 You can, right there. Wow. There we go. Hello. And because, all right, shall we get started? Thank you all for coming. I know people are still coming down. It's running the gauntlet from the top down here. Come on in, grab some seats in here. And thanks, yes, as I said, thank you all for coming and welcome to the world's first PeaceTech Accelerator, a partnership with Amazon Web Services C5 and NS2SAP. Here, we like to say that the sun shines every day in your imagination. But what doesn't happen in your imagination is the hard work that we see here of entrepreneurs from around the world who are working on technologies that can be applied for peace building. Sometimes, it's a startup like Primo Wind or Sieb Energy with patented tech that can improve food or water security in fragile states. Sometimes, it's companies like Hala Systems or Red Crow that are using data to better manage risk in hostile environments. And other times, it's companies like Anona from Kenya or Superfluid Labs from Ghana who are working on tech that will benefit small farmers and consumers needing access to credit. But what all of these innovators and entrepreneurs have in common is that if they're successful, their companies will help to drive economic development in places where economic development is vital to peace building. Does that sound familiar? It should because using tech to drive economic development where it's languished. In effect, using tech as a critical enabler, as a critical enabler of peace and prosperity is at the heart of the inspiring life's work of our honored guests today. What Nandan Lekhani has done in India could turn out to be the best example the world has ever seen of true peace tech at scale. For those of you who aren't familiar with his work, let me describe it this way. Millions and millions of people in India, a nation of over a billion people, millions who were once invisible are invisible no longer. They have digital identities, biometric and otherwise, that enable them to access government benefits such as food, healthcare and work. They can access the banking system more easily than ever before and millions have done just that. In short, they have a hand hold thanks to the sheer drive and imagination of Nandan and his team. They have a hand hold on perhaps the single most essential necessity for building and sustaining a just and peaceful society and that is dignity. Needless to say, transformation of this scale is not without its perils and not without its detractors which is why we're glad that Nandan is here to explain in his own words the current state of this work in India as well as to answer a few questions at the end. So be thinking of what questions you have for him. He's become a champion of data protection on an equally unprecedented scale as he is of digital data identities, allowing individuals real control of the data they generate because as he's known to say, data is the oil of the 21st century, enabling and empowering in the hands of people. So ladies and gentlemen, please welcome to the stage the co-founder of the tech giant emphasis, the architect of Aadhaar, India's revolution and digital identity and I might add one of the world's leading philanthropists along with his wife, Rohini. Please welcome Nandan Nilekini to the program. Thank you Sheldon. After my talk, I'll get into my Batmobile and zoom away. So thank you very much for hosting us here and the peace tech initiative and I'll basically talk about data empowerment. I think this is a very topical subject today about data and how do countries, individuals, governments think about data. So we know that we are having a moment of reckoning in the internet. We got used to the notion of exchanging data for services, but increasingly this is looking like a Faustian bargain. And we have to rethink our approach to data. The fact of the matter is data is going to be inexorable. The reality is that with every individual having a smartphone spewing data out, with every device having a sensor spewing data out, we're really going to be entering an era when there's going to be more and more data coming out of every digital interaction. And we have to look at really what, how do we think about this strategically? What's the policies? What's the social and economic and other policies that we need to have? And we also know that if we don't have a strategic view of this, it's entirely likely that the data will be consolidated only in corporations or only in governments. And we have to think about whether an intervention is possible where the data will actually be available to individuals. And that's what we mean by data empowerment. Now, every country in the world is thinking through its strategy on data. As you know, this week is by coincidence on the Friday of this week, May 25th. The Europe will adopt probably the most comprehensive law on data protection, the GDPR, which is coming to effect from May 25th. The Chinese actually have announced their own policy, which is similar but which is more about national security and keeping data within the four walls. In India, we have the Justice Sri Krishna Committee, which has been set up and they're coming out with data protection and empowerment law, which should be out in the next six weeks. And in the United States, we're having discussions about what do we do with all this stuff. So it's very interesting that we are at a very seminal cusp point in India, in the US, in Europe, in China, as different countries around the world are dealing with the challenge of data. And you can see that