AI summit at Bharat Mandapam signals India’s push to shape AI deployment for the developing world, focusing on scalable, multilingual and real-world solutions.
AI summit at Bharat Mandapam signals India’s push to shape AI deployment [Photo: X]
New Delhi: On the surface, the scale of the AI summit unfolding in Bharat Mandapam appears, for some, excessive for what is, at its core, a technology conference. Heads of government from more than 20 countries are attending. Prime Minister Narendra Modi is personally invested in its execution.
The event is dominating international headlines, is all across social media, and has entered everyday conversation far beyond technical circles. Even those with no direct connection to artificial intelligence are paying attention. This is not accidental. The summit is not merely an event. It is a signal.
Technology transitions are driven as much by perception as by engineering. Governments cannot command innovation into existence, but they can shape expectations. When political leadership elevates a technology to the highest level of national attention, it communicates priority to the stakeholders, especially those who need assurance. It tells students deciding their careers, founders deciding what to build, and investors deciding where to allocate capital that this domain is no longer peripheral.
The AI summit is functioning precisely in this way, it is altering behaviour at a significant scale.
The most immediate impact of this signalling is psychological. The saturation coverage across television, newspapers, Instagram, and Twitter is creating urgency and, for many, a sense of missing out. This fear of missing out will act as a catalyst, it will compress decision timelines.
Teenagers will begin to see AI as a pathway worth pursuing. College students will reconsider their technical focus. Entrepreneurs and institutions have already begun exploring AI-driven solutions. When enough individuals move simultaneously, an ecosystem begins to form.
What is striking, however, is that this movement did not begin with the summit.
Even before AI signalling reached the highest political level, innovation was already emerging from unexpected places. Walking through the event, I encountered individuals not from venture capital networks or elite research labs, but from tier 2 and tier 3 cities. Many have no investor backing. They are not funded by Silicon Valley firms or global technology giants. They are building independently, driven not by capital incentives but by personal experience.
A young engineering graduate told me how he began building an AI-based application after losing his mother to cancer last year. His conclusion, drawn from personal loss, was stark. His mother did not receive the most appropriate treatment pathway available to her, not because the treatment did not exist, but because he, as a non-medical professional, lacked the knowledge to evaluate options. He relied entirely on the treating doctor’s recommendation.
His application attempts to address precisely this information gap, helping patients understand treatment options and navigate decisions with greater clarity.
This pattern reveals something fundamental about India’s emerging AI ecosystem. Innovation here is not confined to large corporations. It is emerging from individuals responding to lived problems. These builders are not optimizing abstract technological benchmarks. They are solving concrete problems rooted in the realities of Indian society, healthcare access, education gaps, agricultural inefficiencies, and service delivery constraints.
This distinction matters because AI ecosystems reflect the environments in which they evolve.
The AI systems emerging from the United States are optimized for high-income, English-speaking populations with stable infrastructure. Chinese AI systems are shaped by tightly integrated digital platforms and centralized coordination.
India’s requirements are fundamentally different. Its population is multilingual, economically diverse, and geographically dispersed. Its infrastructure varies dramatically across regions. AI systems built for India must operate under constraint, across languages, and at price points accessible to millions.
These constraints create a different kind of technological discipline. Systems built for India must be efficient, adaptable, and scalable under real-world limitations. They must work not just in metropolitan centers, but in smaller cities and rural regions. They must address problems specific to societies transitioning from developing to developed status.
India, in this sense, represents more than its own population. It represents a structural model shared by much of the developing world.
Large parts of Asia, Africa, and Latin America face similar conditions. They share demographic scale, infrastructure unevenness, income sensitivity, and linguistic diversity. AI systems that succeed in India will already be optimized for these environments. Tools built for India’s 140 crore people will naturally extend to other countries undergoing similar transitions.
This creates a strategic opportunity. India may not dominate the creation of the largest foundational AI models, an area requiring enormous concentrations of capital and computing infrastructure already present in the United States and China. But it can dominate the application and deployment layers of AI. It can build systems optimized for real-world complexity, cost sensitivity, and population scale. It can define how AI functions for the majority of the world, not just its wealthiest segments.
The summit is an attempt to accelerate this process.
Many well being individuals, while participating in the presser by Ashiwini Vaishnav, were worried about the non arrival of India’s ‘Chat gpt’ moment. Indian innovators will not make another Chatgpt, they will come out with something that will move beyond it as the numerous health and education related AI apps that are on display at Bharat Mandapam indicate.
Innovation ecosystems often begin quietly, driven by individuals responding to personal necessity. But they scale only when broader systems begin to support them. The summit represents that transition point, from isolated individual effort to coordinated national momentum.
If this signalling is followed by sustained investment in infrastructure, education, and deployment, its effects could be profound. The individuals currently building in isolation will find greater institutional support. More students will enter the field. More startups will emerge. More applications will be developed for Indian conditions.
The cumulative effect may not be immediately visible. But over the next two years, the impact could become unmistakable. India’s AI ecosystem, currently fragmented and emergent, could become structured, scaled, and globally relevant.
The summit, therefore, is not the culmination of India’s AI journey. It is the beginning of a deliberate attempt to accelerate it. By transforming artificial intelligence from a technical subject into a national priority, India is attempting to ensure that when the global AI economy stabilizes, it is not merely a consumer of systems built elsewhere, but one of the societies shaping how those systems are built and deployed.
