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Thank you so much. Thank you, ma ‘am. Welcome to all the ladies and gentlemen who have found time to be present here today as we explore the topic of MedTech and AI innovations in public health systems. There’s a lot of AI being branded about here. What we aim to explore during the session is in the public health care and with the three pillars that have been traditionally associated with the public good, public health being a public good. The cost of delivery. That public health care. scales, cost of delivery from the government side and also the cost of public health care for the individual also and what can AI bring in terms of having more cost effectiveness.

The second one will be on the care coordination and how do we ensure that the longitudinal health record get built and the clinicians are better equipped to utilize emerging technologies and AI for better care and the third one is on the efficiency, operational efficiency in terms of how do we ensure that the patient standing the line is treated in the best possible manner and in the lowest possible time with quality being associated. So with these three anchors to public health care, I welcome the panel and let me start with you Saurabh, my counterpart in the government of India. When we talk about population scale deployment of AI systems in health care, how do we ensure that the population is

Thank you, Saurabh. So I would like to inform all of you. Most of you must have also learned about the recent healthcare strategy that has been launched by government of India. It is called SAHI, the Strategy for Artificial Intelligence in Public Health. So as part of that, lots of activities in the field of AI are already happening. So if we see in terms of, we know there is a lot of lack of specialists, even especially in the rural areas. So through these AI techniques, the kind of services that are being provided, whether it is through scanning X -ray images or through diabetic retinopathy, also screening is possible through AI tools. So through that, in the resource constraint settings, we are able to provide good quality healthcare services to the citizens.

We have the eSangevani platform, the teleconsultation where… a person who is there in the PSC, a doctor who is there in the PSC, they can take expert opinions from the tertiary care hospitals. Also, I see artificial intelligence in terms of overall reduction of out -of -pocket expenditure because that is also one of the main important goals of ensuring universal health coverage. So by building more and more such systems, by bringing up trust, safety considerations in the public health system, we are actually also creating public trust in the public health system so that people actually come towards public health system and they rely less on the private health care and thereby reducing the out -of -pocket expenditure.

Similarly, also lots of digitization is happening, lots of records. We have the digital data. And through this digital data also, we can improve upon the overall workflows that are there in the hospitals. We have the resource constraint settings. So in terms of supply chain, management also. So lots of innovations. the health ministry is looking for in terms of ensuring that we will be able to provide the universal health coverage to a person even who is in a remotest of area should get the best quality coverage and also at the least cost. So that is how the strategy of government of India is as far as the adoption of artificial intelligence in healthcare. Thank you.

So you have talked about innovation emerging as a centerpiece in public health and with the strategy of AI adoption in healthcare, the SAI strategy we may look at a UHI movement just like there was a UPI movement where we have the digital public infrastructure in health being set up all the interface layers getting established but that also means bringing a lot of innovation ecosystem to the healthcare. So with the work that you have done Shivji over a lot of time how do you look at the innovation to institutionalization framework in the sense how do we while we every other day there is a health tech startup coming in, how does it get integrated in a structured manner with the public health system.

Thank you and good afternoon everyone. One of the important things which we need to recognize at least in AI is currently solutions are looking for problems not the other way around. Therefore it is important to marry what is the problem which is important for the state and then bring the solution together. So the first step of institutionalization is about how do you apply technologies and who sets the agenda and who is setting the priority. Like the way Andhra government has set up the Center for Applied Technology which has put out a call to say these are the problems we solve for our frontline workers. I think that’s the first step of institutionalization. Second is about the whole although I said solution looking for problem and problem looking for solution these are not either or.

I think both are important. We never knew we all needed a smartphone. But at the same time smartphone has become a problem now. Right in some sense. So I think continuous bridging and very critical element is to taking it to the ground and actually sharing the real evidence because every innovator will want to say their technology is fantastic. Have you come across any innovator who says their technology is not good? All of them will say it’s fantastic, it works the best, it is the best. That’s okay. That’s what an innovator is supposed to do. Whereas I think the state has the responsibility to test it on the ground, to look at the feasibility, to see does it actually change health outcomes.

Does it actually save cost, as sir was mentioning. And the third element of institutionalization, sir, is also the use case library that we need to build. I think there is a lot of discussion around this can do that, that can do this. Where’s the evidence? Where’s the use case library? Where has it worked? Has it worked with tribal communities? Does it work for the last poor woman, tribal leader or ordinary person? Right? That becomes very, very critical. And the last part is around the AI policy. Policy and processes. where the guardrails are built so that the state is also able to have a very clear policy towards how do we share the data, how do we ensure that we are able to, all the data that is shared by the community is actually monetized for them.

Thank you. You started with a great point that most of the time innovators come with solutions and they are looking for problem statements. But in Andhra Pradesh, we have articulated the problem statement clearly in terms of how do you at population scale drive an AI -enabled public healthcare system. And that’s where one of our partners is TataMD, which is represented by Saraswati Imam here. I believe you have set a fantastic stall also on the digital system that you have set up for healthcare delivery. So would you want to talk about your experience and how do you bring up a private sector ecosystem into public health and enable a public healthcare system?

Thank you, sir. As the sir mentioned, I represent TataMD. please visit our stall in hall number 5 where we have showcased what we are doing but I will just explain it in simple terms public health system we are looking at AIS assisting the entire public health system so I will just divide it into 3 or 4 aspects one is for the medical officer how can the medical officer gain from the assistance of AI so what we are looking at is how can the so normally when you go to a PHC the doctor would ask for the vitals to be taken the basic he would ask what is the complaint for which the citizen has come but it tends to be episodic it’s not longitudinal so we are looking at how with the help of AI we can share with the medical officer in a structured manner the entire longitudinal data of the citizen so that the doctor knows this is not just an episodic care we are talking about we are talking about continuum So how can we ensure that we understand the citizens?

So if a NCD patient comes, shows, mentions that he has HbA1c of 8, but has it been the same? Has it come down or is it increasing? So that trend will help the doctor to decide the medication. Or normally they would also ask what is the medicine that you are consuming and they would say either continue or stop. But with the longitudinal data, they will be able to say, okay, is this medicine actually working? Is it not working? How do I ensure that the patient is taken care of better? So I think it helps the data to be structured in a manner in which the doctor can use. Secondly, there are sometimes because of the busyness all of, I mean many of, I don’t know how many of you have actually visited a PHC and seen the workload that a medical officer faces.

Many times they are just rushing through the citizens, right? So they do not have the time. Sometimes they do not have the time. Sometimes they may miss an investigation which is required for a particular. So AI can do that prompt saying that, okay, this is the history, this is the data. Maybe we should get a foot examination done for this diabetic patient. He has not done it for last few days. So basically we are looking at AI as assisting the medical officer with the clinical support system so that nothing is lost, there is no oversight. Plus there is a evidence -based treatment guideline which can be shared with the doctor. And finally, the decision maker is the doctor.

We are not here to say that the AI will decide. The decision maker is doctor. The AI is to assist. So this would be more on the clinical side. Similarly on the operational side, right, all of us know the time that is spent in detailing out what the conversation with the patient is. So we are looking at how that can be made in a meaningful manner in a rural public health system. We all know in a closed room probably the listening can be better, the ambient listening can be easier managed. But in a closed room, we are not here to say that the AI will decide. We are looking at different dialects. We are looking at different dialects.

We are looking at different dialects. We are looking at different dialects. different contexts, how can we make that better? So that’s the second part for the clinician. Looking at the frontline workers, I think some of the AI bots, we are looking at how we can help them with their tasks. So if an ASHA is looking at 50 pregnant mothers, how can she prioritize who is the one she needs to look at, who is the high -risk mother whom she needs to prioritize? Because all of them are loaded with work, but AI can help them in scheduling their tasks, do their tasks in a better manner. And lastly, if I had to look at it from a public health system, the government, public, the health department, we are looking at AIs, how can it provide the analysis in a manner that it makes better sense for the government to see the trends?

How can the data show to them that, this is the… key problem in this particular area. We’ve been talking about with Andhra Pradesh government on creating wellness course, composite wellness course which looks at patients, looks at environment, creates a score which can tell them how to look at where the problems are and provide solutions. So basically this is going to strengthen the health department in identifying the risks, predicting the risks and looking at ways to do a proactive preventive care. So this is the way we are looking at ensuring that AI is providing support to all the stakeholders by using the data that is being provided and there’s a lot of deeper work while what I say is on the surface, the deeper work is how do you understand the data, how do you capture data across different geographies.

So that it’s more meaningful and it’s

I’ll come back to you in terms of the challenges you face while working with government, especially looking at the fact that government is probably implementing 25 odd public health care programs and the flavor is from preventive health care to maternal child health to genetic care and so on and so forth. But bringing a different stakeholder into the conversation, Mr. Seth, you represent a social impact organization and have been working on tobacco control. So where do you think is the maximum value of AI in health care driven from your perspective while you’ve heard the other people talk about digital platforms and enablements and innovation? What in your mind will be the biggest AI value generation for public health?

Thank you. Thank you, Dr. Mr. Gaur. You know, large public health programs, TB programs, prevention, tobacco control. adolescent health, the real question is where AI can actually help them day to day rather than in theory. And if you see most of these programs across states, the failure is not because of the design, because they’re not reviewed, but because of variable implementation across areas. Now, the data exists, reviews are also done, dashboards exist, but you find we very often find out what’s going wrong after the event when the failure has already occurred. I have heard so many senior level IS officers lament, the dashboards only tell me what I have not done. They don’t tell me what I am supposed to do.

Now, that is where I think AI can come in and add a huge amount of value. By, you know, helping and telling you where the failure is likely to occur. Identify where it is happening. Who has to take action on it? And then that action, I mean, you know, the person can be informed. And then, of course, you know, where the action is, that’s so important to pushing it. But for this, AI has to exist inside the delivery system, not on top of it. So, in my mind, that is where AI fits into the delivery system.

Typically, a PHC, a Primary Health Care Center… that supposed to see around 40 patients or doctor ends up seeing 60 patients, at least that is the statistics for Andhra Pradesh. You have all been to institution like AIMS where heavily what do I say, the fact that there is no care coordination or absence of care coordination, so everybody seems to be ending up in a tertiary healthcare setup. And there representing a tertiary care unit Dr. Rakesh and coming from AIG hospital, how do you augment, how do you do clinical augmentation for the doctors and in private healthcare, what are the lessons that have been learned and can be adopted in public healthcare also? Dr.

Thanks, Saurabh. In fact, I would start by saying, A is going to reduce the cost both in public and private health not by replacing the doctors but for the early diagnosis and intelligent triage. See, for example, as my co -speaker has said, this is a 1 .4 billion country. And as of now, I think it is going to be around $20 billion. And I think it is going to be around $20 billion. And I think it is going to be around $20 billion. And I think it is going to be around $20 billion. I mean, it’s growing day by day. So any hospital, it can be a primary, secondary, or tertiary care hospital, has got a huge volume of input. And it’s very difficult for anybody, even a robot also cannot match the human scale.

So these need -based innovations are something which we really have to look upon. See, suppose I, in my hospital, have got something where I have difficulty in doing it, and in other hospitals, something else. So need -based innovation we have to catch and then try to solve it. For example, in private setup, as I told you, there are some use cases which we had a personal experience since the last three, four years. I’ll tell you a little bit of economics on this. There is an algorithm which we developed with a pure AI model, costing 500 rupees to pick up a fatty liver, versus getting a machine which is 1 .2 crore and charging 5 ,000 rupees per piece. So this is something which is a need -based innovation for me as a gastroenterologist.

I’m a hardcore clinician, and I look upon any metabolic disorder which is the crux of the entire metabolism. And if you have tools like this, that will give you a lot of value in terms of fast diagnosis as well as the economics getting scaled down at your level. Then there are other use cases where you have the EMR and ESR. In fact, Sarabhiji, there’s a lot of chaos which happens when you have the admissions in hospital. So in that, we have a use case where the patient, they stand there, and the discharge summaries will take 8 to 10 hours for them to come out of the hospital. So we have an AI -enabled system where the discharge summary will happen in half an hour max once I say my patient has been discharged.

Vis -a -vis when you have electronic medical records and you want to have the patient bed management made. so one patient there’s a huge line where you start it’s all a personal experience in this hall everybody goes and you’ll be standing in the queue and even it’s not that you have to blame the hospital authorities but again that is something where in those areas you need these AI enabled systems so it can be digital health or a clinical related AI system so that’s where we have to concentrate on

so while you do that my question is again to you only go around the panel in a reverse order now and so while you do that and look at private sector efficiencies being coming out how do you think you can collaborate with state governments or governments at large in terms of the fact that you will be an early deployers of medtech solutions and the fact that you will have built it in your cost economics to use them faster how do you think you can accelerate their adoption in government ecosystem also

it’s a very valid point in fact the the see as a private sector person we have the early adaptation and adoption compared to getting into the public but in fact on that note i would say i couldn’t bring the aim foundation which we have which is working closely with the government of anupadhyay and other governments so what we did is we formed a platform with triple it hyderabad indian school of business iat delhi the fit and it is a neutral platform where anybody can come and then pitch their idea we handhold them nurture them and then make it validated at our clinical level and once we have the products for example the journey mitra which is launched in the government of anupadesh as the co -speaker told so that is something the asha workers can pick up with the a enabled system about the high risk pregnant mothers and then the nutritive value to decrease imr mmr so tools like this which we can do at our level and once we validate and we feel confident then we can give it to the public systems so there should be a public private integration, which is the main strength for this country.

And then only this will scale fast because time is running fast and nobody waits for us. And we have to keep up with that and then try to get the solutions because we cannot adopt the Western world solutions to us. Ours is entirely a different system. So we can never take any Western AI algorithm and then try to adapt. We have to have our own algorithms and we can do fast because of the population we have, the volumes we have, and of course the zeal we have.

That’s great. In fact, we are working with the AIM Foundation and then looking at setting up a biodesign lab in Andhra Pradesh with the AIM Foundation, with all the other institutes that you’ve talked about. And I see a lot of facilitation of deployment of Meta -X solutions happening through the CAT, the Committee on Advanced Technology. And the biodesign lab. But while we talk about all these Meta -X solutions, the core is something that… we believe as a state also that preventive health care has to be strengthened. And that’s where for prevention as an entry point, where do you see AI playing a role in terms of preventive health care being strengthened, Sanjay ji?

So I think prevention programs, as we all agree, prevention is better than cure and preventive programs will have the highest ROI. Unfortunately, preventive programs are not politically supported. Right. And that is where AI, I mean, if you take adolescent health, you know, student health, nutrition, you know, and I mean, if you take non -communicable diseases, we are talking about behavior change across entire populations, and that’s become the most important, you know, today maximum number of deaths are taking place is because of NCDs. That’s where preventive. So, you know, health comes in. Now where AI can really, I mean why AI really fits into this, because these programs operate at scale. They require continuous and repetitive activities to be done.

And they also show very predictable gaps during implementation. Okay, where the drop -offs are taking place, where the failures are taking place, and actually, you know, prevention, or sorry, the program implementation being done. And the number of variables are also very large, because as soon as you talk of behavior change, you are talking about, you know, huge amount of different cultures taking place. Different cultures respond differently to behavior. And if you take the mass of data, this is where AI can really support the programs and bring the, you know, not just the cost down, but the effectiveness of the programs to take place. But as I said earlier, AI needs. You know, I’m going to do this, and I’m going to do that.

And I’m going to do that. be within the delivery thing, not as a layer on top. And if you then, if we focus on, you know, how these schemes or programs result in outcomes, all right, this is where I feel that AI can give a very vast feedback. All these different entities, facilities, units are feeding a huge amount of program data coming in. AI can analyze where the likely failure rates are. It can escalate it to the appropriate level, bring it to the attention of the senior people, and that will result in far, far better delivery outcomes.

So can you be more specific in terms of, for example, the tobacco control program that you run, Sanjay ji? Right. Where is it, do you think that the fact that if we are doing it across, say, 20 ,000 schools and I do not know how many schools you are doing it with, are you able to do those kind of predictive outcomes? Outcomes or prediction in terms of where the program is. is probably bound to fail or is looking at a failure condition and the actions that need to be taken.

Oh, yes, we are getting, we are running tobacco control program in Antara, as you know, more than 20 ,000 schools. And each school is supposed to do a standard set of nine activities. All right. So very early, we are able to see which, you know, schools are not doing some certain activities. All right. And if we manually, if we start analyzing across 20 ,000 schools, there’s no way we can do it. All right. So AI helps us to analyze the data and say this block, this district, this area, there is a failure taking place. These schools are not taking action. All right. And then when we in terms of after the analysis, what we are doing is also the schools which are acting when they do the activities, they upload the activities.

Now we do image recognition. And decide whether the activity is done correctly or not. And we’re very high, 98%. accuracy we are able to see whether the activity has been done correctly or not and that tables enables us to give feedback immediately look within I mean as soon as he enters it within shortly after that he gets feedback you haven’t done this properly please repeat it all right then once we are talking about you know informing people so we are now sending out in Andhra Pradesh for instance 40 ,000 teachers get messages from us personalized messages you know for each person in the language he prefers in the language you know in the tone he prefers and that makes the motivation or the you know the way they act much faster than they used to earlier so we are seeing orders of magnitude improvement in terms of effectiveness of the program taking place I believe is you know works for I mean not in Andhra but in other states we are seeing this same thing happening across other programs which we have been working on

This is very heartening to see. So while for example you may be doing a tobacco control program with us in Andhra Pradesh, there is a cancer care program happening in Tamil Nadu that we got exposed to in one of the workshops. There are other states doing fantastic work. I saw Odisha for example, the stall today. So while and I probably picture to you Shivkumar ji that while we have all these islands of excellence and innovations, what is it that prevents them from scaling up and what are the structural barriers that probably government is not able to while we talk about ease of doing business, what is that ease of doing governance, public governance and public health care system that will make them scale up?

in place and the data quality actually improves. AI models really can’t work on top of it. And there is an exception in terms of, you know, you’re doing surveys and various other data points are there. Most states don’t have it. Right? And therefore, the processes unless they throw data out, I think we can all dream about AI, but really having the kind of value that we are talking about is going to be very, very difficult.

Thank you. Thank you. That’s a great point. And while we are at a certain maturity level in state government of Andhra Pradesh, there are other states which do equally well and there are states which probably are lagging. But with the national framework being put and with the ISHMA and Bharat Digital Mission, I bring it to Saurabh, my colleague. Where do you think in government of India, how do you facilitate all state governments to at least come on par and how do you see AI within the national health systems which become say gold standards or standards at least for all the other states to follow?

So as we know that health is a state subject, so ultimately government of India works in collaboration with the state government. And we understand that the kind of AI systems, the algorithms, the applications that have been developed for AI, ultimately the quality of output that comes out from those systems depends upon the data on which it is trained. And that is why it is very, very important that the data should be representative. It should be from every region because every region has a different kind of disease profile, every kind of various kind of demographic profiles. So this is very, very important that the data should be representative. Data quality, as was mentioned, should be very good.

And in fact, through this Aishwarya Bharat Digital Mission with more and more of digitization, we are and with every person now being provided with the ABAID, which is a… Actually, an ID which is linked with the health record so that the health records can move. with the person. So with all this digitization, with all the data that is being generated, we are able, we can do lots of usage of AI in terms of disease surveillance. We can use it for modeling of various diseases. We can use it for imaging. Lots of MRI because as I have mentioned earlier, still there is a lot of issue about the availability of specialist doctors, especially in the rural settings.