this is really getting the headlines. Everybody's talking about it. What do we do about it? How do we look at data and make sure that the platforms are responsible for the content on the platform? How do we look at data and make sure that we don't have winner-take-all models and innovation and competition can continue? How do we ensure that privacy is protected? So these are all very fundamental questions that are being raised around the world and this is a very topical moment. But most of the conversations in the world have really been about, they make a tacit assumption. They make a tacit assumption that the data is going to be accumulated by businesses for monetization and by governments for surveillance or whatever and assume that really the individual has no particular control or visibility into his own data. So the entire conversation, the entire narrative, is about how you can protect a person from his data being abused, how you can make sure that they are protected. So it's really about data protection for the person. But we believe that there's a different way to look at data and that is how can we look at data as a way to empower people? In other words, if every individual, I think we have the team from ID40, they can come here in front. We have reserved chairs for you. So the question is, can we think of empowering people with their own data? Now what does that mean? It's very important to realize that in many parts of the world, people are going to be data rich before they're economically well off. Now that's very different from the western world, the US and Europe. Because in the US and Europe, people were already economically well off before they became data rich. If you look at per capita incomes, $40,000, $50,000 per person, and then the data revolution started. So the natural business model that emerged from that was business models that use that data to sell better to them or business models that use that data to send them a more precise ad. So it was all about the fact that people were already well off, the data was plentiful and was there a way to use that data to sell something to them. But what is happening today is while the western world is data rich after they were economically well off, in a large part of the world, they're going to be data rich before they're economically well off. In other words, the per capita income in the US may be $40,000. The per capita income in India would be $2,000. But if you have a person in America using a smartphone and making digital payments, and you have a person in India who's also using a smartphone and digital payments, the level of digital footprints both have is the same, right? So both have the same level of digital footprints, but they are different income levels. Now clearly, if the per capita income level is $2,000, it's unlikely that business models that are about selling to people will make that kind of money. Just to give you an idea, the US spends hundreds of billions of dollars in advertising between digital platforms, the TV and print. And a large part of that is captured by the internet company, the digital side. The total spending in India on advertising, there's a billion people, is only about $10 billion. That's the amount of money spent on advertising. And the total money out of that spent on digital advertising is about $1.5 to $2 billion. So you can see that if your business model is to make money by showing ads to people, you're not going to make too much money in that world. On the other hand, you're going to have a few hundred million people who have a digital footprint which is very exhaustive. And therefore, can we think of an architecture where people who have these strong digital footprints can use their data to improve their lives? Can I use my data to get better healthcare? Can I use my data to get more skills? Can I use my data to get loans from the bank and so on? So we have to think of data as something that can empower individuals and they can put their data at work to improve different aspects of their life. And this is what we mean by data empowerment. How do you invert the data and make the user at the center of his own data? And therefore, how do we empower and protect? And that's what we're talking about, where the user is at the heart of every transaction. And we are looking at a set of simple principles that users must control the data and users have the freedom to exercise decisions and very importantly, that this requires a set of public goods to implement these things. And what we mean by public goods is, if you really look at the Internet, it was really a set of public goods. It was investment in the U.S. Government Department of Defense and every major innovation in Internet came out of taxpayer spending, whether it was the HTML protocol, HTTP protocol, SMTP protocol for email, browser technology, mouse, everything came out of some government grant. And therefore, we have to extend the concept of public good to a new class of issues which are really required to empower people. In other words, we think that the public goods aspect of the Internet has actually slowed down and there are things which the Internet has to do, which is available as a public good, and that becomes a prerequisite to really implement a data empowerment way of thinking. And therefore, one aspect of this is, of course, how do we have meaningful choice between interoperability and data portability? Because I think part of the challenge we have is because of the nature of the network effects of technology, you end up with creating