are sitting in this economy in 2026 and you believe that next 2 trillion is going to come in next 10 years so I don’t have to tell you out of 2 trillion dollars many of you can get many of million dollars or billion dollars entities or business benefit to yourself so that’s why the India is a bull case scenario and AI on top of it is the absolute bull scenario I don’t think there is any challenge to that someone asked me this question in some other audience so I said like in Ramcharitmaras if Pran goes but Bachar doesn’t go similarly if Job goes but AI doesn’t go Well, job jai hoga nahi, kyunki people will have method to discover themselves to do more productive job.

So, in my opinion, the traditional kind of work, main job ko yaha pe metaphorically use kar raha hoon, traditional kind of work versus the AI kind of work. And so, that will happen. Main completely maata hoon, main isko apne tarike se express karna chahta hoon. Humare kisi bhi system ko banane ke liye, koi bhi job karne ke liye company ke andar ya kahin pe bhi, kahin na kahin koi bottleneck hota hai. Aur ab increasingly, ap log ye figure out karoge, ki wo wala bottleneck pe eliminate ho jata hai. Iska basically matlab hai ki sadak chodi ho gayi. Problem kahin aur shift ho gayi hai. Problems disappear nahi hoti hai, wo bas move kar jati hai kisi aur jaga ke upar.

Outer ring road ke road block se bhi hume yahi bata chalta hai. Ke road, sae pehle Nehru place pe ruka karta tha jam, ab jaake wo airport ke paas lagta hai.

Very good. Main samajh gaya. Achha sab na aya. Okay. Toh ek aur related question tha, ki opportunity. is a lot. And when we look at the exhibition hall, someone is making a chip, someone is making a data center, someone is making a foundation model. So should we all make a foundation model in India? Should we make our own chips? Should we make our own applications?

Very good. So first I want to say one thing. To make a foundation model, English, we’ll speak English and Hindi, okay? And you should use AI to live translate. No, it’s okay. We’ll definitely, I understand, and we will do it. So in my opinion, India has to build a foundation model. This is no compromise statement. Not because that we can make a better financial foundation model or not, but because we as country have to move on from services culture. I mean. There is nothing wrong about IT service business. There is nothing wrong about BPO and business services, but it is an obligation on us. that we should move up in the value chain. It’s like growing up in the life.

You do not move up in the value chain. You rather continuously make something for someone. So most of founders or capable technology people in Silicon Valley will have a significant mix of Indian people. So as a people, we are able and capable. So can’t our country have enough amount of resource allocated to those individual capable people that they can make foundation model? We have to do it and we have to prove it. So I first of all applaud Sarvam that they have launched it and they have launched a perfectly, I would say awesome foundation model from India. And I want many of us to do it so that this race does not look like that there was none or only one.

There should be tens of foundation model to prove in the world that Indians can do it and Indians are doing it in India. That is why we need foundation model. And now the question of that whether it is on an ego or is it on a use case basis, the advantage of India financial or made in India sovereign model will be that the amount of… nuance and biases that happen so what is a foundation model it is like aggregated knowledge of what you give the feed off now obviously all of us have our own perfect understanding of whether this is correct or not so after the power a lot of us would know this like for a small kid making them eat banana or curd in the night is considered as if next day they may have some cold or some other thing but when you go on internet internet is half and half divided it doesn’t believe it and it believes it and then you’re totally confused but when you go Ayurveda then it says something else and obviously it follows the bath with and also at cuff kind of philosophy so that the answer is not the straightforward as straightforward as we believe but it has an answer in a different way versus an answer in a different way in a Western medicine if you will now I’m not saying Western medicine is right or wrong I’m saying I need my opinion I need the culture or the knowledge that we have inherited extended towards the next generation And I want that to happen by the intelligence that we will query, which means that that can only be done by somebody who is making for it.

If we don’t make for it, our all compounded historical knowledge will be lacking in the next generation. So instead of adding on top of it, we will not be able to take it further. So it is a case for India to build not just a perfect foundation model, also retrained models. Like you remove the biases that it has, you trim it to the ability that you want. And you make it for the purpose that you want. And that I believe India’s opportunity is even bigger beyond just the foundation model.

So I have a two -part follow -up. All right. So I believe that this is probably just a way to discourage people. And it may be more than that. But we often hear, mostly on Twitter or from these large foundation model companies, that if you don’t have 10 ,000 crores, why are you even in this place? What is your viewpoint on that?

I mean, literally, PTM, we both of us, put more than 10 ,000 of you, put 25 ,000 crore on the table for making this humble QR which is everywhere now. So there is an ambition and there is a commitment. So the question whether you put a billion or two or four or five, it’s not that question. I think the question should be whether there will be enough business model of that. Will we have a skill to market it? And remember, the world is right now not just made in America. There is a European model and there is a China set of models. We need our models. And the proving point now that there is, I would say, enough knowledge of model creation is that it is not literally about a billion dollar or two or ten.

It is also about the kind of smartness that you can run on the model creation. RL and so on and so forth. And there is a lot of chemistry and math or let me put it rather better way of saying it. There is a lot of physics and gravity handling capabilities that are discovered now that you can build model in a much less cost. So whether, I mean, another question that a couple of us were asked when I was roaming around, why don’t you build an LLM? So actually, now I should ask you, what kind of LLM are we or you building when we talk about that there should be a model made in India?

Yeah, this was going to be my question to him. But it’s always fun to answer. I think that models are just not what we know them today to be question and answer knowledge sharing sessions. There are models that have capabilities for reasoning, problem solving, for building agents. So you want to give them agency, the ability to act and so on. I think that to answer your question of whether we should build or not, I believe that there is much more than a question and answer chatbot model. And we believe that we should build models that actually solve problems that actually have. the committed confident agency to take actions on behalf of others

okay so you’re saying and this is for everybody to know we are making models but their models for a vertical problem not for a horizontal scope so instead of talking about 200 billion token model we would be making four or five billion token model or 20 billion token just in case like we should open the questionnaire from the audience also because some of you may have and we will not have a time after that sort of line item so while we are talking you can just raise the hand and I’ll just allow this to okay now that’s you literally are asking good vertical problems to solve

oh it’s easy I mean imagine it’s a classic triangle of mess laws hierarchy the first is the financial foundation that you need to solve for financial services so there is a risk fraud control models that some of us are building and we built it and then you go on top of it like food food chain which is agree and processing of this if you want to produce high quality you food outcomes at a more yield, there is a tremendous amount of data that gets generated in visual amount of data and the farmers need access to it, nuance of it. We just heard how Prime Minister was able to ask a question that maybe a cow is not able to say that I am ill, if you have an ability to discover that the cow is ill.

Maybe a tree may not say or plant may not say that I need this mineral or supply and maybe you can read it. So there is a tremendous amount of vertical like I just told you example of finance, agri, husbandry, you can go towards now industrial, you can go towards the problem of pollution which is we are sitting in this country, this city. So there are many vertical solutions that we can build a specific use case.

So let me just help you guys think of it another way of it. Mr. Banj discovered an engine and that engine then got percolated by many to be made. so LLMs are like an engine and they are literally called intelligence engines just in case, now if you had access to an engine, can you make engine? you should, so that you can make your own vehicle use case, you can make a small car, you can make a big car, you can make a truck, you can make a trailer, there are so many use case of a transport so think of it in that eyesight, then you will say, well do I need to make a small car in India because I heard that there is a small car being made elsewhere no, you will need to make it, I can confirm it and so like in automobile industry, making an engine is an art, I would say making an engine equivalent of LLM is an art, yes, but many of us will have it and many of us are making for those

yeah, finance is the one of the few things which is part of vertical, but it is also one of the few things horizontal every industry requires and has a financial department food, finance, both both

yes so all of all humanity needs these foundations so you are into both vertical and horizontal at one go

Yeah, that’s right.

So, perfect. Now, question to you. The classic financial use case, considering we just heard it, what kind of use cases you believe the LLMs will save or solve for the problems that you see in financial industry and what you believe fundamentally could be solved for wider financial industry and we might have solved them or not yet?

I think that the best favor or the best value we can add is to remove biases in decision making that we already see in our financial system, which are actually a complete antithesis to the whole inclusion aim that we all have.

Beautiful. Give me an example of it. So, a very good starting point would be detecting whether a particular transaction should go through or not. There is a lot of bias today in the rules that we set, in the checks and balances that we set in allowing… transaction through and you can invariably say that if there is a loan officer deciding on whether you should be getting a loan, there is a lot of bias that creeps into it. We are not able to measure that today but when you ask a machine to make that decision, you actually remove those biases. So the person who may not present themselves very nicely is unlikely to get a loan but the machine doesn’t care about how you present yourself.

So you are saying that when you make a financial decision, when financial industry or system makes a decision, there may be unknown known biases and the unknown biases can be removed even if you want to keep the known biases. The good thing is that the machine actually now helps us identify what the biases used to exist which you are very used to. And speaking of financial industry and going beyond, one of the things that I want all of us to know. So classic. Financial inclusion is not just about payments inclusion or bank account inclusion, which I would say that India has perfectly solved. It will continue to grow towards the next requirement. And most of us need access to credit, access to insurance, access to classic wealth solutions.

Right now the wealth in India is only when you have crore or plus or less, somewhere around that amount. But a normal auto rickshaw driver who has 20 ,000, 50 ,000 or 2 lakh or 5 lakh saving also deserves to have a financial wealth model. And that person, poor fellow, by hearing or hearsay may invest or risk the capital at large. So the access to the financial services can become further and dramatically scalable once you add the power of AI to it. For example, like capability of auto rickshaw driver like I just now contextualized you. Imagine that person has 2 or 3 lakh rupees. You can suggest based on your risk and time horizon, you should put it in FD, you should put it in a gold or a sovereign gold fund or you could put it in let’s say some index fund.

Because you’re talking about 15 year forward money that is for your house, family, daughter or kids education or something. Now, those things can not be told by a commoner around them. And then you can make it in a language and you can answer the questions like ChatGP today answers the question in a language they speak and the language they get the answer. So in my opinion, AI will enable financial inclusion to a next level because it is not just about the access to the smartphone, but the question answers that you get from smartphone can become far more native and continuously possible. So let’s say this guy is busy whole day, can start in the evening when he is waiting for some customers and start talking to it and can take a decision.

Now, at least he has a second opinion and can do it. Similarly, healthcare, huge amount of capabilities because many of us literally just say, but you need to check whether there are some more symptoms or not and what the blood test report says. So the dramatic amount of inclusion of education, finances, healthcare will be such a catapulting impact. On the society.

