By Naina, 22nd May 2026

A quiet shift is underway in the architecture of the global economy. For most of the post-war period, emerging economies were defined by what they lacked: depth of capital markets, breadth of physical infrastructure, sophistication of regulatory institutions, density of skilled labour. The path to convergence with advanced economies was understood to be linear, and in most cases slow. A country that wished to industrialise had to begin where every other industrialised country had once begun, and follow broadly the same sequence. That paradigm is now visibly breaking down. The economies of India, Brazil, Indonesia, Vietnam, Nigeria, Kenya and others are not waiting to recapitulate the development experience of Britain, Germany or the United States. They are building parallel digital architectures of their own, and in several measurable respects they are pulling ahead of the advanced world.

The numbers tell the structural story. Emerging market and developing economies now account for roughly 45 percent of global gross domestic product on a market-exchange-rate basis, up from 25 percent at the turn of the century. On a purchasing-power-parity basis, the share is closer to 60 percent. The World Bank's January 2026 Global Economic Prospects report describes the recovery from the 2020 recession as the strongest five-year expansion in more than six decades, though it cautions that the gains have not been evenly distributed and that more than a quarter of emerging market and developing economies still have per capita incomes below pre-pandemic levels. The April 2026 World Economic Outlook from the IMF, written in the shadow of renewed conflict in West Asia, projects global growth at 3.1 percent for the year, with emerging markets continuing to outpace advanced economies despite a softer external environment. South Asia is expected to grow at 6.2 percent in 2026 and 6.5 percent in 2027, the highest among any regional grouping in the world.

What sits beneath these headline figures, and what gives the current moment its distinctive character, is the rise of a new growth model. It is digital-first, infrastructure-led, demographically powered and increasingly outward-facing. It is also, for the first time in modern economic history, being designed and exported by emerging economies themselves rather than imported from the developed world.

The Digital Public Infrastructure Revolution

The clearest example is India's Digital Public Infrastructure, now widely abbreviated as DPI. Built around three foundational layers — biometric identity through Aadhaar, instant payments through the Unified Payments Interface and consent-based data sharing through the Account Aggregator framework — DPI has compressed into roughly a decade what most economies took two or three generations to achieve. The Unified Payments Interface processed 21.7 billion transactions worth more than 28 lakh crore rupees in the single month of January 2026, with 691 banks live on the platform. The International Monetary Fund's 2025 report on retail digital payments recognised UPI as the world's largest real-time payment system by volume, accounting for roughly 49 percent of global instant payment transactions. Within India, 81 percent of retail payment transaction volume now flows over UPI rails. The Institut Montaigne and similar policy research institutions estimate that the economic value added by DPI to Indian GDP, already 0.9 percent in 2022, could climb to between 2.9 and 4.2 percent by 2030.

The Indian model has not stayed within Indian borders. As of February 2026, the Government of India has signed memoranda of understanding with twenty-four countries on DPI cooperation, covering identity, payments, data exchange and service delivery. UPI is now live in eight foreign jurisdictions including the United Arab Emirates, Singapore, Bhutan, Nepal, Sri Lanka, France and Mauritius. What is being exported is not a product but a set of design principles: open protocols, modular layers, public-private separation of concerns, and a deliberate refusal to lock developing countries into proprietary vendor ecosystems.

Brazil is running a parallel experiment with comparable results. Pix, the central bank's instant payments system, onboarded over 70 million users within two years of launch and is projected to contribute roughly 37.9 billion US dollars to Brazilian GDP by 2026, equivalent to about two percent of the country's output. The new national identity card, anchored to the taxpayer registration number, has proved structurally important during emergencies including the Rio Grande do Sul floods. Conecta GOV, an interoperability platform connecting nearly a thousand public services through open APIs, has saved an estimated 800 million dollars in administrative costs. The Brazilian and Indian approaches differ in their governance philosophy and their relationship to the private sector, but they share the same fundamental insight: digital public goods scale faster, cost less and reach further than the closed proprietary systems that defined the first generation of financial technology.

Fintech as a Continental Phenomenon

The DPI story is part of a broader fintech transformation that is now visible in every major emerging region. Fintech companies in emerging markets raised 17.3 billion US dollars in venture capital in 2024, according to CB Insights, distributed across India at 7.2 billion, Latin America at 5.8 billion, Southeast Asia at 2.2 billion and Africa at 2.1 billion. McKinsey projects that fintech revenue in emerging markets will grow at between 25 and 30 percent annually through 2030, against 10 to 15 percent in developed economies. The structural reasons are not hard to identify.

