The Bangalore Consensus, India’s AI Ascent, the Compute Imperative, and the Case for Global South Leadership in an Algorithmic Age
For decades, the narrative of technology and development has been written from the global North. It is a story of innovation concentrated in Silicon Valley, Shenzhen, and a handful of metropolitan clusters where venture capital, research universities, and engineering talent converge. The rest of the world—the Global South of Africa, Latin America, Southeast Asia, and South Asia—has been positioned as consumers, not creators; as markets to be captured, not innovators to be reckoned with; as sources of data to be extracted, not partners in governance.
That narrative is becoming obsolete. It is being displaced not by rhetoric or aspiration but by infrastructure, policy, and demonstrated capability. India, as S. Krishnan, Secretary of the Ministry of Electronics and Information Technology, argues in the accompanying essay, is no longer content to be a peripheral participant in the AI revolution. It is building the compute capacity, cultivating the talent, and deploying the applications that position it as a leading power in the development and governance of artificial intelligence. More significantly, it is articulating a distinctive model of AI development—one rooted in digital public infrastructure, focused on population-scale public problems, and committed to the principle that technological progress must serve human welfare rather than merely corporate profitability.
This is not merely a national aspiration; it is a Global South project. The compute platform that India is constructing, with more than 38,000 GPUs allocated to researchers, startups, and government departments, addresses what Krishnan identifies as “one of the biggest barriers for countries in Africa, Southeast Asia, and Latin America that want to build advanced AI systems.” The solutions that Indian startups are developing in health diagnostics, agricultural advisory, educational tutoring, and governance platforms are not designed for the affluent consumers of the global North; they are designed for the diverse, resource-constrained, linguistically plural populations of the global South. And the regulatory frameworks that India is developing, balancing innovation with trust, are not imported from Brussels or Washington; they are being forged in Delhi, responsive to Indian conditions and scalable to Indian scale.
India’s AI journey is not complete. Research intensity must increase; domestic hardware capability must be developed; data quality and cybersecurity must be continuously strengthened. But the direction is unmistakable. India is moving from being a consumer of global technologies to a contributor to frontier innovation. And in doing so, it is challenging the assumption that the AI revolution will be written exclusively in the code, capital, and values of the global North.
The Foundation: Digital Public Infrastructure as AI Enabler
India’s AI capabilities did not emerge from a vacuum. They are the product of a deliberate, sustained, and strategically coherent investment in digital public infrastructure that began more than a decade before generative AI captured the world’s imagination.
The Aadhaar system, launched in 2009, created a foundational identity infrastructure that now covers more than 1.3 billion residents. It is not merely a database; it is a platform for inclusion, enabling previously excluded populations to access banking, subsidies, and services. The Unified Payments Interface (UPI), launched in 2016, revolutionised digital payments, processing billions of transactions monthly with a reliability and efficiency that rivals—and in many respects exceeds—comparable systems in advanced economies. DigiLocker, the Open Network for Digital Commerce, and the Ayushman Bharat Digital Mission have extended this infrastructure into documents, commerce, and healthcare.
These systems share common characteristics that make them uniquely valuable as foundations for AI. They are built on open standards and interoperable protocols, enabling diverse applications to integrate and communicate. They operate at population scale, generating vast datasets that reflect the真实 diversity of Indian society. They are publicly governed, accountable to democratic institutions rather than to corporate shareholders. And they are designed for inclusion, with low-cost, low-friction access that extends to the poorest and most marginalised citizens.
The significance of this foundation for AI cannot be overstated. Machine learning models are only as good as the data on which they are trained; India’s digital public infrastructure has generated high-quality, standardised, interoperable data on an unprecedented scale. The same infrastructure that enables a street vendor to accept UPI payments also generates the transaction records that can train credit underwriting models for the unbanked. The same platform that authenticates a farmer’s identity for subsidy disbursement also generates the data that can train crop advisory systems. India does not need to import training data from California or Shanghai; it has generated its own, reflecting its own conditions, its own populations, and its own priorities.
This is the foundational advantage that Krishnan identifies. India has “systems that can support AI-led innovation in governance, public services and commercial applications.” It has not merely adopted AI; it has prepared the ground for AI through a decade of patient, cumulative institutional investment.
The Compute Imperative: Breaking the Barrier
The second pillar of India’s AI strategy is the recognition that access to computing power is the new determinant of technological sovereignty.