Yes. So do you think the whole banking system will become redundant? Because today if I have to make a transaction, I’ll use Paytm. If I have to invest, I’ll use Paytm or a Zerodha. So why is the banks existing? Because with whole human interface, it’s becoming very difficult. They don’t solve our problems. And second thing is, is education, school is going to become redundant because what they’re learning is going to be not valid, will not have a shelf life at all.

Okay. I have an opinion on schools. I will, I will, I will. So it’s a very easy thing. If people start to make food at home, will the restaurant become redundant? Yeah, the demand may be a different kind and the need may be a different kind. So first of all, none of these things will become redundant because they do not offer literally the verbatim statement that we just made in this sentence, but they offer much more beyond that. For example, like banking inherently is about extending credit. So ability to have… I have a… well regulated place where you can deposit money and that deposit money is taken care of so that when you need it, it is available to you and when the economy needs it, it is available as a credit which is a business model of a bank is an ability that will never go away.

I mean it is an obligation for them to become even more able and capable in both parts. That security safety of your deposit and ability to disperse credit to the needy. Now that is a part called bank. If we treat bank as a bank branch and your question that you may not walk into the branch, fair. That may be the case because you will not need a branch to make the banking reach places. It can be very perfectly extended using a smart phone and now in a chat bot instead of just an app and that is what the beauty of this inclusion I am calling. The core machine of a financial institution is needed and even more because you will have even further bigger load coming in.

The bank, the way it is served through let’s say a branch could extend itself when the ATM happened through an ATM when apps are happening through an app. You can have a third party app. That’s not a problem but the core activity of the bank still belongs to the bank. And similarly, like you were saying for the schools, schools are not the place only for going in the classroom. How many of us did not attend the class when the teacher was in the class? And I’m talking about college days. I’m the one. Now, that does not mean the college was a bad experience. Rather, college is a social experience of meeting like -minded people and understanding and self -discovering beyond the syllabus in the class.

And I definitely agree with you that maybe a single method of teaching on a blackboard, whiteboard that you’re teaching and the people are writing and just putting up back in exam is changing. Then you have these different MBA institutes like Harvard is popular for case -based education. So those things can be extended towards now in the classroom of a common or other mass level of school. You know, that is what the power of AI is. I’m not a believer yet that you will not need it. I mean, homeschool is good, work from home is good, but going to office has its own use case. just like we just saw in the COVID days that everybody was working from home you can be selfish that I want to work from home because I have 10 more things to do that is always taking care of it and then sort it out but ultimately there is a value to go to a thing a place and that is in my opinion will perpetually remain whether it is school, bank or any other such institution your answer on the education no I completely no no no I am not going to say that the core philosophy that the bank is in a branch definitely changes the philosophy that they will be a let’s say bank manager or somebody to approve a loan will evolve but the core work will remain of the bank so DeFi etc are very different cultures or I would say they all are technology nothing wrong about them again the core philosophy that you store money at place and you demand for it banking.

I completely agree on the school front. School is not just education, also the social experience, and we’ve all benefited from that. So nothing more to add. But I do find an interesting theme across finance, healthcare, education, that it allows you to have more access and more personalized access. Like your doctor is your doctor. It’s not the doctor that says the paracetamol example. Same with your teacher. And I think that makes a very radical impact.

It’s amazing. My mother who had a heart and then stroke, which ended up becoming a stroke, heart disease. And then now I leverage the power of AI to keep a track, including she has a Fitbit that generates a feed that goes to my agent. And it triggers to me notification that if all is well, you should check it. Now, that possibility, the kind of nuanced care that I’m talking about, couldn’t have been possible without a doctor support. So I’ll give you another example. And this is something that you should check out. If you have a situation like this. So there was some medicine the doctor gave her. for certain rhythm control and suppressing beats and so on and so forth as the case was.

But some of them ended up taking her palate taste away, making her not feel to eat, and she was becoming weaker. So I talked to the chat GPT, I threw the prescription, and I said, she’s not eating, is there a problem here? So then chat GPT would tell me that I think this combination at this hour, which is pre -lunch, makes this a situation where she may not have a tendency to eat. If you move it to this time, and if you move this earlier, there will be enough window for her to eat. And any which ways, by seeing your heartbeat that I’m seeing, at this time, this beat compression that was needed was not required.

So I’m just suggesting that you should do it, and then obviously put a disclaimer that you talked to doctor and so on. So basically, same medicines in a different time schedule potentially will solve for it. And I sent this to the doctor, and the doctor was like, Like, the good thing I want to tell you is, doctor replied three words, you can do it. Yes, you can. Actually, he said, yes, you can. I said, sir, do you think, I very frankly shared, because doctors don’t feel, and any person who’s skilled in that domain may not feel that you’re bringing cocky input. So I said, my mother was this, and I understand it is tough to go through this nuance.

I tried asking this, it is suggesting this, and this is a brief if you want to read the PDF, but net output is, instead of it to be done before lunch, I can do it after lunch. He said, okay. He said, yeah, you can do it. Yes, you can. That’s the three words of life. And I think then after that, she became better in terms of, because she’s already taken. Now, what we are talking is, that medical, education, finance, agriculture will catapult into a very different stage and age. I can promise you this today, if many of us, the new Gen Z generation, who’s used to a smartphone, must be asking, really? How were you living the life before?

so I can promise you one thing by the time 27 passes and now we are in 25 so some of us are seeing it and I am literally putting a deadline of 27 which means 2 years that by 27 passes every new generation will think that oh are you saying that you used to search and then click on every page and then scroll down to read and then decipher with your cognitive load that what is happening and how did you decide that it is correct or not did you not have an alternate opinion and this and that 2030s will be so shockingly different that we will look archaic in 2010s and 2020s that I can definitely promise

question from you your mother’s example made me believe that yes these days chat GPT is probably more humane than a doctor himself because this kind of conclusion might not be given by a doctor my question is Since you are giving such wonderful use cases, it’s about the curd and banana example. I would like you to elaborate on that. Would you rather want an Indian -specific model for the purpose, or you are talking about Indian context data, or is it that only Indian children get affected from that banana and curd?

Yeah, very good. I like it. Classic future mother, maybe. Perfect. Number one, the data gets trained on what is on digitized and available Internet, because it literally browsed and scrolled Internet and brought it. And then it gives the weight to the common knowledge. So if more people said, blah happened, it believes that, because there is nobody who answered every question for it. How does it believe that what I just now said should happen is that because it learned more number of times it was said. It’s a very surprising method of adding the bias to the model. for example like let’s say we may have a very contentious statement of history if more people tell the model, model will start believing it, did you know this it’s exactly how the social media, viral news sometime which is wrong could be believed to be true because that is how the brain thinks, we also why are you saying this, why are you saying this I feel like I should eat banana or I can eat Lato at night the more of what you see you start believing is the thesis that the model goes through now do you fundamentally believe that internet is filled up with domestic or let’s say Indian or Indian background or history and culture and richness answer is probably not because the western English civilization has brought lot of content which is in English and there is a much less content which is generated, translated in English repeated a number of times, people are discussing so it’s an inherent limitation of our own knowledge that did not propagate on internet, who would have guessed that whatever is written on internet is the ultimate truth versus what is written in the book Now that gap lacks and creates this gap of knowledge of the model.

Can international model do it? Yeah, if you want to tell that no, no, no, on this, this is the truth model. And the problem of the international labs is that how do I know this is the truth model? I can’t. And a lot of people need to say this, that it is a truth model. So there is a limitation of international labs who are making these models. Then can this be done by India lab? India lab will respectfully say this, that why don’t you learn what is written here first and treat this as that it is your definitive knowledge. And there it goes, the obligation to build in India.

And I’ll add just one more thing. There is absolutely a very strong risk of bias from the inputs that you give. But the model has the advantage of also knowing you, who is the person who is asking the question and can reason through all the inputs and still give you a balance.

So this is a very interesting thing. I want to tell you that my model, because I have started to give it a lot of things, that many of you used to be doing claw and so on and so forth. it starts to say, oh you are in Delhi this is what Indians do it literally said that, I was trying to ask that what do you think okay it’s a very interesting product feature that I was discussing with it, about wealth system, KTM money and I was trying to ask that give me the cultural nuances of managing money that Indians have which I could add in my feature and the kind of feature I was like, oh yeah that’s good, I would have never thought as a logical person to think like that so yes, it can, you had a question chat GPT you are asking this is very good, very cute Amitabh, he is reading the essay again and again but it’s a good thing chat GPT versus meet our agent sometime later we will do this our agent will talk to your agent then they will decide whether we should talk to you or not this is coming, very seriously now Uber if you order Uber, your agent will call you first Uber, this is expected I have to go now and I am leaving then PJ will pop up and say should I call Uber?

yes, call it should I call the same one? I am not getting this I will make it quick all those things that we all experience that will be so dramatically different guys it is real you will be represented by your agent and that is the fight all these clients are running that is why agent came and that is why agent is hyped up at this time because your agent could be based on OpenAI or Gemini or Claude or whomsoever that is why they are talking about it and your agent will be working with you he has tagalongs right there back to you read from that

so my question is do you believe the future of stock brokerage industry will be an AI native from the ground up where AI agent can be the primary interface or you think AI will be just the feature in the stock brokerage platform

And I’m not saying that it is the perfect way to do it, but I’m saying that we are all in committed towards agent -first interfaces. Vijay, I have a follow -up question around this agent -first interfaces. So when an agent, on behalf of me, goes to the Uber app, will it see ads? Will it have to log in with Google? Well, nerd question. And for every one of you, the point that he’s trying to bring it is that will every other stack also change or not? Well, that is the beauty. The agent will talk to agent. Uber will also become an agent. And instead of trying to authenticate my login, et cetera, it’ll say I’m coming and I’m an agent on behalf of.

Just like your ambassadors used to say, I’m the king, I’m from his court. And he has sent this message. You take this VATS. You take this VATS. This is how it will be. I’m the king, I’m from Harinder Thakkar’s court. And he wants Uber. What kind of Uber do you want, VATS? They want such an Uber that can take them so fast. So I feel that at this time, you can throw so much money. So how will you want to pay? Will you want to do it with a token? Will you want to do it like that? Will you give it later? No, take this money. And you can call Uber. It will happen like this.

And this is real. I am telling you, you are laughing, I am laughing. One day someone said, I will stand here, and I will press the button, and the car will come. That day, he laughed at him. Before that, I will stand here, I will make a call, and I can talk to someone through the call. He laughed at that. Then, I will stand here, I will make a video, and my video will be in London, and he laughed at that. So all these technologies, which we natively, as normal as it happens, what kind of trick question it is, in near incremental future, the way this is going to be is so dramatically different, that those who are making apps, I have only one suggestion to them, that make an app with a non -icon based interface.

