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Indian Express
31-05-2025
- Business
- Indian Express
Land tokenisation using tech will unlock huge opportunities: Siddharth Shetty, co-creator, Finternet
Siddharth Shetty is the co-creator of Finternet and CEO of Finternet Labs. Among other co-creators of this concept are Nandan Nilekani and Pramod Varma (of UPI fame). Finternet, called a vision for the future financial system, is based on the structural principles of the internet, and aims to build a universal interoperable infrastructure to support seamless transactions of multiple assets. It can help you sell, buy, lend, or pledge assets like land, vehicles or shares in a tokenised format and in a seamless manner linking all ecosystem players like governments, banks and financial institutions. Siddharth has previously worked as an advisor to the Union Finance Ministry on Digital Public Infrastructure, and was involved in designing the India Stack, personal health records for Ayushman Bharat Digital Mission, DigiYatra for aviation travel, and Digital Sky for integrating drones into civilian airspace. He was also the co-founder of Sahamati, an industry alliance that works on Account Aggregator adoption as intermediaries for data sharing with user consent. Siddharth spoke to on the need for Finternet, the challenges it would face for adoption in India, the value that it could unlock, and the safeguards that tokenisation of assets would have. Edited excerpts: Siddharth Shetty: Today, people find it difficult to buy or sell assets like land or apartments, as there are multiple regulatory agencies and systems, and these agencies and systems do not interact with each other. There is the survey department, which holds the maps, the revenue department, which holds the records of your ownership rights of the land, the property registration department where your land or apartment is registered in your name, and the municipal body where you pay your property tax. A lot of the time, the data on your property is contradictory in these databases. So, if you want to sell, buy, or pledge your assets, there is a huge bureaucratic process, and it would take quite some time. Imagine if it were tokenised; made simple as if one were holding a share in the stock market and that would make it easy to sell, buy or raise money using the land/apartment asset. In simple terms, it means that the tokenisation would authenticate that you own the asset, say land, and there are no loans on the same, show a clear title, and would help in easier transactions of the title. Such a tokenisation of land/apartment assets would help not merely in financial transactions like buying, selling, and pledging but would also help in inheritance planning, loans on parts of the property, and even simplify rental agreements. Nearly two-thirds of all civil cases in Indian courts are related to land. The average time to resolve a case is 15 years. The economic cost of this inefficiency is huge. Our GDP can be boosted simply through land record digitalisation. One must understand that lack of a tech-like tokenisation and how it has impacted India. India ranks 154th globally in the World Bank's Ease of Doing Business index for registering property. On average, it takes 58 days to complete a property registration here, compared to three days in some countries. Land dispute resolution is ranked at 136th globally, with an average resolution time of 1445 days versus the global average of 358 days. Apart from its impact on real estate, it has uses in a wide variety of areas, like agricultural markets and simplifying access to healthcare and education through tokenised vouchers. It could support the issuance of green bonds for sustainable projects, manage carbon credits more effectively, or facilitate the distribution of subsidies for energy-efficient tech. Siddharth Shetty: The tech behind tokenisation is the tech behind Finternet per se; it is a combination of public blockchain, credentialing tech, wallets and token management. It is based on the principles of interoperability, immutability, and composability. It establishes a foundational architecture for decentralised, interoperable financial ecosystems. It also has open networks as a critical component. At its core, land tokenisation converts rights associated with real estate and property into tokens recorded on an immutable ledger. These tokens are not just digital certificates. They carry details of the asset rights, ownership history, legal rights, restrictions and real-time updates. The asset is not just represented, but can be interacted with, transacted on and in a trusted manner. With tokenisation, land assets can become easily verifiable. A plot