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India’s ambition to become the world’s “AI use-case capital” is backed by real public investment—not just publicity. But a growing number of applications, models or GPUs is not evidence that people are healthier, better educated, more secure or earning more. AI can help solve defined problems; it cannot substitute for the public services, jobs, infrastructure and accountability that development requires.
What does “AI use-case capital” mean?
The phrase can describe several different goals: adopting existing AI in Indian businesses and public agencies; building applications for Indian languages and sectors such as agriculture or health; developing domestic models and computing infrastructure; improving outcomes for people; or positioning India as a technology provider to the Global South. These ambitions overlap, but they are not equivalent. India could deploy many applications while remaining dependent on overseas chips or cloud platforms. It could also develop research capability without making public services more accessible.
The useful question is not how many use cases India can announce. It is which goal an initiative serves, who benefits, and what evidence shows that it works.
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A serious mission, but announcements are not outcomes
The Union Cabinet approved the IndiaAI Mission in March 2024 with an outlay of ₹10,371.92 crore over five years. Its seven pillars cover compute capacity, foundation models, datasets, applications, skills, startup financing, and safe and trusted AI. The government’s original plan called for at least 10,000 GPUs; later official material reported more than 38,000 GPUs being made available. That figure describes government-reported availability, not necessarily the capacity actually used, where it can be accessed, or whether a small research team or public institution can obtain it on workable terms. See the Cabinet announcement and the India AI Governance Guidelines.
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The wider programme includes the AIKosh datasets effort, support for Indian-language models, startup finance and public applications. The government describes BharatGen as a multilingual, multimodal model supporting 22 Indian languages; that is a description of its stated scope, not proof of equally strong performance in every language or setting. India has also announced Centres of Excellence in healthcare, agriculture, sustainable cities and education. The Principal Scientific Adviser’s overview characterises the November 2025 governance approach as light-touch, risk-based and techno-legal.
These are meaningful policy commitments. But there is a long distance between allocating a budget, making infrastructure available, launching a pilot, reaching routine use and demonstrating a durable public benefit. Every initiative should be judged by its users, baseline, measured outcomes, independent evaluation, error reporting and plan for accountability when it fails.
Use cases cannot replace the institutions around them
AI can lower the cost of processing information, translating it, identifying patterns or assisting a decision. Development also depends on whether someone has the resources and authority to act on that information. A farmer may receive sound advice but lack affordable credit, water or access to a market. A health worker may get a useful alert but have no medicines or referral service. A student may have a tutoring tool but no reliable device, connectivity or qualified teacher.
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This is the last-mile problem: information is not the same as the capacity to act. AI can support public institutions, but it cannot by itself resolve weak schools, understaffed clinics, low wages, insecure land tenure, inadequate transport or limited local administrative capacity. Nor should “AI versus nothing” be the standard comparison. The relevant alternative may be more nurses, teachers or agricultural extension workers—or a simpler database, translation service or workflow redesign.
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Three sectors, three tests
Agriculture: does better advice change farm outcomes?
AI may help identify crop pests and disease, forecast weather and crop risk, optimise irrigation, analyse satellite imagery, share market information or provide agricultural advice in local languages. These are plausible uses, especially where information is hard to reach. But they address only part of the problem. Fragmented holdings, inadequate storage and transport, volatile prices, weak extension services, costly credit, insecure tenure and climate shocks cannot be fixed by a chatbot.
A credible evaluation should test whether an intervention improves yields, cuts input costs, raises realised farm income, reduces crop losses or builds resilience—not merely count queries or downloads. The answer also depends on whether advice is accurate for local conditions and whether farmers can obtain the inputs needed to act on it. Mila T. Samdub’s May 2025 essay in Scroll argues that many social-sector claims, including agricultural ones, remain speculative. That is a critique to test against project-level evidence, not a basis for declaring every application ineffective.
Healthcare: access, accuracy and responsibility
Potential uses include triage and referral, assistance with medical images, clinical documentation, patient communication and translation, disease surveillance, and managing appointments or supplies. Such tools could assist overstretched services. They could also benefit better-resourced hospitals first, or make administration more efficient without expanding access to care.
Health systems need evidence about diagnostic performance across languages, ages, sexes, regions and disease prevalence; the consequences of false negatives and false positives; and when a qualified person must review an output. Patients need clear information about data collection and secondary use. Institutions must establish who is responsible for clinical errors, how systems fit into public-health workflows and whether investment complements rather than displaces doctors, nurses, primary-care centres and medicines. The fair comparison is with the best available non-AI intervention, not with no care at all.
Education: does learning improve?
AI could support tutoring, feedback, translation, lesson planning, accessibility and some administrative work. It can also produce incorrect explanations, expose children to surveillance, reinforce language or social biases, and encourage dependence on private platforms. Device and connectivity gaps matter; so does the risk that automated instruction is treated as a cheap replacement for teachers.