Yes,

One question here is, for the students who are going through their degrees right now and not in the data tech AI space, in the functional domains like finance and accounting, like HR and so on and so forth. So what do those poor people do? How do they think? Because, you know, how they are running through the education system, that’s old, that’s outdated, that’s still that. So what’s future for them?

So it’s a very interesting question. A lot of people tell me that, does it mean that we should study only, I mean, some days back we used to talk about we should teach programming to your kids. Like IIT preparation was done from class number five. It’s a joke, it’s a stretch joke, you can imagine what I’m trying to say. You came in class six, do IIT preparation. So by doing this, when people were born, they were given a programming language book with a book. So that will end, right? Because you have no more programming. So we are in a flux. Yes, ma ‘am. as we speak in 24, 25, 26. Okay, 24 mein kuch kam log the, 25 mein logon ko laga, 26 mein most of us will go through.

Your question has come in 26. This question did exist in 24. And there it goes, that we will remain in a flux to decide whether this is needed or not. Some will do it out of their intent and commitment to do it and learn, and some will do it for obligation. The fact that we always assumed that education will give us a job, and now the job is disappearing, so uncomfort is that, do you do it out of your vocation and interest and passion, or you do it for the outcome of a job? If you’re doing it for the outcome of a job, I’m not skilled enough in, let’s say, humanities or art, but I can produce an art which is sounding logical.

One of my teammates who was roaming around in Kerala and wanted to figure out what is a good Ayurveda outcome that he wanted to seek for some problem, and he just used Claude to generate questions and nuances and formulas and so on, and then he went to the… Kerala labs and then they were like haan ye toh bahut achha hai, aapne kaise socha, ye toh bahut acha soch ke laay ho. Now the poor fellow doesn’t even know that he does not have a skill of Ayurveda. And he’s talking to the super skill of Ayurveda as if that person is skilled. So now there goes this question. Did you need Ayurveda education to become that intelligent?

Answer is not necessarily. You were able to use the tool of AI. So to the person whosoever it is, whether it is a programming language and computer science student or whether it is a humanities student, I’m not going to say don’t do that what you’re doing. I’d rather say definitely learn how to use jaisa ek time kotha na, computer seekhlo bete, uski bhot kaam ainge. So ussi tarike se main bolna chahta ho. Leverage and make your work AI first as much more because you will be relevant. And this is not a question of these people who are right now graduating because there is something that will remain for some more time. If you think about 2030 onward when this is all prevalent.

Ab jaisa painting hoti thi, aapne dekha hoga shole ka jo studio ka jo poster bana karta tha, wo haat se banaye tha. And making posters, in Bombay, there used to be an art. Studios, posters of Mahbub studio and RK studio are called legendary. Now where are they? And they are made in a spot like this. And Ghibli art is made like this, you make this. So the point is that, does it mean we don’t need a new cartoonist? But we will need one who is making with a computer. And he will say, I want this kind of nuance, it will become an art fashion. So if the today’s artist only uses, let’s say, paint, then it will be so unique and rare that that person will have its own value.

My God, handmade art. Like today, there is a tradition of cold hand pressed oil. Which was very popular at one time, that you are not doing industrialization. Oh my God, this is perfectly hand pressed juice to you. It is made with a jug of sugar cane juice. So that is it

Thank you, Vijay. Fantastic and energetic talk. Thank you. So, a little while ago, you told me that LLM, Foundation Model, should be done. Yes, sir. And the thing is, both while making a foundation model and during the inference, actually a lot of data is needed. And just like in the finance industry, actually, there is a lot of regulation and this will keep coming. Actually, in such a constrained data environment, how do you kind of make a good LLM and inference time? So, let’s say this could be applicable on any industry, that this industry does not have a lot of data. How do we make a good LLM of that industry?

Short answer is that you work with that industry players, whether it is regulated or not. I apply for everyone. And you find out all stakeholders and pursue them to learn what you could bring on the table. And people understand the need of it. If not all, some will. And in my opinion, the training model and the why are you training it, if you are able to articulate it well, I mean, the process. Progress, even progress is a very interesting thing. People. the regulation is not for not progress. Regulation is for not what it can slide and fall. So remember, regulation is also for progress. And that is always the case. Regulators are the reason that we have such vibrant financial system in this country.

At the same point of time, what they protect us from is that not falling apart of the system. So there is a respect and value of what they do. And if you talk to different industry stakeholders, you will get access to the insights and data. And there are different regulators starting from their sandbox programs and so on. So they allow it. And I think I’ll just add one more thing. Data, just double click into that data. There is only some kinds of data, like my personal information, that is usually something that you do not want. The regulator doesn’t want, you don’t want. But outside of that, there is plenty. There is plenty.

Yes. So as you mentioned about AI and all these things forthcoming with the Uber, the IFPS interface, do you think that… growing as AI will be growing fast and forth there will be more inequality or will it remove inequality in terms of will power get sustained in few hands

perfect this is a very interesting question inequality I can take it from money perspective because inequality from other perspective I am not taking it AI for the first time is one of that technology that will be easier to use for everyone because you are talking in native language and you can speak even if you do not know how to type or write so it’s a very inclusive technology for itself and its outcome is very profoundly powerful that a rich person or a able person so let’s say that you are trying to fight a battle of certain skill or ability which are rich in terms of money or able in terms of skill you can very comfortably write this person so AI is that horse or super car or a rocket ship that you can ride easily and then you can go ahead of anybody in a zero sum business if you are in a wholesome business you can expand yourself So AI not only is inclusive, AI is actually the superpower that would reduce the gap between rich and poor and be more inclusive.

And that is what I’m trying to say, that it is not the technology of rich. Rich has a fear. You must have seen, you must have read something on social media, that parents should be given education and old should be given health and women should be given beauty. Such business models are written on social media. So there is a model written in it. I want to say that it is written for the rich to give them safety. Because rich has done this. They want safety, security, exclusivity. Why? Because they don’t want others to take away that what they have. So they want to stay disconnected from the world. That’s why rich’s lounge is different from the normal people’s lounge.

But sir, having said so, there is also a risk with AI, which we are underestimating at this point of time. So what do you think could be that potential thing?

I think risk is in driving the car and coming here. And also in using the phone. so risk I will not say how much risk how much not this gauge is more important the generic line that it is risk is not complete line it could be that is it a risk that every common person can take care for example like kids are not allowed to cross the road alone but as an adult you are expected to cross the road alone and there is a risk that’s why you are supposed to do the left and right check and so on so AI surprisingly is that less level of risky that even a commoner could do it that’s what I am trying to say yes yes ma my model my model is right now in the beta and we are using it and just like she mentioned very nice your infectious enthusiasm to drive this technology that’s very commendable thank you thank you so much for that and you are at the driving state so to speak thank you so like like she mentioned my always my concern is about how to solve this distribution problem of AI of AI like cool No, the problem was…

And how to make it a public good. Yeah, that’s right. I really didn’t like that concept. I think I want to tell you that technology distribution happens on a terminal. So as you remember, there was a time when computer used to not be with people. Laptop was not with people. And the smartphone was not with people. So governments used to give free laptop, free computer program. Then some free smartphone and tablet program also came. Politicians put it in the well wishes of different states. I think anyone who has a compute terminal internet connected, which is said to be a data positive country in India, everyone has access to it. So if you have access to a computing device with internet connection, you’ve got full AI access.

The good thing is it is not installed in your device. It is not a version of a software in your device. It is not requiring high amount of compute or memory or something on the device. I mean, we created an AI sound box, which is a very natively small device. And it is as capable. As even as somebody else’s, Sam Altman’s computer device. That is the beauty. about it. So AI is far more inclusive and very easy to diffuse. I’m glad to hear that you being in the driving seat, think it as a problem. That’s very nice.

And I have one more thing about this Asantic AI. It has this very human trait of trying to please you.

Yeah, because they were written to be written like this. Oh, you are here about asking about this thing. I think you should see left and right. Actually, agents are they’re not human agents. They are no they’re untrained beast of abilities. You prefix them and they behave like that. And it is literally an instruction. So risk, just like ma ‘am was asking, what risk can there be if someone says behind you always let him do this. You know, that kind of

Yeah, yeah. So PTMS played a very important role in diffusing the fintech to the mass. So what is the plan for diffusing AI to our country?

I think fundamentally I looked at it that you diffuse it to the consumers or do you diffuse it to the small businesses? Consumers -wise, I think it is tough to fight these three, four gorillas that you are seeing. So I started to work towards small businesses AI or small merchant AI So the vegetable vendor should know that when he goes in the morning, he says, brother, these days tomatoes are coming. But tomatoes will get spoiled because it is going to rain. So you bring tindas, bring potatoes. So from here, the knowledge from the core of small businesses, what should they do in the business, to the problem like, why didn’t I get money? Who cut the money?

And will I get a loan or not? The question and answer we don’t get from anyone, and they try to do it on the phone call, that scale is not possible. And then the person, individuals, it is not possible even in a very sophisticated computing system, the level that AI brings. So here we are, we are building. India’s AI for small and micro, small and micro merchants and distributing it through them. So, my belief is that big people will be helped by big people. Small people will be helped by us.

Sir, there is one more question. Thank you. AI sound box. Sir, actually, our education system that is designed for industrial era. So, what do you think like in AI era, do we still need to follow this education system because in AI…

Look, opinion on education system… My father is a teacher, so I won’t give it to him because my father will beat me up and I will do it from a distance. So, I don’t understand that you haven’t understood it yet. You didn’t study in class. I was a topper, by the way. Class. Just in case. That’s when I was beaten. I think this question of what is a good education system will evolve as an answer in next five years further ahead. We are literally in the beginning of… In other words, you land at the airport right now and ask, do you have Indian food here? Go to the city, stay for two days, then ask where the Indian food is.

So it is a problem to be solved. It will and it will evolve much later. Yes, your question. Oh no, no, the lady in the back, sorry. This is the visual identity goes through that. Are you a doctor? Oh, it looks like.

I have a question. What is the one thing that will never allow AI to perform in payments domain? Specifically in the payments domain.

Oh, easy. You would not want it to have full control of your bank account and make payments. Because if it does some stupid thing, you have allowed it. It’s like saying, we will not give our standing and Ram Rajan. Do you understand? Do what you think is right. We will not do that. So full control should never be given to your bank account. This is the thing.