Training large language models and deploying advanced AI applications requires immense computational resources—specifically, graphics processing units (GPUs) that are manufactured by a handful of companies and allocated according to commercial priorities. Countries without access to these resources cannot train their own models; they must rely on models developed elsewhere, trained on data from elsewhere, and optimised for conditions elsewhere. They become perpetual dependents in the AI value chain, consuming technologies they cannot produce and adapting tools they cannot design.
The IndiaAI Mission, launched in 2024, directly addresses this dependency. Its national AI compute platform, equipped with more than 38,000 GPUs, is already operational. More than 22,000 GPUs have been allocated to 291 end users, including central and state government departments, academic researchers, students, and micro, small, and medium enterprises. This is not a symbolic gesture; it is a strategic intervention in the political economy of AI development.
The significance of this intervention extends far beyond India’s borders. As Krishnan notes, access to compute is “one of the biggest barriers for countries in Africa, Southeast Asia, and Latin America that want to build advanced AI systems.” By alleviating this barrier for its own researchers and entrepreneurs, India is demonstrating that compute access need not be a permanent constraint. More ambitiously, it is creating a model that other Global South countries can study, adapt, and perhaps eventually replicate. The compute platform is not merely an infrastructure project; it is a proof of concept for technological sovereignty in the AI age.
The Talent Multiplier: Scale as Structural Advantage
India’s third strategic asset is its workforce. The country ranked second in the Global AI Vibrancy Rankings published by Stanford University’s Institute for Human-Centered AI. It possesses one of the world’s largest pools of engineers, coders, and data scientists. This is not merely a matter of headcount; it is a structural advantage that cannot be easily replicated.
AI adoption is not a discrete event; it is a continuous process of integration. AI tools must be embedded into healthcare workflows, supply chains, government processes, and industrial operations. This requires not a handful of elite researchers but a broad base of technically competent professionals who can understand, adapt, and deploy AI systems across diverse domains. India’s workforce provides this base.
The scale of India’s talent pool also enables a distinctive model of AI development. Startups in the Global North often focus on high-margin, low-volume applications serving affluent consumers or specialised enterprises. Indian startups, addressing a market of 1.4 billion people with immense diversity in income, language, and infrastructure access, must develop solutions that are affordable, scalable, and linguistically adaptable. This constraint, imposed by the market, has become a competitive advantage. Indian health diagnostic models, crop advisory systems, AI tutors, and governance platforms are designed for conditions that characterise much of the Global South. They are not inferior versions of Northern products; they are distinctive innovations developed in response to distinctive conditions.
Krishnan’s observation that “India’s workforce gives the country a long-term structural strength that many advanced economies cannot replicate” is not chauvinism; it is demographic arithmetic. Advanced economies with ageing populations and restrictive immigration policies cannot reproduce India’s pool of young, technically trained professionals. This advantage will compound over time, as experience accumulates and networks deepen.
The Governance Question: Trust Without Restriction
The fourth dimension of India’s AI strategy is its approach to governance. Krishnan articulates the objective with characteristic precision: “India aims to achieve trust without restricting progress.”
This formulation rejects the false dichotomy between innovation and regulation that has paralysed policy debates in many jurisdictions. It recognises that regulation is not merely a constraint on innovation; it is a precondition for sustainable innovation. Users will not adopt technologies they do not trust; investors will not fund ventures that operate in legal uncertainty; societies will not tolerate systems that amplify bias, violate privacy, or operate without accountability.
India’s governance approach is rooted in its experience with digital public infrastructure. Aadhaar, UPI, and other platforms have demonstrated that publicly governed, standards-based systems can achieve scale while protecting rights and enabling innovation. The same principles are being extended to AI. The IndiaAI Mission includes components on responsible AI development, ethical frameworks, and safety norms. These are not afterthoughts; they are integral to the strategic vision.
India’s governance choices will have significance beyond its borders. As Krishnan notes, “global regulatory frameworks must evolve to support innovation while also addressing concerns over misuse, bias, and safety.” India, as a major AI developer and deployer, will inevitably influence this evolution. Its choices about data protection, algorithmic transparency, and accountability mechanisms will be studied and, in some cases, emulated by other Global South countries. Its voice in international forums such as the Global Partnership on AI, the OECD, and the United Nations will carry increasing weight. The governance frameworks developed in Delhi will not merely regulate Indian AI; they will shape the global conversation about what responsible AI development looks like.
Conclusion: From Consumer to Contributor
India’s AI journey is incomplete. Krishnan acknowledges the gaps: research intensity must increase; domestic hardware capability must be developed; data quality and cybersecurity must be continuously strengthened. The compute platform is operational but must be sustained; the talent pool is large but must be continuously replenished; the governance frameworks are emerging but must be tested and refined.