So at least if the AI solutions are available, the basic, at least 90 % of the imaging can be taken care of by the AI. So that only the most suspected cases can be referred to the tertiary care hospitals and the basic healthcare can be managed at the facility level. And as my colleagues have also mentioned, one of the issues in public health delivery is totally preoccupied with lots of administrative work. Lots of data entry, lots of portals that they have to enter data into the portals. That takes a lot of time beyond what is expected out of them, which is their clinical duties. So with this more and more, the AI application and more and more systems getting digitized, we can have a system where the data which is fed into one portal can be automatically populated all across the portals and the administrative work of these healthcare workers, which are our frontline healthcare workers, can be substantially reduced so that they can focus more and more upon the actual clinical work.

So AI is ultimately, it’s about improving efficiencies. It’s about improving the workflows. We have the supply chain management also. It is about optimizing of supply chain management. And in this entire journey, in the adoption of AI, we take states as our partners. Because ultimately, when both government of India and states work together, only then we can have a very robust AI system which can actually deliver quality care to our people.

We are here to work with the government of India very closely and establish those models. But the point you made, Kher, I actually cannot think of working as a NM myself also, despite being, adding a state health department. The sheer fact that a poor NM or MPHA male, the multi -purpose health assistant or the nurse on the field has to work with 25 programs. And while there is this national architecture coming up, there is so much of digital literacy challenge, not just digital literacy but adoption and using all these apps. This is a real challenge we face at the state level also. And with TATA, when we are building the digital backbone through project Sanjeevani that we are doing together in a collaborative approach, in an example of public -private partnership, I would want, Saraswati, you to play the devil’s advocate role and tell us the three key technology integration challenges that you see.

And please be critical of the system. But tell us that what is it that you would want to see. what are the challenges that you face day in day out when you look at building this care coordination oriented digital backbone for public health in Andhra Pradesh

Thank you sir, tough question to answer especially in a public forum but I will do my best, so one of the things like we have spoken about in a PHC there are lot of things to be done and like you mentioned sir, all of them have a lot of activities lot of programs, lot of reporting that they are doing introducing AI as something like Sanjay ji said as something additional or bringing technology as something outside is definitely a challenge, so our aim and what we have realized is if it is not integrated in their workflow, if it is not something that they find value in, the adoption is going to be a challenge like Shukumar ji mentioned that people are collecting data and just sending data But is that data really helping them?

Is it helping the people who are collecting the data? Is it helping them to do their work better? Are they able to benefit from the work that they are doing? If the answer is no, definitely they will not take it up. So one of the things is how do we integrate in the workflow? And they find value for what is being introduced. The moment that we are able to reach that sweet spot, I think they will start utilizing it. So one is how it’s integrated in the workflow. Second, I think, is it’s less of technology management and more of change management. Whenever there is something introduced, people look at it. I mean, there’s a cycle of adoption similar to what even we all face whenever we get introduced to anything new.

There will be a set of people who are ready to adopt it and are forthcoming. Then there will be a lot of people who are resisting. Slowly, the pattern changes. And people start seeing benefits. So what we are trying to… get at is who are those people who are seeing value for it who are those early adopters how can they bring in and probably with them we train the model like shikumaji rightly said if we do not train the model correctly you’re not going to get the good benefits so who are those people who can be utilized to train the model and who will not resist and then you give the trained model to people who are resisting so that they can see value so i think it’s a lot of change management related resistance which is what we need to address and lastly while i mean andhra is a very progressive state and we see this not as a challenge here but generally the connectivity the power availability all these tend to be one of the other challenges that for doing it a system wide change how these could probably be the this as i said in andhra thankfully those are not issues that we have seen but finally i think it’s about the incentives, right?

What are the incentives for people to adopt? If there are incentives for them to adopt both from the state side and from their personal side, the adoption tends to be easy. So, it’s a lot of work that we need to do to make sure that this is taken at scale, sir. Thank you.

So, I think we have a round time for one more quick round and I want to keep it short. Thinking aloud, what do you think? And the question is to all of you in the panel. What is the one maximum impact zone or maximum impact innovation that you feel based on your engagement with public health or with healthcare that should be happening? And I start with Dr. Akesh. What do you think will be the one most impactful thing that we can do? That’s a

very difficult question. There are many things to do. But what I would say in a nutshell is with the current scenario where we are, so the clinical insights from the doctors, the engineering capability from the bioengineers or the AI engineers, and the policy support from people like you, so this is something which will make the AI -related or metric -related innovation to go from the lab to lives. So that should be a collective holistic approach which we have to do and join hands together. I would say in that way that’s the need of the hour. Thank you.

I’ll make it simpler for you, Sanjayji. In preventive healthcare, which is one most impactful innovation that should happen?

Since I’m working in that area, I guess that is where I will obviously state, but if you look at it, Andhra Pradesh, 48 ,000 deaths every year because of tobacco usage. If you take any of the adolescent health, the future of our youth is how well our adults are. lessons grow. As I said, preventive is the highest return on investment, and it is not glamorous. It is very dull. It requires enormous amount of day -in, day -out discipline. But as a state, if you’re looking at what can really give you the maximum amount of benefit, I’d argue for preventive health. Thank you so much.

To you, Saraswati ji, in terms of engaging, and in the public -private partnership board, which is the most impactful thing that can be done?

I would probably respond slightly differently. I think in terms of bringing back the trust in the public health system, that would probably be the focus, and that hinges on quality of care that we are able to provide in the primary care, and that is what is going to ensure that the need for tertiary, secondary, and the disease burden that we are envisaging, that would probably… probably be managed if we strengthen the primary care with the trust in the public primary care. Thank you.

And Shivji, with you heading our committee on advanced technologies and doing all the work with innovation ecosystem, which do you think is the most standout innovation that you have seen that can be impactful for public health?

Sir, I’m going to be a little controversial on this. I think technology is just an enabler. I think our single biggest problem is going to be work culture. Nature. Work culture, incentives, and today every officer feels that they need to see a dashboard and tell their team what to do. I think if we have to really make AI help everybody decide, I think the work culture around evidence, the work culture around data is going to be the biggest one. But I will answer your question. The biggest innovation should be people’s data should be owned by data cooperatives. Nellore is a district. In Andhra, Nellore people should own the data through a data cooperative. and we should have reverse tokens where people pay for their data.

And we are feeding the AI engines and I think our people should gain from that. When we reverse that, sir, and when we reverse the incentives and the work culture of use of data, I think automatically you will find people coming and telling you this is how I am using it. Thank you.

That’s very interesting. And what about you? What do you think that can be the most impactful at a national scale also for the health innovation that can be there?

I would just also like to address the work culture issue that you have mentioned. In fact, if we can just sensitize, if we can make our doctors, our health workers confident that the outcomes are predictable, outcomes from the AI systems are good. And actually by adoption of AI systems, their productivity is improving. The kind of work that they have to do in less number of hours, in less time, they are able to use that. Use this. Use the same. Do the same kind of work. And they can do better in terms of their clinical approach and their productivity approach. I think. I think our health workers, they have adopted very swiftly to the technology. And if we can show them the reliability, the outcome that is certain, and overall improvement in their productivity, definitely workforce will adapt to this technology.

And as far as coming to your question, I think diagnostics will play a very, very important role in terms of the adoption of AI. And we are seeing it in the area of tuberculosis and also in diabetic retinopathy, where through the scanning of these images, the doctors can make a very evidence -based decision in a very less time. So in the same time, if they were seeing 10 patients, now with the support of AI, they can see 20 patients or 30 patients with much more accuracy. So the kind of shortage of doctors we have and the kind of patient load we have, especially in the tertiary. I think diagnostics will be playing a very important role. huge role in the field of AI.

Thank you.

I think that’s all the time we have. We still have time for one or two questions from the audience. If somebody would want to, the gentleman at the back actually raised his hand first or I spotted him first. There’s a mic behind you.

For mental health perspective. Because that requires additional safety, security as well as sensitivity. But I have not seen anyone touching on yesterday also as well as today. Mostly we talk about medical imaging and that takes because radiology as well as radiology all the innovation. there are developments also but I was thinking I will get some insight but so far

Dr. Rakesh you want to take it?

I think I have a point on that it’s a very nice question so on the mental health there are people in the western world who have got some apps and they are doing it but in India unfortunately there is no robust system to collect the data in fact if you have suppose you are working in a private hospital you have a robust EMR, EHR then you must be having a questionnaire on which you can build up these things but that uniformity to come it takes a little more evolution but there are people who are working on it in Indian sector probably it will take a little more time in fact you will be the one who can start that at your level

no we are working actually we are struggling I am at Eames Bhopal and what we have audio recording like we don’t have medical imaging either you have mental status examinations or video recording and based on that voice recording similar like detection of suicidal ideations, detection of depressions, anxiety and those kind of things. So there I was seeking if some assistance or guidance can

So I will probably just respond to this. What we have done in Andhra Pradesh is we have worked with psychiatrists and so there is a methodology called QPR, question, persuade, refer, which is actually proven in the sense that it is patented, where we worked with them and said that okay, especially with let’s say our students who go through high pressure, people who are in intermediate education, 11th and 12th, and there is pressure to perform an examination, parents are pushing them. So out of those 10 lakh students who are appearing for examination, which are those and our estimate is say around 15 % need to have special focus being paid. So taking all of them through this QPR methodology, working with this organization called Suicide Prevention Foundation of India, SPFI, and working with them, we have been able to at least look at which are those students who need to be given specific focus who are having those kind of ideations or having those kind of vulnerabilities and what kind of messaging needs to go for them and while it is a challenge and I am not saying there is a lot of AI into that because there is lot of what do I say privacy issue also associated with this but the other point you said about a scribe or essentially since people are talking you can actually get into the behavioral insight and understand whether what kind of ideation is happening and getting answers out of that I think that is a great point and would love to work with any innovator who would want to do it as a sandbox with us.

Thank you. One more question. Yeah. No, no, we will go to. You are an in -house person. Yeah, please go ahead.

So first of all thank you, Saurabh Gaur, sir. The first thing about that MedTech challenge I think this is the first time we have seen state government or government is opening up and telling that why don’t you guys innovate as come with your solutions including small startups like us. and we will give you a platform to pilot it and then finally help us in scaling up. My question more is to Saurabh Jain sir because we need to replicate something like this at a central government level. At the start -up, we definitely cannot go to all states and keep on doing pilot while MedTech is a segment where almost zero VC or private investment is there.

So it’s largely we running on either government grants or our own save money or loan or everything. So is it central government can create a platform which Saurabh Gaur sir or other state government can take those validated solutions and scale up them and we don’t just keep on repeating the same thing?

I think yes. ICMR is developing this kind of sandbox in which the start -ups can come up with their innovations. You can test it in the sandbox. So ICMR is actually developing this kind of mechanism to test the models. And ultimately, it’s about replication as you have mentioned. So once it is tested and it is tested across various settings depending upon the… outputs… Definitely it can be scaled up.

Thank you so much. The audience, you deserve a round of applause for being very patient audience. And I thank all the panelists also for their very valuable insights. Thank you. Thank you, everyone. There is a moment to give also. We can quickly hand over the moment to Dursu. Thank you. Thank you.

Very few individuals have done more to bring revolutionary and transformative technology into the hands of millions than Matthew and Rajan. Matthew Prince is the co -founder and CEO of CloudFlare, a World Economic Forum on Foreign Relations, Forum Technology Pioneer, and a Council on Foreign Relations member. He has degrees from Harvard, Chicago, and Trinity College, and co -created Project Honeypot, the largest community tracking online fraud and abuse. Matthew’s founding mission for CloudFlare was to help build a better Internet, a goal that has become increasingly critical in the age of artificial intelligence. Rajan Anandan is one of India’s most influential technology leaders and investors, currently serving as Managing Director of Peak15 Partners, formerly Sequoia. He is the founder of Sequoia Capital India in Southeast Asia, where he focuses on backing founders building transformative, technology -led businesses.

With decades of experience across entrepreneurship, investing, and global technology leadership, Rajan has played a pivotal role in shaping India’s startup and digital ecosystem. Orchestrating this conversation is Rahul Mathan, who brings the perfect blend of legal insight, policy depth, and the ability to ask the questions everyone else is thinking. Rahul is a board member and partner in TriLegal’s Bangalore office and has their technology, media, and telecom practice. He has extensive experience advising on high -value TMT transactions in the country. He has worked with companies across sectors, from telecom majors to Internet and data service providers, offering advice on regulatory matters and operational issues. So please join me in welcoming three awesome leaders on stage, and with that, the stage is yours.

Thanks, Rahul.

And since I haven’t worked with you, I’m going to square that circle. Matthew, I just heard your keynote up in the big 3 ,000 -seater hall, and you ended with a very powerful statement, which is that that this wonderful AI technology should not be built by a handful of companies in the same postal code. And that, in many ways, seems to be the driving motivation for having this discussion. But it’s easier said than done in that AI is a very big and complicated stack. And a lot of that stack actually involves complex hardware. And it’s hard, really, to move that hardware around the Internet. So if we are to democratize AI and if we are to come up with the infrastructure construct that would democratize AI, what would that look like?

And what is your idea, your vision for how this would be, if not now, but sometime soon?

Yeah, so let’s talk first about why AI is hard and expensive today. So the first thing is AI requires lots and lots and lots of chips. And the fifth thing is AI requires lots and lots of chips. And the sixth thing is AI requires lots and lots of chips. largely produced today by one manufacturer, NVIDIA, that use a ton of power and are very, very expensive. They were never built to do this. If we’re totally honest, the NVIDIA chips were built to power gaming consoles, right? And then for a while to mine Bitcoin and then magically to create a superintelligence. But if you had started with, let’s create the superintelligence, you would have designed those chips somewhat differently today.

That’s challenge one that keeps AI very, very hard. Challenge two is that it requires a real specialized set of knowledge. There’s a very small set of people in the world that know how to build these models and how to run these systems. And so you have to ask, why is that not something where everyone knows it? If you had known that you could specialize in this in school and literally make $100 million, there’s a year. We would all have studied AI, right? And yet if you go back just five years ago, the people who were studying AI were kind of the weirdos. Why was that the case? Well, because AI was one of these fields that kind of had promise in the 70s and had promise in the 80s and had promise in the 90s.

And then everyone was kind of like, you know what? We’re tired of this. And so the AI professor was kind of shunted off to the side. And so if those are the things that today make AI extremely expensive, the question is, are those things permanent states or are they going to change? Well, we can measure one of them already. Already, if you look in enrollment in computer science programs across the world, it is up dramatically, even though supposedly there’s no future for computer scientists, in just the last two years. And then secondly, the enrollment in specifically AI theory courses is off the top. Every university that used to sort of shutter their course is now standing it up and building it like.

like crazy. And so I think that over time, we’re gonna have more and more people who are able to do this. And so having to pay enormous salaries for those people, that’s probably not going to be the future. On the chip side, you know, if you have literally a company going from being an obscure gaming company, to the most valuable company in the world, obviously, a whole bunch of people are going to chase after that. And if you look at the history of silicon, anytime there has been a silicon shortage, it turns into a silicon glut over time. And with GPUs, it’s kind of had hit after hit after hit. I think that what we’re seeing, at least is that both from startups, as well as incumbent players, as well as from the hyperscalers and other players, they’re getting involved.

There are so many people who are making this this silicon, that no matter what the price per unit of work done is going to come down. The other thing that I think is encouraging is that if we look at the actual AI models themselves, it doesn’t appear that this is necessarily a one company is running away with it. It’s sort of like Google gets a lead, and then Anthropic passes them, and then OpenAI passes them, and then someone else passes them, and then Google does it again, and it keeps leapfrogging itself. That, to me, suggests that the actual model making is more likely in a steady state in the future to be something like a commodity.

And if that’s the case, if the cost of creating the models is going to come down, if the models themselves are more commodities, I think that we can’t assume that the literally hundreds of billions, if not trillions of dollars that are going into building the leading AI companies today, that that might come crashing down. And my prediction would be that you’ll be able to build models. Models that will be on the frontier. They’ll be more specialized, but they’ll be on the frontier. for tens of millions of dollars in the not -so -distant future. I’ll put a date out there. In five years, you’ll be able to build a frontier -like model within a specialty for $10 million or less.

Rajan, about a year ago, one of these companies and one of these postal codes were here, and you asked a question, you know, what will it take for India to compete? And you were told it’s hopeless, don’t compete. And yet at the summit, we’ve come out with a model that’s, by all accounts, haven’t yet played with it competitive. So what Matthew is saying seems to be working out, but he’s putting a five -year timeline. I would argue that perhaps we could be more aggressive with that timeline. So what is your view on this as someone who is actually, you know, in India, working with some of these really smart people who are working under constraints?

but are yet putting out some fairly impressive models, interesting use cases. What’s life like at the other end of the absolute front? I mean, what people call the absolute frontier of these models. What is life at the other end of it where there are many different applications, many different use cases, and many different types of models? Yeah.

Firstly, hi, everyone. Great to have all of you here. So I think the first thing is, look, Matthew, I don’t know whether you’ve been following a company called Sarvam. I think, firstly, it’s important that India is not trying to get to AGI. With 1 .4 billion humans and a million Indians turning 18 every month, AGI is not the thing that we need. Our focus really is to uplift 1 .4 billion Indians. And I think our ecosystem, our innovators, our government, our investors, our technologists, our engineers, our engineers, our engineers, our engineers, our engineers, are all of the view that we have. But to really do that, you don’t need trillion or five trillion parameter models. What you need are highly performant, extremely low cost models that are a billion parameters to maybe 100 or 200 billion parameters.

And actually, for the amount that you mentioned, we have launched 30 and 100 billion parameter models that are SOTA in Indic languages. In fact, I don’t know whether you know this, Matthew, but if you look at voice AI in Indic languages, SARVAM today is both SOTA in speech -to -text and text -to -speech and is a fraction of the cost of the global models, including the global leader in voice AI. So I think what you’re going to see is the government’s actually – and by the way, the reason SARVAM is able to do that, because of tremendous support from the government, but it’s not just SARVAM. There are 12 large and small language models. By the way, just – I don’t know, I don’t know.

a clarification because when Sarvam launched, I think somebody said India is really good at small language models. The last time I checked Chakchipiti and Gemini, anything above 30 billion is actually a large language model. So we are actually in the large language model race. Just a clarification there. So basically, we have 12 companies, actually 11 companies and Bharat Chen, which is part of IIT Bombay, that are building these models. I think this number goes to 15, 20 very, very quickly. And I would say actually well within this year, Matthew, in many, many things that India needs, right? We need to uplift 100 million farmers, and for that, we need to build basically models that work on feature fonts, right, in local languages.

That’s done, right? That was actually launched on Wednesday and so on. So I think that’s the first thing. And now when you ask this other question of, look, true frontier, which is, you know, the frontier today, let’s call it a few trillion, maybe it’ll go to 5 trillion, 10 trillion. I think it’s going to be very, if you define frontier that way, part of it is also definition of frontier and more importantly, what’s the objective, right? If you define that frontier that way, I think it’s going to, it’s not going to be, Indians are not going to be able to do it with these set of architectures. But what we are going to do is to Matthew, to the point that you made, which is, look, LLMs are the most inefficient compute machines ever.

I mean, this is the, you know, these are not efficient architectures, right? We believe that this is the beginning, not the end. We believe there’s going to be many more to come after transformers. And I think that is where the bets are going to be made. In fact, I mean, you know, Jan Lukun is here and he sort of said that, look, you know, this is not sort of the, this is not going to lead us to AGI. We don’t really want to get to AGI, but even if you just look at where AI is going to go. So I’d say at the model layer this week, India entered the race, but we are going to play this race differently, which is IE.