can be divided into fractional units and transferred. The real world and digital assets will be represented as tokens which are self-describing and self-contained, and which can be chained together. The advances in cryptography in verifiability, security, and privacy make it a trustworthy exercise. Finternet also enables quick dispute resolution, provides for a lifetime history of all assets, and helps with online dispute resolution with digital evidence and easier auditing. It could save much time spent in disputes in court. Siddharth Shetty: Land tokenisation is one small part of the whole of Finternet. Finternet looks at tokenisation of other assets too, like vehicles, stocks, gold, silver, agricultural assets and even energy resources. Finternet emerged from a paper co-authored by Nandan Nilekani and Agustín Carstens of the Bank for International Settlements. It was later developed by Nandan Nilekani, Pramod Varma, and me. Siddharth Shetty: There are a lot of pilots going on across the world, in the US, UK, Latin America, Middle East, and places like Korea. In some places, certain assets have been tokenised. These pilots are also looking at the various legal aspects of tokenisation. The key is to see that it works at scale. Korea is talking about tokenisation of its agri produce and meat products. In Dubai, land tokenisation is being tried out in closed user groups. In India, too, pilots are going on in Mumbai among lenders of financial products. Siddharth Shetty: The tech is maturing very fast, and within three to five years, I predict that we could see adoption of some facets. My reading is that within 10-15 years, Finternet will take off worldwide in a big way. In India, confusion exists, and clarity needs to be developed on different aspects of digital assets, crypto, tokenisation, and cryptographic infrastructure. What needs to be understood is that tokenisation is not just for financial services, it's critical for even availing basic services around property management. We are working on technology that is safe, secure and reliable. We understand that if there are not sufficient safeguards, it would not pass regulatory scrutiny or even public approval. In simple terms, it is a technology like Android for smartphones, an underlying tech. On top of it are apps for users, built by various companies for various sets of users. It is wrong to blame Android for an app that is not helpful or does not work. Now, the Union government has come up with a draft bill to replace The Registration Act of 2008. The draft bill, which has been placed for public feedback, introduces provisions to support online registration, electronic presentation and admission of documents, issuance of electronic registration certificates, and digital maintenance of records. Siddharth Shetty: We have set up Finternet Labs in India and the US. They are also being set up in Singapore and Switzerland. These would test the hypothesis on the ground, work with early adopters, test the legal frameworks, and other issues. We are working with various stakeholders across the globe to mainstream this idea and the tech. The adoption of this tech would be asynchronous. It would not be linear. Some governments or regulators would latch on early, and not everyone and every aspect would go live at the same time. It would be in phases. Perhaps among the states in India, there would be some early adopters, but it is also a learning process. It would be like the Aadhaar and UPI adoption in India. Studies by experts said that it would take five decades for Aadhaar to be adopted in India. It got off within a decade. From five million merchants in 2016 to fifty million did not take much time. The same was the growth from 30 million users to 500 million users for UPI. We see the Finternet taking off in a non-linear and exponential manner. Siddharth Shetty: The hurdles would be of three types. First would be building the tech infrastructure, with a focus on digitisation of land records and digitisation of other databases. And it should also reach a certain maturity. Second would be the legal frameworks and changes in legal and public policy around land tokenisation. There is a need to remove the legal and regulatory barriers that hinder the adoption of new technologies, particularly those related to tokenised assets. Third would be the challenge of onboarding and enabling the commercial partners for this ecosystem, which would help these partners to unlock new markets and meet consumer needs. It would also require collaboration between the public and private sectors. Public authorities should build digital infrastructure, establish regulations, while the private sector should lead in providing the services.