But if we go a little deeper into technical ways when we are talking about ISO. Or 2022 or maybe RTR. payments, whatever. I really liked that you’re talking about nerds. But because of this technology, the payment didn’t increase. We made it IP guys. So you’re a very old technology person. Actually, sir, I have a job in Canada. You won’t believe, RTR is going to come there. In Canada, they’re going to be front -line and they’re going to be Mr. Stargazer. So I feel very proud there. They say that I’m from India and we use Paytm. So kudos. Thank you so much. Thank you so much, sir. Thank you. Namaste, sir. So my question is this. What is the minimum…

I’m serious, man. I love you guys. The problem with Gen Z is that you have to think a little. Oh my God. I should ask this guy. This is the risk. Because if he says something like this one day, go home and fight, then you leave this friend. Then he won’t believe the whole thing. Look, before us, Gen Z people used to say, they’re on the computer, they’re on the screen, they don’t go out to play. They don’t go out. They don’t read the newspaper. Go to the beach. Go there. Go out. So we were told this. Now we’ll tell you. What are you doing? You’re taking all the questions like this. Use your brain.

But your t -shirt will be, I don’t use my brain, I use tokens.

Sir, one more question.

I will do a quick question. I will make a question out of four questions and answer it. Okay, okay.

Sir, what is the minimum effective strategy a tier 3, 4 student can follow so that he can do very well in AI? Tier 3, 4 students. Tier 3 or tier 4 students.

Tier 3 or tier 4 students. What does tier mean? Class 3 class? No, no, sir. City? Yes, city, city. Like rural area. Okay, okay. I am also tier 3 from Aligarh. So what they can do so that they can do very good in AI and opportunities. I got it. Basically you are trying to say what to do if you have a student there. What is your question? Go ahead. How do you say it? Farms become cleaner. Air becomes cleaner. Productive becomes dreamer. what will be left for labor market? Oh, this was a very earlier question. I had raised my hand. So, he is saying, when farms become greener, air becomes cleaner, productivity becomes heavier, what will we be doing?

Okay, I got it.

Do you see AI as a leveler in a fintech segment and how will you compete with your peers if they are also adopting AI and AGI?

No problem. I get it. Okay. Any other questions? I’ll answer these things. Go ahead. If you can speak, I will look. Sure. Go ahead.

So, I would like to ask one thing that I would like to fall back on your point that you said that agents will do the talking for us, humans. So, I have seen…

How will I talk? Okay, go ahead. No, no. That’s a different question entirely.

My question is that the agents have started forming their own websites. If you search…

Yeah. So, what is the question that you have?

I have the question that can… How can this economy succeed that can we integrate agents in the human economy that we have currently?

Okay, perfect. Okay. So the inherent question of tier 3 school kid is, which is the problem of every one of us to believe that whether it will be inclusive or not. I have a very simple answer to give. You consider this, like for using the internet, for using the computer, you had to get a QWERTY keyboard and then you had to learn programming. So in AI, just try to get all your work done with AI. And then leverage it as an extension of what your education allows. If you study engineering, if you study art, then you should ask, tell me the comparison between Shakespeare’s English and what was in India going on in that time and how were they writing in Hindi.

Ask the questions and enhance your curiosity as a student. You could be in tier 3, you could be in tier 1 city. But the curiosity and then fulfilment. Filling it using AI will give you the superpower that nobody will have. it so enhance your curiosity because you have access to AI no more questions please yeah and your question was on labor market so the labor market is very simple I don’t think the labor stands for less labor for all of us are also labor and I’m not treating just the physical labor as a labor so the the AI’s ability is that whatever is digital it can perform it superiorly so now imagine is you are you tactically doing exactly what the keyboard typing is or are you also thinking and doing something that you are asking this so you become more productive and the work market becomes even more richer and fulfilling for you so your job will become fulfilling and the businesses will be able to expand the places where the otherwise could have not expanded so subcassad subcassad thank you thank you and please exit from the left Thank you.

Thank you. Thank you. Thank you.

defining moment for disaster risk governance. Around the world, the frequency, intensity, and complexity of disasters are increasing. Climate variability is compounding existing vulnerabilities. Urbanization is concentrating risk, and cascading hazards are challenging traditional response models. At the same time, we are witnessing unprecedented advances in AI. So, at this point of time, how does India bring or develop a model with AI for resilience? We believe that the next frontier in DRR is not better algorithms alone, it is institutionalizing AI within national resilience architecture. Thank you very much. Thank you very much. from pilot projects to national and global resilience systems. Before we start the discussion, let me invite and call on the stage for the panel discussion His Excellency Dr.

Avinash Ramtohol, the Minister for Information Technology, Communication and Innovation from the Republic of Mauritius. Welcome, sir. He is a key contributor to national strategies for AI in resilient infrastructure and South -South cooperation. I would like to invite Ms. Beth Woodham, Senior Manager from UK Met Office. She is a specialist in disaster risk reduction via forecasting innovations and AI explorations for prediction. Welcome. I would like to invite Mr. Som Satsangi, former SVP and Managing Director for Hewlett Packard Enterprise India with industry insights on AI deployment in geospatial and climate analytics. Welcome, Mr. Som. I would like to now call upon Mr. Nikhilesh Kumar, CEO and co -founder of Vassar Labs. He is an innovator in leveraging AI for DRR.

Welcome, Nikhilesh. And lastly, Mr. Pankaj Shukla, Head of Customer Engineering, Google Cloud India, for Practical AI Applications, Hazard Mapping, Predictive Analytics, and EWS Scale -Up. Thank you. Thank you. and focus on double integration during this panel discussion. So my question first to Minister for IT, Communication and Innovation, Republic of Mauritius. Minister, the small island developing states face existential climatic threats. From your perspective, what policy reforms are required to institutionalize AI -enabled early warning and alerting systems within national governance frameworks? And how can countries with limited resources ensure sustainability in such ventures?

Thank you and good morning, everybody. Thank you for the opportunity to be here amongst you. First of all, I would like to say a couple of points before I get into the actual response there. Today, just like we have the physical world, we have a virtual world as well in which we all live. And that virtual world is so much bigger. Than the physical world we can see here in front of us at the moment. And just like disaster can strike the physical world, and that is the scope of the discussion, disaster can also strike the virtual world. And as we grow in dependency on the virtual world, on our digital systems, we should be well aware that disaster is not just the flood, the cyclone, the drought.

Disaster can also be the cybersecurity attacks that can actually create havoc in our lives. Therefore, it is very important that the scope of the discussions when we look at disaster be also extended to the virtual world and cybersecurity attacks. Now, having said this, in terms of policy reform, it is very important that we also create this bridge between the physical world and the virtual world. And I will explain myself. Just imagine, as we speak here, there is a big fire that breaks out in one organization. There is a big fire that breaks out in one organization. and because it broke out there are you know automated connections that go to the fire services to the medical services they will proactively now start driving to this place but when they come to this place where would they know where are the people because their main objective is to save the lives of the people secondary the material where would they know where are the people do they have a plan a structural plan of this this space do they know where do the pipes cross now I’m talking about a digital twin it is really important that we create that digital twin which will be the bridge between the physical world and the virtual world and the architectural map of that digital twin should be accessible to a certain set of operators the medical the fire services now this is part of the reform that we are looking at And in a small administration, it becomes easier to do it, as opposed to a huge administration like India.

Now, there’s one more thing in there. As, let’s say, we also have the structural plan, how do we know where the people are? Can we have heartbeat indication? Can we have the thermal map of the place so that we know wherever there’s 37, 38, 39 degrees, well, 37, 38 is better. Do we know where the people are located so that when the fire services come, they go straight to that spot? So this is very important. And another reform that is important that we be aware of is that when there is some kind of a pandemic, there is, and which is contagious, there is human -to -human virus transfer. Now, we are all very excited. We are very excited about artificial intelligence.

intelligence but we are also aware that there is this possibility of virus infecting systems right and just like virus infects people virus also infects systems and virus get contagious in computers as well we all know that therefore we need to also have mechanisms to protect because if we have a message that goes through an early warning system to people this already creates an alert in the minds of people the adrenaline surge starts already but if that message is infected it can create a lot of disruption in our daily lives and this we need to be very careful of therefore in terms of reform the decision making process and i think it was mr somebody earlier mentioned in the previous panel the decision making process is automated now automation can be and 100 % automation in the field of AI where it concerns the lives of people can be dangerous.

Therefore, human in the loop or human on the loop is critical in these kinds of environments. And this is also part of what we are looking at in Mauritius. Yes, it’s true that as a small island developing state, we call it SIDS, we have our own set of flash floods that can actually occur. Within a couple of hours, we can have flash floods and we can see cars floating around already. And this has happened in the country. And we don’t want that to happen again. Therefore, there are early warning systems that we are deploying, like cell broadcast systems, which we have planned to deploy. Now, again, the message that goes into that system should be a message that is human verified.

That is, decisions like these that are sensitive, highly sensitive, cannot be 100 % automated. That’s part of our policy. as well and want to ensure that humans are involved because machines cannot decide for humans human decide for machines and this needs to be critical and needs to be given the attention that it deserves and I believe our Prime Minister Modi ji also mentioned in his intervention yesterday that there is a great necessity to ensure that human remain part of the decision -making process in the application of AI for disaster management so these are a few points I wanted to mention thank you

insights in developing resilient governance frameworks which actually are scalable across the nation’s and this is a way to go resilient system which is resilience to even cyber attacks and of sustainable meaningful and not giving any say fatigue you you of alerts also is very much vital for a robust system to be effective across all disasters. So we now come to the second panelist, Ms. Woodham. My question to you is the national meteorological agencies play a crucial role in operational forecasting and early warning delivery. From the perspective of the UK Met Office, how can AI complement physical weather and climatic models to improve forecast, lead time, and impact, basically impact -based warnings to gain public trust?

And what institutional partnerships are necessary to ensure that AI -driven meteorological insights translate into actionable decisions and actions at national and local levels, with special emphasis on low -resource countries?

Hello? Yeah. Right. Thank you. Thank you for your question. Thank you, it’s a real honour to be part of this panel. So at the Met Office we are currently developing machine learning weather models and we absolutely do not see these as a replacement for our physical models. Our plan over the coming years is to step by step implement these models through blending. This could be hybrid models, physics based and machine learning based. It could be blending the output from both of these models after they’ve run. The truth is we don’t know what the answer to this solution is yet but in order to build the trust amongst the users of our models, the customers of our data we’re certainly not going to have a complete shift.

We are going to do this step by step increasing our blending. As we become more confident with the data. so it’s from this conference you know it’s very clear that um companies from the private sector are developing these models in the public sector of course we’re developing them too and sovereign capability remains really important but for public sectors we really need to have that co that co -development um at the met office we have a long history of co -developing with partners like india so through wc ssp india and through age um wiser asia pacific we have these partnerships we’ve co -developed physics -based models and we really want to do the same with machine learning models as well at the met office we’re starting standardizing our benchmark and evaluation benchmarking and evaluation we really want to make sure that when we’re doing comparisons between machine learning and physics -based models we’re being focused on the same thing so we’re really wanting to do the same thing with machine learning and we’re really wanting to do the same thing with machine learning and we’re really wanting to space models.