We’re not going to try to build trillion parameter models and we’ll do it at super, super low cost. Now, coming to the chip player, that’s harder for India, but I don’t know whether you know this, Matthew, but 20 % of the world’s semiconductor designers are in India. Four years ago, we had no semiconductor startups. Today, we have about 35 to 40. They span the spectrum from low -power, call it 20 nanometer chips, these all SoCs, all the way up through – actually, two weeks ago, we announced an investment in a GPU company. It’s a very seasoned team, Intel, AMD, a company called Agrani. Monday, this week, we announced an investment in a company called C2I, which is going to make memory, focus on memory.

So we’ll see even at the chip player, because what is very clear to us and I think to many in India is we have to have the sovereign stack. Our friends are no longer friends, or sometimes they’re friends, or sometimes they’re not. And as India, we just need to have the sovereign stack. And we can’t – we, of course, are going to have alliances. And today, I think a very important alliance. This alliance was announced with Paxilica and so on. But we’ve got to actually have a sovereign stack. So whether it’s the chip layer, whether it’s the compute layer, I think it’s great that both Adani, Reliance announced $100 billion investments into AI infra this week at the model layer.

Where we have excelled is on the application layer. And what I can tell you is at the application layer, I joined Google in 2011. At the time, India had 10 million connected smartphone users, no venture capital, and no unicorns. Today, we have a lot of venture capital. We have 900 million smartphone users. We don’t have enough capital, but we have enough venture capital, and we have 125 unicorns. At the application layer, I can confidently say, whether it’s consumer, whether it’s enterprise, Indian companies will win. Because the traditional formats of consumer consumption, which is called search, or now Gemini, ChatGPT, et cetera, will scale to, in my view, will probably scale to 200 or 300 million. They’re not going to scale to a billion Indians.

To scale to a billion Indians, you’ve got to. You’ve got to have image, you’ve got to have video, you’ve got to have highly local language, and it’s got to be. ultra, ultra low cost. So I do think we have a shot. I think what you’ve just described, we ship this week. I think by the end of the year, we’re going to ship 100x more because, Matthew, what’s happened in India is we don’t, we can’t, we’re trying to do things differently. Payments is an example, which I think the whole world knows about. And you’ll see that playing out in many other things. And I’ll last end with this. Matthew, 2015, India had two space tech

And I would just say, don’t sell yourself short. Like you may not, India may not need AGI, but India may still build AGI. Right. And I think that the thing that actually might end up holding back the biggest AI companies is that they are so unresourced constrained. If you look at what was the biggest over the last two years, innovation that really drove AI forward, it wasn’t anything that Google or OpenAI or anyone else did. It was actually DeepSeek, and DeepSeek’s ability to say that within the constraints of the chips that they had access to, that they would – they had two incredible innovations. They would prune the tree more efficiently, and they’d be able to process that pruned tree much more quickly.

I wish DeepSeek had been an Indian company, not a Chinese company. It would have ended a little bit more constrained. I think we have a thought room. But it is – I actually think those places with constraints, and I would not be scared away if you’re an Indian AI company by hearing the hundreds of billions of dollars that the big U .S. AI companies are pouring in. That seems like an asset. That seems like an advantage that they have. But in some ways, it’s blinding them to what will be the real innovations that cause AI to become more efficient, that cause AI to become – more scalable, and there is no way that the long -term solution to this is you have to turn up a mothballed nuclear power plant.

So we’re going to get more efficient, and I would bet that that efficiency comes from places just like this.

So if I can push back to both of you, I think there’s – not that I disagree with any of this, but to say that – so one of the things that DeepSeek did was it came up with this reasoning model. I guess other people were working on it, but they did a really good job of doing reasoning really well and really powerfully. I mean the real – the DeepSeek innovation was being able to say that you’ve got to build this giant tree if you’re building an AI model. And they were able to say probabilistically there’s a whole bunch of branches on the tree that we can ignore. Like there’s a bunch of things in your life that have happened to you that your brain is just really good at forgetting about, whereas there are a few just salient moments that have formed who you are as a person.

What DeepSeek did is did a better job of pruning that. The big US AI models don’t have to do that because they can just – well, let’s just buy another H200, right? And let’s just keep throwing more money at the problem. By having the constraints and the specialization. in this case, the memory constraints, it forced DeepSeek to come up with a better pruning algorithm, which allowed them to then just be able to deliver AI at a much, much, much more efficient level. And I suspect Sarvam did something similar because, I mean, I spoke to Pratyush and he said that one of the things that the big guys have been coming around when we told him was how do you do this with 15 people?

And it’s certainly some of those constraints that they’re at work. I wanted to talk along similar lines on this idea of open source, open weights, perhaps as a stick with open weights because open source is a controversial definition. A lot of the early models at that time, there was a lot more open weight stuff coming out. Of late, that’s gone down. And the power of open weights, of open weights models and perhaps open sources different is that you can actually, I mean, develop open weights and you can actually develop open weights and you can actually can actually tinker around with the model. and customize it to their use case. But increasingly, we’ve seen a sort of a drop -off other than the Chinese models, Kimi and Qen, which are still open -weight.

I wanted to just discuss among the two of you, perhaps from different perspectives, maybe the use case perspective and perhaps just the whole internet infrastructure perspective, how important open -weight is. And some of the backroom chatter I’ve been getting is as these models get more performant, it becomes increasingly dangerous to put out highly performant models as open -weights because it’s something that OpenAI called malicious fine -tuning and the fact that as these models become better, it’s easier to undo the fine -tuning guardrails that have been established so these models don’t do bad things. And so that’s why it won’t be delivered. So I know that the ecosystem needs open -weights because not everyone has the time to do the training and the time to do the training.

And someone just perhaps wants to do the pre -training and get the model. out. But I’m also hearing from the other side that open weights has this fundamental security challenge. And I know, but we go to security separately, but just on open weights, what’s the, you know, what’s the way We thread this needle between these two things?

Well, I think that, okay, let’s, let’s, let’s, I’m going to tell a story. I don’t know that 100 % of the story is right, but I think it adds up to something that approximates what’s right. Let’s imagine that over time, you are one of these major model makers, you’re open AI, you’re, you’re anthropic, you’re, you’re Google. And you look at this and you say, huh, if we keep playing this out, then this is a commodity. And the only way that we win is if we restrict as many people from getting into the game as we possibly can. So how do you do that? I mean, one of the best ways to do that is just to scare everyone.

that if everybody has this technology, that the world is going to end. And so the next time you come across an AI doomer and they say, if everyone has this, the world is going to end, just keep pushing them. Just be like, okay, and then what happens? And then what happens? And then what happens? And basically, you know, the most likely, the scariest scenario is these things can design very bad maybe pathogens or other malicious vectors, biological vectors, that could then get synthesized and spread around society, to which I say, well, then shouldn’t we be regulating the synthesizers, not regulating the sort of technology that’s out there? But, again, it gets to be very hand -wavy.

But if you think about it as a strategy, if you believe that these ultimately are commodities, then what you want to do is actually regulate. Regulate them in order to make it so that yours is the only company that can be safely trusted to handle this. And I think that that’s, again, somewhat cynically, a lot of the explanation for why the people that are building these horribly dangerous, scary things keep telling you how horribly dangerous and scary they are. I’ve never seen another industry that has done that. You never – you don’t see like the automobile industry be like, you know, this could plow through a crowd of people and kill and be used in a mass murder event, right?

You don’t – that just doesn’t make any sense. And so the only way that I can make sense of that world is if, from a business perspective, if it’s actually trying to do some sort of regulatory capture. And so I am pretty discounting on what the risks are here. I tend to think that more open is going to win, and I tend to think that the Chinese approach right now is the smartest approach to take on what looks like this enormous kind of just money machine which the U .S. is creating. And so it’s – I think that as India thinks about how it’s going to regulate AI – I would be careful about listening to sort of the AI doomers.

I would be especially careful about trying to regulate the output of what is fundamentally at least a pseudo non -deterministic system. We have built machines that act like humans, and yet we think we can regulate them like machines. The better way to regulate them is actually more like humans. Look to the criminal code, not the engineering code, in order to figure out what that regulation should look like. And so I am very much pro -open. I think we should think about what these risks are and what these dangers are. We should definitely be testing and looking for those. But I tend to think that they are somewhat overblown. And if you want to understand why they are somewhat overblown, I would argue it is because it’s a strategy in order to keep the people , they’re currently in the lead going forward.

I think you may be absolutely right, because on that big stage that you were at just a short while ago, yesterday, there was a call by one of these companies for an IAEA for AI, that it should be regulated like nuclear technology. And the other example I keep giving is, you know, at the turn of this last century, people were just walking around the streets and getting electrocuted, because electricity is highly dangerous. And yet we sit in this room, which literally the walls are buzzing with electricity, and we’re completely safe. And this is the nature of all technologies. But on the positive side, Rajan, all the AI deployers that we have in India, a large number of them are relying on open source.

And if the open source pipeline starts, to diminish, where are they going to go? I mean, Sarvam can certainly deliver these models, but how important is it actually to the community of people that are, I mean, AI and chat GPT and all this is all well and good, but it’s really those applications, the voice applications that people need. How dependent are they on open source? What can we do to continue to keep this open?

Look, I mean, firstly, as Matthew said, look, if you invest a trillion dollars, okay, you can’t give it away for free. It’s as simple as that. It’s just economics. So you can position it any which way you want, but fundamentally it’s about economics, right, and how do you build a business, especially if you have to invest so much. No, look, open source is absolutely critical. You know, I think the – I mean, Lama is the most recent example, right, where the reality is if you’re going to launch the next state -of -the -art version of Lama, it’ll be closed because otherwise how are they going to monetize this, right? And especially if you have – you know, they’re spending $80 billion, $100 billion.

There are other ways to make money. They don’t make money. Even for them, $100 billion a year is kind of a lot, you know. So anyway, coming back, look, it’s super important to the ecosystem. I don’t know. I don’t have the answer to that. I think in March. In March, you’ll see one of the big companies make a big announcement, massive, massive announcement on their commitment to open source. But it is, you know, I think the only way you do this is there has to be a different path, okay, because if you’re going to have to invest hundreds of millions of dollars to build a new model or billions of dollars to build a new model or tens of billions of dollars, that doesn’t, that’s not really open.

You can’t keep those models open, right? So to the first question that you ask in Matthew’s response, you really need to have a different way of doing this, right? So actually, by the way, if you look at voice AI, for instance, I’ll give you some data. You know, India has very low labor costs. If you look at human cost of voice today in India, it’s five rupees a minute to about 20 rupees a minute. Five rupees a minute is the lowest you can get. Amex would be probably 40 or 50 rupees a minute. Today’s voice AI costs about three rupees a minute. So already, and that’s why you’re beginning to see voice AI really begin to take off in India, right?

But even with today’s SOTA model, you can get to about maybe a rupee. Right now, the question is, even at a rupee, now you’re one, you’re twenty one fifth the cost of humans. So it’s going to really take off. But if you want to make voice the primary medium through which one point four billion Indians will access AI, that’s still too expensive. Right. You’ve got to get it down to maybe five paisa or ten paisa. And that’s actually not about open source. It’s about compute and it’s about the cost of inference. So if you ask me, open source is really, really important. Important. But we have to find a way to get the cost of inference down.

Obviously, model size, all of these things matter. And, you know, we can talk about that as well. But a short answer is, look, it’s really important. But it is not clear to me how you do this, especially in the current game that we’re in. Because anybody that wants to be at the frontier, the way a frontier is defined today actually has to go out and invest. Right. And honestly, I don’t know how the Chinese are doing it because, you know, it’s a bit opaque as to exactly how much are they investing. You’re right. It’s kind of a hedge fund.

Which is basically what deep seek is and they have this on the side META is the fascinating question here because it took me a really long time to understand META’s strategy like why are they doing all this VR why are they doing all of this AI what they learned was the lesson that if you are caught on the wrong side of a platform shift and you then become beholden to some other platform where in the past they were on the web and that was fine and social worked and no one controlled the underlying platform and then the platform shifted and all of a sudden it was on mobile and they were beholden to both Apple and Google that put them on a back foot and it really limited their business.

So they are so desperate to whatever the next platform shift is stay in front of that platform shift and so for a while that looked like it might be VR that was less likely today although never count these technologies out The real next platform shift is almost certainly going to be AI. And so if you control the social graph, which is an unreplicable kind of asset that they have, they need to be – they need to make sure that whatever the next platform is, that they control that or at least have an equal seat at the table of everyone else. And so if they continue to invest in open source and you’re like, why are they spending so much money in order to do this?

It’s to make sure that as the next platform shifts, that it’s going to be – that they aren’t in the same back -footed position that they were with Apple and Google. That would be my analysis of META.

I don’t want to comment either way, that AI is going to accelerate cyber attacks because agentic swarms, et cetera, can do things much more dangerous. So what’s the evidence that you have for this?

Yeah, so I think this is sort of a long -term good news, short -term scary headline story. The long -term good news – let’s start with short -term scary headline. There are going to be a whole bunch of scary headlines of bad things that AI does. There will be a story about an Indian family who lost all their money because they wired it to some criminals that made it seem like their daughter had been kidnapped. I mean, we’re already seeing the level and sophistication of phishing scams go through the roof in terms of what is being done. And so the bad guys are going to use that to attack. The other thing that we’re seeing is – so there was an example.

There was a company called SalesLock. It had a program called Drift, a piece of software that was connected into hundreds of thousands of Salesforce instances. SalesLoft got breached by a Russian hacker. The Russian hacker didn’t understand how Salesforce worked, so they kind of fumbled around for a really long time. Had they just used AI, which is what we’re now seeing a lot of North Korean and Chinese hackers do, they would have been able to just be instantly knowledgeable on how to get as much information out of Salesforce as quickly as possible, and the breach could have been orders of magnitude worse. So those are the bad stories, and there’s going to be real hardship and real pain that’s caused from it.

The counter to that is that folks like us, I was just with Nikesh from Palo Alto Networks, Jay from Zscaler is here. We’re all using AI. We’re all using AI in our own systems to make them smart. In fact, Cloudflare, we would have never described ourselves this way, but the whole theory of the company was let’s get as much Internet traffic flowing through a machine learning system to be able to predict where security threats were in the same way that three years ago we all looked at ChatGPT and were like, whoa, that’s amazing. internally about three years ago was the first time the system said, bloop, here’s a new threat that no human has ever identified before.

And that went from being something that happened once in the first 15 years of CloudFlare’s history to now it’s happening on an incredibly regular basis where the machine learning is able to win. And so I think the good news is that the good guys will always have more data than the bad guys do. Again, a caveat to regulation preventing us from using it in order to do cybersecurity in various ways. But largely, we’re able to do that. And I think that we will actually use AI in order to stay ahead of these threats. That’s what we’re seeing. It’s going to require some change in any part of your life where you are today relying on what someone looks like or what they sound like in order to verify who they are and give them access to anything, secure, confidential.

That’s got to change. And so the simple thing that you should all do with your immediate family at your next holiday meal. is decide on a family password. And that seems silly, but I guarantee you at some point, some hacker is going to call up and say, hey, your son or your dad or your grandmother or whatever needs money. And if you say, hey, what’s the family password? And they say, I don’t know, Aardvark, you’ll know that it’s a scam, right? So it’s a simple thing that you can do. And it’s going to be these simple things, which I think are going to get translated. And I think businesses have got to go away from, oh, the person looked right, so we let them in the door.

Like that can’t happen in the cyber world. And so we’re going to have to lock systems down. There are going to be some scary stories, but I would predict again that in 10 years, we are more secure online than we are today.

Rajan, I wanted to talk about data because a lot of the conversation is around how the models that we have, CERN accepted, are models that are largely built in the West and therefore are Western systems. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question. And I think that’s a very important part of the question.

And I think that’s a very important part of the question. And I think that’s a very important part of the question. and I know there’s something of a land grab going on for the data so as far as the data companies in India are concerned the companies are actually hoovering up the data, annotating it, making it ready what’s the business model for them? Are they feeding this all up back to that one pin code in the US or what’s the negotiating part and I ask this question because we have a lot of data in this country but I know that there are countries in Africa where the deal is already done and the data is out the door there are deals I know for 25 years worth of medical data out of Africa in exchange for setting up an EHR system because that’s a deal they’ve done.

And I was wondering whether we are thinking about this in a nuanced way and then I know Matthew you’ve got some ideas on crawling, I’ll come to you on that. What is actually happen on the ground with this?

I think the first thing is we don’t have as many I mean there are there are you know initiatives or NGOs like AI for Bharat that are collecting data but if you look at the leading global data companies they’re not Indian right India probably has a handful of startups that are that are that are actually in the quote unquote business of AI data for AI so first and foremost I mean because these companies are global you’re absolutely right all the data that Indians are generating is actually going to you know going to those sort of few handful of companies now that being said look firstly you know for for Indian companies to actually keep the data here we have to have model companies right like you know what I mean otherwise you have to sell it because if you’re in the data business you have to sell it to somebody but I think the benefit we have is you know like honest like you know we’ve only collected probably one less than one percent of the data we actually need if you really want to get to AGI right like if you look at but physical intelligence and things like that.

And India really has a competitive advantage. In fact, we’ve been looking for startups we could find, fund that would basically do all kinds of data collection for robotics and things like that. So that’s number one. I think number two, we’re also beginning to see companies that are leveraging their proprietary data in a very, very interesting way. I’ll just give you one example. So we have a company called Cloud Physician. It’s an Indian startup. They run these remote ICUs in tier two, tier three towns. In India, they’ve been doing that for four or five years. They’ve got this extraordinary amount of proprietary data that they’ve now used to actually build about a dozen or so specialized models in healthcare.

And now they’re actually taking those models to market in the U .S. And the kind of data that they have, which they’ve collected over four or five years for a different, for a sort of a healthcare delivery business, if you will, has been very valuable. So, you know, in our portfolio, we only have a handful of companies in different spaces that are using. And data is an advantage to actually build a final proposition, which is usually tied to some sort of domain model or something like that. But I do think, you know, we need probably some sort of – firstly, we need a lot more innovation around this. I’m surprised we don’t have more companies that are actually trying to sort of build businesses around this India’s data advantage.

And second, we need to have – I do think we need to have some smart regulation. I don’t know where the regulatory framework is on data. I think that’s going to be super, super important. I do know, like, AI for Bharat, et cetera, are being quite thoughtful about who they share data with, which is great. So, yeah, that’s sort of where it is. But it’s a huge opportunity for India. I don’t – you know, my real view is, like, look, basically, you know, it’s all the data on the Internet. That’s accessible to everybody. You just need, like, literally large amounts of capital. Most of the data that we need to get to AGI we don’t have yet.

And we have 1 .4 billion people. Well, created.

But, Matthew, you – You wanted to intervene in that whole thing. You had – is something called – maybe you didn’t call it, maybe the media started to call it pay -to -crawl, and you may have something more sophisticated like an AI audit or something like that. What’s the idea behind that? Because that’s also part of this democratization of AI as I see it.

So firstly, correcting a little bit of a misconception, all the money in the world and you still can’t even crawl the Internet. So how much less of the Internet does Microsoft see than Google? Microsoft Bing, they’ve thrown a ton of money at it. For every six pages that Google sees, Microsoft sees one. OpenAI knows how much of an advantage that is. So every 3 .5 pages that Google sees, OpenAI sees one. But that means that two -thirds of the Internet is hidden to – the most sophisticated model. Anthropic, it’s almost 10 to 1 in terms of what’s there. And so if you want to ask why did Gemini just leapfrog open AI, I don’t think it’s the chips. I don’t think it’s the researchers.

