Dipak Kurmi
The rise of Artificial Intelligence as the defining technology of the twenty-first century has fundamentally altered the architecture of global power, placing data, computing capacity, and algorithmic capability on a strategic pedestal once reserved for energy reserves and financial dominance. Nations are increasingly aware that the future geopolitical hierarchy will be shaped not merely by military strength or economic size, but by the ability to design, control, and deploy intelligent systems at scale. It is against this transformative backdrop that the India-led AI Impact Summit in New Delhi, scheduled from 16 to 20 February 2026, assumes exceptional significance. The gathering is not merely another technology conference; it represents a deliberate attempt to reframe how AI is conceptualised, governed, and operationalised. Its distinctive emphasis on inclusion and developmental outcomes marks a conscious departure from prevailing narratives centred on scale, monopoly, and technological dominance.
India’s intervention arrives at a pivotal historical juncture often described as the “AI Techade,” a period in which the most powerful AI models remain heavily concentrated within a narrow cluster of countries and large corporations. This concentration has raised legitimate concerns about digital dependency, technological asymmetry, and the risk of algorithmic colonialism for developing economies. India’s proposition seeks to counter this imbalance by advancing a model of democratic, frugal, and sovereign AI explicitly tailored to the institutional and economic realities of the Global South. Rather than replicating capital-intensive Western approaches, the Indian framework prioritises affordability, adaptability, and broad-based accessibility. In doing so, it attempts to widen the global AI conversation beyond the traditional centres of innovation and to reposition emerging economies as co-creators rather than passive consumers of advanced technologies.
A central pillar of India’s strategy draws upon its experience with Digital Public Infrastructure, a domain in which the country has already demonstrated an ability to build scalable, population-level digital systems. Leveraging this foundation, India has positioned itself simultaneously as a laboratory for large-scale technological deployment and as a bridge connecting cutting-edge innovation with resource-constrained societies. The architecture underpinning this ambition is notably layered and strategic, spanning five interlinked components: applications, models, chips, infrastructure, and energy. Together these layers form a vertically integrated ecosystem designed to sustain national AI capabilities while reducing excessive dependence on external vendors or geopolitically vulnerable supply chains. In an era marked by growing concerns about the weaponisation of technology and supply chain disruptions, such strategic autonomy carries considerable policy weight.
India’s operational focus on task-specific and medium-scale AI models represents a deliberate departure from the global race toward ever-larger foundation models. These systems are being optimised for population-scale governance, service delivery, and productivity enhancement by aligning model design with real-world use cases such as agricultural advisories, welfare targeting, health diagnostics, and education delivery. Supporting this ambition are concrete investments, including the establishment of a 10,000-GPU national compute grid and initiatives like BharatGen, which aim to democratise access to advanced computing resources for startups, researchers, and public institutions. The guiding philosophy, often described as “diffusion first,” emphasises widespread deployment and local usability rather than confinement within elite research environments or proprietary corporate silos. This approach reflects a pragmatic understanding that technological impact in developing economies depends less on model size and more on accessibility and contextual relevance.
By systematically reducing training costs, improving inference efficiency, and foregrounding return on investment, India is presenting a credible alternative to capital-heavy AI paradigms that remain beyond the reach of most developing nations. The results of this strategy are already visible in global comparative metrics. International assessments consistently place India among the top three countries in terms of AI talent availability and preparedness, a ranking that underscores the depth of its human capital pipeline. More importantly, India has demonstrated a rare capacity to deploy AI at population scale, effectively functioning as a systems integrator capable of aligning advanced technologies with governance structures and developmental priorities. This capability distinguishes India from many innovation-driven economies that excel in research but struggle with nationwide implementation.
The political framing of India’s AI vision has been articulated with increasing clarity in international forums. Senior leadership has conveyed an unambiguous message: India does not intend to remain a peripheral participant in the AI race but seeks to act simultaneously as a producer, tester, and global partner. Among the most consequential components of this ecosystem is the country’s sustained investment in language technologies. With twenty-two official languages and hundreds of dialects, India confronts one of the most complex linguistic landscapes in the world. Addressing this challenge is the Bhashini initiative under the National Language Translation Mission, which provides open and interoperable AI services for speech recognition, translation, and voice interfaces. Designed as a public digital good, Bhashini enables governments, startups, and civil society organisations to build local-language services at affordable cost, thereby lowering the barriers to digital participation.