winner-take-all models. And when you have a winner-take-all model, you have very difficult to have new innovation because the existing incumbents have such a strong market position and there's such a strong number of users that, you know, even if somebody has a new innovation, it doesn't have the network effects to bring the innovation into force. And therefore, you need to think about how to make things interoperable, portable. So new innovation comes and users can migrate easily and the users of the new system can talk to you on the old system and finally, the better system will prevail. So there's a part of this which is about competition and portability. But we also think that it's about creating public goods and these public goods are a whole new kind of infrastructure. So just as Web was a public good, Browser was a public good, are there other public goods and we talk about that. And we think that we need to do this because if you don't have these public goods like identity, like authentication, like digital payments, then you don't have a mechanism by which users can be in charge of their own data. In other words, these public goods are a prerequisite to allow users to be in charge of their own data. And the belief is that you also need new business models to empower users to use their own data. And we are calling these as data fiduciaries or companies who are incentive aligned with the user's view of data. So data fiduciary is an entity which acts as a traffic cop between data producers and data consumers on behalf of the user. So I as a user can direct various data producers of mine to give my data back and send it to a particular user to deliver a particular service. So you need this kind of architecture to really at scale provide individual empowerment of data. And therefore, the missing piece of the puzzle is an entity which we call as a data fiduciary. The data fiduciary does not own data. The data fiduciary does not store data. The data fiduciary does not read your data because the data flows from the producer to the consumer in an encrypted manner. So the data fiduciary cannot see the data. And then you'll ask what is the business model of this data fiduciary and we'll believe that business model is really transactional revenue on all the transaction it does but not from the data it has. So it provides a very important thing and you need to make sure the incentive and the technical, legal and institutional infrastructure is there to enable people to have access to these data fiduciaries so that they have an easy way to access their own data for various purposes. So this whole thing is encompassed in something we have thought about called the data empowerment and protection architecture where you have these data fiduciaries which are utility companies, the utilities and you have many data providers. So people who have data about you which are stored in their respective systems and you are out there and you can make a request for data and get it from any one of these providers and I'll give you a specific example later and this can be given to somebody else who's a consumer of your data but with your permission. So you're giving consent to use your data for a particular purpose. And this consent is qualified consent. It's not saying you can do whatever you want. You can say that you have the consent to use this data on a one-time basis for one transaction or you can say that you have this consent for one week. So you can actually put stipulations around consent and it's like programmatic consent. You can actually put rules on that and that is something that can be done by the user. So this is an architecture where you have the fiduciary who helps the user take data from providers and gives it to consumers. So this is an abstraction of the model. Now how would this work in different industries? And a good example is the financial sector and the reason I've selected the financial sector is because at this point this is the most developed sector to implement the concept of data fiduciaries because they're very fortunate that we have a very good regulatory environment and we have four financial regulators. We have the Reserve Bank of India for banking, SEBI or Security Exchange Board which is like the SEC. They regulate capital markets. You have IRDA which is for insurance and you have PFRDA for pension and all the four regulators sit together on a common regulatory board and they all collectively come out under the leadership of the RBI with a form of financial data fiduciary which they call as an account aggregator. So an account aggregator is a data fiduciary for the financial sector and it has all these attributes. It does not store data. It acts as a data pass-through. It cannot read the data and its business model is transactional income. And you can have many of them. So it's not about a monopoly. The regulator can regulate and give licenses to operate account aggregators to multiple players. So there can be many people competing to be good efficient regulators. And these regulators are connected to what are essentially the people who have the data on you, the financial information providers. So it could be a bank. It could be a mutual fund. It could be an insurance company. It could be the income tax platform. It could be the GST platform. So all these data providers can plug into the fiduciary. And the way that these financial information providers plug into the fiduciary, data fiduciary is designed on standardized interfaces. So there's a whole set of protocols on how to make the data