I actually think it’s the data. And I think getting access to data is important. And so if we want to have a level playing field, there’s a real risk that Google is going to leverage the monopoly position they had indexing the Internet yesterday in order to win in the AI market tomorrow. And that’s something that we’re really concerned about. And I think we have to do one of two things. We either have to bring Google down and say that they have to play by the same rules as all the other AI companies. That’s something that you could do from a regulatory perspective. And that’s something that the U .K. is looking into. Canada is looking into.

Australia is looking into. The alternative is how do we give all the other AI companies the same access that Google does. And that’s, I think, an opportunity to also solve some of the democratization challenges out there. One of the things I really worry about. is that AI is going to disrupt the fundamental internet business model. The fundamental internet business model was create content, drive traffic, and then sell things, subscriptions, or ads. That was it. I don’t care if you’re B2B, B2C. I don’t care if you’re a media company. That was it. Create great content, drive traffic, sell things, subscriptions, or ads. AI doesn’t work that way. So just take a media company. If AI scrapes your ads and takes it, let’s say it’s the New York Times, or the Times of India, or whatever it is, you can now go to your AI and just say, show me all, summarize all the articles from the New York Times that would be of interest to me.

And you’re going to read it there. Now, that’s great for you as a user. It’s better as a user experience. So it’s going to win. But now the Times of India isn’t selling a subscription or an ad. Now the New York Times isn’t getting anyone to click on an ad. And that’s going to make it harder. And to make this clear how much harder it’s gotten. Ten years ago, for every two pages that Google scraped on the Internet, they sent you one unique visitor. And then you could monetize that visitor again by selling them things, subscriptions, or ads. Today, what is it? 50 to 1. Actually, excuse me, 30 to 1 in Google’s case, 50 to 1 in Bing’s case. That’s the good news.

In OpenAI’s case, it’s 3 ,500 to 1. In Anthropic’s case, it’s half a million to 1. They take half a million pages for every one page they give back. So AI takes, but it doesn’t always give back. And if the currency of the Internet has been traffic, that traffic is gone. And it’s getting harder and harder to then make money through the traditional business model of the Internet. So one of two things happens. One is, well, the Internet just dies. But that’s not going to happen because the AI companies need the content. They need the information. They need the things that are out there. And so the Internet. The alternative is a new business model emerges. So what happens?

and that’s what’s going to happen over the course of the next five years a new business model is going to emerge for the internet and think how exciting that is think how rare new business models for something as grand and as large as the internet are how often they emerge almost never and yet we’re all going to live through it and that’s an incredible opportunity and i don’t know quite what it is but it has to be some way that the people who are creating the content and creating the value get compensated for the things that they are creating and what the encouraging version of this is to think about the music industry the entire music industry 22 years ago was valued at 8 billion us dollars which is a lot of money but it’s not that much money because that was the beatles and rolling stones and like everything right why was it that well because napster and grokster and kazan all these things had commoditized they were basically taking a music and musicians weren’t getting paid for it and they were getting paid for it and they were getting paid for it and they weren’t getting paid for the music anymore what changed one day steve jobs walked on stage and he said it’s going to be 99 cents per song right itunes launched almost 22 years ago to this day and that wasn’t the business model that won but at least was a business model and it started the conversation and that evolved into what is the business model that won which is something closer to spotify which is now i don’t know what it is in india but in the u .s it’s like ten dollars a month and what’s incredible is that spotify last year sent over 12 billion dollars to musicians more than the entire music industry was worth 22 years ago and that’s just spotify there’s apple music and and uh title and tiktok and youtube and tons of people there’s more money going into music creation today than at any other time in human history by an order of magnitude now different winners and losers and we can debate whether or not the right people are winning the right people are losing but there is more money going into music creation today than any time in human history and so as we figure out what the next business model of the internet is going to be, let’s try not to make it one that’s worse.

Let’s try and learn the lessons because traffic was always a terrible proxy for quality. So let’s actually find something that is a proxy for quality and let’s reward the people who are creating that. And the good news is, I think that’s what everyone in this room wants, but it’s what Sam wants. It’s what Daria wants. It’s even what Elon probably wants. And that’s the sort of thing that is actually going to drive not only a healthier internet ecosystem, but I actually think that a lot of what’s wrong with the world today is that we have monetized traffic. And what that has meant is we have monetized basically making people emotional or angry or whatever it gets to click on things, which is part of what’s driven society apart in a lot of ways.

I think if instead what we monetize and what we reward is the creation of human knowledge. That’s what the AI companies want. That’s what we all want. And I think that’s what we can actually do to actually bring our society back together in.

I want to turn it over to the audience for questions. I don’t want to be the only one asking questions. I’ll take – hands are going. I’m going to take three questions at a time.

I like Indian audiences. They ask questions. Like you go to the UK and everyone just sits on their hands.

No, no. Indian audiences are very, very – now we’ll have to shut them up because we don’t have time. I’m going to take this one. I’m going to take that one. I’m going to take this one, right? So first up here, yeah? And I have a rule, a question, not a statement. So it has to end with your voice going up a little bit. Then I know it’s a question.

Sir, this is for you. You’ve touched upon a lot of interesting topics across domains. First of all, I remember you talking about the deterministic AI outcomes. Now AI having crossed the threshold –

Give me the question.

Okay. So how – So what, in your view, would make AI trustworthy? Is it something to do with explainability, deterministic AI, and what would be the pathways?

Let me get a couple more. Otherwise, we won’t get through. The lady at the back there. So one is, I’ll keep track of it. How do we make AI more trustworthy?

My question is for Matthew. So you mentioned about the paper crawl. We see robots .txt getting ignored. My question for you is, what makes you believe that AI companies would be equally invested in a creator -based compensation when AI creates the Internet and is not giving back attribution or compensation?

Trustworthy, and how do creators get paid? And attribution. I think she also wants to do attribution. This gentleman here.

Hi. My question is to Rajan. So, Rajan, what do you think you were explaining about the consumer and vertical part in the application layer? So what do you think? Where are we in terms of investment from a venture capital side point of view in terms of how can we match the Y Combinator and AI 16Z level in terms of investments?

Great. So AI is already more trustworthy than most humans. The simple fact is that AI is a better driver than ninety nine point nine nine percent of humans that are on the road today. Literally, since I started talking within a kilometer of where we’re sitting, there was an accident where between two cars. I mean, we just know that’s happening. We’re sitting in Delhi. Right. You will not be able to find any news about that anywhere on any publication anywhere on Earth. And yet, if one of those two cars had been a self -driving car, it would have been front page news around the world. There are expectations for AI are too high. We have built a system that acts like humans and we need to think of it as acting like humans.

The smartest CEO that I know in terms of doing this is Robin Vince at BNY Mellon. In their case, they actually have AI employees. The AI employees get an employee number. They get an email address. They get a quarterly review. They can get fired if they don’t do a good job. They can get promoted if they do a good job. I asked if there are any AIs that are supervising humans. He said, not yet, but it’s inevitable. That’s the way to think of it, right, is that they act like humans because they are like humans. And, again, we are all fallible, and we’re all going to make mistakes, but already we see in certain disciplines like driving, AI is better than human beings are.

In terms of getting paid, I think the empirical evidence is that when you’ve actually seen – forget robots at TXC. That’s like a no trespassing sign. Anyone can ignore it. When you actually block the AI agents, which is what we have done, then they come to the table. And so with big publishers like Condé Nast, DotDash, Meredith, and others, where starting July 1st, we said all of the AI companies are blocked, they actually came to the table, and they were able to get paid. Things done. In the case of Reddit, Reddit was willing to block everyone. including even Google. And as a result, they got the public number is seven times as much for the Reddit corpus licensing that than the New York Times did, even though the two corpuses are about the same.

So again, I think that the first step in any market is having some level of scarcity. As long as you’re making it easy for anyone to take your data, then you’re not going to get paid for it.

Yeah, I think on the question on consumer AI, I don’t know, very few people know this, but India today has more consumer AI startups than the US. In fact, on Tuesday this week at the Pitchfest, just our firm, one firm, we announced five new seed investments in AI companies. Four out of the five are consumer AI companies, right? And the reason is, and we think this is going to explode because we have 900 million Indians on the internet, 850 million of them are active every day, seven hours a day on the internet. and every space has potential for tremendous innovation, right? If you take education, education hasn’t been accessible to a large part of online education because it’s just been too expensive, right?

But today with AI, you can have a 99 rupees a month plan with an AI tutor across. In fact, the fastest growing AI education company in the world is in India and nobody’s really heard of it because actually, fortunately, these guys are all just being stealth and just building, which is very good. So I think it’s a great time to be building consumer. Actually, it’s a great time building AI companies, but especially in consumer AI, we’re going to see some breakouts. Look, the world’s leading consumer AI companies in education, healthcare, entertainment, et cetera, will be either here or in China. They won’t be in the Western world because we just need it.

The one beautiful thing about this summit is there have been so many wonderful, rich, diverse conversations. This is one of them. Matthew, Rajan, thank you so much. Thank you all for being such a good audience. Thank you.

And it’s such a pleasure to be here with such lovely panelists and an audience who’s possibly going to skip some of the lunchtime to join us today in our discussions. Let me get started by really talking about, you know, we are towards the end of the week. It’s been a fantastic week, lots of conversations. And one thing which I reflect back on most of the conversations has been what is the most defining question of our time, which is who all is artificial intelligence really benefiting and with what rules? If I look at it, AI’s enterprise infrastructure, AI’s public sector capability, AI’s even geopolitical leverage is what we’ve seen across all these days. But more importantly, AI has become a part and parcel of our daily lives.

It stretches from everything from making our work life easier. to making sure that we get our entertainment as and when and how we require it. And more importantly, from healthcare to hiring to anything you can possibly imagine. When we really focus on inclusion in AI, one thing which has kind of stayed as a thought for the last five days is inclusion in AI is way beyond equitable representation in data sets. It’s, you know, it’s everything. It’s about access to compute. It’s about standards. It’s about having a right policy framework, which encourages everyone, everywhere. And more important, it’s also getting clarity on regulations, which are there across countries, to see how it can really be beneficial.

Now, to take the discussion ahead, today’s conversation is going to be really about trade -offs. Excellence and inclusion. It’s been interesting on how to navigate both these terminologies whenever you think of any policy or a framework. So I’m going to start with my first question to Dean. So Dean, you know, you’ve been working at the frontier of AI policy. You’ve been at the institutional design through the Foundation for American Innovation. There is a lot of growing debate between self -regulation and innovation -first approaches. Where should policymakers really draw the line without really undermining national competitiveness?

So I think it’s a, first of all, thanks for being here. And thank you for having me. It’s an honor to be here. The way I think about this is that, you know, we will govern AI through a very large intersecting web of different things, right? It’s not just going to be one day one bill is going to get passed and that’s going to be the AI bill and then AI is regulated, right? AI is currently regulated today. It’s regulated by many different things. It’s regulated in the United States by things like liability doctrine and a lot of existing products. regulations and things like that. So I think step number one for government is let’s take the existing bodies of law, you know, many of which just as, you know, as in India and the United States, we’re quite proud of.

Many countries, you know, are very proud of their regulatory and legal traditions. We have a common law tradition in the United States that we are proud of. So let’s take those things and let’s figure out how to apply them to AI. And then, you know, the companies, I think, thus far, the major AI labs have been, I think, responsible stewards when it comes to the major risks. Now, I think the area where you might need proactive governance first is, at least in my view, is really this domain of tail events, potential events that could be very serious, have very serious consequences that are relatively unlikely. So, So, you know, pandemic is an example of a tail event.

And I think AI might have some tail, you know, sort of catastrophic type risks associated with it. And so this is an area where some proactive governance, I think, is needed. And I’ve written supportively about transparency laws in the United States along those lines. So I think that’s where when we have a clear and demonstrated threat model and we have a, you know, clear evidence that existing law is not sufficient. I think one area, one aspect of AI governance that I often push back on and that I often dispute is there’s this kind of assumption baked in whenever we talk about AI regulation that the existing law is insufficient and that the current status quo is that AI is unregulated in some way.

And I think that should actually be, we should have the opposite presumption. We should presume that existing law is sufficient and that there is some sort of good solution. And then. Yeah. It should be, the burden of proof should be on the person who wants the regulation to show this is why existing law doesn’t work.

Thank you, Dean. That’s very interesting that we go with an assumption. And with that, Gabriela, let me move on to you. So should, you know, how can governments foster open innovation, assuming to whatever Dean said, while minimizing the risk of market distortions?

Well, I think that it’s a very nice segue because I completely agree with Dean that there are a very broad portfolio of policy interventions that has not only to be with regulations. Regulations is looking at the way the technology is developed. But we need to think about this as an ecosystem that needs to be nurtured, that need investments. That needs incentives. that needs institutions and that needs infrastructure. And therefore it’s not only the technological conversation about what do we do with AI, but what kind of an economy we want that is really productive, that delivers for people with AI, and for that you need government intervention. And let me tell you, what is very interesting is we usually tend to think that the private sector is an innovative force and the government is a break.

In the U .S. that was not the case. The U .S. was the place where the massive investment in innovation in DARPA, in the creation of the Internet, all the foundational issues that we are seeing now were financed at some point by basic research that was paid by the government of the U .S. And many countries fill that space and that’s why it’s so important that we invest in research because it cannot be that the research is being done only by the private sector. and then it’s also true that when the government gets into the research it’s open research because it needs to bring everybody around the table and then it needs to be shared which is not always the case when you have a private sector innovation so I will contest this also way of framing the issues in terms of the government’s only creating market distortions because at the end it’s about how the government can be effective to address the market distortions that we see many times emerged in this case I like to see the AI technologies as natural monopolies somebody invented something somebody laid the whole network to operate it and then it was a monopoly and now it’s oligopoly at the end it’s very concentrated so there are market distortions now that needs to be addressed by government policies again there is a wide gap of things that needs to be done to ensure that the main distortion that can occur nationally and globally, which is that this is a story of a lucky few, is prevented.

Thanks, Gabriela. And you know, it’s interesting you mention that because at least in India, whenever we speak about public -private partnerships, it’s all about how we are moving from a culture of competition to cooperation, to really working together so that the markets stay healthy. With that, we move over to you, Amanda. So, Amanda, you know… Eva. Sorry. Yeah, Amanda is missed. So, Eva, let’s talk about the global AI governance strategy at Wipro, right? Many organizations are developing a responsible AI framework. How do we move beyond policy statements? Through measurable accountability. and specifically when we have to do that at scale.

Thank you very much and it’s great to be here and thanks to all of you for joining. So I have what I say often, I have the best job in the world, which is basically to translate a lot of the things that we’ve been discussed over the last few days into practice. So basically means we’ve heard democratisation, we’ve heard inclusion, we’ve heard how it’s important that AI is inclusive and by inclusivity, it’s not just about access, as was said, but it’s also making sure that many get the opportunity to participate in the design of this technology, but also in the decisions around what we are producing and who is going to be benefiting from that.

We, I think in a lot of our work, a lot of organisations, what happened over the last few years when generative AI came about, a lot of organizations we had to face something quite dramatic if you think about it because before then AI was very much for engineers for scientists to work with if they think about machine learning people who knew about AI then what happened a few years ago is that generative AI came and everybody got access to it did you remember and do you remember how companies started to scramble with who’s got access do we leave people our employees to access this the systems do we create our own private instance how do we navigate the fact that we want people to play with these tools with the fact that we have to be safe and secure as an enterprise and then things evolved and a lot of organizations if you know how the debate around governors started and you know a lot of organizations started to set up governance boards and they started to set up ethics boards and all of it and I think we realized at some point and I took on the challenge of AI governance from a privacy standpoint the reason for this and many people in organizations took on AI governance from a privacy standpoint not only because a lot of AI harms are actually privacy harms but also because privacy professionals knew about risk management and then we realized that actually governance of AI is much more than that it’s much more than risk management it’s much more than compliance we realized and I think this summit show that really clearly that AI governance is really about a strategic capability that an organization must have to create long -term value What does that mean?

It means that you have to do two things. First, you have to look at what you want to deploy or develop and that is where you need to embed privacy, security, legal protections, resilience into the products that you’re working on. That is not an easy one. It’s not an easy one. It requires knowledge. It requires investment in privacy enhancing and security enhancing technologies. It requires what, for example, India is promoting which is a techno -legal approach. It’s not just about the law but it’s also about how you translate the law into technical tools. So you have to do all of that and then you have to look at what happens once the product is in production.

So how do you monitor it once it’s out in production? How do you make sure that if, for example, you’re using AI to fire and fire, as sometimes it happens, you have tools to pull the trigger if something goes wrong? Now we are into the realm of agentic AI. If you’re interested in this, I’ve just published an article on the World Economic Forum of a subject I’m really fascinated in, which is what is the design for trust in agentic AI? So, for example, governance means that you do design these agents, but you give people, according to security standards, but also according to their own preferences, the right to intervene when they don’t want the machine to make a decision in an autonomous way.

And then you make sure that you protect from cascading hallucinations, from model drifting, all of that. So governance, to me, is very much about… . the capability that organizations have to think laterally about AI, which means impact, design choices, the trust stack that enables people and employees to trust the product. And one element which to me is very important is to make sure that companies bring their employees with them. That is a very crucial part of governance because the work is going to change. People are going to change the way that they work. They’re going to, and it’s important, the people are going to know best how to use AI are the people working in a company.

This is why I’ve seen successful companies developing a lot of use cases based on their activity and asking their employees, how should we innovate this? This is a fundamental part of governance, I believe, because it brings people with them. us. So very encompassing approach to governance. I think we are evolving and changing how we see it but certainly I think it’s become very clear over the last sort of few years and especially with things like this summit talking about impact that it’s way beyond compliance and it’s way

Thanks Eva and that makes me very curious enough to ask you a very quick question. So do you still feel you’re underestimating the risks because you spoke about AI trust?

No, I think let’s be honest here. Over the last few years we’ve seen amazing benefits coming from AI, right? Beautiful stuff, fantastic. Every day there is a piece of news that makes us hopeful that we can improve our well -being and we can feel better in the world we live in. But at the same time we’ve seen the risks too and we’ve got to be honest that looking at the success without looking at the risks is very naive. We can’t. because we’re not going to be able to deploy AI successfully if we don’t look at the risks. We’ve seen disinformation. We see deepfakes. We have seen AI softwareizing existing inequalities into decision -making around people, future, rights, and livelihood.

That’s not okay. So we’re not underestimating the risks. But we can’t approach governance from a risk management control. We have to shift our approach and do AI for good and change the way that we look into this. So we have to engineer fairness into the systems that we create. We have to engineer inclusivity into the systems that we create. And, of course, we have to manage the risk. But the mindset has to really shift.

Thanks. And that gets me back to Dean. So my question to you is, inclusion at the national level often intersects with computer access. And research infrastructure. You spoke about public -private partnerships, spoke about trust in… emerging technologies like artificial intelligence and maybe even quantum going ahead, should governments treat compute as critical infrastructure?

Yeah, I think they should. The data centers that power frontier AI systems are going to be a part, you know, like ports or railroads. They’re going to be critical infrastructure of the future. I believe that’s true. Prior to my current role, I worked in the Trump administration in the White House Office of Science and Technology Policy. And in particular, I was one of the people that shaped the administration’s AI action plan and AI export program, which my former boss, Michael Kratios, was just here talking about and announcing some next steps on. I was really excited to see that. One of the key messages of that that I feel was I feel this is maybe a communications failure on our part.