There’s a lot of metrics we can look at that show machine learning models are doing well, but are these the metrics that are most important to users? Therefore, not only do we want to co -develop the actual models with partners, we want to co -develop the benchmarking and the tests that we do on these models. Thank you.

Thank you, Beth, for giving your insight into how the National Meteorological Agency’s may plan to use AI to the systems. So now we move towards the technologies so that how do we really create resilient systems for forecasting. So my first question would be to Mr. Som Sasangi. The private sector innovation has advanced rapidly. So there are first foremost question I think which comes to my mind is how can technology providers design AI systems that are interoperable and with sovereign data architectures because that is the crucial issue to be cracked. So we have to design AI systems that are interoperable with sovereign data architectures and compatible with diverse governance ecosystems. For a country like India with federal government and the state governments so this is a very vital nut to be cracked from the technology’s point of view and similarly what standards of explainability are necessary when AI informs life -saving decisions.

Thanks Manish. Really a great question and probably in this room I’ll be calling something which is very very important because just when I walked in I heard Mr. Martin and he spoke a couple of points which are so important. and critical for a country like India. He spoke about the government. He spoke about the procurement policies and the scale. So all these three things are so important and critical when we look from India’s standpoint with 1 .2 billion plus citizens. I’ve been the managing director of Hewlett Packard Enterprise for the last nine years, and I know I’ve been involved almost in all large critical infrastructure projects, whether it’s UIDI or any kind of transaction, COVID, all applications.

And we know that all these things we have developed at this scale and delivered. Probably when we look, the most important aspect of the human life with the climate change, the disaster which is happening across the world, the length and breadth of India on coastal side. So, but somehow, are we ready to do it? I don’t think we are ready. While, and I’ll give you some pointer which are very important and why it’s not happening to the Mr. Martin point. And I’ll just, while India has very ambitious plan for national supercomputer mission way back in 2015, where India said, okay, we’ll be investing 4 ,500 crore to develop some of the supercomputer which will be the high class.

But in last 10 year, what we have developed is some 37 supercomputer with just the 40 petaflop of capital. is that sufficient? Now we are planning, okay, we’ll add another 50 petaflop. But when you look at the global level, the kind of infrastructure which is, if we have to manage in a real time this alert and warning system, and I’ll give you one or two examples in United States. The top which have been developed and deployed to do these things, they have got a capacity of almost one to two exaflop. And one exaflop is close to around thousand petaflop. Whole India we don’t have even today 100 petaflop of data. And US, there are multiple systems which provide this real time information and each of them, one is Al Capitan which is 1 .8 exaflop then Frontier system which has been deployed by the Oak Ridge National University has got 1 .3 exaflop.

capacity. Aurora, which is recently deployed by the Argonne National University, has got one extra flop of power and capability. So these are the kind of systems which are deployed so that actually they can take the power of AI in a real -time environment, whether it’s geospatial data or satellite information data or it’s any kind of live information, and analyze these things with the help of AI in a real -time environment and provide the alert much ahead of those things. Somehow we are not able to provide. So in India if we want this early warning system to be done, I think our main focus needs to be how we can have the core infrastructure which will meet the requirement of this.

And last couple of days in every discussion, this is what is coming out with the global CIO and CEO, that probably India need the core infrastructure which we have not developed. Now, we might say, okay, we are doing 10 ,000 to the AI and all, but that is getting distributed to a large number of tech and SMB guys who are developing the application. But what government needs, because it’s a sovereign data, that government need to buy this kind of infrastructure. But I know each of the system will cost anything between 400, 500 million dollars to a billion dollars. Government may not be able to spend that kind of money. So probably that’s a place where private partnership becomes very, very important in cricket.

So my request is that probably department should be looking how the large global institution and technology partner can bring the core infrastructure and technology, because today technology is not a barrier. It’s an infrastructure and the scale and the procurement process and some of these policies. How the various data will be getting integrated is a problem. So if we can address these things, to the scale probably India has done on the DPI side where we have implemented and the best example to the global level where the UIDI is being used almost by almost more than 800 -900 million citizens in the country. We can deliver that. We have got a capability and with all these AI transformation which is happening, our Honorable Prime Minister already said that India is going to be leapfrogging on those things and going to the global leader in the AI space with all those technology embedded along with the capability what India has got.

Only what is required the infrastructure, but infrastructure will come with a huge cost. When you are going to get the infrastructure, another element comes is the power, energy and water. That’s going to be very critical. So somebody has to look at all the three aspects. You can get the infrastructure if you don’t have the power. So we need to have the power which can help and power these kind of systems. So alternative power resources are going to be very, very critical. They’ll be all water -cooled system because they will have hundreds of thousands of GPU and CPUs running together kind of thing. They will require a huge power and huge water capabilities. So need to have that thing.

So India need to start thinking on those lines to create that thing. If we have to protect and we have to get the right early warning alert to save the life of millions of citizens in the country. Thank you.

Thank you. And definitely DRR also offers an opportunity for us to ponder. So taking forward from Mr. Som, I’ll go to Mr. Pankaj Shuklaji, the head of customer engineering at Google Cloud AI. Basically, cloud computing and AI platforms enable real analytics, real -time analytics at scale. So what are the critical infrastructure investments which are essential to support AI deployment in low connectivity and high -risk environments? That’s very vital, looking at the geography of our nation. And how can AI -driven dissemination ensure last -mile inclusion while mitigating misinformation risk? So your insights on that.

Good afternoon, everyone. So irrespective of the technology, when we talk of disaster management and resilience, essentially what we are trying to do is, we are trying to turn the chaotic reality on the ground into actionable intelligence. so for example the data fragmentation which is sitting with multiple ministries and social media various places all across all of that first of all needs to be brought to a place or at least should be you should have an ability to bring all of that data and turn into a living intelligence so once the data is there which is structured as well as unstructured data then we have the ability of our AI models today which are multi -modal to make sense of completely chaotic data noisy data into a real intelligence at unimaginable speed that is essentially what AI is all about so when it comes to the real implementation of this entire architecture and panelists spoke about multiple aspects that how can we use AI how do we actually implement it on ground if you look at what we need and essentially if we talk of AI broadly it has it is it is there at the five layers.

One is the infrastructure layer. Second is the operating system layer which runs on top of infrastructure. I am not talking about just servers and data centers but an operating system layer which scales from a central to an edge location to a multiple regional locations. Then on top of that the services which are required, platform services which are required to basically build the AI applications, make the use of the right models etc. Then you have got the models, the multi -model ability of the models like Gemini and various other hyperscalers which provide and for example the NDIA mission, lot of Indian providers are building models. Ma ‘am spoke about lot of the models which for example other companies are building.

So the question is that how are we able to make the use of all diverse set of models in a dynamic manner and use agentic AI on top of that. To build applications and turn that into a real action. which can be disseminated at the places where we want. For both proactive during the response and as well as after the response. So the question is how do we implement it? So implementation of this will require a framework or an architecture which essentially has a central living intelligence of all the data on which you are basically experimenting and pre -training the models and tuning using different type of models and building applications. The real application of that is going to happen at a place which might get completely disconnected from a central place.

So you should be able to actually build all of these applications, AI applications, make use of that data, make a single central single truth centrally but ability to send that intelligence back to a tactical location. Today it is exactly possible. So organizations for example Google and many other organizations are actually trying to build. Basically bring all the goodness of hyperscaler cloud. for the entire infrastructure and managed services layer as well as AI tooling to on -prem and then ability to also run those in a completely disconnected air gap environment in a zero trust manner. So you have the security of your data and your applications and then also you should have an ability to connect the edge locations in a federated manner to the central place but if required during a disaster you should be able to carry it a rugged device which basically has a basic small set of central intelligence sitting into that with all these necessary models to basically take action on the ground and that action could be related to either finding out where are your assets where is the maximum impact which has happened, how do you actually kind of send the information to various places.

So all of those things are absolutely possible today and while there is huge amount of infrastructure at its own set is required actually to train models, build models. but that’s happening across the country we should have an ability to bring all the good models, the models for the right thing to basically run it on prem at a smaller set of infrastructure and make a smaller set of that which can run in a tactical location which sits possibly in a very limited infrastructure and compute that is what we

Thank you Pankaj, I think for giving Google’s insight into building rugged systems at scale deployment of AI solutions in low cost and high risk environments we have another contributor to the DRR particularly AI deployment in DRR could be from the startups and we have Mr. Nikhilesh Kumar CEO and founder from Vassar Labs basically Nikhilesh you can enlighten us about how startups can contribute in developing a DPG at population scale for DRR particularly for countries like India

The modeling layer, which is transforming the data into various insights, hazards. The asset and the people layer, which is getting impacted, where we need to know today in personalized and in precision, where exactly we talked about, like if flood is coming, which road, which houses, if a landslide is coming, which area. And the fourth, which is most important, is the workflows to translate the actions. And this is where we see a role of DPI and DPG, because all these four layers are not done by one person. They are looking to data scattered across various agencies. And we need to have DPIs and DPGs, which are built across this data right from. different institutions which are bringing metrological data, some institutions which are bringing water and other asset related data, some institutions which are creating different layers like survey of India in case of India on earthquake and various other layers.

Now we also see a role of AI today being playing a role and I will just give an example for that. So we see as today extreme events are happening, one of the first pressure is in the water sector where we see extreme floods sudden gush of water coming into the large dams and dams is one of the most extreme and vulnerable asset that we are all impacted to it and we would also see that the large dams are perhaps we have got a good handle to control them during the disaster but we have big number of dams in the country where they are unregulated and they are scattered across in large numbers and there is no forecast available for them.

So how do we churn in real time, in near real time, both hourly and in days time for close to 1 million water bodies which anytime can be vulnerable. So one of the solutions that we recently saw was translating and making leveraging actually the data gap utilizing AI and AI sitting on real time satellite data 30 minutes interval, radar data coming from, currently all these assets are available from IMD and translating that into a nowcast. Now that nowcast layer translated into hydraulics to each of the dams and in cyclone month close to 5000 dams were given in real time. So such use cases you see is where data is getting connected is available in real time in an interoperable format and then there are players who can translate this data into actions.

I see that contributions where such platforms are brought at national and state scale and these use cases are also packaged and made available for different recipient departments to translate them into actions. So I would say that sir and one more thing I would just like to add taking this as a forum sir. Risk assessment and risk reduction both has a very big gap when it comes to data especially various events across take about earthquake, take about other type of disasters where… where parametric measurements historic have not been available and knowing a frequency… of location specific frequency of these hazards has been actually lacking because you don’t have a database. Now AI can play a very good role here where the information is lying in lots of news which has happened and which contains unstructured information on location, unstructured information on the hazard that has impacted the damages.