fiduciary and the information provider talk to each other. The good thing about this model, it's completely open-ended to extend. So tomorrow there may be a new class of financial companies. All they have to do is just plug into the system and they join the left-hand side of the financial information providers. Similarly, there are different people who could be consuming this product. Some people are those who are offering loans or lending projects. Some people are providing wealth management services. Some people are providing, you know, some kind of personal finance managers. They're all different consumers of that data. But all the data that is consumed is only with the specific consent and approval of the user on the terms that they decide. So it's really putting the power of the person in that. And this is a very important construct. And this is a new construct in the area of data because nobody's actually done this in this way. And now why is this important? Because now for the first time, you actually have an infrastructure where people can use the data in a practical way to get things done. For example, if I'm someone who's come completely out of the informal economy, I've had no history of transactions and I get an Aadha number, I get a bank account. I start receiving my money into my bank account, my salary. Over six months, I have a history of my transactions in my bank account. And now I want to get a loan to buy a motorcycle or some vehicle. I can go to my bank and say, give me my last six months transactions because I want to show my lender that I have a job and I get regular income into my account. So the bank gives that person the last six months transactions in a very secure manner. And then he gives it to the lender and the lender says, oh, okay. So you have had a job for six months and looks like you had a regular income of 3,000 rupees a month and you only spent 2,000. So you have some savings. So clearly you have the potential to be a good credit risk. So I'll give you a loan of 100,000 rupees to buy a motorcycle. So in other words, you're using the flow of data to help give access to loans. And that's an example of how data is being used to improve somebody's life when he uses data to get a loan. And this is really the philosophy of empowering people with their own data. And this is something which the account aggregator standards have already been published by the central bank and I think the final technology standards are getting, they also been published per month. So it's all, it's all out there. And we hope in the next few months we'll actually have multiple fiduciaries in the financial sector who will then start offering this service to people. And why this is important is that just this one application alone which is lending in financial services itself has a dramatic impact on the economy because people who are shut out of the economy, people who didn't have access to credit now can use their data to get access to credit and become part of the formal economy. So this is a very important thing from an inclusion perspective. Now the same principle of financial data can also be applied for healthcare records. As you know, one of the big challenges we have including in this country is how do we put the user in the center of his health records because your health records are there in your insurance company or pharmacy in the PBM here, there, everywhere. And you don't really have access. When you create this kind of infrastructure you are a health data fiduciary who can take, who at your request can get your hospital records, your lab records, some of the diagnostic records, bundle it together and give it to a specialist to check out some second opinion on something that you have. So the same concept of fiduciary that you can apply for financial services can be applied for healthcare. And similarly you can do the same thing for skills because you want, how do people get better jobs? How do they show their employer or their customer that they have these skills? You can actually get a system where they build these skills. It's all about using data and reducing knowledge of symmetry and improving transactional trust at a scale, at a scale of a billion people. And the same thing can be done in the telecom industry where I can get my telecom data and then give it to somebody else to check something and get me some service. So once you have this fundamental architecture of this data fiduciary, you can then apply that in multiple sectors to create this kind of instrumentation that allows a person to get access to all his data. Now, but to make this happen, you need building blocks and that's why I refer to the need to have a new set of public goods as building blocks. You, if you want to make this to happen, you must be able to do millions of transactions every day where any individual can ask any data information provided to give his data in real time in a few seconds so that he wants to give it to somebody else. Therefore, you need a way to identify the person online. You need to make to ensure that he's the person he claims to be. You need to authenticate him. You need the ability to pay for these transactions. You need the ability to sign documents like a loan document digitally. You need a way to, you know, give consent. So all these are the building blocks, the plumbing to make this happen. And all these building blocks have been built in India and it is because of the presence of these building blocks that we can even conceptualize the ability to have data empowerment. In other