Yet the direction is unmistakable. India is moving from being a consumer of global technologies to a contributor to frontier innovation. It is no longer content to adopt tools developed elsewhere, adapted to conditions elsewhere, optimised for populations elsewhere. It is developing its own tools, for its own conditions, for its own populations—and, increasingly, for the populations of the Global South that share those conditions.
This is not autarky or technological nationalism. India remains deeply integrated into global technology ecosystems; its researchers collaborate with international peers, its startups raise capital from global investors, its companies serve customers around the world. The objective is not isolation but agency—the capacity to shape one’s own technological destiny rather than merely respond to forces beyond one’s control.
The AI Impact Summit 2026, which Krishnan mentions in his concluding paragraphs, is a symbol of this aspiration. It brings together policymakers, innovators, and deployers from across the world to discuss the governance, deployment, and scaling of AI systems. It is not a gathering of the Global South alone; it includes participants from the Global North as well. But it is hosted in India, reflecting the country’s emergence as a convening power in global technology governance. It is not a protest against the existing order; it is a demonstration that a new order is possible.
The AI revolution will shape the world’s economic and political order over the next decade. The choices made by global stakeholders now will determine whether this transformation is limited to a few technologically advanced countries or extends to include the large, diverse, and dynamic societies of the Global South. India is making its choices. It is building compute capacity, cultivating talent, deploying applications, and developing governance frameworks. It is asserting its right to participate in the AI revolution not as a consumer but as a creator, contributor, and leader.
The Bangalore Consensus—if such a term can be coined—is not a fixed doctrine or a detailed blueprint. It is an orientation: a conviction that the Global South need not be a passive recipient of technologies developed elsewhere; that digital public infrastructure is a foundation for technological sovereignty; that compute access is a strategic imperative; that talent at scale is a structural advantage; and that governance frameworks must balance trust and innovation. It is a work in progress, subject to revision and refinement as experience accumulates and circumstances change.
But it is also a declaration. It declares that the AI revolution will not be written exclusively in the code, capital, and values of the global North. It declares that India—and, by extension, the Global South—will be a protagonist in this revolution, not a spectator. It declares that the future of AI will be shaped not only in boardrooms of Silicon Valley and research laboratories of Shenzhen but also in the policy corridors of Delhi, the startup incubators of Bengaluru, and the digital infrastructure that connects a farmer in Maharashtra to a crop advisory system trained on Indian data and optimised for Indian conditions.
The declaration is not yet fulfilled; it is a promise, not an achievement. But the direction is clear. India is moving from consumer to contributor. And in doing so, it is changing the story of what the AI revolution can be.
Q&A Section
Q1: What is the role of India’s digital public infrastructure (DPI) in enabling its AI capabilities, and why is this foundation described as a “strategic advantage”?
A1: India’s DPI—including Aadhaar, UPI, DigiLocker, and the Ayushman Bharat Digital Mission—provides three critical enablers for AI development. First, data standardisation and interoperability: these systems generate high-quality, structured data that can be used to train machine learning models. Unlike the fragmented, unstructured data ecosystems of many countries, India’s DPI produces data that is immediately usable for AI applications. Second, population scale: the DPI operates at a scale of over a billion users, generating datasets that reflect the真实 diversity of Indian society. This is particularly valuable for training models that must perform across linguistic, regional, and socioeconomic variations. Third, public governance: the DPI is accountable to democratic institutions rather than corporate shareholders, enabling a distinctive approach to AI development that prioritises public welfare over commercial optimisation.
This foundation is described as a “strategic advantage” because it cannot be rapidly replicated. Countries that have not invested in similar infrastructure over the past decade cannot conjure it into existence to support their AI ambitions. India’s DPI is the product of sustained, cumulative institutional investment; it is not a legacy of colonial extraction but a deliberate creation of the post-colonial developmental state. This gives India a structural head start in the AI race that cannot be overcome by increased spending or political will alone.
Q2: What is the “compute imperative,” and how does the IndiaAI Mission’s national AI compute platform address the barrier of compute access?
A2: The compute imperative is the recognition that access to advanced computing resources—specifically graphics processing units (GPUs)—is the new determinant of technological sovereignty. Training large language models and deploying sophisticated AI applications requires immense computational power that is concentrated in a handful of companies and regions. Countries without such access cannot train their own models; they must rely on models developed elsewhere, trained on data from elsewhere, and optimised for conditions elsewhere. They become perpetual dependents in the AI value chain.