So AI can actually uncover these informations and create a structured data sets each hazard wise and these will also feed into various DPGs that will further unlock to the insurance sector to make this which will basically benefit from knowing location specific intensity and also frequency of the risks. I will just close with that sir.

Thank you Nikhilish. You have aptly summarized that startups in this sector, definitely can play a very vital role particularly for developing a rugged AI systems for India at population scale. Now we have heard the panelists from the Indian perspective since we are running large systems. So we would like also to have all the members to have the benefit of the insights of how the national systems are functioning in India and how technology is basically being deployed at scale for DRR perspectives. So firstly I would like to get insight from Dr. Prithunjay Mahapatra. Yes. DG of IMD is here and IMD can elaborate on how robust AI based systems can be deployed, are being deployed at population scale in the Indian context.

DG of IMD is here and IMD can elaborate on how robust AI based systems can be deployed at population scale in the Indian context. Dr. Bapa.

Namaskar. Good morning to all of you. Respected Dr. Komal Kishore sir, Adas Nand sir, our Krishnamurthy sir and distinguished panelists and their delegates, friends and colleagues. On the outside, I congratulate NDA. For organizing this session, which has given a lot of thoughts to each of us who are represented here. I’ll just start with what the initiative has been taken up by the UN and the WMO. A clarion call was given in 2022 that early warning for all. And when you go for early warning for all, it includes all the countries, all the people and all sectors, all the strata of the society. So with that, when it came of that, actually less than 50 % of countries had the early warning at that time.

Now the number is increasing. but still the time is short now by 2027 we have to achieve the 100 % what’s the early warning for all it is a long goal and if we review now we find that during this last 5 years there is a huge jump in technology and AI is one such technology which is helping for extension of the early warning for all looking at the various components of that you need first the risk knowledge at each and every point what our friend told us like Nikhil and it is not possible with the existing network of any country to have the risk knowledge at each and every location but at the same time there are unstructured data as it is told which can be utilized to create the knowledge to create the risk hazard vulnerability assessment which can be the historical knowledge which can be utilized in real time when you go for the prediction of any severe weather event Next point comes the early warning.

Yes, over the early warning aspects, you will see that there has been also a huge jump in recent years with the inclusion of many AI -based models. You will find that each and every large NMHS, you can say, established NMHS, they are utilizing AI. IMD is also utilizing AI for taking a decision with respect to the early warning. At the same time, I will tell you, AI has come off as a hybrid model along with the physical models. We cannot get away with the physical models because physical models provide you the physical understanding, the reasoning. And hence, the human knowledge gets into the picture with the help of these physical models. So therefore, AI has to be suitably connected with the physical models.

That’s what everyone is doing, starting with European Center or Indian Center. And to do that also, there’s been many collaborations and integrations towards that. So after that, if you look at the basic backbone, which is the modeling, the modeling starts with the basic assumption that weather forecasting is an initial value problem. You cannot give weather forecast if you do not have what is the initial status of Earth, ocean, and atmosphere. So therefore, the basic thing which we are talking of now, that is already defined in the physical modeling system. The system, unless you improve the data, initial data, with all types of observational tools and techniques, you cannot improve the weather forecast. So therefore, here, by collecting or by creating the data with the help of AI also will go a long way in improving not only the AI models, but also the physical models and the hybrid models.

Once you have the good data, the quality of the data can be improved with the help of AI. There are, I’ll tell you, from satellite we get data, a lot of data, but only five. Five percent of the data from satellite is usable. We cannot use the data from satellites because of the quality. And further is that the quantity. you cannot accommodate all types of data in the physical modeling system as our friends have been telling that you need infrastructure, you do not have a computational infrastructure where you can utilize 100 % satellite data so yes you are true that in India we do not have sufficient computing infrastructure, we have at least now 28 petaflops in IMD and outside of course with National Subcontinent Motion we have come up like that, but that is not sufficient and therefore there is scope for public engagement for the augmenting the computational infrastructure and other digital infrastructures but at the same time there is another scope because of AI, now box model has come up a poor country, a poor small island nation cannot venture or cannot even dream to have a high performance computing system, they can go for AI system, a box model has come up where you can give it a small island nation and there with the help of a few GPU nodes they can have the forecast and that has come up and it will grow gradually and we will have the affordability to early warning with the help of AI.

of this GPU -based AI -driven or data -driven models. So after that comes the forecast. Once you come to forecast, now we have already come up to AI consensus. But physical consensus plus AI consensus, then again you will go for the final forecast. Then finally you go to the sectoral applications. There is a huge scope here with the improvement of economic conditions and societal conditions in every country to improve our decision -making for each and every sector, and there AML can play a role. So I urge upon all the industries, academia, R &D, and think tanks to collaborate with NMHS, especially with the India Metrology Department and other organizations here, to have very authentic, specific, and judicious utilization of AI with limited reasonable resources available in the country.

So thank you very much.

Thank you, Dr. Mapatra, for your valuable insight. Now, since NDMA is the APEC’s national body, which basically will integrate… all the varied systems into creating a rugged AI… systems. I would like the entire audience to get the benefit of the vision of NDMA from member and HOD Dr. Krishna Vassa. Sir, can you please elaborate how NDMA intends to take this forward to create a sustainable, low -cost, at -scale model for the country?

Thank you very much for giving me this platform. I would like to mention that we already have a huge amount of data that exists in relation to almost all the hazards. Look at the earthquakes. We record all the micro -earthquakes for the entire country. The kind of data that exists for the earthquakes which are below 3 also can give us a very good indication of the kind of earthquakes that we can experience in the Himalayas. and other regions. And this data, the availability of data is going to increase exponentially as we are investing in the observational networks. Almost every mitigation program that we are doing, we have included a significant aspect of early warning systems. In the next five years or so, every village in India will have an automated weather station.

We will have a large number of instrumentation for measuring landslides. We are going to at least quadrupling the seismometers, strong motion accelerographs. So we will be investing a huge amount of money in improving our observational networks across the hazards, which will mean that we will have access. to still a larger amount of data. What is important is that we need to have that capacity to process the data and apply the AI models and improve the precision of early warning. That is an area that is the sphere where we are struggling right now. It’s one thing to set up the observational network. The another thing is to how to collect the data, process the data and generate the information that could be used and more so when it comes to informing the common citizens.

Scientists is one thing. You are getting a huge amount of data, but we are not doing it for the scientists. We are doing it for the people who get affected by disasters. So how do we go about it? And the roadmap is not sufficiently clear and I keep talking to all kinds of people. somebody would come and say that you set up a huge data center. No? Okay, that’s fine, great. But people also say if you are setting up a huge data center and you are not really empowering all the early warning agencies, then how are you going to justify the investment in data centers? The data comes to individual agencies. How do the data center and the individual early warning agencies interact so that we have a good model available?

And we don’t have unlimited resources. So the point is, this is where we need more clarity. How do we go about using our existing networks to improve the precision in early warning risk information through a gradually incremental way of building capacities that of course include the data center that should include improving our connection with the LLM models. But it’s also very, very important that we need to find a way of improving the overall architecture. That is one area where we are struggling and where we need some guidance. Thank you very much.

Thank you, sir. I think we are coming to the close of the discussion. I’ll request Krishna Vassa, sir, to please give a moment to our panelists. Just give the back. also then request all our dignitaries in the front row to after this memento is over for a quick photograph and then we vacate the room. Thank you. Thank you. I’ll request the leadership from the states of Tamil Nadu and Andhra Pradesh, Telangana also to come, please, in the front for the photograph. We are very happy to inform that most of the states are also represented from the State Disaster Management Authority. Thank you very much. Thank you.

Artificial intelligence is real and it is influencing governance, markets, public services and even geopolitics. The question before us is not whether AI will shape governance. The question is whether governance is going to shape the artificial intelligence. It’s transforming governance in fundamental ways today. Through decision intelligence, public service delivery at scale and national security and strategic stability. Moreover, the governance challenge becomes uniquely complex as AI introduces speed, opacity to a level, concentration, global reach and dual use. This creates a governance paradox. Regulate too slowly and risk harm. Regulate too slowly and risk harm. Regulate too heavily and risk stagnation. The answer is not control versus innovation. The answer is intelligent governance. Therefore, the principle of AI governance should necessarily include human -centered design, transparency and accountability, risk -based regulations, global cooperation, and adaptive policies.

AI does not recognize borders. We need interoperable frameworks, shared safety standards, and cooperative oversight mechanisms. Governance frameworks must evolve as the artificial intelligence evolves. Static policies cannot manage dynamic intelligence. In this era, we move from individual, national -level policies to coordinated global norms, and that’s the necessity for today. History will not judge us by our sophisticated algorithms. It will judge us by the wisdom of our governance. The industrial revolution reshaped economies. The digital revolution reshaped communication. The AI revolution will reshape decision -making itself. With that power comes great responsibility. The digital revolution which we are undergoing today, surely we stand at crossroads. One path leads to inequity, instability and uncontrolled disruption. The other leads to augmented human capability, smart governance and inclusive prosperity.

The difference between these futures will not be determined by machines. It will be determined by us, the policy makers, the people who use it and each and every person who is involved in the process of AI governance. Therefore, we need to commit today to ourselves for building AI systems that are safe, secure, transparent, equitable and sustainable. and aligned with core human values. And that’s what we are going to discuss here today. Last but not the least, the message that this panel discussion and the fireside chat is going to give is let us govern intelligence with wisdom. Thank you so much.

Thank you, sir. We are truly honored to have with us today a leader who has been forefront of technology -driven governance in Maharashtra. I now request Shri Ashish Shailar Sir, Honorable Minister of IT and Cultural Affairs, Government of Maharashtra, to grace us with a keynote address.

Good morning to everybody. Respected guests, dignitaries, Excellencies and all the policy makers, members of the media, dear friends, young challengers, ladies and gentlemen. Namaskar, Vande Mataram and a very good morning to all. India today is not merely hosting an AI summit. India is helping to ride the operating system of AI age. We meet at Bharat Mandapam under the banner Maha AI, building safe, secure and smart governance. As a part of AI Impact Summit 2026, the first in its global series to be hosted in the global south. Over 20 heads of state, 60 ministers and hundreds of AI leaders from the industry and academia are here. Reflecting a shared vision. A shared conviction that AI must be inclusive, responsible and resilient.