Evaluation should measure learning, retention, inclusion and teacher workload—not simply the number of students who encountered a tool. A student-monitoring or dropout-risk system also needs safeguards against inaccurate labels becoming difficult-to-contest judgments.
Public services: assistant or gatekeeper?
A chatbot may help someone understand a scheme, find a form, translate a notice or submit a grievance. That makes it an additional access channel. The risk changes when automation becomes a gatekeeper to benefits, identity records or other rights. Incorrect eligibility advice, poor performance on dialects, limited digital literacy, merged records or the absence of a route to a human official can turn convenience into exclusion.
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People should be told when AI is involved, have a way to correct errors and be able to appeal consequential decisions to a responsible person. “Human in the loop” is not enough if that person lacks time, authority, expertise or incentives to reject a machine-generated answer.
Who benefits—and who bears the risk?
Samdub’s Scroll essay makes a political-economy argument: the language of AI use cases can blur development policy with industrial promotion, philanthropy and commercial opportunity, while poor communities are described at once as beneficiaries, markets, sources of data and testing grounds. This is a useful question to ask of individual programmes, not a claim that all public-interest AI exploits its users. The issue is whether people have agency, ownership, bargaining power and remedies—not whether they should have access to technology.
That means asking whether data collection is necessary and consensual, whether its purpose and retention are limited, whether people can seek correction or deletion, and whether data may be reused commercially. It also means involving affected communities in design, publishing evaluations and errors, providing accessible human appeals, and allowing people to use a service without being forced through an AI system.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.More applications do not automatically mean more jobs
AI can create direct work for researchers, engineers, data specialists, translators, auditors and implementation teams. It may also help existing workers produce more. But it can substitute for some routine call-centre, clerical, translation, back-office or support tasks, and it may intensify monitoring and targets for workers who supervise automated systems. The scale and distribution of those effects are questions for labour-market evidence, not settled by the existence of a model or pilot.
The key distinction is between AI-enabled growth and AI-led development. Growth may raise output; development requires gains to reach people previously excluded from it. Who captures the productivity surplus—workers through better pay, employers, platforms, investors, cloud providers or the state? Does an application create decent work, improve wages or merely let a firm handle more tasks with fewer employees? A strategy that counts startup formation and productivity while ignoring job quality, informal work and worker bargaining power leaves out much of the economic question.
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Indian models are not the same as technological sovereignty
“Sovereignty” has several layers. Compute sovereignty concerns affordable access for researchers and public institutions. Data sovereignty asks who controls information and under what enforceable privacy rules. Model sovereignty includes the ability to inspect, modify, audit and maintain systems. Infrastructure sovereignty concerns dependence on a narrow set of vendors. Operational sovereignty asks whether public services can continue if a provider changes prices, terms or access. Democratic sovereignty means people can challenge consequential automated decisions.
A locally trained model or Indian-language interface can be valuable without establishing control over chips, cloud infrastructure, model tooling, capital or distribution. Localisation is not the same as local ownership or public accountability. Procurement should therefore consider portability, documentation, audit access, data control, maintenance costs and an exit plan—not only model capability.
A practical public-interest scorecard
Before calling an AI project a development success, ask:
- Problem: What specific failure is it meant to fix? Is the obstacle missing information, missing capacity, poor incentives or structural inequality? Why is AI preferable to a non-AI option?
- Evidence: Is there a baseline and a fair comparison? Are results independently evaluated, sustained beyond a pilot and reported by relevant language, region, gender, caste, income and disability where appropriate?
- Distribution: Who benefits and who pays? Who owns the model and data, and who captures productivity gains? Are the intended users actually reached?
- Institutional fit: Can the agency act on the output? Is the system integrated into real workflows, usable on low bandwidth and supported by a human fallback?
- Rights: Are people informed? Can they contest an output and reach a responsible official? Are data collection and retention limited, procurement transparent and error reporting public?
- Economic value: Does it raise incomes or reduce costs, create decent work, build lasting domestic capability and remain maintainable without indefinite subsidy?
Warning signs include user counts without outcome measures, no published failure statistics, unclear ownership after a pilot, no post-pilot budget, dependence on one vendor, or a proof of concept that never becomes routine service. National platforms may improve interoperability but can also concentrate data and decision-making. Fraud detection, eligibility scoring and predictive systems can increase scrutiny of marginalised groups even when their technical accuracy appears high.
What a credible programme would require
Public-interest AI needs independent evaluation, public reporting of error rates, meaningful human appeal, transparent procurement, open standards and data minimisation. Communities should help shape systems that affect them; workers need protections where deployment changes jobs or intensifies monitoring. Agencies need funds and staff to respond to outputs, while public investment must continue in the non-AI services and infrastructure that make those outputs useful. For commercial or government buyers, compute, APIs and models are implementation choices—not evidence of social or economic development.
India does not need fewer useful AI applications. It needs fewer claims that applications alone amount to development. The measure of success is not the number of pilots, GPUs, partnerships or locally branded models, but demonstrable improvement in jobs, incomes, health, learning, access and accountability.
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