The IndiaAI Mission’s national AI compute platform, equipped with more than 38,000 GPUs, directly addresses this barrier. Over 22,000 GPUs have already been allocated to 291 end users, including government departments, academic researchers, students, and MSMEs. This is not merely a subsidy programme; it is a strategic intervention in the political economy of AI development. By alleviating compute scarcity for its own researchers and entrepreneurs, India is demonstrating that compute access need not be a permanent constraint. More ambitiously, it is creating a model that other Global South countries can study and adapt. The compute platform is thus both an infrastructure project and a proof of concept for technological sovereignty.
Q3: Why is India’s talent pool described as a “structural advantage” that advanced economies cannot easily replicate?
A3: India’s talent advantage operates at three levels. First, scale: India possesses one of the world’s largest pools of engineers, coders, and data scientists. This is not merely a matter of headcount; it enables a distinctive model of AI deployment. AI adoption requires a broad base of professionals who can integrate AI tools into healthcare, supply chains, government processes, and industrial operations. India has this base; many advanced economies do not. Second, demography: India has a young, growing workforce, while many advanced economies face ageing populations and labour shortages. This demographic advantage compounds over time. Third, market-driven innovation: Indian startups, addressing a market of 1.4 billion people with immense diversity in income, language, and infrastructure, must develop solutions that are affordable, scalable, and linguistically adaptable. This constraint has become a competitive advantage, producing innovations designed for Global South conditions.
Krishnan’s claim that this advantage “many advanced economies cannot replicate” is not chauvinism but demographic arithmetic. Advanced economies with restrictive immigration policies and ageing populations cannot reproduce India’s pool of young, technically trained professionals. This advantage is structural, not contingent—it is embedded in India’s demographic profile and educational investments, not dependent on temporary policy choices.
Q4: What does Krishnan mean by the objective to “achieve trust without restricting progress,” and how does this formulation differ from regulatory approaches in other jurisdictions?
A4: The formulation rejects the false dichotomy between innovation and regulation that has paralysed policy debates in many jurisdictions. It recognises that regulation is not merely a constraint on innovation but a precondition for sustainable innovation. Users will not adopt technologies they do not trust; investors will not fund ventures operating in legal uncertainty; societies will not tolerate systems that amplify bias, violate privacy, or operate without accountability.
This approach differs from both the European model (which prioritises rights protection through comprehensive, legally binding regulation) and the American model (which has historically favoured light-touch, sectoral, ex post enforcement). India’s approach, rooted in its experience with digital public infrastructure, emphasises publicly governed, standards-based systems that enable innovation while protecting rights. It is neither laissez-faire nor prescriptive; it is pragmatic and experimental.
The governance frameworks emerging from the IndiaAI Mission—including components on responsible AI, ethical frameworks, and safety norms—are not afterthoughts but integral to the strategic vision. They are being developed concurrently with compute infrastructure and skilling programmes, not sequentially. This reflects the recognition that governance is not a constraint on innovation but a component of innovation capacity. Countries that cannot govern AI responsibly will not be trusted to develop AI at scale.
Q5: What is the “Global South project” implicit in Krishnan’s essay, and why is India’s AI development framed as having significance beyond its national borders?
A5: The Global South project is the proposition that the AI revolution need not be written exclusively in the code, capital, and values of the global North. India’s AI development is framed as having broader significance because it addresses constraints—compute access, talent scarcity, governance capacity—that are shared across Africa, Southeast Asia, and Latin America. The compute platform demonstrates that compute access need not be a permanent barrier; the talent pool demonstrates that scale is a structural advantage; the DPI demonstrates that public infrastructure can enable private innovation; the governance frameworks demonstrate that trust and innovation can be balanced.
Krishnan explicitly notes that Indian solutions in health diagnostics, crop advisory, AI tutoring, and governance platforms are attracting interest from countries facing similar challenges. This is not accidental; these solutions are designed for Global South conditions—affordability constraints, linguistic diversity, infrastructure variability—that characterise much of the world. They are not inferior versions of Northern products; they are distinctive innovations developed in response to distinctive conditions.
The AI Impact Summit 2026, which Krishnan mentions, symbolises this aspiration. It is not a gathering of the Global South alone; it includes Global North participants. But it is hosted in India, reflecting the country’s emergence as a convening power in global technology governance. It is not a protest against the existing order; it is a demonstration that a new order is possible. The Global South project is not about autarky or technological nationalism; it is about agency—the capacity of diverse societies to shape their own technological destinies rather than merely respond to forces beyond their control.