First, traditional banking infrastructure in emerging markets is thinner, which means there is far more empty space for digital alternatives to fill. Bank account penetration in sub-Saharan Africa is roughly 55 percent against 95 percent in OECD countries. Second, smartphone adoption has accelerated faster than analysts expected: sub-Saharan Africa moved from 25 percent smartphone penetration in 2018 to 50 percent in 2024, and Southeast Asia is now at 75 percent. Each new smartphone user is, in effect, a potential fintech customer who never needed a bank branch. Third, the demographic dividend is real and quantifiable. The median age in Africa is 19, in India 28 and in Southeast Asia 30. Younger populations adopt mobile financial services faster, demand newer features sooner and tolerate friction less than older customers in developed markets.

The Central Bank of Nigeria reported that the country's fintech sector expanded by approximately 70 percent in 2025, and is now rolling out a shared fraud-intelligence model, regulatory passporting for cross-border African fintechs and open banking standards. Kenya remains the global benchmark for mobile money, while M-Pesa-style models have been replicated across East and West Africa. In Southeast Asia, GrabPay, GoPay and GCash have moved from convenience tools to financial primary providers for tens of millions of customers in Indonesia, Vietnam, the Philippines and Thailand. China retains the world's largest fintech user base, with more than a billion digital wallet users, though its model is more state-coordinated and less open than the rails being built elsewhere.

The Demographic Equation

The strategic context that gives the digital-first growth model its weight is demographic. The World Bank estimates that 1.2 billion young people in emerging markets and developing economies will reach working age in the next ten years. By comparison, the entire population of the European Union is approximately 450 million. Whether these 1.2 billion people find productive employment will determine the political stability, geopolitical orientation and economic vitality of more than a hundred countries.

The labour market mathematics is daunting. Traditional manufacturing-led development, which absorbed surplus rural labour in East Asia from the 1960s to the 1990s, no longer offers the same absorption capacity. Manufacturing employment has plateaued or fallen as a share of total employment even in successful developing economies, partly because automation has reduced the labour intensity of production and partly because trade tensions and supply-chain reshoring have narrowed the export-led growth window. The implication is that emerging economies must find ways to create high-quality jobs without relying on the manufacturing-export model that defined Korean, Taiwanese and Chinese development.

Digital services, platform-mediated entrepreneurship and skilled-services exports have become the substitute pathways. India's information-technology services sector continues to generate roughly 200 billion US dollars in annual export revenue, even as the volume-hiring model that supported it for two decades comes under pressure from artificial intelligence. The Philippines' business process outsourcing industry remains a major employer. Vietnam has built a hardware-assembly and design ecosystem that has grown faster than most regional analysts predicted. Nigerian and Kenyan technology talent is increasingly visible in remote-work labour markets across North America and Europe. None of these pathways individually solves the 1.2 billion problem, but together they describe a recognisable architecture of digital-first employment that did not exist a generation ago.

The AI Inflection

Artificial intelligence sits over this transition as both opportunity and threat. The opportunity is straightforward: emerging economies that build sovereign compute, train domestic models in local languages and integrate AI into public service delivery can compress decades of institutional development into years. India's national AI mission, the United Arab Emirates' Falcon model programme, Saudi Arabia's HUMAIN initiative, Singapore's SEA-LION effort and Indonesia's Sahabat-AI all represent serious attempts to participate in the frontier rather than depend on foreign providers.

The threat is equally clear. The IMF's research on the cross-country impact of AI finds that growth gains in advanced economies could be more than twice the gains in low-income countries, driven by differences in sectoral exposure, AI preparedness and access to data and chips. Even with best-case improvements in domestic readiness, a significant gap is likely to persist. Low-income economies dominated by agriculture, with weak digital infrastructure and limited access to frontier compute, face the most acute risk of falling further behind. The same technology that enables a well-positioned emerging economy to leapfrog could relegate an unprepared one to permanent peripheral status.

The capital costs of AI participation are formidable. Frontier-scale data centres now cost between 15 and 20 million dollars per megawatt to build, up from roughly 10 million dollars for traditional hyperscale facilities. Goldman Sachs projects cumulative global AI capital expenditure of approximately 7.6 trillion dollars between 2026 and 2031. The bulk of that spending is presently concentrated in the United States and, to a lesser extent, China. For most emerging economies, the realistic question is not whether to compete at the frontier but how to negotiate workable terms of access while building domestic capability in deployment, applications and specialised models.