I hereby say under the leadership of Chief Minister Devendra Fadanvisi, Maharashtra has positioned itself as a living laboratory for AI in governance. Our partnership with global technology leaders, for example, our AI -powered Mahak Crime OS, showcased by Microsoft’s Satya Nadellaji, has already transformed how we prevent, detect and investigate crime, faster response, shorter investigation cycles and more transparent processes. Simultaneously, our state digital agency, Mahaiti, is building what we call an intelligent government infrastructure, a cloud native, and a cloud -based infrastructure. modular, API -driven backbone that uses AI to integrate services, predict needs, and respond in real -time. This spans smart recruitment, AI -based property mapping for urban local bodies, real -time urban dashboard for traffic, weather, and civic issues, and pilots in flood management and smart mobility.

The philosophy is simple. Use AI not to distance the state from the citizens, but to make governance more human, faster, more responsive, and more inclusive. In other words, scale empathy through insight. Across the world, public sectors are resting with the same three imperatives. So better safeguard digital sovereignty. and adopt AI responsibly. Many countries have realized that interportal, public, date and robust AI governance are becoming strategic infrastructure at par with energy, transport or telecom. For Maharashtra, Maha AI is our response to this challenge. A safe, secure, smart governance stack must rest on five pillars. Computers, compute and cloud at scale number one, high quality public data sets number two, state AI governance number three, and interpolarity and standards number four, and capacity building is number five.

Smart governance is not only about deploying chatbots or dashboards. It is about building resilient, auditable, human -centered, AI systems. into the nerve systems of cities and states, transport, energy, public safety, urban planning, disaster response and welfare delivery, without trustworthy air governance, smart cities’ risk opacity, bias, security breaches and erosion of public trust. In Maharashtra, we see internet health as a core policy concern, just as physical health is essential for individuals, digital health is essential for societies. Disinformation, deepfakes, AI -generated fraud and cyberattacks can undermine democracies, markets and communities with unprecedented speed. Our response must be combined. That is robust cyber security, digital literacy and critical thinking, hybrid verification ecosystem. and that is our response as far as our state is concerned.

I am really happy to be the part of this summit and at the same time giving a response addressing the challenges and our ecosystem under the name of Mahayaya and therefore we are here to present our case as a building safe, secure, smart governance and appeal all the best of the technologies and platforms of the world to associate, coexist and work with us. Thank you so much. Thank you so much.

Thank you, sir. Your vision for a digitally empowered Maharashtra truly sets the tone for everything we will discuss today. And now the highlight of today’s session. May I request all the panelists to join us on the stage please Sri Praveen Pardeshi Sir Yashasvi Adav Sir Dr. Anupam Chattopadhyay Dr. Amit Kapoor Mr. Suresh Sethi Major Ranjit Goswami Ms. Bina Sarkar Mr. Dev Rukhdar Davinder Sandhu Dr. Ganesh Ramakrishnan Vikash Chandra Rastogi Sir Rajesh Agarwal Sir Mr. Suresh Sethi Thank you. Thank you. Thank you. Thank you. Thank you. Thank you. Shri Yashastri Yadav, Additional Director General of Police, Maharashtra Cyber Department, Government of Maharashtra, Dr. Anupam Chattopadhyay, Associate Professor, Nanyang Technological University, Singapore, Dr. Amit Kapoor, Chair, Institute for Competitiveness, Mr.

Suresh Sethi, Managing Director and CEO, Protean EGov Technologies, Major Ranjit Goswami, Head, Corporate Affairs, Tata Consultancy Services, Ms. Beena Sarkar, Customer Success Executive Service Now, and moderating this conversation is Mr. Devaroop Dhar, Co -Founder and CEO, Primus Partners. I now hand over to Mr. Devaroop Dhar to moderate this session.

Thank you, Aditi, and a warm welcome to all our panel members. I’ll start with Praveen Paradesi, sir. So, sir, at Mitra, you have been experimenting a lot with AI. There are multiple AI initiatives which have been taken. If you could share your thoughts, your vision as we start this.

So first, the most important thing about AI is getting our energy, some of the hard things right. So one is energy because, you know, remember President Trump just mentioned that why should we be paying for Indians who are processing most of our answers. So I think we are pushing on green energy, more than 19 ,000 megawatts of that to come up at solar level. So that’s the fuel for AI in future. The second is that capacity building. So all our staff from Mitra and a lot of our other departments, we went to this AI university and gave them a course. And we also hope that there will be online courses available on IGOT where government staff can become empowered to use AI.

Then we look at what is the impact on the economy. So the impact on the economy, mostly people are concerned about the jobs. And that’s a real issue. One analysis from NITI. Shows that from 1950. to 2020, all highly educated people with postgraduate degrees, engineers, they are the ones who had a 95 % plus chance of getting jobs. But from 2020 till now, 0 .65 % is the rate at which physical jobs, that is, masons, bricklayers, home carers, their value and their employability is increasing vis -a -vis highly educated ones. So this is the impact of AI. So how should we do this better? One is, of course, as our Secretary IT mentioned, making it available in government, ensuring seamless access to services.

But other aspects which we don’t look at, which we are working through our state data authority, is how do we also encash the data at a large scale? Because otherwise we become… a sitting target for people to just use India’s data for monetizing their own values. So two big examples are pharmaceuticals. India has the largest population and the number of diseases, experiments, et cetera. Now, this is all health data, and this is very valuable for pharma companies. So the state data authority is working on issues wherein we can make it a single source of proof, make it available also, and if there is a commercial possibility, make those resources available to India, that is to our government, and not to be cashed for free.

So these are some of the applications. Government issues many, many orders. We have issued more than 150 ,000 orders, which are called government GRs. And it’s a maze through which it’s very difficult even for government officers, whose departments it is, to understand what is the latest position on a complicated situation. So we are working with Professor Ganesh here from IIT Mumbai. So Mitra and we are working together with them. And we are working with the government to make sure that we are getting the best results. And we are working with the government to make sure that we are getting the best results. disentangle all these orders through a small language model, not a large language model, and so that you can query at two levels.

One, for the government officers. The government officers should be able to ask in any complicated situation, what is the latest position on whether an additional FSIF or a building permit can be given in this situation or not, what are the Supreme Court orders. And on the other hand, citizens should also be able to ask under those rules. So this is called Maha GPT, and hopefully this will be the first application, which is both available to government officers and to citizens. I stop here.

Thank you, sir, for sharing all these examples, wonderful examples. I’ll come to Yashishvi Yadav, sir. Cyber is another large user of AI. So if you could share your examples, your experience around how cyber is using AI.

Okay, so law enforcement and cyber security. is one of the major concerns for law enforcement agencies all over the world. And I would like to draw your attention that under the visionary leadership of our Chief Minister, Mr. Devinder Fadnavis, who had the foresight to use AI in law enforcement work about five years ago. So we have launched and implemented this Maharashtra Cyber Security Project, which generously borrows AI tools, technologies, algorithms. And we are fighting real crime with these technologies. And the USP of this project is that the best and the most state -of -the -art tools, technologies, experienced consultants in the field of cyber security, and experienced and professional police officers have all coalesced, under one roof.

And there is a live police station as well. So this is how cyber security is being embellished through this project. And the beauty is that the dark web monitoring, threat analysis, social media monitoring, or any type of cyber crime, sextortion, ransomware, cyber bullying, and a lot of other types which cyber crime takes. They can all be undertaken, handled by just one, on the fingertips by just one helpline number, 1930. So this agency, if any kind of cyber issue is with any citizen, they can just dial this 1930 number and all the cyber solutions will be provided by more than 150 cyber consultants. So a lot of AI tools are being used. And this is the reason why we are using this and seamlessly.

And the best part of this whole exercise is that in less than six months, more than 1000 crore of rupees, which would have gone into the hand of the scamsters have been frozen and are being ultimately returned to the victims. What a big relief to the victims. And more than 70 young girls who were being subjected to intense cyberbullying, blackmailing and sextortion and were on the verge of committing suicide because of very efficient AI tracking. They were prevented from taking the extreme step. And 70 lives have been saved in less than six months of its operation. So that’s how AI is at the forefront. Thank you. Is at the forefront of being the bulwark against cyber security concerns.

I would like to draw your attention to only one report that we generated. which is called Echoes of Pahalgam. Now, in that case, while the Indian Army was fighting a conventional war with Pakistan because of the Pahalgam incident, more than one million cyber attacks were launched by nation -state actors, whom we called as APT, groups from Indonesia, Pakistan, and even Turkey, and so many other countries. And they were thwarted by such AI tools, which we call as threat intelligence tools, like Luminar, Cognite, or Pathfinder, which are big data analytical tools. So in the dark net, we still find the traces of these cyber attacks. So cyber crime is now slowly progressing into cyber terrorism and cyber warfare.

So that is what we have to be very, very careful. and before I end this preliminary address, I would like to also draw your attention what beyond AI? A big, big threat is lurking. It is called quantum computing. Now quantum computing can do processes in qubits, hundreds of millions of qubits. It’s at speed and it can solve complex issues in less than six seconds which the best of supercomputers would take more than 50 years to do it immediately. So quantum computing can break the best of encryptions including RSA encryptions of the banking industry, including blockchain technology. Now if these encryptions are broken in less than few minutes, the whole financial system can be lopsided and lots of money can flow into actors or threat actors which we are not aware at all.

Bitcoin can be broken Even credit card Encryptions can be broken Banking systems, encryptions can be broken So right now we have to prepare What quantum computing Can give us as in terms of Pros and what can be The shortcomings or dangers Lurking because of quantum Computing, the cons So we have to prepare because China And other countries have already invested 15 billion dollars Or even close to 20 billion dollars We have invested only 1 billion Dollar till now So that’s how we have to catch up with Quantum computing before it’s too late So this is how cyber security And law enforcement perspective On AI And I would like to pass on the baton To the next speaker, thank you

Thank you sir, that was quite reassuring as well And since you spoke about quantum I want to bring in Dr. Anupam Chattopadhyay. Anupam, you’re working at the intersection of quantum and AI. So if you could share your thoughts, how are things moving in that direction? pooled into the product in order to help this. Thanks, Anupam, for giving a perspective, both from research as well as industry. So I’ll come to Suresh, you. So you’re working extensively in DPIs, and there’s a strong interaction between DPIs and AI. So from your experience, how are things moving in the DPI space, and how is AI influencing?

Thanks, Devroop. I think from a DPI perspective, we are all very familiar. We today have population -scale digital rates. And, you know, that puts us in a very sweet spot because today a lot of times when we start using or embedding AI into any technology, are we ready to embed AI or not? And I think there was reference to data sets, how is data organized, how can you enable AI on top of it? So I think, first of all, the population -scale DPI that we have, that gives us a significant advantage. Whether we talk about identity, we talk about our UPI rails, which is the payment and the transactional layer which comes on top of it.