But. You know, the United States government has publicly said the president. has come out and made as a flagship of his AI policy that we intend to subsidize the development of AI data centers in the global south. That is a policy of the United States under this administration. And we don’t have the interest in exercising control over the technology in the way that I think the prior administration did in some ways. We don’t want to control other countries’ use in the same way that the prior administration did. So I do think you should think of it as critical infrastructure. And I think that you should think of the United States as a partner in the construction of that.

And I think that owning infrastructure of this kind is an asset that states and regions can use for years to come.

Thank you. So… So, you know, it’s been interesting because whenever we speak about AI at scale, when we talk about taking AI to every single person… across the planet, there are always three vectors we look at. So that can be mindset, skill sets, and tool sets. You just spoke about tool sets, which is extremely relevant. And that takes my question to you, Gabriela. When we talk about mindset, should inclusion be framed primarily as an ethical imperative or a competitive strategy or even both? What’s your take on that?

I really like the way this question is framed, my dear, because I’m sure that people think that going for inclusive policies might hinder competitiveness. Who from the public believes that? Can we have a show of hands? That being inclusive might hinder competitiveness? Investing in competitiveness might go against inclusiveness? I really think I’m an economist, and I think in this area we really need to think. We need to think about economic inclusiveness. because if we just think about social policies that might be needed when some people are left behind and therefore we need to invest in communities or in infrastructure or in people, kids that are in deep need of education, those things are very important.

But more importantly, we need to consider how do we foster market economies that are inclusive and that’s the core issue here. And I can tell you because I have been looking at the question of inequalities. Actually, I’m now co -chairing the task force on inequalities financial disclosure. And what we have seen is that when you have market concentration, productivity flattens. And what happened here, and we saw it at the OECD report that we did some years ago, when you have concentration at the top as the one we are seeing now, we saw that the OECD report which is companies having the whole concentration of compute capacities, the capacity to sort out skills and attract the skills, the capacity of having the financial means to invest.

What happens is that the diffusion machine, which is this very important element that trickles down the innovative developments into a broader set of users and benefits, is broken. Now the diffusion machine is broken. And therefore we need to see how do we ensure that the diffusion is faster. And to do that, of course, I agree with Ivana. The question is how do we ensure that we create the capacities of people and economies that are lagging behind. But we also need to see how do we diminish market dominance. And I know that there are many other considerations, geopolitics, competition matters, trade secrets matter, all these things matter. But for me, competitiveness, inclusiveness has to do with creating the highest well -being for people and that’s the outcome and that’s where ethics competitiveness, all of these narratives collide together because at the end what are we looking at?

that we have well distributed 70 % of wealth in many countries, 60 % of wealth 50 % of wealth is owned by just the top 10 % income groups but that’s not sustainable I get into Europe and Mexico and I was asking where do I put my children because I need good schools and they told me choose the right neighborhood that’s not possible and therefore I feel that there needs to be this set of policies and who is there to ensure a level playing field who is the one that needs to be using the tax systems or the incentive systems or the investment systems or to ensure that people are not left behind or the anti -competition or the non -competitive practices who is there to I pay my taxes so that the governments deliver on their promises so I think this is super important and I feel for example that what India has managed, this question of the digital registry I was with Mr.

Murthy when he presented his plan so many years ago I never could believe that you were going to be doing registry for 100 million people every month, it was just like you’re crazy, that will never happen who finances the government and now you have all India with the digital identification, it’s just amazing and then you go with the financial thing so I feel this is this is this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this is the this

Thanks, Gabriela. Since with this vision that the world of tomorrow with AI would certainly be a better world and hopefully be a better world than what it is today, I have a common question for all the panelists. And the question is, what do you see as the most significant blind spot in recent times AI discourse, keeping in all the conversations you’ve possibly had this week and even prior to that? And maybe what we can do is, Dean, we can go with you first.

Yeah. So in terms of blind spots, I think maybe the most important thing I could possibly say here would be one thing I’ve heard repeated a lot in the conversations I’ve had this week is this notion that the frontier models, the best AI systems are not… necessary. You can find good enough models that can, you know, that are cheaper to run. And in some cases, I think that will be true. But I would point to the very significant blind spot there that, you know, I believe that what we are doing is building systems that are going to be smarter than humans at all cognitive labor. That is a very serious goal. The United States is currently spending, like, it’s not a joke, right?

That’s not a joke. That’s not hype. That’s not crypto. We’re spending a trillion dollars this year on that. That’s the plan. We’re going to do it. It’s going to happen, right? And so the capabilities of those systems and the way that that will change the way the world works, I think ambitious people will be able to do unbelievably broad range of things. And I think this could really be an incredible opportunity for countries in the global south and really everyone in the world to participate in building the future together. And this is not something rejecting frontier models out of some sort of belief that that preserves sovereignty because there are existing use cases you can think of that can be done you know with with with cheaper models i would use a think of think of frontier ai as being useful for stuff that uh we don’t even have words for today right concepts that you will invent uh and you know that we will all invent together that’s the future that we’re building um and uh i think that’s a it’s an easy thing to miss and i think missing it is basically missing the ball game

that’s very interesting thank you for sharing that gorilla what about you

i would say a traditional education education education okay i am really that takes awake my sleep why aren’t we upgrading massively the education pedagogy Why are we changing the way we go in school? Why don’t we invest in our teachers for them to understand how these technologies can help improve student outcomes and at the same time make their life easier? I see a lot of teachers complaining about all the administrative work that they need to do that don’t leave any space for them to invest in quality changes with their students. And I’m not seeing that happening. And we need that pipeline. If the future that Dean is projecting is going to happen, is going to arrive, we need people to be very well equipped.

And where do we get that equipment? I’m fine to invest in the workers in the market. That’s very important. And I think that we need to upgrade that too, the skills of the people in the market. But the school system needs to be upgraded. And actually, I haven’t seen it really happening anywhere. This is a challenge. This is a challenge for North, South, East, West. and I invite that for all to confront this challenge.

Okay, I love the fact that you brought education and skilling as a part of it because building AI readiness has become so essential to ensure natural competitiveness no matter which market we are talking about. And just to share an incidence, India was one of the few countries where AI was introduced as a school subject way back in 2019, even before the COVID era. So students could learn AI as they would possibly learn a biology or a physics. But yes, that’s a major challenge which we’re trying to work on. Ivana, your book.

I was very impressed yesterday when your prime minister spoke. And I was very impressed by one thing that he said. But he said, you know, develop here and serve humanity. And I think that to me went to point that it’s been very strong. here and where I see because the I like also something that has been missing so far so he said something very very important and he said that AI needs to be used for inclusion for economic well -being inclusion as we said as access but also as participation for many as reduction of the gap between areas of society in geographies across India inclusion also as creating models that respect your languages and your dialects and the ethical norms bind in this country together because the eye that we have now is often not reflecting of the diversity of the world one thing that following on this one thing that has been good has been to see many leaders coming from all over the world.

One thing that I’ve always supported is how we haven’t aligned, I’ve always thought this, how we haven’t aligned on what the red lines of AI are. Are there things that us as a society or as a world, we are never going to do or we don’t want to do? Regardless of, are they, and we’ve seen appeals coming over recent years. We’ve had massive debates around ethics of AI in different ways, whether it’s the US, whether it’s Europe, whether it’s, in different ways, or everyone in different ways. But I think when it comes to something which is far more than technology, because AI is far more than technology, AI’s power is geopolitics, is earth, cables, sea, so much.

I think one of the things that probably we are overlooking is how we, and if we will be, as a world able to come together and have some red lines and say, well actually, we’re not going to go

Thank you. So I just want to take a moment to thank the panelists and maybe I can ask Dean for you to sum it up.

Well, I think there’s a lot of different things. Unfortunately, the subject of AI governance is so difficult because it’s so capacious, right? It’s such an enormous topic. But look, I think we have a very real infrastructure development sort of challenge ahead of us. We have a huge complex of new types of institutions and old institutions that are going to change and evolve in various ways. And there’s all sorts of interlocking work to do on things like that that are going to be critical for the governance of AI for both everyday types of harms and also sort of catastrophic things that feel futuristic. But I think that are going to be real parts of our lives in the pretty near future.

And then I think, you know, another thing I would I would kind of double click on is this need for competitive. Yes, which I feel. agree with. And one of the things that I think is exciting about AI is that the price per token of models does drop quite quickly. And so there are a lot of good competitive dynamics here. There’s also centralizing tendencies. And so I think working together to figure out how to prevent those tendencies, I think that’s going to be extremely important. The concentration of power in AI and that issue in the long term is going to be, I think, one of the most important parts of the political economy of this topic.

So yeah, I think that’s how I see it.

Thank you. That’s fantastic. We spoke about trade -offs, we spoke about potential solutions, and we spoke about building AI readiness for national competitiveness. Thank you so much to all the panelists, and it was lovely having a conversation.

Thanks to the moderator. Thank you.

Sharing is learning with the rest of the world. One, an AI that is independent. From large global AI to empowered, scalable, sovereign AI. Sovereignty. The generation sitting right in front of me grew up taking it for only political and geographical individuality. Fast forward to now, the world has a completely new landscape for its definition. I’m growing up knowing it’s to be more around something I may like to call digital independence. And achieving AI sovereignty has become a global imperative. And then I’m seeing an emergence of very AI models which are not just differentiating from the rest of the world. by scale, computer parameters, but by the very approach different nations are building them with. While US leads the global AI models and the technology sector drives innovation, China likes to keep its control centralized with rapid scaling and strong international governance.

While Europe likes to build it more with trust and compliance with the world’s first comprehensive AI law, Middle East positions itself by building AI hubs in the infrastructure layer contributing critical nodes in the AI boom. Well, India is digging into sovereignty. Data sovereignty, infrastructure sovereignty. And most importantly, talent sovereignty. And I’m glad. That is what my country needs to boost its economy. Two, an AI that is inclusive. From the artificial general intelligence race to responsible, democratized AI inclusion. The democratization of AI with inclusion, which I touched upon in my keynote at the EIFGO Global Summit in Geneva last year, has become a core focus area for not just India, but even for the United Nations and the rest of the world.

I’m seeing how India is leading a shift from the artificial general intelligence race to the AI. Two, responsible, democratized AI inclusion. The democratization of the AI course as a key enabler for India’s digital public infrastructure 7500 data sets and 273 models have already been deployed as natural resources to build AI solutions across sectors. Allow me to share my two cents on the affordability of AI compute power under the India AI Mission. Well, to your surprise, it is less than 2 cents per minute. How’s that for democratization? Inclusion of different Indian startups, researchers and developers. Social inclusion of different cultures, languages, disabilities and even gender equality. Overall inclusion of human capital, innovation, social empowerment and the list goes on.

Third, AI is free and AI that is impactful. From safe, innovative, actionable AI to impactful AI. Let’s move to impact and let’s do it a bit differently here. How about I share my own use case of an AI model just released by India. Thanks to my recently completed certification course from the India AI Mission, I observed how every single bit of content was exemplified with an India specific use case impacting lives, businesses and industries. So here’s my back story. When I was six, I written a book on AI. Are you born with AI? This had been made available globally on Amazon and even had been acknowledged by His Excellency, Secretary General of the United Nations, Sir Antonio Guterres and the Ministry of Education, Government of India.

Thanks to the full stack AI sovereign model now in place, Sarvam AI, I’m able to translate my book into 22 different Indian languages, boosting the sales of my book and contributing to India’s GDP. Here’s a sneak peek into this. So you can see here that I’ve translated it into Punjabi, Tamil, Hindi, and then 19 more languages, but obviously I can’t fit on the slide. Impact? One, it helps me live my dream to drive A -L -O -C to all my friends out there breaking language barriers. Two, it helps me support the National Education Policy 2020 of the Government of India by introducing A -L -O -C from Grade 3 onwards. Democritization checked. Three, it helps to have a wider reach as an author, boosting the sales and the royalty I get from the book.

Business impact and GDP contribution checked. So, if a Gen Alpha can contribute to AI literacy countrywide by first writing a book on artificial intelligence, then using AI tools to make illustrations to make it relevant for young minds, and then further use Indian AI tools to translate it into multiple Indian languages, boosting the sales of his book and the royalty, then, to contribute to India’s GDP at age 8, I am confident that each and every one of you can leave your impact with relevant Indian AI models. Amalgamating, be you geopolitically driven or an inclusive AI -impact fabric, and there is no assurance that any country will get it all correct and do it truly. My simple yet important message here is that we can all learn from each other and share our learnings to make this world more empowered with AI.

And that is exactly what India is all set to do once the GP AI Council members convene and define the multilateral cooperation for responsible and inclusive AI, keeping in mind the value of a human connection. Also, me and my generation are part of this AI revolution too. We understand and observe how AI is being shaped up globally. Be it governments, be it tech giants, be it start -ups or even scientists. We are not just at the receiving end. Do not forget we are born with AI around us and we will contribute and be the true agents of change of what you all build today. I stand for I, Generation Alpha. I stand for India. I stand for impact.

And the world will witness all three when they have been raised to the power of AI. Sarvajan Hitai, Sarvajan Sukhai. Thank you.

Thank you. Thank you. Thank you, Renvi. We have our next panel, which is next generation of techies. May I now invite Mr. Arvind Jain, Founder and Chief Executive Officer, Glean. Ms. Navina Singh, Founder and Chief Executive Officer, Credo AI. Malhar Abide, Chief Technology Officer, Origin Bio. And the panel will be moderated by Mr. Ranirudh Suri, Managing Partner, India Internet Fund. In the meantime…

I think even in the panel before, there was a conversation around that. And I’m going to, over the next couple of slides, just take you through why we see this as a window. Last, about, say, 15 years has really been, you know, the streaming or the consumption era as we know it. It was predominantly, you know, passive consumption. About 20 years back, a bunch of companies like Netflix, etc., they got content from studios, from broadcasters. And prime time basically became my time. There was search, there was recommendations, etc. But format hasn’t changed. Because seven years later, when they did their first original show, they pretty much did what HBO was already doing. So we haven’t really seen much change in format for almost now 20 years.

Cut to now. We’re seeing the era of creation, and that had already commenced in the short video space. But the short video space. was what I call as an augmented space, 30, 60 second stories, augmented with music, augmented with background. You can call them stories, but they were not really complete as it were. I believe with the manner in which video generation models are developing, creation costs are collapsing, production cycles are now within hours, and AI will make India a creation civilization. Why do I say so? So when I look at, you know, what I term as the four pillars of an AI storytelling civilization, it starts with the fact that every creator is already a studio.

That is the reality now. I mean, you could literally be able to speak, and there is a component around, you know, an output that will happen as far as. Second, every language is global. We don’t need to. We have platforms where there is already auto translation, and this will continue to progress even more. Even the ones that you wear, I will be speaking in English, you will listen to me in French, you can reply back in Spanish and I’ll still comprehend. Our stories are becoming participatory. We’re beginning to see branching of narratives. I’ve been on forums like this for the last 15 years. Many a times spoken about terms like, which are more often than used, abused, which is convergence.

But today it is truly beginning to happen because we have conversational AI within characters. It’s already happened within gaming and it’s beginning to happen in this. And lastly, to me, culture is truly an opportunity of export in a very different way. I think from our stories, whether they’re mythological or folklore, we have an ability to extend these. And why do I say so? Because I think the technology stack… which is getting laid out, will make this a possibility. From production pipelines, we’re getting into what we call as creative intelligence systems. We already have generative engines. There is an autonomous sort of creative cycle and agents which are doing this from camera work to the kind of manner of lighting, etc.

We have the layer of narrative engines. You will have more interfaces for immersiveness, etc. And that leads to multi -path and components which we’ve seen parts of. But I think where we are heading is I come from a gaming world as well. We used to take two, three years, make a game, and then do seven years of live ops. I believe with categories like micro dramas, etc., for the first time, we are in for a live op scenario where I will make 10 shots. They’re ready, ingested. By the time you’re watching the fourth, you know, basis that feedback, basis that conversion sort of consumption pattern. your 11th and your 12th and your 13th episode is getting created.

So a vision which was typically one to million, that of a director, scriptwriter, etc., is now heading for a million to million kind of interaction and interface. So why 2030? I believe the camera will no longer be the primary tool of storytelling. Intelligence will. And why do I say that? You see, we are already seeing parts of this. You know, one of the first most significant, in fact, I believe in 20 years, micro dramas are the first truly digital format that have emerged. When we made films, a filmmaker could take four minutes, five minutes in setting up a character. When I did original shows, you know, which could be extending to four hours, I could take eight hours to show this person as an alcoholic, very elderly person, sort of, you know, whatever.

And by the seventh minute, you’re finding that he’s a genius as well. Here, in 15 seconds, they are unabashed, they don’t care, they will put, they’ll show you a face, it’s a billionaire playboy and there will be a little thing coming around and in one stroke, that’s the kind of, so it’s a format. And it is a format of narration which a generation who hasn’t seen things in horizontal is embracing at a pace which is unprecedented. We’re seeing that and I feel where we are heading is a world which I like to describe just from a visual point of view like a cube of sorts. Up till now, we’ve all consumed content as, you know, audio, video, game, live, extended reality.

We’re going to kind of move from one to the other seamlessly and that is why it is exciting. From multi platforms to stories which will no longer live on platforms but platforms will live inside these stories. And why do I say that, you know, because as I said, the creator explosion has already commenced. this is what typically it looked like if I really wanted to be very very generous let’s say for every author who’s been living and has been published for every lyricist who has written a song for every singer for every director every filmmaker in any form anyone from a literary sense if I were to out of eight billion people right now my sense is that number or whatever would probably be about 10 million but if I took the entire creator economy of what’s happening I’ll probably jump a little more we are heading from that world to potentially billions of sort of creators across this entire space and that is the reason why I feel the next Disney our own YRF or Marvel may not be a company but it could very well be a community which is coming and therefore So, you know, let me, no talk is incomplete on media entertainment without, you know, some perspective, you know, on our most visual form, which is still the most sort of, you know, expansive form of theaters.

I don’t believe, you know, that we are going to see the end of that. What we are going to see is more eventized immersive screenings, more mixed reality environments, and hopefully interactive participation. So the formula one is storytelling, premium, spectacular, and experiential. And with this in mind, I feel now coming to the last two slides of why India can lead. In an era where cultural depth becomes a comparative advantage. It’s important, and I really hope that, you know, this is something, you know, we’re a nation with so much of history. The first is the fact that we have demographic energy. We all know this, right? We have linguistic complexity. I often say, you know, we were hosting the American delegation three days back, and there were 120 of us.

We were the first group of them. And I said to them, I said, you know, this is probably the most somber American delegation I have seen across industries. And I asked them, I said, you know, why is it? I mean, is it because of whatever is happening in the traffic, et cetera? Or is somebody actually sort of, you know, concerned or has been using the T word with you all? So I said, you know, as far as I know, intelligence is still pretty much duty free. But having said that, the point I was making to them was I said, listen, even our models here in India have been trained on chaos. And complexity of language and nuances have a huge opportunity.

I think we have massive, you know, cultural depth. Five, six thousand years of storytelling experience. And finally, we’re a nation of startups, you know. We’re an entrepreneurial ecosystem across every sector. And that is one of the reasons why I feel. This is certainly a category where India can lead and show the world what’s possible. So with this in mind, my sense is by 2030, I believe 10 million AI -assisted creators, regional studios, real -time cinematic production, immersive, devotional, cultural platforms, and leading to mainstream sort of events. There’s a thought I’ll leave for you. With all of this, it looks very good. But there is, you know, nothing looks sort of just hunky -dory, as it were. And the thought is we’re also moving from a world of finite.