words, you can't have data empowerment without the building blocks to access these things at scale. And this is not, you know, it's all there. It's called as the India Stack. Aadhaar provides the identity. There's something called Jam. It provides authentication. UPI, the payment system, which is rolled out 200 million transactions last month. E-Sign, which is a digital signature and a consent layer for sharing this. And I just want to say that you should consider yourselves very fortunate because the architect of all this is sitting here, Dr. Pramod Verma. So, and Pramod will, whenever the questions become hot, I'm going to call. I just do the bullshitting part. So these are, this is called as the India Stack. And the India Stack is not vaporware. It's real. You know, we have 1.2 billion people on the identity. There are a monthly, there are about 1.5 billion authentications that happen. We have about half a billion people who have used authentication at some point or the other. The payment system does 200 million transactions a month. The signature system has done 33 million signatures so far, but that will grow with GST. And the consent architecture has already got 2.4 billion documents that have been electronically stored. For example, all of India's driver's licenses and vehicle registrations are there. Securely in a database which you can access. So you don't have to carry your driver's license. You can just split up on your phone and show it when you get caught for speeding or whatever it is. So this is infrastructure at scale which enables people to leverage their own data to improve their lives. And a small business can now use his business records to get a loan so that it can grow his business. And this is very important because we think this is also about economic growth because the notion that companies will hire... One company will hire 100,000 people. I think those days are over because these companies are going to use automation and AI and all that. Then they're going to hire people. On the other hand, if you have 100,000 companies which use this technology to get credit and grow their business, they'll all hire one person each. So tomorrow's jobs will not be large companies hiring lots of people, but many small companies hiring a few people. And for that, you need to make sure that all these small companies are empowered with data. So that's what all this does. And similarly, today there is no premium for expertise. I could be an entry-level carpenter or a master carpenter. I don't get paid the same because the market is not able to distinguish my skills. Whereas once I have a trusted way of signaling to the market, these are my skills, then you have an incentive to improve your skills to get a premium on your job and therefore it creates huge opportunities. So I think to end by saying that all of the world data is being used to sell things to people. So really we have four models on the planet, on data. We have the US model, which we don't know what it is, but we have to fix it. So nobody knows what to do, but it looks like we have to do something, right? That's the US model. You have the Chinese model which is about the state and companies working together and face recognition at every traffic light and that kind of stuff. You have the European model which comes into effect on May 25th with this GDPR which is really a defensive model saying make sure nothing happens, make sure people are protected. But it also creates layers of administrative bureaucracy. And then you have the Indian model which is how do we use data to empower users? And we think this is a model which has a lot of legs and we think this model is something that the world should look at for all its various things because then data becomes not something that's only used by companies to sell to you or governments to keep tabs on you, but individuals and small business can use data to make their lives better. Thank you very much. Sheldon, where's my Batmobile? All right. We have to take some questions. We got to turn up the mic. Okay. Today or yesterday about this rollout of this plan, of the cashless plan in India and as good journalists often do, they only talk to four or five reasonably illiterate people. How do you envision either... This was done more on the question of unbanked people but also undated people. How do you roll this out in a country in which you would know this number better than I? A quarter of the people are not particularly literate and not particularly tech-savvy and not particularly trusting. Well, actually, it's already rolled out so it's not like how will you roll out because we have 1.2 billion people on it, 300 billion people have opened bank accounts the last four years. And I think it's important to understand that digital does not mean self-service. It does not mean that the person does everything on his own phone. That's how we think of it here. Digital means that you have a digital backbone but the actual service is given by somebody to the person who doesn't have the tech skills or the literacy skills. So we call that as assisted service. So, for example, if somebody wants to open a bank account, he goes to a business correspondent, gives us the Aadha number, authenticates and the system opens the bank account and that guy gives the customer his whatever details. So, assisted service is a very important way of reaching the people, especially those who have challenges of literacy or technological complexity. Hi, thank you very much for that wonderful presentation. I was interested in understanding