by Shri Varshit Kumar Reddy E.
India AI Summit concludes Bharat Mandapamwe need to shift our attention from optics to structural economics. AI is currently reshaping productivity, labor-capital dynamics, and fiscal federalism, but India lacks a coherent regulatory framework. In this context, the Jammu and Kashmir AI Framework and proposed Center of Excellence positions Jammu and Kashmir as a governance-driven and inclusion-focused testbed that has the potential to outperform wealthy states in policy innovation.
India – AI Impact Summit 2026 Held at the Bharat Mandapam, the conference displayed the familiar ceremonial energy of national ambition, with ministerial declarations, podium demonstrations, and the sophisticated choreography of a country that has decided, with some legitimacy, that it belongs at the center of the next global technological order. But what no summit agenda can choreograph is the macroeconomic reality that the event itself reflects. Artificial intelligence has ceased to function as a technological sector and has instead become a factor of production that reorganizes the conditions of the economy: how output is generated, how income is distributed, and how fiscal capacity is allocated across a heterogeneous federal structure.
This distinction carries weight. When economists talk about capital deepening, they mean the process by which an economy increases its output per worker through sustained investment in productive assets such as more machines, deeper infrastructure, and better tools. Artificial intelligence extends this logic into the realm of cognition. Businesses will invest in data, algorithms, and computing to leverage strategic disciplines once reserved for plant and equipment. Productive assets are now built into software that learns and adapts, rather than steel that just works. Total productivity can be significantly increased. The distribution of these benefits follows the logic of capital accumulation rather than broad wage growth, and that difference is at the heart of the problem.
Geography of concentrated revenue
India has encountered this asymmetry in a weakened form before. The expansion of information technology services in the 1990s concentrated tremendous productivity gains within narrow geographies and narrow occupational spheres, while the broader labor market absorbed the second-order effects slowly and at considerable social cost. Artificial intelligence is a harbinger of a more serious version of the same pattern, for structural reasons. At the very least, the value that AI creates requires minimal labor relative to the volume of production, unlike outsourced services that require large workforces in the cities in which they operate. When value is created by a model that is trained in one jurisdiction, owned in another, and consumed in a third of jurisdictions, the architecture of taxation will be challenged in ways that India’s existing fiscal arrangements – the Union List, the State List, and the Concurrent List – were not designed to address.
On the other hand, the labor market is divided along more distinct axes rather than the familiar axis of skilled and unskilled workers. A notable difference is between those who can oversee, audit, and challenge the output of algorithms, and those whose work is simply mediated by such systems, without meaningful involvement in their design or accountability. Pay disparities will follow and are already evident in ongoing structural changes in the day-to-day management of India’s financial services, logistics and public services.
When governance becomes infrastructure
India’s experience with digital public infrastructure offers useful lessons here. Aadhaar, UPI, and open networks for digital commerce were acts of economic architecture designed to shape market outcomes before private platforms established irreversible dominance. Governed by transparent rules, public railways reduced transaction costs, expanded access, and disciplined the monopolistic tendencies that disintermediated platform economies invariably generate. Artificial intelligence demands the same institutional instincts, with considerable complexity, as AI models continually evolve, operate probabilistically, and resist auditing by static tools that were sufficient for early digital systems.
IndiaAI Mission’s governance guidelines represent a credible first move towards accountability, risk classification and responsible deployment across sectors. However, its scope is dependent on the federal government’s machinery, and its capabilities are very jagged. States with the administrative resources and political will will seriously get involved. Other companies implement frameworks that are designed as centrally as possible, with limited input on design and revenue impact. The country that has built some of the world’s most comprehensive digital plumbing has yet to assemble a governance architecture commensurate with the intelligence currently flowing through the country. In the meantime, markets are filling the gap, with predictable consequences for fiscal fairness and competitive concentration.
Jammu and Kashmir: First Principles Laboratory
It is within this federal context that Jammu and Kashmir is of analytical interest, and why it deserves to be included in serious discussions about national AI policy. The Union Territories’ 2023 Artificial Intelligence Framework addresses data exchange platforms, high-performance infrastructure, ethical protocols, privacy protection, and human capital development with a consistency that many large states have yet to achieve. The Center of Excellence for AI, GIS, and Emerging Technologies is currently under development in partnership with BISAG-N and will bring institutional specificity to the goals of digital governance and sustainable regional development. These constitute a policy architecture assembled in a region with unique constraints, such as fiscal dependence on transfers from the EU, geographic rigor, and security obligations, reinforcing rather than compromising the values of deliberate design.
The economic logic of AI-enabled public service delivery in J&K is based on mundane rationales. Applications in land records management, complaint handling, and predictive analytics for power procurement and water allocation reduce administrative friction across multiple layers of governance. For Union Territories that are structurally dependent on central subsidies, sustained improvements in fiscal precision and public sector productivity yield multipliers that cannot be replicated through budgetary transfers alone. This ripple effect radiates outward by stabilizing local procurement markets, improving planning cycles, and providing managers who currently rely on incomplete and delayed data with an information base for decision-making.
Shared computing infrastructure in publicly managed clusters, accessible to universities, departments, and start-ups without having to negotiate their own arrangements, addresses structural inequalities that are not resolved spontaneously by market provision. The governance of such infrastructure has proven to be as important as its capacity. Pricing principles, data ownership rules, and model portability criteria will determine whether the computing commons truly decentralizes functionality or merely assists in the concentration of familiar benefits under different institutional names.
In agriculture, where J&K’s mountainous terrain and climatic fluctuations repeatedly cause severe disruption, AI systems trained on region-specific topography and rainfall data have the potential to reduce income disparities among farmers in ways that national models tailored to different ecosystems cannot structurally achieve. Tourism presents a complementary case through real-time analysis of visitor flows and carrying capacity. This allows sustainable limits to be enforced without sacrificing the income that local economies rely heavily on, making AI a tool not only for economic optimization but also for environmental governance.
Human capital policy requires similar consideration. The temptation is always to chase common credentials. What is really needed is to build interpretive capacity, the capacity of frontline officials, agronomists, and health workers to challenge algorithmic outputs, identify failure modes, and maintain accountability in systems that increasingly mediate access to land, welfare, and justice. Governance sandboxes designate specific districts or administrative domains as controlled pilots under transparent audits and accessible grievance mechanisms, transforming regulatory uncertainty into iterative institutional learning. Small mistakes with public liability are the raw material for lasting policy.
Choices not available at the summit
The summit ended, as summits do, with the immense satisfaction of a country that had been convinced of its global importance by communiqués and promises. The more important choices lie in the details of the system, which cannot be resolved in plenary session. Whether India builds a governance architecture that can equitably distribute the productive benefits of AI, or allow it to integrate into familiar enclaves of capital and connectivity, will not be resolved by a single declaration from a single podium.
Regions like Jammu and Kashmir cannot just wait for the decision. They can prototype their solutions by demonstrating, at a scale where institutional choices remain legible, that AI governance can be created to provide economic resilience, rather than simply accelerating the efficiency of those already in place to ensure economic resilience. A summit meeting is a statement of intent. Whether that intention solidifies into a design will, in time, become visible in a much quieter place than the Bharat Mandapam and will be quite beneficial in the long run of India’s economic history.
(The author is a practitioner involved in government policy and public institutions. The ideas are personal.)