Scroll.in
28-05-2025
- Business
- Scroll.in
The hollow hype over India as the ‘AI use case capital of the world'
In February 2023, Indian Prime Minister Narendra Modi called on citizens to 'identify 10 problems of the society that can be solved by AI'. In 2024, Nandan Nilekani, the IT czar who has been a driving force behind India's digital journey over the past 15 years, declared that India would soon become the ' AI use case capital of the world '. In January 2025, the Ministry of Electronics and Information Technology's IndiaAI Mission issued a call for proposals to build Indian foundational models, the software that underlies contemporary generative AI development. One of the criteria was 'identifying and elaborating use cases that address societal challenges at scale.' And last month, the Gates Foundation and the IndiaAI Mission announced a partnership on 'AI solutions for better crops, stronger healthcare, smarter education & climate resilience'. The discourse of AI use cases for socio-economic development is one of the most distinctive features of India's AI policy. Its promise is that AI will 'solve' difficult problems in classic sites of postcolonial development: agriculture, health, and education. It discursively links socio-economic development in rural India to industrial strategy at the cutting edge of global AI technology. However, lacking an account of political economy, the 'use cases' approach makes for a poor policy programme. Instead, this seductive vision serves as a hype machine, paying lip service to development to legitimise a range of other interventions, from claims to geopolitical leadership to the marketisation of populations new to the internet. The discourse of AI use cases has been foreshadowed by the digitalisation of development that followed the Aadhaar digital identity system in India. Launched in 2009 as an intervention that promised to streamline India's rights-based welfare apparatus, Aadhaar brought hundreds of millions of Indians into the purview of digital systems. Its promoters in the software industry used the promise of financial inclusion , especially following the founding of the IndiaStack project in 2015, to legitimise granting the Indian software and financial industry access to these digitalised Indians as customers. With access to private credit, impoverished Indians would now, in the financial inclusion playbook, ' enterprise themselves out of poverty '. While poverty remains an enormous challenge , state investment in public-private infrastructure has undergirded an expansion in the software industry, spawning, for example, a new fintech industry. The targeting enabled by Aadhaar and similar systems also heralded the rise of the BJP's ' new welfarism ', shifting away from public goods like public health and primary education to the provision of cash transfers for private goods like gas cylinders. Rebranded as ' Digital Public Infrastructure ', these systems are being exported around the world as a model for the use of technology in development. Building on this approach – and promoted by a similar set of actors in the state and industry – the AI 'use case' discourse frames India's societal challenges as a resource for software capitalists. In practice, AI use cases in these domains are largely speculative. In agriculture, for example, dozens of vernacular language chatbots promise to better inform farmers about weather conditions and planting times. In healthcare and education, the promises of AI are largely in streamlining administrative processes, such as hastening India's transition to digital health records, which is supposed to improve efficiency while delivering huge amounts of data to hospitals and insurance providers. Despite the lack of tested applications, the discourse of AI use cases portrays the numerically vast market constituted by the poor as a national opportunity in the global AI arms race. The AI supply chain – composed of datasets, models, and computing power – is controlled by just a handful of US Big Tech actors, eliciting industrial policy responses from several states . In India, the poverty market – where poor people are figured both as users and providers of data – is imagined as a driver of growth that can give the nation a competitive advantage in an increasingly concentrated global AI market. To be sure, Indian AI industrial policy also relies on more traditional tools, including massive incentives to build domestic capabilities in semiconductor manufacturing, cloud resources, and models. But the national champions of the Indian AI economy are imagined in the 'use case' discourse as emerging from software applications for socio-economic development. 'To [unlock] India's potential with AI', Nilekani proclaimed in 2023 , 'the trick is not to look too hard at the technology but to look at the problems people face that existing technology has been unable to solve'. The promise of use cases, in other words, blurs the lines between the marketisation of poverty and national industrial policy that hopes to make India globally competitive in cutting edge technology. Of course, this is too good to be true. The discourse of use cases ignores the political economy of both the AI industry and of development. From the perspective of AI sovereignty – a major focus in India's technological doctrine – it will do little good to become the 'use case capital of the world' if semiconductors, cloud resources, and models remain concentrated in the hands of US Big Tech. Today's generative AI, even more than other digital technologies, runs on semiconductors sold by a single company – Nvidia – which are fabricated by a single factory in Taiwan – TSMC – on equipment made by one Dutch manufacturer – ASML. Meanwhile the cloud computing data centers and models required by AI are overwhelmingly controlled by Amazon, Microsoft, and Google. AI use cases are imagined as a way to promote the growth of the domestic startup industry, but most Indian startups in the AI space and beyond don't appear to be interested in the poverty market. A 2024 survey of over 120 generative AI startups in India, which have collectively raised over $1.2 billion in the last five years, showed that 70% are providing solutions only for enterprise clients. In keeping with Indian tech's historical bias toward enterprise services, the industry appears to be largely focused on backend