how blockchain may or may not be playing a factor into this. I know that there's cities in India and lots of entrepreneurs that are embracing blockchain in various financial and economic communities in India that are using blockchain. So I was just interested in understanding if there's how that might work into that. No, you'll be obviously looking at blockchain and seeing what is the applicability and I think I'm sure there are a class of challenges or business problems where blockchain is the appropriate choice. But it's not really the solution for everything and we feel that what we have achieved here gets you the benefits of blockchain in many ways without having the cost of blockchain which is the massive maintenance of public ledgers. So, but we will keep looking at and if in any anywhere in this system a blockchain solution is more appropriate, we will do that. And identify yourself please when you... Sure, it's Kay Turner at US Treasury. Was interested in your thoughts about data localization, particularly in innovation and in smaller jurisdictions that perhaps don't necessarily have people that have been like yourself that have gone and created infrastructures. And then secondly, just wondered if you could share any thoughts on the role of public and private sector in this new data world that you're talking about the way that you think about it. Sure, yeah, no, I think data localization is obviously a very hot topic today in the area of data protection and privacy and for example, the China is very clear that the firewall and everything has to be inside the firewall. I think different countries are grappling with this and it's often as much a political issue as a technology issue. And I think in India I can say that there are two strands happening. I mean the central bank of India has past resolution regulation that payment data must be localized and that conversation is happening because they have given a six month window for that. And there's an emerging data protection and empowerment law which is going to be released in six weeks. That is also going to say something about data localization. So I mean, I think it's a very complex issue because obviously companies don't want these would rather have a globalized way of dealing with data because it simplifies things for them. But countries have a different view about data privacy or data leaks. I mean, a lot of this happened after this Cambridge event. So governments have got nervous about these things and so they're reacting to that. On the second question, actually there's a huge role for the private sector here but it's not the role in the way of accumulating data. It's in the way of providing services for people when they give the data. To just give an example, the infrastructure of data fiduciary in financial services which in turn leads to increased lending for people who don't have access to capital is estimated to add about $600 billion of market cap to the Indian financial sector in the next few years. So this is serious value being created but in a way that it's spread out there will be 10 banks that will do well or 10 companies will do well because a lot of the choke points of technology have been removed and anybody can come in and build a business but they have to innovate and provide better services because they can't do it by controlling payments or controlling data. I'm Hari Shurhari, professor of computer science at the University of Buffalo. 20 years ago, Tom Friedman wrote this book, The World is Flat. 15. 15 years ago. 2004. And he credited you in that book that you are the one who suggested that phrase that the world is flat. And today I love the title of your talk in the beginning, Data Empowerment, Leap Frogging. So when the world is flat was said, it bore out with India taking such a powerful role with the coming of the internet, the world became flat. And with this data empowerment, it seems like another starting point. So give your thoughts about the time frame when India is going to leap frog to the level of whatever, US or... Well, that's a long story. But I think we have a working India stack which does billions of transactions and the data fiduciary architecture is getting rolled out in the financial sector. So over the next couple of years, you're going to see a direct practical use of this architecture for all kinds of financial products. And we believe that that will show the way. And I think countries are trying to find a balance between they don't want to adopt a state model. They don't want to adopt a free, completely free market model. Nor do they want to adopt a defensive model. But they want something which is balanced. Think of doing this, but that will take time. Let's go over here. Thank you very much for this very interesting presentation. So I'm Nomeh Lahimar. I'm from Tunisia. I'm founder of a civic startup there. And my questions are about... Do you use this for voting, for example, in India? What? Do you use this data or systems for citizens' votes? The question is, how do you protect anonymous identities on the platform? And is there a reward system, like when people accept or consent to give their data, do they get something in return? Yeah. So the voting system does not use Aadhar for two, three reasons. One is that Aadhar is meant for any resident, whereas voting rights are really for people under the Representation of People's Act, which is for citizens. So you have to establish that he's a voter, not just a resident. Secondly, you know, voting is an area where you don't want actually to know