If I look at content today, in whichever platform it is, right? We make 1 ,500 films. Hollywood makes 250 films. We have 900 TV channels. We produce so many hours across the world. It is this. We have so many radio networks. We have 2 ,500 print publications. It’s all finite. In a gen AI leading to… an AGI world, we will move from finite to infinite. Now, no industry is in a position to, if I’m doing cement and I have 30 million tons of cement capacity, I know India is growing in a particular way, I add a couple of million tons and that’s fine. But if I go in that category and start adding 30 million tons all over again, you know, it’s not sustainable.

So there is a thought, there have to be reimagination of, you know, business models. And to me, the biggest reimagination of this is no more linkages to just advertising and the traditional subscription, etc. This ecosystem is made for commerce. We need to get and engage into that world from community to commerce is an integral part of leading this way. Which is why I feel that civilizations are not defined by the tools they use. They are defined by the stories they tell. Thank you. And artificial intelligence will be built everywhere, but the next storytelling civilization… can rise right here. By 2030, let it be said that India just did not scale AI. We narrated it. Thank you very much.

Thank you, sir, for your wonderful remarks. For our next keynote, we have Mr. Naveen Tiwari, founder and CEO in Mobi. We welcome you to the stage, sir.

So good to see everybody here. Thank you. Firstly, I must congratulate the event organizers, the AI Impact Center.

Deep work on working on fragmented data silos. As you all know that AI, it thrives on data. And today, most of the LLMs, what they have done is, they’ve definitely scraped internet and they’re doing really well. But the value of the work or what an answer an LLM would give is present based on what it can fetch from the actual data, which means in enterprises and organizations, there’s a wealth of information. There’s a wealth of information stuck in PDFs, stuck in documents, which people have a fear of not giving it to AI. So there is a fear, there’s lack of trust today, and that data… data, it stays where it is, like digitized. So, for example, there is there could be an entrepreneur, say in Nagpur, wanting to know about the scheme applied for the biotechnology plant that she wants to put up in Nagpur.

Now, if you see the MSME industry has a scheme for her, for women, for biotechnology. And, you know, it’s very good subsidy that that’s available. But where is it stuck? It’s stuck in a government notification which came out, which she’s not aware of. And what she is doing is she’s actually going to LLMS and asking that question and she’s not getting it. She’s also searching it on various places. She doesn’t get it. So that’s the divide, the information divide, which is existing. And the information which is there has which which is there stuck in documents or in even digitized form. That has to be AI ready. so that in a safe, trusted, and these two are very important, safe and trusted manner, the data can be linked, made useful and then made available.

Now, this is a long journey. This is a long journey. It’s not an easy journey because the data journey is about how you clean the data, you make it ready, you link it, you make it relevant, you make it useful and then present it in a manner so that the choice, and you want to have a choice of various elements of, you know, I mean, we live in the age of choice, right? We don’t want to be locked into anything particular. So that’s the data problem that we have in front of us. The opportunity is humongous because there is, I’ll give you an example, 3 ,000, I’m talking to an organization which does 3 ,000. Thank you. entities and the 3 ,000 entities actually manage 5 million new compliances in a year.

They have those kind of queries, 5 million queries on new compliances. Forget existing compliances because there are new compliances which get generated by the government, by various bodies and then they have to search. So the problem is humongous and it can be bridged. It can be bridged but we have to think about how to make data interoperable useful and AI ready. So with that background, I’d like to get into our panel and talk to some of the experts that we have today. My first question is to Rohitji who is from Mosby. So India generates a vast amount of statistical and administrative data. Mosby actually for all of you, it calculates a GDP for India. they have the source of all the data at village and taluka level so the data is there but as you think about making data AI ready what do you think is the responsibility of institution how and yours is an institution to make the data trusted safe and available to all

thank you Salini ji good morning everyone so trusted safe and ready for everyone AI ready my I like all of you to take a step back on this and just let us understand do you have uniform definition of what is AI readiness at this point in time do we have and I’ll not say that it’s not there in the ecosystem it’s there in the ecosystem that but do we have an agreement about it so there are two issues we need to understand when we talk about AI readiness of data. One is that, so let me just go back to today’s conversation I had with one of my colleagues over WhatsApp group, you know, we all are very active there.

So one of my papers has just been accepted in one of the largest conference and it’s about AI readiness of data and he asked me what’s so great about it. So I asked why, what is not so great about it? So he asked me, I put Bangla into ChatGPT and it completely understands. So what’s new you are doing? So the point I’m trying to make is people are not aware what it takes to make data AI ready. We all understand and then he asked me that no, but it’s not understanding and he talked about some of the dialect of this country and we have a huge number of dialects and Salneji, I asked him and he asked me that how do I train ChatGPT on this dialect?

I said, it’s not my job, it’s Sam Holtzman’s job. So the issue here. is that we don’t know. And that is the biggest responsibility of our institutions like MOSB to make people aware what AI readiness is all about. And then AI readiness means if I start, you know, talking about there should be a context file, there should be semanticity, there should be metadata, but many of us sorry about that, many of us it looks it would not make sense. So the first idea is to create a framework agreed framework, say people not only me, it’s not about my way or highway, me all of us work together create that framework, put it up for people to know.

I would do, the first thing I would do and I plan to do it literally is try to create a slide deck saying what AI can see and what human can see. So my folder, if it has 10 versions of budget 1, 2, 3, 4, 5, 6 and if I ask a question from that folder budget some answer will come from budget one some answer will come from budget two because unlike human where i am focused on this question ai is designed to take scan the entire thing available so it’s a big difference between human and ai i can be focused ai when once i give a thing to ai it will just scan everything it has in its domain so i would say starting point and just you know not taking much of a time uh starting point should be that let us create this framework let us have a shared understanding let us have a core ai readiness part and an aspirational ai readiness part and work on

yeah i think that’s very relevant because you cannot leapfrog into everything you have to be like i mean you can have aspirational but the foundation is very very important and and everybody joining that foundation that that that foundation exercise is is really important um i’ll go to you preb uh and talk about let’s talk about data Data Commons aims to make public data more accessible and usable. You’re from Google, and you have put all this in open source. You’ve been working on US Census data being available. Tell us some more about your experiments and how Data Commons is kind of ready or prepared to work on this challenge.

Thank you for having me here on this panel today. I think one of the areas I’ll start with is the importance of coming to that understanding on AI -ready data, but understanding that the field itself is moving quite quickly at the same time. So whatever agreements we come to today in six months, it feels like we’re dealing with a brand new technological landscape that we’re staring down. What Data Commons tried to do was say that if we can get… If we can get our data in that machine -readable format, which means… structured, which means machine -readable metadata also, and a format where that format specification is not stuck behind a 500 -page PDF, right? Can we make that in a way that the machine can understand it, interpret it, and then use it?

Our theory behind this is that idea of a knowledge graph from that data combined with the large language model gives you a much better chance of success to answer your question. So at Data Commons, what we try to do is we try to bring multiple data sets globally together in a common knowledge graph and then put an AI search engine on top of it so that you can quickly access that data. You can play with this yourself at datacommons .org. But what we did is we open -sourced the entire stack because this idea that that data is centralized with one source is also the dangerous part, and it shouldn’t be, right? The data should be federated.

It should be located at every organization and governed locally by the organizations that are… using it. And so one of the things we’ve done by open sourcing that stack is allowed, for example, the United Nations, the United Nations Statistical Department to use data commons as their back end. And so, you know, UNSDGs, WHO data, ILO data, so on and so forth, is all stored in this common interoperable database now, where instead of a data analyst spending 80 % of their time renaming column headers, they can actually focus on the data analysis so that we can get the impact and the outcomes we want to see. Hope that helped answer the question.

Yes, yes, no, absolutely. I’ll poke you a little bit more to understand on data commons, what’s a vision you have?

So a very simple vision, right, which is make data aware decision making the easy answer to take. Today, right now, the majority of the world is flying blind, whether you’re one of those 74 million MSMEs in India. you can’t afford a bevy of computer scientists and data scientists that you can hire you pay a tax to play with any data if you’re a policymaker thinking about climate change poverty education health these are holistic problems it’s no longer i can go to one ministry pull one spreadsheet and solve poverty i need to endemically understand how does education how does health outcomes how does income and economy how do all of these affect poverty locally right and that’s the problem we have today that the world is a multi -dimensional problem the other problem is our brains are not inherently multi -dimensional our brains are great in three dimensions you add a fourth dimension which is time and we’re okay right like look at climate change you add time and it’s greater than our lifetime we can’t think about it which is why we’re not solving it right but the majority of problems are 50 60 dimensional problems machines are really good at this but by the way.

And humans are good at using tools that are good at doing things we’re not. And this is where we have to approach AI as a tool we can use. Not as the answer, but as a tool we can use to derive the answer, to supplement our brains in the areas we’re

I’ll poke you a little bit more, but later on.

Saniji, I just want to take a second stab on that. And just a quick interjection on that. I’m a statistician. So I’ll be very happy if some of my work can be done by AI, you know, all those lab language models. I just read a paper today in the morning. It’s been written by two undergraduates from a Canadian university. And they said, and they proved it, that if you give same prompt to AI with the same data set, it gives you two types of analysis. So this is something I just wanted to flag. That we should not be really gung -ho about things, which is still untested. But yes, I would be the first to accept adopt an AI and use it for my work, but it needs to be, as you rightly put it, trustworthy.

Yeah, I just comment on this, the stability of an answer, that’s what you’re talking about. We are actually working to create a benchmark onto this because the same thing we are doing, like Amul AI was launched today in the morning, I mean, by the Prime Minister, and the same thing applies to Bharat Vistar, and we are actually working to see that the same question if you ask, multiple times across LLMs, and also to one LLM many times by different farmers, both options, you get different answers. And that, can we make it as a benchmark? That’s what we are working at also because this is a benchmark which is needed really on the ground, right? So that’s a part, so I wanted to comment.

I’ll go to Mr. Ashish. You’re from the industry, and you work with IIIT, . Bangalore. Tell us more about the research in the data area, plus how institutions can help build it all together.

Right. So I think my perspective is more as a practitioner because the last almost three decades I’ve been a solution builder. So I have seen data not from the data side, but from the solution side, trying to exploit it, trying to use it for the solutions. And I’ll come to the institution part of it. But, you know, when I look at the data and the challenges which are there associated with it, then for the last 10, 12 years, I’ve been for AI, for social problems or digital, like women in child health. I worked for almost a decade. Now, one of the problems that I realized is that the world is fast moving where you don’t manage a transaction.

You manage a journey. OK, and that is the agentic AI and all those things that we are talking about. Now, when I was working a few years back on the women and child data. I realize how fragmented it is the two main data sets if you look at a child’s health his anthropometric data, his nutrition data is with women and child development through their Anganwadi program if you look at the birth data, the humanization data and a lot of other data it is with the health and family welfare department and if you have to have an integrated decision making across for that child what needs to be done and then you have to look at both the data but that burden of orchestration comes on to the person who is solution making the data does not by itself flows through the workflow and that is one of my biggest problem that we have to solve that we look at data sets in isolation but we don’t look at how it flows through the process the second thing which I said the contextualization we all have read the book at least some of them that raw data is an oxymoron data always resides in a particular context and with some standardization associated with it so that you can make some sense out of it.

Now with education, when we are working recently, we realized that LLMs are becoming increasingly good, at least with the main languages, not with all the dialects, but in good translation. The moment they hit any domain -specific vocabulary, that’s when they start failing. Even the class 6th physics question, all these frontier models, is not able to properly translate. So we came up with a solution of using a glossary combined with the LLM so that it does a decent job in terms of overall translation. The user is transparent to contextualization. And the third thing which I faced a lot is that when we talk of public data, a lot of it is declared data and not verified.

Not verifiable data. Especially when a lot of planning depends on surveys. and lot of survey data is actually declared data whether you have a hypertension or not yes, no, whether you have this problem yes, no, what is the verification no doctor has actually verified that and you are going to make a decision based on that so in my opinion the AI ready data has to solve these three big problems it has to be interoperable it has to be contextual and it should actually the third problem that I was saying that you know verifiable, it should be verifiable and governable as an extension of that

very relevant I think you have posed the right challenge so Prem I am going to come to you right what is how let’s just pick one of them which is contextualization because I am increasingly seeing that domain information is needed and people are creating these glossaries to add like even in Agri when we had to roll out like we are going to do like we are going to do Mahavista, we actually created glossary of 5000 terms which is it is in Marathi so it has to be in Marathi and those terms being used and I know we did some experiments and we have created a sandbox environment you have done it for India so why don’t you explain that how contextualization and domain can be added to Google Data Commons and how it can be helpful

I think this idea of contextualization and localization is very important at the end of the day these are large language models, language being the key word there they’re not data models and so to what Mr. Bhardwaj said earlier what you want to be able to do is use them to write code to manipulate data because code is language but you don’t necessarily want them to be producing data on their own and one of those problems that you have today is also those large language models are essentially created largely off the web which has its own biases inherent in it, both language and locality -wise. And then on top of that, the example you used on the full folder of all the budgets, right?

The example I like to use for this is actually if you ask a large language model about a celebrity that recently had a breakup, they’ll tell you they’re together because it doesn’t know what just happened over the last month, right? It’s very sad. And so this is where you can use, though, the combination of, you know, you called it a glossary, I always call it a knowledge graph. What is that factual basis of information that I can put together? Now, it’s always going to be a subset of the whole, right? I might be able to cover maybe 0 .1 % of the world’s information with a knowledge graph. But if I can ground it in those facts, can I then utilize the intelligence of the large model to then help me produce some knowledge from those facts or fill in the gaps in those facts?

And so this, I think, is an opportunity that we actually have in the technology to move it forward. This is one of the areas that we’re actively working on as a team. But again, to do that, you first need that glossary of facts, right? This is where having that knowledge graph of statistical data, even if imperfect at this moment, because it is survey collected. It is dependent on the quality of the question asked, the error bar shown, the quality of that metadata, so on and so forth. But it is a starting point from which you can get more information and use that intelligence to potentially even find those outliers or areas that don’t match what you might be hearing on the ground.

So that’s the opportunity I think that we have.

Because I absolutely agree with you, but I will say it in more direct terms. Because sometimes we feel that LLMs or in previous version, the AI models are the solution. They are not the solution. They are only one of the inputs to the solution. And they comprise 10%, 15 % of what you’re trying to do. It is what is the rest of 85%. is doing yes llm will give different answer how are you compensating with guardrails human in the loop risk assessment these are the tools which are available today so i if you have to build because at the end of it it’s a probabilistic model okay come what may and i was talking to a mathematician from mit and he explained why it will never become perfect why it is it is grounded that fact is grounded in mathematics that it is it cannot ever become as perfect that every time consistent that we are wanting it to be ever because then you are taking the main source of its creativity away from it so what you have to focus is outside not inside that that’s all i ever wanted to say

if i agree with you completely and i started by saying it’s a tool right and we use tools to supplant ourselves not to replace ourselves right to supplement our knowledge not to replace our knowledge so i do agree with you it’s a tool but we have to be careful throwing the baby out with the baby and we have to be careful with the baby and we have to be careful with the baby and we have to be careful with the baby and we have to be careful with the bathwater here in the sense that That tool now makes things available to the average person. It upskills the average person in a way that they couldn’t themselves before.

So if we immediately go to put guardrails, prevent access, things like that, we’re preventing a large part of society. And I’ll say as somebody who worked on Google Search for many years, there were many arguments in Google Search that we, for example, shouldn’t put health information on search. Because the average person isn’t smart enough to be able to deduce information about their own health from Google. But the average person can’t afford a doctor also, right? There are endemic problems in society that prevent you from doing that. So does the answer to that question suffer, or does the answer to that question do less harm and give people a pathway that they can learn from? And so that’s an important question to ask ourselves here as we think about AI, which is, yes, it is imperfect at this moment.

Can we understand? Can we educate? Can we work inside the system that exists? we can’t ignore it either. We can’t say it made one mistake, therefore I will not use it. And I will also call out the imperfection of us as humans is also very much there, right? So there are many times we look at these systems and, you know, we look at, you know, a way more autonomous vehicle and we said, look, it had six accidents last year. The 30 ,000 deaths from car accidents in the U .S. a year, right? And so statistically speaking, this is still much safer, right? And so these are the sorts of examples that we have to look at, understand where to apply it, how to apply it, and what the overall societal good is from using it.

Yeah. No, thanks. I think a very relevant discussion that we are having, and there’s always a fight between should we have RAG architecture or should we just, you know, teach, give all to LLM to do it because it has more capacity and more, you know, GPU. But either or is not possible. There’s like, it’s like so much about the world. It’s like, you know, it’s like, you know, it’s like, you know, it’s like, you don’t want to give you maybe want to keep the data and the sovereignty comes in. it a lot. And this has been a discussion in the last two days. Most of the panels that I have been that you want to keep your data.

Countries want to keep the data with themselves and they actually don’t want to train because choice of LLMs is like you want a lot of choice and you want to use here, there, everywhere. So I’ll come back to you Rohitji and see we talked about administrative data and you talked about a framework. So my question is that how do you think alternate data, secondary data beyond administrative data, how can that be also brought in and your framework which you talked about that there should be a foundational framework if that framework is adopted by industry. One, is it possible? And two, what kind of data economy it can start?

So this is early morning. Let me take an audience poll on it. How many of you think that what Salini asked is a governance issue? Or is it a, I mean, just raise your hand if you feel it’s a governance issue. Anyone who feels it’s a governance issue? How many of you feel it’s a technological issue? What she asked. How to make alternative data ready for AI. That’s what the question was. So how many of you feel it’s a technology? There’s no prizes for it. There’s no punishment for it. So feel free to raise your hand the way you think. It’s a technology. So, okay. So I am with that gentleman. I feel it’s a governance issue.

And I’ll also work on it. So what are we talking about? We are talking about data generated from different sources, be it alternative data sources, be it like administrative data sources. The panelist with my co -panelist just talked about getting data from different sources not aligned to each other. So it’s a governance issue which we need to understand first. We need to create. And, of course, I completely agree with Salini when she said that we need a federated model. Perhaps Prem said that. We need a federated model. There cannot be one whole sole owner for a data. of this country or for that matter for any country what as as somebody needs to play the role of data steward somebody needs to orchestrate this data ecosystem and that perhaps being from nso i have my own biases i’ll say nso can do it but of course that’s something for the people to decide now let’s understand this what do we need when we need ai ready data we need first a cataloging of it i’m just going to take one minute on it cataloging of it you should have everything catalog any industry any government organization this is my data set these are the indicators these are the definitions and so on and so forth i’m not getting that deep into it you need a catalog of your data and if that’s not there second thing is that catalog should not be pdf that catalog should be as she was saying machine readable json file probably you need a catalog of your data and if that’s not there you need a catalog of your data and many other ways are there but let’s talk about you JSON file.

Second point, you should have metadata for it. If you don’t have metadata for it, I mean other day I was with another panel with Prem, I said the thing which irritates me the most is lack of metadata. I don’t know. I’ve been driving in blind. I don’t know what the word frequency means. It may mean hundreds of things. So you should have metadata and again not in PDF. So when I’m, whatever I’m talking about is, I’m not, I mean JSON or XML, there are so many ways, but machine readable. Let’s put it that way. Third is, you should have a context file. So now machine has read it. Now but it wants to know that where do I find the meaning of frequency?