software components for use in industry, not consumer-facing software products, let alone for socio-economic development use cases. This is reflected in the sectoral data. Despite the buzz, agriculture does not figure as one of the top five sectoral applications for generative AI startups. While education and healthcare do figure in the top five, these are lucrative markets for the middle and upper classes; it appears unlikely (though we need further data to definitively conclude) that AI startups in these sectors have developmental goals. This makes financial sense for startups and venture capitalists. The Indians who would be targeted by the proclaimed AI use cases are, after all, very poor with little spending power to sustain startup business models. As a recent venture capitalist report put it , the poorest billion Indians are 'unmonetisable' for startups. AI use cases are also the wrong answer to issues of socio-economic development. Entrenched developmental problems in agriculture, health, and education need structural reforms rather than the quick technical fixes promised by AI . Indeed, the evidence over the past decade shows that reliance on digital systems such as Aadhaar to solve developmental challenges may have harmed the poor more than it helped them . Perhaps most of all, after decades of economic growth concentrated in low-employment sectors like software, Indians need mass employment, which AI use cases will not provide. We should understand the focus on use cases, then, as a particularly Indian species of technology hype, an inflated promise that makes things happen. In the US, AI hype has most often been premised on the emergence of an 'Artificial General Intelligence' with unimaginable, humanity-threatening capabilities that is supposedly right around the corner. These inflated promises have driven a massive surge of speculative investment and pushed market valuations of AI companies to new highs, despite little proven demand for the technology. In contrast, development as AI hype appears to offer a reasonable and socio-economically grounded alternative. Oriented not only toward the future but also toward the periphery of the capitalist system, it promises that those who have been on the margins of economic growth can serve as a source for data and a market for AI applications. This discursive structure may not be driving massive investment similar to US AI hype. Nevertheless, it serves a range of powerful constituents: 1. For the ruling government domestically, it projects an image of benevolent, technocratic developmentalism. Alongside its Hindu nationalism, this high-tech image has been a key plank of the current government's appeal . It is no accident that the exemplary AI use cases are chatbots, which are personalised technologies that provide a one-on-one interface with citizens to access targeted services. As such, AI use cases track with the BJP's shift away from the provision of public goods like basic health and primary health to the techno-patrimonial provision of private goods under Modi. 2. Globally, the 'use case' hype enables India to claim moral leadership on behalf of the global majority in the midst of a great power rivalry. A NITI Aayog AI strategy describes India as ' the AI Garage for 40% of the world ', suggesting that the AI use cases that India develops domestically will be exported to the global south. 3. For global development funders, like the Gates Foundation, who are pushing such initiatives elsewhere in the world under the label of AI for Development, the AI use case approach is the latest in a long line of digital interventions in development. It fits neatly within the philanthrocapitalist dogma that the solution to poverty is marketisation. 4. For the domestic software industry, the discourse of use cases legitimises the state-supported marketisation of a new digital population within India. It enables the extraction of citizens' data under the guise of development, though the financial value of these data and these customers is open to question. It offers the poor as test subjects in developing their products, while also opening up potential export markets in other developing countries. 5. For global tech giants, it offers a path to legitimise their activities in India. One of the most enthusiastic supporters of AI use cases is Microsoft CEO Satya Nadella, who recently (echoing Nilekani) remarked that India had become the 'AI use case capital of the world.' One of the most widely cited examples of AI in action for socio-economic development is the Jugalbandi chatbot, developed by Microsoft and IIT Madras, which provides vernacular language information about government services, and was released amidst a PR blitz in 2023. Adoption and usage statistics for the chatbot are unavailable. No further news has been released since 2023, and the project's website is no longer active. The discourse of AI use cases is seductive because it poses an excellent question: Why shouldn't the poor benefit from the most advanced technology? Unfortunately, socio-economic development use cases as currently articulated won't succeed within the contemporary conjuncture. The hype is unlikely to benefit the poor or India's AI ambitions. It leaves dominant power structures undisturbed and doesn't challenge the monopolistic and extractive practices that undergird Big Tech-led AI. Instead, it is empowering a range of powerful actors. What would it look like to centre poor and marginalised people while challenging Big Tech in an AI age? Most of all, it would require a shift away from treating people merely as end-users, data sources, and testing grounds of AI, but as its owners and producers. While genuine alternatives to the current set-up are largely speculative, initiatives imagining and working toward AI as a commons may provide inspiration for the kinds of changes that would be required for AI development to go hand-in-hand with socio-economic justice. Mila T Samdub researches the aesthetics and political economy of digital infrastructure in India. He is a Visiting Fellow at the Information Society Project at Yale Law School, a CyberBRICS Fellow at the Center for Technology and Society, Fundacao Getulio Vargas, and an Open Future Fellow. The article was first published in India in Transition , a publication of the Center for the Advanced Study of India, University of Pennsylvania.
