India Public AI Strategy 2026

Building Public AI in India: A Strategic, Infrastructure-First but Flexible Approach

Syllabus:

GS 2

• Artificial Intelligence
• Technological development in India

Introduction: The Emerging Debate on Public AI

• Artificial Intelligence (AI) has emerged as a transformative technology with far-reaching implications for governance, economic growth, national security, and social justice.
• As countries compete to establish leadership in AI, India has sought to frame the global conversation differently—especially from the standpoint of the Global South.
• Drawing inspiration from its experience with Digital Public Infrastructure (DPI), India proposes that AI development should prioritize public purpose over pure commercial gain, similar to how environmental clearances prioritize public interest over unchecked development.

Understanding Digital Public Infrastructure (DPI)

• Digital Public Infrastructure refers to foundational digital systems that enable a wide range of public and private services.
• In India, examples include Aadhaar for digital identity, UPI for payments, DigiLocker for document storage, and CoWIN for vaccine management.
• These systems are not merely government-owned platforms; rather, they are public-interest infrastructures designed to promote inclusion, competition, and innovation, much like how environmental impact assessments aim to balance development with ecological concerns.

The Role of the State in AI Development

• Although DPI emphasizes market participation and competition, the state plays a decisive role in its creation and maintenance.
• Similarly, in AI development, the government cannot remain a passive observer.
• AI systems require significant investment in compute infrastructure, data curation, research ecosystems, and skilled human capital.
• However, AI is capital-intensive and emerges at a time of fiscal constraints.
• Governments must carefully evaluate whether public funds should be used to build domestic AI models or data centres, especially when private firms are already heavily investing in these areas.

Innovation versus Diffusion: Rethinking Priorities

• Public discourse often focuses excessively on innovation at the technological frontier.
• Governments are tempted to invest in cutting-edge AI models to showcase technological prowess.
• However, the real impact of AI depends not merely on innovation, but on diffusion, much like how environmental clearances aim to ensure sustainable development practices are widely adopted.

AI as Infrastructure: Identifying Foundational Layers

• Not all components of AI are equally suitable for public investment.
• Policymakers must distinguish between foundational infrastructure and end-user applications.
• Foundational layers include high-quality datasets, base models that can be adapted for multiple uses, language translation systems, and evaluation benchmarks.
• These layers function similarly to roads or electricity grids in the physical economy. Once created, they enable numerous downstream applications.

Caution Against Public Funding of Large Language Models

• Many governments justify building domestic large language models (LLMs) on grounds of strategic autonomy and digital sovereignty.
• They argue that dependence on foreign AI companies may compromise national security and data protection.
• While these concerns are understandable, the AI ecosystem is highly layered and globally interconnected. Controlling one layer, such as compute infrastructure, does not automatically ensure control over models, data, or applications.

Domestic Data Centres and the Illusion of Sovereignty

• Compute infrastructure depends on imported semiconductors, global cloud services, and international hardware supply chains. Merely building data centres within national borders does not guarantee technological independence.
• Additionally, demand for AI compute is highly variable and difficult to forecast. Excess capacity can lead to underutilization and fiscal waste, while insufficient capacity can create bottlenecks.
• Given the rapid pace of technological change, expensive infrastructure may also become obsolete quickly.
• Thus, while some level of domestic capability may be necessary for resilience, large-scale public investment in data centres should be approached with caution, similar to how environmental clearances are granted cautiously to prevent ecological damage.

High-Quality Datasets as a Strategic Priority

• Among all potential public investments in AI, the creation of high-quality, representative datasets stands out as the most promising.
• AI systems are only as reliable as the data on which they are trained. Poor-quality or biased data can result in discriminatory outcomes and flawed decision-making.
• However, data reuse must be accompanied by robust governance frameworks. Privacy safeguards, consent mechanisms, data minimization principles, and legal oversight are essential.
• The right to privacy, recognized as a fundamental right by the Supreme Court in the Puttaswamy judgment, must guide AI-related data policy.
• Investing in datasets not only improves AI performance but also ensures that systems are representative of India’s diversity. This enhances fairness and inclusion, much like how environmental impact assessments aim to ensure equitable development.

Open-Source Infrastructure and Multilingual Inclusion

• Open-source AI infrastructure offers a strategic advantage for countries like India.
• It reduces dependence on proprietary vendors, enhances transparency, and encourages collaborative innovation.
• Given India’s linguistic diversity, investment in shared language resources, translation systems, and multilingual AI tools is particularly important.
• General-purpose translation models and open benchmarks can enable the development of regionally relevant applications.

The Limits of an Infrastructure-Only Approach

• Although an infrastructure-first strategy is desirable, it is not sufficient.
• Certain public-interest objectives may require targeted state intervention beyond foundational systems.
• For example, AI research in climate modeling, disaster prediction, or national security may not attract adequate private investment.
• In such cases, mission-oriented public funding is justified, similar to how the Forest Conservation Act provides targeted protection for critical ecosystems.

Governance, Ethics, and Institutional Capacity

• Building public AI requires robust governance frameworks. Ethical principles such as transparency, accountability, non-discrimination, and explainability must guide system design and deployment.
• These principles align with established environmental jurisprudence, such as the polluter pays principle and the precautionary principle, which can inform AI governance.

Fiscal Prudence and Public Accountability

• AI investments compete with essential sectors such as health, education, and infrastructure.
• Therefore, fiscal prudence is critical. Governments must evaluate the opportunity cost of AI spending.
• Symbolic or prestige-driven projects may generate short-term visibility but fail to deliver long-term value.
• Outcome-based budgeting, periodic audits, and social impact assessments can enhance accountability.
• Public trust is essential for the success of AI initiatives. Transparent communication and citizen engagement can strengthen legitimacy, fostering a form of environmental democracy in the AI domain.

AI and Inclusive Development

• AI has significant potential to advance inclusive development.
• It can improve agricultural productivity through precision farming, enhance healthcare through early diagnostics, and personalize education through adaptive learning systems.
• However, AI also poses risks, including algorithmic bias, job displacement, and widening digital divides.
• Policymakers must address these risks proactively through skilling programs, social safety nets, and inclusive system design.
• An infrastructure-first strategy ensures that AI tools are accessible to marginalized communities rather than concentrated among elite users, contributing to a more equitable and pollution-free environment in the digital realm.

India’s Global Leadership Opportunity

• India’s experience with DPI positions it uniquely to shape global norms around public AI.
• By emphasizing inclusion, affordability, and public purpose, India can offer an alternative to purely market-driven or state-controlled models.
• Through digital diplomacy and South-South cooperation, India can share best practices and collaborate on open-source AI infrastructure.
• This enhances its global influence and aligns technological development with democratic values.

Conclusion: Tactical Ambition with Strategic Flexibility

• Building public AI requires neither unchecked state intervention nor blind reliance on markets. Instead, it demands a careful, tactical, and flexible approach.
• Governments should prioritize foundational infrastructure such as high-quality datasets and open-source systems.
• By being infrastructure-first but not infrastructure-only, India can harness AI as a tool of public empowerment rather than mere technological competition.

UPSC Mains Practice Question

Q: “India–US trade agreements raise complex questions of sovereignty and international law.” Examine this statement in the context of WTO obligations and the principle of non-intervention. (250 words)