The platform’s reliance on participatory data creation, including crowdsourced language contributions, ensures that AI systems capture local idioms, cultural nuance, and contextual meaning rather than reproducing imported linguistic norms. This linguistic capability carries significance far beyond technical convenience. Language access determines who can participate meaningfully in the digital economy, access public services, and exercise civic rights. By embedding linguistic inclusion into its AI architecture, India advances a powerful political proposition: digital citizenship should function as a universal entitlement rather than a privilege mediated by language proficiency. In many ways, this principle lies at the moral core of India’s broader AI diplomacy.
The international resonance of this model is particularly evident in Africa, where demographic dynamism, linguistic diversity, and developmental urgency mirror many of India’s own structural conditions. With more than two thousand languages and a rapidly expanding youth population, African economies face challenges strikingly similar to those India has navigated over the past two decades. This convergence creates a natural foundation for South-South technological cooperation. Techniques developed in India for training low-resource language models can be directly adapted to African linguistic ecosystems, enabling local languages to function as effective interfaces for governance, education, and commerce. Such alignment supports the objectives of Agenda 2063 and its Second Ten-Year Implementation Plan for 2024–2033, both of which identify digital transformation and AI as central drivers of inclusive growth.
Beyond the language domain, India’s DPI-driven AI solutions offer ready-to-deploy templates across multiple sectors, including healthcare delivery, financial inclusion, agricultural extension, and logistics optimisation. Instead of importing expensive proprietary platforms, African countries have the opportunity to co-develop sovereign AI capabilities that preserve control over national data while benefiting from shared architectures and open standards. The economic implications of such collaboration are substantial. Strategic AI adoption can significantly boost productivity, reduce transaction costs, and integrate fragmented regional markets. Properly implemented, these technologies could help operationalise the African Continental Free Trade Area and strengthen regional groupings such as the East African Community, the Economic Community of West African States, and the South African Customs Union by streamlining customs procedures and optimising cross-border supply chains.
India’s experience in fintech-enabled credit assessment illustrates another dimension of AI’s developmental potential. Algorithmic risk modelling has already expanded credit access for small and medium enterprises in India, and similar frameworks could unlock financing for African SMEs that form the backbone of many national economies. Equally important is AI-driven manufacturing optimisation, which can support Africa’s transition from dependence on raw material exports toward higher-value industrial production. Such structural transformation is essential for long-term economic resilience and diversification in the face of volatile commodity markets.
Concerns about employment displacement remain a persistent shadow over global AI debates, yet India’s approach offers a notably different narrative. Rather than emphasising labour substitution, the Indian framework prioritises skill augmentation and workforce enhancement. AI tools are deployed to improve worker productivity, support decision-making, and expand service coverage across sectors. For African economies, where millions of young people enter the labour market each year, this distinction is critical. AI-enabled vocational training delivered in local languages can accelerate skill acquisition and align workforce capabilities with emerging sectors such as digital services, green manufacturing, and intelligent logistics. Within this framework, AI becomes an engine for decent employment rather than a catalyst for job erosion.
The broader global significance of the AI Impact Summit lies in its contribution to the evolving architecture of international technology governance. By foregrounding impact, accessibility, and safety, India has catalysed a wider conversation among developing nations about data sovereignty, algorithmic bias, and the ethical deployment of intelligent systems. The strong participation of governments, multilateral institutions, and industry leaders signals growing momentum toward a more inclusive techno-legal order. For many countries in the Global South, the summit represents the first credible platform where their developmental concerns are integrated into the core AI policy discourse rather than treated as peripheral afterthoughts.
In strategic terms, the India-led AI Impact Summit marks a pivotal moment in the evolution of global artificial intelligence. By advancing a sovereign, frugal, and inclusive model, India has expanded the horizon of what technological leadership can look like in a multipolar world. Its AI stack demonstrates that innovation need not be synonymous with exclusion or monopolistic scale, and that advanced technology can be aligned with equity, autonomy, and shared prosperity. The deepening convergence between India and Africa further illustrates how South-South cooperation may shape the next phase of the digital revolution. As AI continues to redefine economic structures and social contracts worldwide, the principles articulated through this emerging partnership—openness, sovereignty, and human-centric design—may ultimately determine whether artificial intelligence becomes a bridge toward collective advancement or a barrier that entrenches old global divides.
(The writer can be reached at dipakkurmiglpltd@gmail.com)