Business Standard
23-05-2025
- Business
- Business Standard
The Ethical Compass: An upGrad Learner Explores Decision Science in India's Business Landscape
Introduction: A World Shaped by Data In recent months, the Indian tech ecosystem has been abuzz with conversations about the rapid adoption of generative AI, the implementation of data-driven strategies in governance, and ongoing debates surrounding data privacy legislation. These discussions underline a critical shift in decision-making, where data is no longer a supporting actor but the protagonist shaping India's future. However, as the reliance on data deepens, it brings with it an ethical conundrum: how do we ensure that data-driven decisions serve society equitably and do not amplify existing disparities? Consider the controversy over AI-based lending systems. These tools, while efficient, have been shown to inadvertently exclude underprivileged borrowers due to biases embedded in training datasets. Such incidents highlight a broader concern—without rigorous ethical oversight, even the most sophisticated data models risk causing harm. This article explores how India's business and governance sectors can harness the power of decision science responsibly, integrating bias mitigation, storytelling, and ethical frameworks into their strategies. Data-Driven Decisions: The Lifeblood of India's Growth India's ascendance as a digital powerhouse is intrinsically tied to the rise of data-driven decision-making. From the seamless operations of e-commerce giants like Flipkart and Amazon India to the predictive capabilities of fintech leaders such as Paytm and PhonePe, leveraging data has become central to the success of modern businesses. Recent studies by NASSCOM estimate that India's data analytics market is poised to reach $16 billion by 2025, growing at a compound annual growth rate (CAGR) of nearly 29%. This transformation is not limited to the private sector. Governments at both state and central levels are adopting data-driven frameworks to improve governance. For instance, digital platforms such as IndiaStack have revolutionized public service delivery by linking Aadhaar data with various welfare schemes. These systems not only enhance efficiency but also help reduce leakages and fraud. Yet, the effectiveness of data-driven decision-making relies heavily on context. Misinterpreted or misused data can lead to catastrophic outcomes. Take the healthcare sector, for example. During the COVID-19 pandemic, predictive models played a crucial role in allocating resources, but they often failed to account for on-the-ground realities, such as underdeveloped healthcare infrastructure in rural areas. This dissonance underscores the need to balance data insights with a nuanced understanding of socio-economic conditions. Navigating Ethical Quandaries in Decision Science Despite its undeniable potential, decision science is fraught with ethical challenges. The risks of algorithmic bias, data privacy breaches, and the misuse of AI loom large. A particularly pressing concern in India is the role of AI in recruitment. Several organizations have adopted automated hiring tools to streamline processes, yet studies reveal that these systems can unintentionally disadvantage candidates from underrepresented communities by favoring certain demographics based on historical data. Beyond recruitment, decision-making under uncertainty presents its own set of moral and practical dilemmas. Whether it's the allocation of climate adaptation funds or decisions around credit disbursement, incomplete or biased datasets can skew outcomes, disproportionately affecting vulnerable populations. For example, while predictive models can forecast monsoon rainfall, their limited precision often leaves farmers—who depend on accurate weather predictions—struggling to make informed decisions. To address these ethical challenges, India must prioritize transparency and inclusivity in decision-making frameworks. Ethical decision science is not just about compliance; it's about fostering trust and accountability. Bias Mitigation Strategies: A Blueprint for Change One of the most pressing challenges in data-driven decision-making is the mitigation of bias—both human and algorithmic. Heuristics, or mental shortcuts, often drive quick decisions but can lead to systematic errors. For example, the availability heuristic might lead policymakers to focus disproportionately on high-profile disasters like floods, while neglecting slower-moving crises such as groundwater depletion. Organizations can tackle these biases through several strategies. First, diversifying datasets is crucial. India's socio-economic diversity is unparalleled, and datasets used for decision-making must reflect this reality to avoid perpetuating stereotypes. Second, fostering multidisciplinary collaboration is essential. Decisions informed by data scientists, ethicists, and legal experts are more likely to address complex challenges comprehensively. Global best practices—such as conducting regular algorithmic audits and implementing explainable AI—can also be adapted to India's unique context. Transparency is key: businesses and policymakers must ensure that stakeholders can understand how decisions are made and challenge them when necessary. A recent initiative by NITI Aayog to promote responsible AI development is a step in the right direction, but it must be backed by stringent implementation. The Power of Storytelling: Bridging Data and Decisions Even the most precise analytics can fall flat without effective communication. This is where storytelling becomes a critical tool, transforming abstract data into narratives that inspire action. During the pandemic, Kerala's government set a precedent by not only leveraging data to manage the crisis but also