The Risks That Sit Just Below the Surface

The digital-first growth model carries risks that have only recently begun to receive serious analytical attention. The first is the data-protection gap. UPI alone produces a behavioural dataset of staggering depth, and India's Digital Personal Data Protection Act of 2023, while a meaningful step, leaves substantial implementation questions unresolved. Brazil's framework is more developed but still evolving. African and Southeast Asian frameworks vary widely in quality. Systems that were built to extend economic participation can also enable large-scale extraction of behavioural data without commensurate safeguards. The same characteristic that makes DPI economically valuable — its capacity to generate continuous, granular information about user behaviour — makes it strategically sensitive.

The second risk is concentration. The fintech ecosystems of most emerging economies are dominated by a handful of platforms. A regulatory shock, a cybersecurity incident or a corporate failure at any one of these platforms could disrupt payments, lending or identity verification for tens of millions of users. The collapse of a single major super-app in Southeast Asia, or the suspension of a major mobile-money operator in East Africa, could produce systemic effects that traditional banking regulators would find difficult to contain.

The third risk is geopolitical. Digital sovereignty has become an explicit policy objective in capitals from New Delhi to Brasília to Jakarta. The choice of cloud provider, the location of data, the source of frontier models and the standards governing cross-border data flows are increasingly entangled with foreign-policy considerations. The emerging-market response has been to invest in domestic alternatives and to push for multilateral standards that prevent either Washington or Beijing from setting unilateral terms. Whether that approach proves durable will depend on whether emerging economies can coordinate effectively in venues such as the BRICS+ grouping, the G20 digital working stream and the United Nations.

The Indian Example, in Context

India's position warrants particular attention because it sits at the intersection of every variable that matters. It is the world's most populous country, with a median age of 28 and a labour force expanding by roughly twelve million workers a year. It is the fastest-growing large economy. It has built the most extensive digital public infrastructure of any developing nation, and it is actively exporting that model. Its private technology sector is large and globally competitive. Its English-language skills, legal system and growing private equity ecosystem give it a foundation that few peers can match.

The India Skills Report 2026 records national employability at 56.35 percent, the highest figure on record, and notes that more than 90 percent of Indian employees have begun using generative AI tools in some capacity. ServiceNow's modelling suggests that India will add 33.9 million workers to its workforce by 2028, including roughly 2.73 million in technology-intensive roles. PwC estimates that AI could contribute approximately 23.8 percent to India's GDP by 2030. These figures suggest a country with the demographic, institutional and infrastructural conditions to make the digital-first transition successfully.

The qualifying observation is that these figures depend on execution. Bernstein Research's recent open letter to the Prime Minister highlighted the deepening quality-of-employment concern as AI compresses the routine work that has anchored the IT-services sector. Domestic compute capacity remains limited. Skills training has not kept pace with the displacement happening at the lower rungs of the white-collar workforce. The gap between announcement and implementation continues to be the structural feature that international investors flag most often.

What Comes Next

The trajectory of emerging economies over the next decade will be shaped by three policy choices that are no longer optional. The first is investment in digital and physical infrastructure simultaneously rather than sequentially. The old development model, in which physical roads and ports were built first and digital systems layered on top, has been overtaken. Countries that build power generation, broadband connectivity and compute capacity together will outperform those that treat them as separate projects.

The second is human capital. The technology shifts now under way will obsolete a significant share of existing job categories within a decade, and the pace of obsolescence is accelerating. The countries that emerge strongest from this transition will be those that have built nimble retraining systems, that have made tertiary education affordable enough to scale, and that have created credentialing pathways outside the formal university system. The 1.2 billion young people entering the labour market do not need degrees as much as they need bankable skills that the market actually rewards.

The third is governance. Digital-first economies generate concentrated economic rents, concentrated data and concentrated political power. Without competitive markets, robust privacy frameworks, transparent procurement and credible dispute resolution, the gains of the digital transition will be captured by a small elite while the costs are distributed across the population. The political economy of every emerging market is being reshaped by these dynamics, and the countries that handle the governance question best will be the countries that translate digital-first growth into durable prosperity.

The defining feature of the global economy in 2026 is that the centre of gravity is shifting. Emerging economies are no longer the recipients of growth models designed elsewhere. They are the architects of new models that the rest of the world is beginning to study. India's DPI, Brazil's Pix, Kenya's mobile money, Vietnam's manufacturing-to-services pivot, Indonesia's digital-financial ecosystem and the Gulf's sovereign-AI programmes are not isolated experiments. They are early entries in a new chapter of global development, and the next decade will reveal whether the chapter ends in genuine convergence with advanced economies or in a more complicated story of selective leapfrogging set against persistent and possibly widening gaps.

The opportunity is real. The execution risk is equally real. The window is open now in a way that it has not been for a generation. Whether emerging economies use it will determine, more than any other variable, the shape of the world in 2035.