An influential opinion piece in The Hindu on July 31, 2026, by IAS officer Srivatsa Krishna, has advanced a provocative policy argument: that India should treat Artificial Intelligence the way it treated identity, payments, and data — as a Digital Public Infrastructure (DPI) to be commoditised and made near-free, rather than left to be monopolised by a handful of global technology companies. This argument, building on India’s undisputed global leadership in the Aadhaar-UPI-DEPA stack, arrives at a moment when India’s IndiaAI Mission is scaling GPU compute capacity and when questions about AI sovereignty, affordability, and equitable access have become central to global technology policy debates.
The significance of this topic for India’s governance and economic future cannot be overstated. India currently occupies what the article terms an “extractive” position in the global AI economy — supplying vast quantities of data, talent, and training labour to power Silicon Valley’s frontier AI models, while Indian startups and citizens must rent back the resulting intelligence as dollar-priced API tokens subject to export controls and hosted on foreign servers. This pattern, familiar from India’s colonial-era experience with raw material extraction and finished goods import, has direct implications for India’s technological sovereignty, economic self-reliance, and the affordability of AI-driven public services in health, education, and agriculture.
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For UPSC and SSC aspirants, this is a genuinely high-value, forward-looking Science and Technology topic that intersects with economic policy, digital governance, and India’s broader Atmanirbhar Bharat and Digital India strategic architecture — precisely the kind of interdisciplinary, policy-oriented theme that features prominently in UPSC Mains answers on emerging technology.
Background and Context
India’s DPI success story rests on three pillars: Aadhaar (biometric digital identity, enrolling 1.4 billion people), UPI (near-zero-cost digital payments, processing around 20 billion transactions monthly), and DEPA/Account Aggregator (consent-based data-sharing architecture). This combination — described as globally unique because no other country has integrated identity, payments, and data-sharing into a single interoperable public digital rail system — has been credited with dramatically lowering the cost of financial inclusion and digital participation in India.
Five Important Key Points
- India’s IndiaAI Mission, backed by an outlay of approximately ₹10,372 crore, is building public-private partnership compute infrastructure, having onboarded over 38,000 GPUs with plans to scale to 100,000, offering eligible startups compute access at approximately ₹65 per GPU hour compared to global market rates.
- The article proposes that India build foundational AI models as public goods, arguing that any AI model developed using state-subsidised compute or public datasets should be released under an open-weights license, mirroring the UPI philosophy of the government building rails while private companies compete on user experience.
- A proposed “Unified Intelligence Interface” (UII) is envisioned as an “API gateway for AI” analogous to UPI, enabling interoperable access to AI models across sovereign, private, open, or proprietary systems through standardised interfaces for identity, consent, billing, and safety.
- The piece highlights that between September 2016 and 2019, India’s cost per gigabyte of mobile data fell from about $4 to under 30 cents, illustrating how competitive infrastructure policy previously drove a similar “make it free” transformation for connectivity.
- The proposal suggests diverting a portion of subsidies — such as a tenth of the fertiliser subsidy — toward funding free AI token access for research institutions, students, and small businesses, extending the logic of India’s successful “freemium” digital public goods model.
Constitutional and Policy Framework for Digital Public Infrastructure
While India does not have a dedicated constitutional provision for AI policy, the framework draws on existing digital governance architecture, including the Digital Personal Data Protection Act, 2023, and institutional bodies like the Ministry of Electronics and Information Technology (MeitY), which administers the IndiaAI Mission approved by the Union Cabinet in 2024. The mission itself operates through seven pillars, including compute infrastructure, foundational models, application development, data quality management, and skilling, reflecting an ambitious whole-of-government approach rather than a narrow technology procurement exercise.
Economic Implications — Compute, Energy, and Market Structure
The economic argument for treating AI compute as critical infrastructure hinges on the observation that inference costs — not just training costs — determine whether AI becomes broadly accessible. The article’s proposal to integrate AI-related electricity demand into national grid planning, treating compute clusters analogous to how coal linkages once served the steel industry, represents a sophisticated recognition that AI policy is fundamentally intertwined with energy policy, an area where India’s renewable energy capacity expansion under schemes like the National Green Hydrogen Mission could provide a competitive advantage.
Governance Concerns and Implementation Challenges
Significant governance challenges remain unaddressed in translating this vision into policy. Data sovereignty concerns arise when aggregating anonymised public datasets (legal rulings, agricultural data, multilingual corpora) for AI training, requiring robust anonymisation and consent frameworks under the DPDP Act, 2023. Questions of accountability also emerge: if government-subsidised compute powers privately-deployed AI applications, regulatory clarity on liability, bias auditing, and algorithmic accountability becomes essential, an area where India’s regulatory architecture remains nascent compared to the EU’s AI Act.
Geopolitical Dimensions — Technology Sovereignty in a Fragmenting World
The push for an indigenous, open-source AI ecosystem also carries geopolitical weight. As Western nations impose export controls on advanced AI chips and models, India’s strategy of building sovereign compute and model infrastructure reduces exposure to potential future restrictions, aligning with broader trends of “technology non-alignment” that mirror India’s historical foreign policy posture, now extended into the digital domain.
Way Forward
India should institutionalise a National AI Public Goods Fund, formalising the proposal to redirect subsidy allocations toward compute and token access for public institutions. The government should also fast-track energy policy integration for AI data centres, ensuring dedicated renewable capacity allocation similar to how solar parks were developed for specific industrial clusters. Finally, a regulatory sandbox mechanism, jointly administered by MeitY and the RBI-style sectoral regulators, should be established to test open-weight foundational models for public sector use cases in health, education, and agriculture before wider deployment, ensuring safety without stifling innovation.
Relevance for UPSC and SSC Examinations
For UPSC GS-III (Science and Technology, Indian Economy), this topic is central to “Developments and their applications in everyday life,” “Indigenisation of technology,” and “Issues relating to intellectual property rights.” For GS-II, it connects to Digital India governance themes. For SSC exams, static facts include IndiaAI Mission’s approval year and budget outlay, DPDP Act 2023, and the UPI/Aadhaar/DEPA framework. Key terms: Digital Public Infrastructure, GPU compute, open-weights model, API gateway, and JAM trinity.