communicating its strategies transparently and empathetically. The result was widespread public cooperation and trust. Storytelling is equally impactful in the corporate world. The Tata Group, for instance, has consistently used narratives to align its sustainability initiatives with its core values. By framing their data-driven strategies in humanistic terms, they have built lasting relationships with stakeholders. Businesses must recognize that data is only as persuasive as the story it tells. Whether it's communicating a strategic pivot to shareholders or launching a new product, framing insights through the lens of storytelling can bridge the gap between numbers and people. The AI Revolution: Navigating the Ethical Frontier Artificial intelligence represents the cutting edge of decision science, offering unparalleled predictive capabilities. In India, AI is being deployed across sectors—from optimizing traffic flow in Bengaluru to automating loan approvals in financial institutions. However, the speed of AI adoption has outpaced the development of robust ethical guidelines. A particular concern is the use of AI in surveillance. While facial recognition technology is being used to enhance security in urban areas, it raises significant privacy concerns. Who decides how this data is used, and who holds these decision-makers accountable? To ensure AI serves as a force for good, India must invest in homegrown standards for responsible AI. Initiatives such as the IndiaAI program and the ethical AI framework proposed by the Ministry of Electronics and Information Technology are commendable, but they must be operationalized with stakeholder engagement at every level. Conclusion: Towards an Ethical Framework for Decision Science As India cements its position as a global leader in data analytics and AI, it faces a unique opportunity—and responsibility—to define the ethical contours of decision science. The journey forward demands a collective commitment to transparency, inclusivity, and fairness. By integrating bias mitigation strategies, embracing storytelling, and upholding ethical principles, India's business leaders and policymakers can set a benchmark for the world. Ethical decision science isn't just about avoiding harm; it's about unlocking the full potential of data to benefit society. In the words of Mahatma Gandhi, 'The true measure of any society can be found in how it treats its most vulnerable members.' If India's decision science can rise to this standard, it will not only drive progress but ensure that progress is meaningful, just, and lasting. More details about the Doctor of Business Administration program can be found here. *This article includes internet and third party-based data points as supporting elements. About the Contributor: Jennifer Vasantha Ruby is an upGrad learner and a Senior Service Delivery Manager at Microsoft. She is currently pursuing a Doctor of Business Administration with a focus on Digital Leadership from Golden Gate University, San Francisco – powered by upGrad. With expertise in decision science and ethical AI frameworks, Jennifer is passionate about transparent practices and actively contributes to thought leadership in data-driven decision-making.
India Today
24-04-2025
- Business
- India Today
Middle-class stuck in debt trap, says Marcellus Investment's Saurabh Mukherjea
India's middle class is falling into a dangerous pattern of debt-fuelled consumption, warns Saurabh Mukherjea, founder of Marcellus Investment Managers. The dream of upward mobility is increasingly being financed through easy credit, and the fallout has already data from the Reserve Bank of India, Mukherjea estimates that 5-10% of middle-class households are now stuck in a debt trap. These are not isolated incidents, he says, but symptoms of a larger shift driven by social media-fuelled aspiration, cheap loans, and pandemic-era mindset two years at home during Covid, people were utterly convinced that whatever their financial means are, it doesn't matter—they too can live the good life,' Mukherjea said in a podcast with The Federal. From luxury vacations to designer gadgets and upscale homes, the illusion of affluence is now just an EMI away. Consumption, once tied to income, is being decoupled through credit.'You're told every minute that you should have the lifestyle of an IPL cricketer. You don't have to work for it—you can get it on credit,' Mukherjea pointed this shift is the India Stack—Jandhan, Aadhaar, and Mobile—which has made credit more accessible than ever before. While a success story in financial inclusion, Mukherjea argues it has also removed key friction points that once forced consumers to think twice before allows people to take on a ton of debt almost unthinkingly,' he distress is unfolding in predictable stages. First came defaults in microfinance. Then trouble in the unsecured loan segment. Now, credit card delinquencies are rising, and even two-wheeler financing is under pressure.'Whenever I see credit cards being handed out at airports, the analyst in me starts worrying,' Mukherjea now, home and car loans remain stable, but Mukherjea sees them as the next pressure points if the current cycle continues make matters worse, household savings have dropped to their lowest in 50 years. Middle-class income is being funnelled into loan repayments or risky investments in equities, leaving little safety net. Banks are feeling the strain as well, with deposit growth solution is a bold macroeconomic reset: 'We need the RBI to step in with a 2% rate cut, fresh liquidity, and a 10 to 15 percent devaluation of the rupee. That's the kind of decisive action that could pull us back from the edge.'Until then, the middle class remains locked in a fragile balancing act—living the good life on borrowed time, and borrowed money.