So machine should have a context file where the source is written. You go there and see. You will find the meaning of frequency. So metadata will not have frequency, meaning of frequency. It will only write frequency means quarterly. So machine now needs to understand what does that frequency means. So that’s what she was talking about and Tim again was talking about. We need to have a, that makes us, bring us to the, we need to have a business glossary. We need to have a business glossary. He also talked about a knowledge graph. I mean, just a sophisticated version of business glossary. That we need to have. So once we have sorted this out, we need to work, what type of codes are we working?

So the gentleman just beside me just talked about two data sources using different codes for different thing. I mean, same thing. So then we have to standardize that codes. And then lastly, we have to structure our data. Data needs to go in a structured database. It should be defined and that’s not new I’m talking about. It should be defined by dimensions. It should be defined by attributes. It should be defined by its role. So time means temporal. You can’t write time and expect LLM to understand what does time mean. You have to say time means temporal. And once you have these ready, these available in a, so there are two use cases. And just last, last quick of the One is that am I using it for my own use case?

Am I training my own model for it? Then I can put all these in one file and feed it to my model. But if I’m expected to create a MCP for my database, then I have to create separate files, put it up in a URI or URL where any model can go, the connector can direct it to that model, that place, that resource place, and then the things happen. And this is all I’m talking from my personal experience when we, and Salneji knows about it, when we developed our own MCP server

loving it the amount of reach out which has happened to use the data data sets for you know you can actually find out you can ask a question of what’s been the price of how has the price of moong dal been in the last whole year or whole quarters or month wise so that that capability is there now and it has happened because it was always there they do the calculation of the wholesale price index the commodity price index so that in from the data was there it’s just that now it is ai ready for people to consume take and and then ask and it is connected to claude and chat jpt ashish i’ll go to i’ll go to you uh for because building on what uh roheji stopped at which is the use cases and you come from the solution part of it uh how do you visualize and imagine solutions and use cases and how do you visualize and imagine solutions and use cases you combining say administrative data and alternate data I’m not going into personal data because there’s a lot of consent there but at least a lot of secondary sources of data which is available and how do we combine and make it more powerful

I think as you rightly pointed out I come from the solution perspective and a solution now with agentic AI coming in we look at every solution in form of a journey. We are going past the mechanism of point solution that you ask it reverts back to the answer and now the use case has to decide at which part of the journey what data is that you need and that will dictate whether it is additional data sets which are outside or it is a public data set it will be due. The only challenge which I see here is the who is accountable for that data Thank you. Thank you. in the solution at the API level, at the policy engine level, which are actually going along with the solution, and it should happen, it should be enforceable automatically.

If you are thinking that a human being will actually enforce that policy, it will break. It will break in no time. So that is what we are trying to do, is to create those reusable artifacts as DPIs or DPGs, it will fall into one of those categories. But where it allows those policies to be set for a data set in an easy reusable way so that everybody doesn’t have to recreate from scratch those kind of policies, and then that’s the way to move forward.

You mentioned your lab. I’m sorry, I just spoke you into that. Tell us more about your lab. What more work they are doing?

So that’s my current job. Previously I was heading a Gen AI company, by the way, and I will talk separately later on PDF challenge, which we thought we had solved it. We didn’t fully, but we were on the way. But the current lab, which is very exciting, it’s a collaboration between Microsoft and IIIT Bangalore. A4I stands for AI Innovation for Inclusion Initiative. That means we create large scale. The idea is not here to run pilots that we do this small thing here, we diagnose, not that. It should be population scale and we want to launch it as a DPG so that it can be largely. So we are working on education, school education area.

We are working with teachers in terms of making their life easy. We are working in terms of accessibility. How blind children can actually be taught STEMs so that they can actually become a mathematician. They can hope to become a physicist, mathematician. Today it’s very difficult. How to even read a book? And the third one we are doing is working with the last mile health workers. Our current solution is a rack based AI combination, but we are looking at exactly that problem that you mentioned that either it is this or that. I think there are plenty of answers which are in between. The. That was what we are exploring.

Thank you. Thank you so much. Prem, I’ll again build on the concept that we were discussing on the use cases, which can be. I mean, I just want you to paint a picture of if you have data in knowledge graphs, like what you mentioned, if the data is there and data commons is present. I just want you to visualize that what more use cases can be possible with secondary data. How can India benefit and not just India, Global South benefit from this? And please feel free to paint the use cases which you have built in the sandbox environment that you have. You can just take those examples.

Yeah, I’ll give two very. These might not be exactly where the sandbox is today, but where it could go tomorrow. Right. And so I’ll give two very different examples here. One is. At the end of the day, the Ministry of Statistics does a lovely job collecting as much information as they can. The whole ministry does. The government does. it’s a top -down data collection.

I’m sorry, I’ll just interrupt you. I think Rohitji will say it’s not top -down. It’s actually at the field level, it’s bottom -up.

That’s fair, that’s fair.

He will say that, it’s bottom -up.

That’s fair, that’s fair. You’re correct, it’s bottom -up. That said, we have alternate data sources also that are there. Sometimes they supplement and they further show, yes, the data collected is correct. At times they disagree. And those disagreements are also interesting to understand to the point of where is the survey question flawed or where is the civil society seeing something or has visibility into something that we don’t have access to. And so the more of these data sets that come together, these points of friction, again, this is where the human intelligence comes in. Show me the points of friction. I have a haystack full of needles. Which needles do I pay attention to? Right? So this is one example if I’m at the government or the statistics, you know, ministry of statistics level.

Now let’s go to the completely opposite end. I’m a small business owner. I’m setting up a physical shop. Where should I set it up? Right? Where I set it up depends on mobility traffic, depends on the demographics and affordability in that space, depends on all types of things. Right? It’s a large data question. But that MSME owner is often ill -equipped to answer any of those questions, is often taking a shot in the dark. And that shot in the dark is a costly shot in the dark if they’re wrong. Right? Because they are taking the full risk of that decision. Now with the data commons that we’re building, the question becomes can we reduce that risk for that individual?

Can we help them model, understand, de -risk the decision they’re making? And that’s what we’re doing. And that’s what we’re doing. based on the audience they want, based on the footfalls they want, based on the location that they’re choosing. That’s a very specific example now. But these are two very opposite examples of how bringing all of this data together, which we often think about as more aligned towards, you know, the international organizations or the government minister, but is actually usable on the ground by an individual too.

Tell us a bit more about, like if suppose someone wants to put up a Data Commons instance, how can they get started?

It’s actually quite simple. It’s easy enough that I can do it myself, which means you can. But it’s datacommons .org is an open source platform. We have a 20 -minute guide to get started. You can set the whole thing up on your computer, have your CSV data set, bring it in. And the thing is, once you bring one data set in, it overlays with all the data sets already in Data Commons. This creates sort of a network effect between the two. To the data, right? So once I bring in, you know, if I am a chain store in India trying to figure out that next store location, if I bring in all my per store sales revenue data once, then suddenly I can compare that to the 50 ,000 data sets and overlay them that are already in data comments.

Before, if I wanted to do this as a chain store in India, I would normally have my people come up with maybe 10, 12 different hypotheses. Because then I have to get those 10, 12 different data sets and I have to form 13 different data transforms, right? So they’re all in the same format. That prevents us from being able to have that level of creativity we want where we can look across the entire landscape of the problem set. And so this is sort of one of the things.

Right answer. And it was a matter of trust for NSO also. That, you know, people are getting different answers for the data which is created by NSO. That makes sense. It makes us look toward MCP server. A, it is open. so it makes our data interoperable for all the almost all the AI system. I am not saying all the AI system. Otherwise what would happen? Be aware that every LLMs have their own standards of API. So you create those APIs first and then you somehow manage the LLM to approach that API. With this connector, it’s like C socket for the phone charger, if I may use the parallel, where you can just plug in any C socket you can use for anything.

That’s what the MCP is. So data comes and the LLM comes and plugs into MCP. And it allows any LLM to come. But what you have to do now, that you have to connect that small tool with that LLM. So that’s a one minute job and it’s available on our website. You go there, www .mosfet .gov .in and the offering section, everything is available. You can do it in one minute. One minute, maybe two minutes at the most. Anyone. But still there is one challenge, which I must tell you, is that somehow need to ensure that this becomes a default tool. The user does not have to add it. Somebody says somebody forgets it. Then the same situation starts happening again.

So right now people have to add it to their tool. But the biggest advantage I see is that people don’t have to come out of their workflow. So if I have taken a very costly pro cloud, then I don’t have to come out of it. Go to my portal to get the data analysis. I can keep using the intelligence of cloud or chat GPT. I don’t have a preference for that. With the verified data, as he talked about, verified data of Mospy. And the use cases are innumerous on the web now. I mean people have just lapped it up. My favorite is that there is a Tamil song which talks about a lot of grains.

So one of the messages I got, and I’ll share the link also. It’s on Twitter also. I mean X now. That somebody created a CPI for all the grains which was talked about in that CPI is consumer price index which basically talks about inflation uh which talks about all the grains and they just took the grain out of the uh song that you know now wheat and and created CPI index for it and they have named it like p index or something which is like songs name so I’m not very conversant pardon me for that in Tamil but I’ll share that link so that’s my favorite use case so people so what I mean to say that people can use the data the way they like it that’s the that’s the bottom line and that’s that’s what the NSO’s idea

is most interesting use case I would have I would have seen and I really want to see uh it and and say yeah yeah I’ll have a look at it so one more thing which I want to tell uh the audiences that uh the work uh see several like the use case uh Rohitji mentioned about that someone can just pick the data uh so we have created a data and we have created a data and we have created a data and we have created a data and we have created a data and we have created a data and we have created a data and we have created a data and we have created a data and we have created a concept called as data boarding pass concept called as data boarding pass concept called as data boarding pass so this is a data boarding pass so this is a data boarding pass This is like for AI ready India.

This is a physical copy, but actually the concept is that once your data is ready and it is it has a set of checklists. Which it passes, then as a B2B player, you could be a policymaker, you could be a researcher, you could be a market player wanting to build on top of it. You can take this, you know, this concept of data boarding pass and get onboarded onto the date or for the data usage so that you can pick the data and then start using it in your applications. So data boarding pass is, say, at a district level, you have and I’m just painting a scenario. You have a data commons where graph knowledge graph and data have been all combined together, created all together, right context and everything.

And some organization. Now wants to know, say, the automobile. MSME manufacturer wants to access it and give information to dealers as to where scooters are being sold, where motorcycles are being sold and what’s been the income of of that region over a period of time that that can be possible now. Right. So the data boarding pass enables it, makes it possible. And if you want to physically see it, how this exactly works, visit our booth at a step foundation on Hall three in on the first floor. Do visit that. And my team would be there to show you the actual generation of the data I think we have given a lot of things. I want to just, you know, we have less time, but I want to take a couple of questions from the audience.

So feel free to ask. We have four minutes so we can have like two, three questions from the audience. audience. I saw that first, sorry, and then I saw you. So next to you. Yeah, please go ahead. Can someone give him a mic, please? Otherwise, I’ll hand mine.

Thank you very much. I wanted to ask you about the business models of these platforms because it is obviously extremely important to have high -quality data, but high -quality data is also expensive to collect, to maintain in the time. So did you work, besides, on how you can maintain these kind of platforms during the time? Does it have to be, I don’t know, publicly paid or whatever models you may have? And it’s also for everybody, I think.

Go ahead, then I’ll also add. Then I’ll also add.

So, Jasmo, I just have a quick clarification on that. And National Statistics Office India is fully funded by the Government of India. It’s a… I mean, as we all know, National Statistics Office India is fully funded by the Government statistics office over all over are like public funded through public money. So it’s our job to create data and make it available to the public. At the same time, just one quick disclaimer on that, that open data is not free data. So somebody has paid for it. So when depending on the use, we provide the data. So if the use is research and things like those, I’m not getting into details of it, then it’s free.

But if the resource, you know, the use is commercial, then, of course, there is a system. There is a policy for it. And people have to pay accordingly.

Yeah. So I’ll also answer it because we have done a good amount of work. I would encourage you to see a paper that I’ve put up on our People Plus AI website, which talks about the give data, give model for data. G is guaranteed trust. And we talked about it. I is incentive. Incentive. Why should I bring the data? What will I get it get from it? The V is the value. If the data has no value, nobody is interested. And E is exchangeability. right which is can i share the data so i’ll focus on the i the incentive there has to be an incentive for someone to bring the data and there has to be an incentive for someone to use the data and that value will be monetized that is the data economy if you ask me this data economy is actually running without a formal mechanism there’s good amount of money people in selling data buying data lead generation i mean there’s huge amount of things which are happening this formalizes that so they will be but what will be the price that the economy the data economy i mean that has to stabilize that has to happen at region level with private sectors so we have been working in that direction so that the incentive model is clear but the actual price is is a discovery mechanism

and it’s very uh very interesting to hear all this that’s amazing one of the very key scenario that we see every day and we get little bit trouble is we see a road making getting made and stuck after few days I mean yeah it might not feel good but that’s how it is because it somewhere it feels like a disconnection in the data or somewhere decision in the policy making so do we have some way to kind of get this kind of pieces applied in those like know whatever the tender ecosystem or whatever that like you know you have a road made and then a duck for a pipeline after a very short window

yeah maybe I’ll answer it see if you see India has put the whole digital public infrastructure in place these are the DPI thinking whether UPI Aadhaar DigiLocker DigiYatra they were about digital rails which were put together this data infrastructure that we talked about today is going to be that rails is it going to be dug up maybe maybe maybe no problem Promises, right? Is it going to be dug up? Are there going to be holes in that? Maybe. But I think it’s a journey that if we don’t do it and don’t start it now, it’s going to hit us later on. So no promises, but yes. Rohit, do you have to add anything on that?

I just wanted to add that we need to keep working on these data sharing platforms and all the philosophies we just talked about, like accessibility, sharing, analysis, use of AI, and things will improve slowly but steadily, I’m very sure about it.

Time is up, and the next session is going to start. So thank you so much for listening in to the AI -ready data, and please visit the booth to see it actually in action. Thank you. Bye. Bye. Bye. Bye. Thank you. Thank you. you you you you Thank you. Thank you. Thank you.

Hi and welcome a very good afternoon to all of you thank you for staying on I know it’s the last day of a long productive summit and I think maybe not the last but the second last session maybe the third last session so thank you for being here I’m excited about this discussion that we’re going to have over the course of the next about half an hour or so, 35 minutes I’ll quickly introduce myself and then I’ll get our panelists to introduce themselves. We’re talking of course about the team of the next generation of tech entrepreneurs, tech founders, tech leaders in the world I’m Anirudh Suri, I run a venture capital fund called India Internet Fund and I’m Anirudh Suri And I’m also an author of a book called The Great Tech Game and a podcast by the same name that looks at the intersection of technology and geopolitics.

So I might bring in a little bit of geopolitics into our conversation, even though we’re talking mostly about tech founders. I’ll start with my left, Malhar. Today, I earlier had the opportunity to interview a very young panelist, a young speaker, Rahul. Who’s, I think, one of the youngest speakers at the summit. He’s 16. We did a podcast with him earlier in the afternoon. And so I’m especially delighted to have another young entrepreneur, college dropout. On my left, Malhar, if you can briefly introduce yourself.

Yeah, thank you. Hi, I’m Malhar. I’m the co -founder and chief technology officer of Origin Bio. We’re a Y Combinator startup that is using AI to make safer genetic medicines for diseases. Like cancer.

Thanks Malhar. Navrina

Absolutely not a college dropout not very young but I’m the founder and CEO of Credo AI we are an AI governance and trust management platform. For the past five years I’ve also been advising the White House on AI policy and work very closely with governments across the globe to really think about what AI guardrails should look like so to your point I think there is a very strong intersection of technology and policy happening right now excited to be here. Thank you

Arvind, last but not least.

Thank you everyone my name is Arvind I’m the founder and CEO of Glean. Glean is an enterprise AI company about seven years old and think of us like Google or ChatGPD but inside your company. Glean is a place where you can go and ask any questions or give it some tasks and it uses all of the world’s knowledge just like how ChatGPD does it but also uses all of the internal company’s data and knowledge to help people with their questions or their tasks.

Incredible. I think we have a great set of panelists across various sectors and I think various angles of the AI entrepreneurship market. We’re going to have a focus on how entrepreneurship is evolving, right, in this session. I’m sure in other sessions in this summit you’ve heard a lot about deep tech entrepreneurs. You’ve heard about probably all sorts of AI entrepreneurs. Of course, you’ve heard some of the largest AI companies take the stage, etc., including our very own Sarvam out of India. But now I want to focus on how entrepreneurship is evolving. Before I ask my first question to the panelists, can I have a quick show of hands? How many of you here are entrepreneurs?

Big chunk. Wannabe entrepreneurs? Ex -entrepreneurs? People who decided to… too much and went into the corporate world, let’s say? A few. Good. So I think the biggest number is still entrepreneurs and want to be entrepreneurs. I think for some of us who’ve seen the previous waves of technology, innovation, like for example, most recently the consumer internet wave and of course there have been multiple waves prior to that. And for those of you who are not familiar with the history of technological waves, I really encourage all of you to study the previous waves because often you get to learn a lot from how the earlier waves of technological innovation panned out, what kind of entrepreneurs, what kind of companies succeeded.

But a little bit with that historical context in mind, Arvind, I want to come to you first. Compared to the wave of the consumer internet where we saw forms like of course the Googles and the Facebooks of the world, the social media platforms, but then also the cab hailing platforms and a lot of marketplaces and consumer focused platforms emerged. We’ve seen a lot of and at least in India I know this is the period over the last 10 -15 years where entrepreneurship has become a buzzword, a desirable profession, you can drop out of college and your parents will still be happy about it right, compared to that wave how is today’s wave of AI driven entrepreneurship looking to you, could you draw out for the audience and for us how these two waves might be similar for entrepreneurs and how they might be different?

yeah, so well first, you know, I think whenever there’s a new technology wave, it creates a lot of opportunities for new companies to get started that’s the right time for somebody to jump into the entrepreneurial journey and, you know, we’ve been through many of these in the past two or three decades, you know, starting with like you said consumer internet to mobile to social and of course now AI, and each one of these opportunities are similar in some ways like, you know, to start a company to be successful at it you have to have, like, first of all, the ambition to make, you know, to make one, to make a company. You have to have that sort of, you know, real deep desire to, you know, to be able to take the risk and have the courage to actually go start something.

You have to follow the recipe, the right recipes, which is finding, you know, the right business problem to solve, like something that actually creates a lot of business value in there. And it’s not so much about the technology trend. It’s, you know, you can use the technology trend as a way to see if you can solve that business problem better, but it always starts with the right business problem. And then, of course, like, you know, do the rest of, you know, the entrepreneurship journey, which is about, you know, building a great team, having a clear vision, working hard, and making things happen, right? So those are, like, you know, those things don’t change, like, you know, through these ways.

But I think one thing that is actually very unique about… the new AI wave is that it’s not only, I think we always had a blueprint in terms of what an organization needs to look like. When we went from consumer internet to mobile as two big technology trends, the shape of your company, how you build it, what kind of people you need to hire, those things were not actually changing that much. The blueprint was clear that you’re going to hire some engineers, you’re going to have product managers, some sales people. But now with AI, everything changes. In fact, the role of human itself is unclear in what roles seem to exist. In some sense, there’s some more challenge for the AI entrepreneurs, but also more opportunity to actually not know the basics.

You can actually start and chart a journey without actually knowing how to start a company. Because reinventing yourself, thinking AI first, can actually help you build an organization, which is very unconventional. And maybe that is what is going to create big success for you in the future.