And similarly, when we look at data itself, DigiLocker today has millions of authenticated documents that come into play over there. So while we have the digital infrastructure in place, if we can embed an intelligence layer on top of it, and if the question is around targeting subsidy, getting the right beneficiary, putting the money in the hands of the right person, that becomes a very, very important and significant leverage. I will just take two or three examples where we see AI playing a significant role. One is that as we move from static identity to dynamic eligibility. Now, we’ve seen it all happen. Today, we have digitally verifiable credentials. So the moment you are using static identity, you are only able to prove who you are, what do you do.

and then you are applying for getting some sort of benefits or subsidy coming through to you. But if you have verifiable credentials, these are credentials which are machine readable. We talk about the concept of blue dot, which technically means all of us have certain attributes which are associated with us. If these are available in a machine readable format, then AI can actually determine who is eligible for what subsidy. The second part comes to the fact saying, are we being reactive or can we do predictive governance? And predictive governance can be strongly enabled by AI because the moment you are having credentials which are digitally verifiable, you are actually able to predict who needs some sort of subsidy.

Now today, if there’s a distress in income and that can be tracked, you can trigger some sort of benefit to that and coming at a government level. If you can put data. consented data being shared with the government, the same can come through. And last but not least is the important part related to inclusion error and exclusion error. So when we talk about inclusion error, we are talking about leakages. When we are talking about exclusion, naturally we are saying the right person is not getting what is due to them. So your ability to be able to predict precision using verification is going to be very critical. Again, an AI layer can be embedded over there.

But all this very clearly, and we’ve heard it before, all this is very clearly important to have the guardrails around it. So AI should be explainable. The moment we are saying explainable, today a decision taken not to give benefits to somebody should be very clearly explained. And similarly, if there are benefits going out, that should also be explainable. The second part is auditable. So whatever we are doing, there has to be an audit layer over there to explain what has happened. And more importantly, there should be a human redressal pathway because ultimately you can’t put everything to machines. You have to have that human person coming into play and having accountability settled over there. So I think these are critical aspects which can make governance more predictive, more precise, and more proactive going forward by embedding an intelligence layer into the DBI.

Thanks, Suresh. I think very valid and meaningful points. I’ll come to Ranjit. Now, we’re talking about AI. There are a lot of happenings which are there. We have seen, we have heard about so many things at the summit. Now, a major tech company like TCS, which is doing a lot of work in this, how does large tech companies come in and collaborate with state governments? How can you enable that? What can be the steps in that?

Thank you, Devroop. I think… we need to first take a holistic view of what are we trying to achieve with AI. The tagline for this summit, if we go by that, welfare for all, happiness for all is very holistically kind of captures it. As coming from Tata Group, I am reminded that 170 years back almost, our founding father, Jamshedji Tata spoke about giving us the guidance saying that society or community is not another stakeholder in the business. It is the purpose as to why the business exists in the first place. Similarly, if we were to apply the same analogy over here today, I think AI is not a technical tool which is fundamentally going to make the governance more efficient.

It is fundamentally meant for how to bring the benefit, welfare and happiness to the community at large. If we try and approach the question from that perspective, it definitely comes out. And like even Suresh alluded to, how do we make sure that it is inclusive, the people get the right benefits that they are entitled to, and do not go to somebody who is not entitled to. The colleague from the police forces also spoke about as to how the criminal tracking and other things. These are translating the intent into action at the ground level. Lastly, when it comes to organizations like DCS, we believe that each department in the government firstly should not be treated in isolation.

Each department in the government should have the ability to have a common database of people, be able to extract the information, and ensure that the citizen is seen as a citizen of the state or the country, and not as a citizen of the department. So that common databasing is something that we are trying to approach. We have the Aadhaar database. Not every department is still connected to it. Of course, we are trying to find a reason as to how that can become the major point of it. So small steps like that, and of course, bringing in the platform’s intelligence to its core. That’s fundamentally the steps that we have taken.

Thanks, Ranjit. With that, I’ll go to Bina. Bina, I want to talk on the aspect of ethical AI biases, especially you work with women for ethical AI. How do you see biases or maybe biases around gender diversity creeping in and what needs to be done around this?

Thank you, Debru. So, yes, I do work. I volunteer with the Women for Ethical AI South Asia chapter. It’s powered by UNESCO. So one of the key questions that we ask ourselves is what are we solving for? Every time and I’ve been looking at the various solutions that are. Debuting or being showcased as part of the. India AI mission. Many a times when we look at a particular hardware or a piece of any new device through which we are delivering what we now call as AI services, mostly on large language models, I should say that. What we sometimes seem to miss is the wood for the trees. I will just give a very hard example over here.

We do know smart glasses is not a new phenomenon. It was introduced by Google Glasses way back in 2015, I remember 2013, 2015, 2016, about that range. One of the reasons why it was recalled was you had safety concerns because people were taking images, videos without consent. we have seen the return of these glasses and those concerns have not gone away and yet you see them in the market you see it in India being sold in every in any optic store, in my neighborhood optic store I have my colleagues who flaunt it saying that I am so cool, I have taken the latest piece of technology so when we talk about how do you build ethics and governance Yashasvi sir said you are the best, you are the best framework.

So what this means is we are not giving guns to everybody. Right. India has been very, very smart about it. Of course, there are certain countries where owning a gun is is fine. It’s as for their rights. That doesn’t mean India has to adopt it. Right. We have our very able police force. We have the Indian army. Right. So what is exciting outside? One needs to contextualize it, humanize it, see if it is threatening 50 percent of your population. I’m a part of that population and then decide whether it even needs to exist. In that market. So when you’re building out solutions and when you’re building out devices, we have the India Safety Institute. It has been instituted in 2025.

I do know that. What I would urge policymakers is that it should not, while we do have it and I do know we are working with Research Institute, industry, industry, ideally, if any new device comes like this, the first line of defense, so to speak, should be this institute. That actually should determine whether it actually creates a problem for the police force, for cyber security, right? Does it threaten 50 % of the population? We are already seeing it playing out in the UK, US. There is no policy that protects us. Even now, we are not protected as women. Leave women, even children, right? So I think that is something one really needs to take into consideration. While we love technology, trust me, as a lady, I find it extremely liberating to be able to create applications with just language.

But if you use that technology against us by bringing out devices and hardware that endanger us, I feel that’s where it breaks down. So you definitely, as part of ethics, you need to evaluate it from that framework. I call it the Kali versus the Rakta Bija effect. I’m sure some of you know that. Why would you create a Rakta Bija? Create a Kali.

So, Beena, I think a very valid point that you have made. With that, I’ll move to Dr. Amit Kapoor. We’re talking of AI and its impact and benefits. How do you see this benefit percolating? Next level of cities, tier 2, tier 3, other places.

So, Devru, this is a very important question. And as I was hearing all the panelists here, I would like to rake a few points. We definitely agree that, yes, AI can be transformational. But we have to understand a couple of points here. One of them is that, S2, what is the quality of education that we are giving so that people are able to use it in the right earnest? In fact, the issue that Bina was talking about is about ethics, education, and so on and so forth. The larger point here is when you talk about the skill development levels in Maharashtra, out of the 100 % or 9 crore workforce that you have, only about 20 % of them are at skill level 3 and 4.

80 % of them are at skill level 1 and 2. So if you have to move beyond that, you need to do something far greater. That means you need to embed your education system and build it very strongly. And then the second point out here is that how do we use, and if you really want to talk about tier 1 and 2, sorry, tier 2 and tier 3 cities, we have to also understand that what is the level of penetration and quality of internet in these locations. We can tom -tom about internet and everything, but the numbers are not very supportive right now. And you talk about internet and broadband connectivity. There are severe issues with this within the state of Maharashtra itself, which is supposedly one of the finest states in terms of internet connectivity itself.

The average speed of internet traffic in Bombay or in Mumbai is about 58 Mbps on a broadband network. When you are talking about usage of AI, and if you want to take it to the masses, then you have to have far better, deeper internet connectivity and broadband. I think it has to be done on a war footing. Not that we are not getting there, but it will have to be done faster and quicker. The second thing is going to be about inadequacy of supporting infrastructure. So this is where I also see that there is a tremendous level of opportunity that exists in Maharashtra to create this. Because if you really look at it, like 16 % of India’s workforce, IT workforce, or what you call a technology workforce sits in one single city in Maharashtra.

That is Pune. So if you’re really talking about it, that means Maharashtra has the potential, the talent to really take it to the next level. And that is where you have to build infrastructure. You will have to build what you call, when you talk about the data centers, et cetera, the opportunity does exist and things. And last but not the least is about, it’s going to be about cost and affordability. You will have to bring cost and affordability to these services as you go along. But having said that, I think the larger potential, we have not touched here. And when you talk about Tier 2 and Tier 3 cities, this technology has a huge possible impact that can be done at Tier 2 and Tier 3 cities, and that is about nutrition.

Today, Maharashtra actually has a problem on nutrition. Fifty percent of the people in Maharashtra are malnourished even today. How do I use this technology to assess what is happening in my PIN code level or a smaller level of geography in various cities? And location. The second thing is about water and sanitation, access to basic knowledge. What about access to can AI solve my education problem in tier two and tier three cities? In fact, none of us is talking about the elephant in the room. And that elephant in the room is that we are all super excited about AI, but we are not understanding that AI is also going to be the biggest dumping down element for the society.

In fact, when you talk about AI itself, it is going to make bonobos out of us. People out here, when you actually use the doom scrolling, when you talk about Instagram, what is happening to our children? And that is exactly what AI is going to do. How do we set our education system right? That’s what you have to do in tier two and tier three cities. Last point, and that is about the higher education space itself. When you talk about your workforce, and close to about 50 % of it is underemployed. So I have to disagree with Praveen for one small point. He made a very powerful point in terms of saying that how after 2020, there has been a transformation.

In terms of how people are getting jobs, et cetera. I do agree with. But the larger point here is that we are also not preparing our workforce right, and that is happening in Tier 2 and Tier 3 cities. So we need to take it there. Potential exists. As of today, Maharashtra is the engine of growth in India. We cannot debate that. Even today, it is about close to 17 % to 18 % of India’s GDP, and it will define India’s growth story in the future, definitely. But if Maharashtra does it right, then the country can follow suit on this. And that is where things have to be.

Thank you, Dr. Amit. And thanks to all the panelists. So with that, we’ll come to the end of the panel discussion.

Thank you so much. Thank you to all our esteemed panelists and senior officers who are here. May I request all our panelists to just step forward for a photo? Very interesting, sir. May I request you to join our esteemed panelists? on this piece. Thank you. Thank you.