Associated Press
28-03-2025
- Business
- Associated Press
Digital Public Infrastructure: A unified global 'stack'?
03/28/2025, New York City, New York // KISS PR Brand Story PressWire // Over the past couple of years, there has been much international attention about Digital Public Infrastructure (DPI) and the 'India Stack.' And some appreciation/ concern that India is 'offering' India Stack to the world. We have also seen recent calls for setting up an Africa Stack and a Euro Stack. Let me begin by explaining what the India Stack is - and what it is not. For this, we'll take a step back before India began building out DPI. The Internet was the first DPI, developed mainly by US universities with some important contributions from Europe. It connected devices and made it possible for them to interact with each other. Similarly, the US government funded the Global Positioning System (GPS), which was built originally for military purposes. The US Government opened it up for public use in 2000 and has since continued to fund this important global DPI. GPS answers the fundamental question – where are you? With these two layers of DPI, the innovation landscape began to take off, especially in the US but also globally. In the early 2000s, we began seeing exponential growth and adoption of several products and services offered by the 'internet' companies. But the thinking in India at the time was contextual to the country's needs -- how could India leverage the power of digital technology to bring about exponential change not just to improve government services and reduce leakages but also create new capabilities that might lower the barriers for private sector innovation? India's digital ID, Aadhaar, which began in 2009, has enabled hundreds of millions of people to open bank accounts for the first time. It was a major validation of the exponential power of digital technology to drive societal scale impact. This set in motion a series of efforts to build additional digital capabilities as public infrastructure. This resulted in innovations like eSign (for digital document signing), DigiLocker (a secure digital wallet for verified credentials), Unified Payments Interface (UPI) (which revolutionized digital payments), and the Account Aggregator framework (which enables secure digital consent management). The chronology is well laid out at India was building a set of capabilities – a presence-less layer, a paperless layer, a cashless layer, and a digital consent layer. 'India Stack' is an idea that sums up these capabilities. India happens to be the preeminent example of where these capabilities have scaled massively. The Indian example has inspired many countries that are keen to build similar capabilities. India Stack that is not being 'offered' to other countries. The only exception has been the National Payments Corporation of India, which has begun offering the Unified Payments Interface to other countries over the past few years. Therefore, contrary to what seems to be a widely held belief, all the capabilities of the India Stack are proprietary technology built by India for India's needs. With this perspective on India Stack, it would be useful to turn our attention to the current global context. If there was ever a time when the phrase, 'may you live in interesting times' seemed resonant, it is now. Among the many shifts that we are beginning to witness, two ideas that directly impact the work many of us are collectively working towards are: (a) government efficiency as an idea will likely become an important theme in many parts of the world, and (b) the cushion of foreign aid will likely become less dependable for many developing countries. In light of these (and related developments), all of us who are working on supporting governments on their digitization efforts would do well to work towards ensuring that governments can indeed get more value for their expenditures. This is a time to build on each other's capabilities and strengths rather than to pursue exceptionalism. This is a time to coordinate efforts across the world and set in motion a series of steps that build 'public' infrastructure that is inclusive, respects sovereignty and ensures privacy protections, harnesses private sector capabilities, and enables a new genre of entrepreneurs to drive the next wave of innovation that increases public value.