Do we expect, Arvind, do we expect now startups to be even leaner given AI? Because I’ll give you a couple of examples. I have friends who are second -time, third -time entrepreneurs who successfully started, exited startups in the consumer internet wave. And now when they’re starting up in the AI era, they seem to have much lower number of team members to start with to get to that minimum viable product. They seem to have much fewer people doing the coding for them. Many, I would say, a significantly smaller requirement even for capital as a result, because of course in the early days sometimes employee costs or early employee costs are quite high. So do you expect this to continue, leaner startups?

Absolutely. In terms of the product and how far you can go with a very, very lean team, in fact a team of one person, is actually, it’s incredible. can actually build a lot with, you know, with that low cost. So certainly, like, you know, that is, that is a, I would say that, you know, but it’s not like, you know, ultimately when you build a company, you know, with scale, people actually are your asset. And at some point you’re going to start growing, but you can do a lot before. And I think one of the reasons why companies will be more lean now is because it’s always, you know, on an entrepreneur’s mind, you’re always thinking about, especially when you don’t have enough resources, enough funding, you’re always thinking about any piece of work that needs to happen.

You know, can the machine do it? Can AI do it? And so that sort of like that mindset of like, you know, like, hey, you know, I’m going to actually use AI to do most of my, most of the work that needs to get done in this company. It is actually, that is what is going to actually create significant efficiencies and a way to defeat like, you know, the incumbents.

Great. Let me move to you, Malhar. We had the opportunity to speak a little bit prior to the session. Thank you very much. And you’ve, of course, started very recently. So you started technically your first startup, I’m assuming, first startup in the AI age. And so talk to us about how you are both viewing entrepreneurship today. Is it any different than maybe, I’m sure you might have read some books or met entrepreneurs who started earlier, venture capitalists who started companies earlier in the earlier waves. What’s your sense on the question I just asked Arvind as well? And secondly, how are you being AI first in the company that you’ve started? How are you leveraging AI, not just in the product itself, but even in the sort of organization, so to say?

Yeah, I think one thing is that because of how good AI has gotten, knowledge has gotten a lot more democratized. And so there’s less of an excuse to. actually be able to work in different fields in this sort of cross -disciplinary nature. For context, my co -founder, Yash and I, we’ve never studied biology. Our team is five people. One of the people from our team has graduated. Only one person has studied biology and they’re not the same people, the same person either. So I think AI has actually allowed us to study a lot more, read a lot more papers, reach out to more scientists, learn from them, reach out to more customers, understand what exactly they want.

And we sort of use AI throughout. We do fundamental research. We train our own models from scratch. We do research in the wet lab. We’re starting a lot of wet lab work where we use AI to actually be able to predict the results of these wet lab experiments so that we can be cost efficient and ensure that we can work with a very limited budget. I think in some sense what hasn’t changed from when we grew up and we were watching movies like The Social Network is even then it felt like the people who succeeded the most were people who didn’t want permission. The example being Mark Zuckerberg. Even if you look at Jeff Bezos starting Amazon, it wasn’t like Barnes and Nobles that started a website to sell books.

I think with AI that sentiment hasn’t changed, but it’s probably easier to materialize and we’ve definitely gotten the benefit of that.

Are you finding that research is more and more critical to your work compared to maybe earlier waves of startups?

Yeah, I think it’s definitely critical. We’re working on using these AI models to actually design novel DNA sequences. They act as switches. This involves training the models from scratch, working with… public data, starting and warranting experiments to actually get our own proprietary private data. So the entire thing actually hedges on the product or our research producing an output that is biologically and scientifically viable. Even when we want to sell to biotech companies and pharma companies, or if we ever want to pursue our own therapeutic program, there are very rigorous requirements for the thing to actually work. An example, of course, being the FDA and needing clearance throughout, but even actually starting the clinical trial process is a lot of work and actually has a requirement of things actually working.

I’m going to keep coming back to this theme of research for a reason, but before I do that, Navreena, I want to bring you in. We, of course, started off the conversation saying about talking about the intersection of AI with, I mentioned geopolitics, you mentioned policy, you said you’re working closely with folks in D .C., in the White House, and otherwise. This intersection of AI and policy. while for policy wonks like some of us I also work at a think tank as a non -resident but other than for us policy wonks who are looking at that intersection of AI and policy talk to us about why for let’s say a Malhar of an Arvind who are not necessarily spending that much time in DC and with folks in the White House and the policy crowd why is understanding or maybe dealing with this intersection of AI and policy and geopolitics why is it important?

Is it? And if it is, why?

Absolutely and just by way of background I’m an engineer by training, spent 20 years building AI products in research and development at companies like Qualcomm, Microsoft so it is I do want to ground it in why I think as a technology policy is becoming really critical is going back to something that Malhar said which is really interesting right? It’s when we think about the new AI wave, to actually go from zero to one right now is very, very easy. I think what really becomes really interesting is do you actually get that product to be extremely reliable? It’s robust. Can you explain those systems? There’s a combination of, I would say, scientific measurements to build that trust that needs to happen.

But there’s another thing that needs to happen, which is all about making sure these systems work within the regulatory domains that especially require a lot of risk assessment and management. And so what we are seeing is the true moat that is happening for companies like Malhar is not just the technological innovation, because it is, you know, you’re able to do that much faster with a leaner team. But it is how do you do that consistently within the boundaries of the constraints and guidelines? That’s one of the guardrails that a regulatory ecosystem causes. So just as an example, we work with Fortune 500 companies in financial services, in health care, and they are really finding that they’re depending a lot on third -party AI, like maybe tools like Glean.

But how does Glean work in the context if you are building, let’s say, a customer service chatbot? You want to make sure that the chatbot not only is aligning to your brand guidelines, but it is not toxic. It’s highly reliable. It’s doing the things it’s supposed to do. And if it is within the context of a regulatory sector, it is following, let’s say, HIPAA compliance, et cetera, right? So as you can imagine, now it’s not just about building technology, but it is about building trusted technology that can work in the context that we are talking about. And that’s the exciting intersection of, I would say, policy, governance, and tech that I see.

If I can go a bit deeper on that. So has it changed? Because regulation, of course, regulatory risk, policy risk. all of these risks are always there. So Qualcomm, Microsoft, a Jio, a Tata, you take any large company anywhere in the world, of course they’ll have regulatory and policy risk people and of course that’s a big part of what they are tracking, etc. What has changed, if anything?

A lot has changed. And just I think another thing I want to ground us in is it’s not just about regulation. When you start thinking about AI risk, just because of the way these large language models are built, unless you ground it in real data, there are issues of hallucination. Do you actually, are you getting the right outputs? And can these systems be reliable? What kind of evaluation benchmarks do you have? Have you actually done the testing across your entire AI supply chain, etc.? So the thing is now it is not a static tech. It’s actually a very dynamic technology that when it starts to operate in e -commerce, either customer context, or it starts to operate in regulatory context, you have to prove that you can do it reliably.

So I would say that’s the biggest shift that we are seeing with AI and some of the applications.

The other theme that I want to keep going on with this, Navreena, and Malhar, Arvind, please feel free to chime in, is I think at the core of our engagement with AI on the policy front is the fact that the technology is moving so fast and governments realizing the fact that AI as a technology has massive ramifications on people, on existing structures, are saying, hey, listen, let’s rein it in before it goes out of our control. So there’s also a question of control here. So governments want to control so that for both reasons. I think one is governments generally don’t like to give up control to the private sector too much anyways, anywhere around the world.

But the other bigger piece here is when the technology, as you were saying, Navreena, is moving so fast, ultimately if some massive harm happens to people, ultimately the governments and political leaders know that they’ll have to be accountable. So it seems to me that it’s the nature of AI as a technology also, and it’s massive ramifications it’s making. policy and geopolitics risk a big part of what entrepreneurs have to keep in mind. The question I have for you though is has this become a function that every team has to have? So any startup has to have a CTO, has to have a CEO, has to have maybe a CFO and then product manager, etc. Is this becoming a role that is critical?

Malhar, do you have someone looking at this? Arvind, do you have people? Of course you’re a larger company. So let me start with Malhar. Do you have someone looking at this kind of risk?

I think we do have the benefit of being a smaller company that’s not entirely putting things out for public use where they can be harmful until we’ve gone through all of the regulatory requirements and until we have tested things out in a setting where it is safe. But I think from a research perspective people who are working on AI biology models such as us they work a lot on ensuring these models are safe whether it comes to other people being able to design dangerous pathogens and they’re not going to be able to be able to So we very actively in our entire team keep up with that research. We study that research. Everyone on our team is technical.

We study how they actually enforce those guardrails. So for when we actually need to start making things and actually turn them into products, we are actually able to implement that.

Yeah, so first, like, you know, so we are enterprise focused. And in that sense, like, you know, users of our product, they come to Glean and they ask questions which are serious in nature. It defines, like, you know, what work they’re going to do, what decisions they’re going to make. So first of all, you have to be absolutely sure that even though the core foundation of the AI technology is, you know, it’s a stochastic, you know, modeling, it can make mistakes, it’s probabilistic. You have to sort of work on top of that and ensure that you can actually deliver precise and accurate results. And like, you know, refrain from answering questions or doing tasks if you’re not sure.

So so we like it’s a big part of like our product experience is how do you actually use a safely and securely? How do you sort of, you know, do that constant sort of judgment and evaluation of the work that it produces? And do fact checking so that ultimately, you know, not only are you delivering the right answers or task execution, but you’re actually showing the full trail of like, you know, where the answers are coming from, what are authoritative information is being used that is human generated. So so that is very core to just like in terms of, you know, what product experiences that we deliver. But I think you’re also talking about the question of like is like how important it is to think about policy, to think about like.

You know. like setting, working with governments and actually ensuring that, you know, the right regulations and laws are in place. For us, like, you know, as an enterprise company, you know, that, you know, we don’t actually think a whole lot about that. But it is important to sort of have, you know, these rules and regulations in place because otherwise AI can actually do significant damage, you know, in the industry.

Great. The other dynamic I now want to move on to is, you know, the tech industry, as I think I’m sure all of us have seen, is in many ways, tech and entrepreneurship is defined by this idea of creative destruction. Large companies come as startups. They become big. By the time they get big, there’s a whole new wave of tech coming in and then a whole new set of startups come in and disrupt the incumbents. We’ve seen that again if you go into history, time after time, wave after wave, that’s happening. So my question to all of you. is that principle of creative destruction going to continue with AI? Or are we going to see that the big companies of today, the big tech firms of today, with the amount of capital they have, the amount of talent that they can hire with the kind of balance sheets that they have, the global scope of these companies, et cetera, their ability to shape policy, right?

Is this wave of big tech firms different? Or can we expect that the principle of creative destruction of them getting disrupted sooner than later is likely to continue? I’ll start off maybe, Arvind, with you, and then I’ll come to Naveen and Mahal.

Yeah, well, I mean, I think this creative destruction or disruption, rather, you know, it happens, and like always, you will see that, you know, over the last 20 years, companies like Google, you know, Microsoft, you know, these are big… giants and they have all the resources in the world and all the policy making power but yet when you think about innovation that happens in the tech industry often it actually happens outside of those companies and that’s because I think the spirit of entrepreneurism is actually alive, it’s an innate human thing and most bold ideas actually come from a single person who wants to actually who’s passionate about solving a problem and so I don’t think AI is going to actually change that in fact if there’s any indications AI is actually going to make it even more, it’s going to make it easier for people to actually create really interesting products and to serve large players because now there’s more power in their hands you don’t even need to be an engineer, you don’t need to be an AI scientist to actually use these amazing technologies and build and sort of turn your ideas into real products with very few reasons.

resources. So what I expect to see happen is that more and more innovation that is going to happen again in startup land. But ultimately, of course, I think the larger companies are well -established, they have large customer bases, so the model in the industry tends to be that new products are always, innovation comes from startups, but then innovation scales at larger companies.

Malhar, I’m going to come to Navrina in a second, but Malhar, I want to ask you a slightly different question. Navina, did you want to add to that?

You should not be worried about another person or even like AI taking your job. You should really be worried about a person who’s so good with AI actually replacing you. So I think so I have started to think about disruption rather than like in context of big tech or startups like individuals. Right. What what are creators and entrepreneurs going to create just when they can unlearn very fast rather than, you know, we don’t have a playbook right now for how you should be succeeding in the age of AI. So can, you know, new set of entrepreneurs use these tools, unlearn very fast old habits and be open and willing to try new ways of building faster?

I think that’s the construct that’s more healthier than thinking in the context of a company.

Great. Thanks, Navina. Malhar, I want to come to you now and ask you, we’ve spoken about how companies or startups are changing both internally. They might look different, might be leaner. more research focused in the age of AI. You’ve spoken about the importance of policy and regulation and especially in the world of AI, but I want to ask you now about the entrepreneur themselves. Given we’re sitting in India, I’m going to ask you the question from the perspective of India. You’re an Indian entrepreneur, grew up in Bombay, working out in San Francisco, part of the Y Combinator batch, dropped out of UIUC. Tell me from your perspective, Malhar, what does the Indian entrepreneur building a startup in the US today look like?

Is it any different from earlier generations of Indian entrepreneurs in the US? One. And do you find some difference that you can maybe point to between an Indian entrepreneur who’s grown up in India and then is working there, starting up there in the US, versus someone who’s grown up in the US?

Yeah, I think something that has been quite fruitful for me moving to America is that there’s an entire process of actually leaving where you’ve grown up and going somewhere and setting up things entirely new from there. And I think that sort of sets some sort of tone and precedence in even the work you do at your startup in terms of just taking risks, the people you hire, the things you do. I think in some sense that has perhaps stayed the same over multiple years because that procedure has not really gotten easier. It might be easy to get information, to book flights, to stay in contact with people, but the act itself has still been incredibly hard.

I think that is one big difference, and I think something that also probably contributes for more specifics is someone who’s grown up, let’s say, in America, is more aware of systems in America, how do you sell to people in America, what are talent distributions like in America, and how do you sell to people in America? And I think that’s something that has been quite fruitful for me moving to America. Thank you. someone like me who’s grown up in India. I think if we believe that India has a large role to play in things like drug discovery in the coming decade, I know a lot about how drug discovery works here, how hospitals work here, how data is collected, how many patients are treated, how diverse the patient body here is.

So I think there are those very specific advantages to it as well.

If I go to Arvind to ask the same question, can I just see a quick show of hands? Anyone has questions or quick comments? I want to make the last few minutes interactive if you’d like. Any burning questions or comments in the audience? Okay. So while Arvind’s answering, just raise your hand so I have a sense of the room, and then we’ll try and get to you. Arvind, this is your second startup, so I think you might have some perspective on this.

Yeah, well, I think, first of all, in technology, a lot of startups actually are started by Indians, folks in Silicon Valley or, of course, here. One thing which I… I think is interesting in the U .S. and Silicon Valley is the… you know, there is availability, you know, of capital. There’s belief, you know, and specifically in the Indian diaspora in terms of, like, you know, their ability to go and build great companies. You know, today, look at tech, you know, in the tech industry, you know, even in the large enterprises, you know, there are a lot of Indian folks who are CEOs. And I think what has actually made that happen is fundamentally, I think, you know, we are more hungry.

You know, we are, like, you know, I think there’s something about, like, in our culture, you know, and where we are as a nation, there is, you know, that drive, you know, that, you know, Indian people have, you know, and which is what is actually creating, you know, this incredible success, you know, for success stories, you know, for all of us. So I think that that’s one thing that I would say. I routinely, like, you know, of course, you know, I had the same thing, you know, I had the desire to make something. You know, big and, you know, that. continues to drive me, but what I see from, I work with a lot of young folks, a lot of people have actually joined our companies and then went on this entrepreneurship journey and I continue to see that same pattern that it is the folks who actually grew up here and then relocated to the US they are the ones who are most likely to start companies and become entrepreneurs.

I want to quickly open up to the audience, I know we don’t have probably mics there so I’m going to can I see a quick show of hands again? 1, 2 and 3. So we have less than 4 minutes, so what I’m going to ask you is in 15 seconds give us a question or comment. 3 hands I see. We’ll start off here and come to you and then come to you. Hello,

Yep, I have a question for Malhar. So you have a multi -discipline startup right now, so and you also told that you’re not from biology field like you’re not affiliated from biology field so the question is how did you like find this problem to solve great we’ll take these two also you can just shout out while the mic comes Yeah. So my question is so I mean we had this technology and it became a boon and bane and then what started evolving with technology was cyber security the field of cyber security right so now we have this AI and also we have the fear of how AI is being used for the boon and the bane and also you have the additional fear of hallucinations and of course so all these equivalent to cyber security are you going to have something like AI security or is there a new field that will come up and also how can you handle this hallucination I mean you can give a relevancy score to the output?

Hi, my question is for Navina, actually. I also work in an AI governance company called Protego. I had attended sessions today with Amazon and Zoom, especially these big leaders are saying that if we do governance at this stage, we will not see the ROI from AI, and it’s going to stop innovation in some manner. What’s your take on that? How do you advocate AI governance, especially with your hands -on with G42?

Great. So I’m sorry, but I mean, I’m sure we can speak. I literally have a timer that’s in two and a half minutes. Navina, I’m going to give you less than a minute, and then we’ll go across the room.

Yeah, it’s funny. If you were in the Amazon room and they made this comment because they were our first customers, so I’m surprised to hear that. But having said that, you know, it’s actually very clear. We are seeing very clear ROI on AI governance. If you have a very clear visibility, risk management practice, you can actually adopt third -party AI much more faster. And you’re seeing… much more, obviously, productivity gains with that. Secondly, when you have governance, your AI deployment increases, so you can actually deploy more products faster to customer, but also products that can be more trusted by the customers, and as a result of which, you are just adding more to top line.

So happy to share more details from our customers.

I think you can take maybe the cyber question, and then Malhar will come to you for this. Are we going to see a new field of AI and cybersecurity?

Oh, that’s right, yeah. So absolutely, I think AI is a very new technology, and it’s actually very gameable. So there’s a new form of attacks, like prompt injection are coming into place. It’s actually a rapidly evolving new field with a lot of entrepreneurship opportunities. It’s about how you actually… So I’m going to turn it over to Malar. control like what data, what information actually goes to AI models so that they actually get to work on good safe data but then also like whatever output comes back from AI the responses that comes back from AI how do you sort of make sure that those are not attack vectors and similarly I think the other thing you mentioned the related point of hallucinations.

The hallucination is actually a core sort of feature of the current AI technology unfortunately like this is how it’s built and so again from a discipline perspective I think companies that actually detect hallucinations that can monitor it, provide observability on it is also again like a good area and a field of discipline.

Good entrepreneur. Anytime there’s a problem there’s an opportunity. Malhar you have something? 16, 15?

I think for my co -founder and I both it started off as this deep intellectual interest more than anything, being college students that was really what we had to go off with. We were always interested in DNA and how your body regulates different cells, how it sort of maintains healthy functioning and what can really be learned by mining the genome and understanding things from that. So it started off with that and then I think after that we treated it very empirically, talking to customers, talking to scientists, talking to doctors who know a lot more in this field. I think that was sort of the start and how it continued.

Great. I think we are out of time, but I do hope that all of you have taken something away from the session. I hope that this summit has been a two -way conversation. I think it’s more important that, and I want to end with this remark, it’s very important that people sitting on the stage, whether it’s us or other panels, listen to all that you have to say and ask and show. because I think the summit must be a two -way conversation. I think that’s a very important piece, especially since so many students and so many entrepreneurs and would -be entrepreneurs have come here. So please do take the time to find the panelists if you want subsequently.

And now let me end with one best of wishes to all of you in your entrepreneurship journeys and to you. And we hope to see all of you back again here soon. And thank you all for staying here.