who somebody voted for. So you don't want an ID for the voter. That could be, you know, lead to other complications. But there are efforts to ensure that while the actual vote is done without... with anonymization, the fact that you're eligible to vote and that you only vote once, because sometimes in India, people vote in multiple times. In the same election, they'll vote in the morning, go there and all that. So they're looking at how to prevent that fraud and reduce... Also, India is sometimes an issue of multiple voter IDs. So all this will help in... So it's a partial use in the voter ID system. Now, the ID itself is linked to authentication, either biometrically and so on. So it's difficult for someone else to steal your identity because they can't do the same kind of authentication. Unlike if you have a number without any way of, you know, authentication, then it can be used by somebody else. But here, it's a number plus an authentication factor. And while there are no particular rewards for that, it's possible that somebody may have a business model in the future using this infrastructure where they reward people to participate and give data. So that's a market function. We don't get into that. From the World Bank. So here I just wanted to ask, we are getting lot of documents from the data producers that lots of documents are being given by the data producers to the data consumers. So are these documents usable for machine level decision-making or they're only meant for human decision-making? No, no. They're all machine-readable documents. They're all electronically... electronic documents which are digitally signed and encrypted and machine-readable. So how is this information ingested by the system for decision-making? How do you ensure interoperability from these four different systems? Yeah, so there's an entire architecture and Pramod can tell you more on the side which is how these documents should be signed, how they should be encapsulated. Think of it as a TCP IP for data. It's like a way to move data around the network. So all that protocols have been laid out and there's an entire architecture called the Electronic Consent Architecture which is published by the government on how these interoperability should work. Any other questions? I just can't let you leave the stage. I'm not done without asking you one thing. I'm going to move from the sublime to the absurd, I suppose. I've got to ask you what you were thinking. You know, a few weeks ago, we had at one stage upstairs, we had Facebook's head of global public policy explaining the Cambridge Analytica data breach and what they're doing about it going forward. I'm just wondering, what were you thinking making of this situation where Zuckerberg was won the stand for two and a half days on Capitol Hill? What was going through your mind? Well, it so happened that that night I was not sleeping because I had jet lag so I actually watched it live in Bangalore at three in the morning. No, I think this whole thing has brought matters to a head and I think between the impact on the elections and this episode of Cambridge Analytica, I think it's sort of made people realize that at least in this country that we need to rethink the whole architecture of how internet companies will operate. What are the kind of regulations we need in the future? What kind of privacy is required which ensures privacy at the same time allows innovation? How do we make platforms responsible for the content on the platform, which is a big concern? And finally, what do we need to do from a regulatory and architecture point of view to ensure innovation happens so new innovative companies can come and start sort of giving the incumbents a run for the money? So I think all these questions are now coming up but it's complicated to sort this out because there are multiple agencies in the US which are responsible for different aspects of this. So it'll take some time to figure this out. And to your credit, you've answered a lot of those questions with a vision, which we just saw. Yeah, so I think the point we are trying to make is we don't have to go the way of state and data, which is one way, or we don't have to have a way of saying let the market do whatever. Not do you want a way of creating a bureaucratic process, but is there a way to think about building something which allows individuals to take advantage of the data? So that's the thesis that we have. So we are the Peace Tech Lab and we work around the world. We work in places that like Iraq, Afghanistan, Yemen, Nigeria, where, you know, the governments are not generally as benign as you have in India today. And I'm curious, I can imagine a lot of them embracing digital identity because that is a great way of keeping tabs on the citizenry. But how is this idea of data ownership being received around the world? What are you hearing from other countries? No, I think it's still early days yet. I think, but the timing is right because data has become such an important and strategic issue. So at least people are listening. So I think it'll take some time for it to just buckle in here. Early days in that discussion. Well, let's thank Nandan for... Just fantastic. Thank you, Elder. Just wonderful. Before we leave, I just want to thank the organizers. Emerge 85 and Future State that really were our partners through this. Thank you very, very much. And I want to thank the Peace Tech Lab staff and the Peace Tech Accelerator staff who helped organize today's event. And again, thank you, Nandan. Thank you. Great to have you here.