India’s AI Moment: Opportunity, Anxiety and the Challenge of Building a Resilient Ecosystem

Ever since the launch of “ChatGPT” in 2022, India’s engagement with artificial intelligence has shifted from cautious experimentation to mass adoption. Today, India accounts for about 13.5% of ChatGPT’s 700 million weekly users, and AI is no longer confined to a handful of tech firms. A “Tata Consultancy Services”–”Confederation of Indian Industry” survey shows that nearly 70% of organisations now use AI-enabled products or services. This rapid diffusion has created an evolving AI ecosystem — one that is shaping, and being shaped by, labour markets, governance frameworks and digital infrastructure.

The scale of India’s AI adoption and innovation surge

The numbers tell a striking story. Between 2019 and 2025, over 83,000 AI-related patents were filed in India, compared to fewer than 4,000 in the previous eight years. AI has moved well beyond back-office automation into domains such as customer services, finance, logistics, healthcare and governance. This expansion is intensifying AI’s interaction with the broader economy, embedding it deeply into how firms operate, how workers perform tasks, and how public services are delivered.

At the same time, adoption remains uneven. While urban firms and digitally connected workers are integrating AI at scale, large sections of the population face limited access to infrastructure, training and opportunities — a gap that risks widening as AI capabilities grow.

Why AI ecosystems matter more than standalone technology

Globally, the focus is shifting from AI tools to AI ecosystems. These ecosystems rest on three interlinked pillars: the labour market, digital infrastructure and governance. Each pillar influences how AI is adopted and who benefits from it — and each is, in turn, reshaped by AI’s spread.

For India, securing this ecosystem requires more than deploying cutting-edge models. Policies must be dynamic, recognising that AI’s impacts are not static but evolve through constant interaction with users, institutions and markets. Without this vigilance, the same technologies that boost productivity could amplify inequality and economic risk.

The labour market: where AI’s impact will be felt most sharply

The most intense policy challenges lie in the labour market. AI has revived fears of job displacement, skill erosion and insecurity, particularly in the IT services industry that has long underpinned India’s white-collar employment growth. Research increasingly suggests that overreliance on AI can degrade human skills over time, even as it boosts short-term efficiency.

Advocates often argue that AI will create new jobs in development, maintenance and oversight. While true in principle, this overlooks structural barriers. Reskilling is costly, employer-provided training is uneven, and many workers face constraints of time, income and family responsibilities. For large sections of the workforce, transitioning into AI-intensive roles may simply not be feasible.

Coding, automation and the risk of a hollowed-out middle

Software development illustrates this tension. AI tools can already write, debug and optimise code, fuelling fears of widespread displacement. Yet AI does not “understand” problems; it extrapolates from patterns in existing data and struggles with genuinely novel challenges.

This creates a paradox. Entry-level and mid-tier coding roles may shrink, while demand grows for a smaller pool of elite engineers capable of framing complex problems and supervising AI systems. The result could be a polarised labour market: fewer jobs overall, higher skill thresholds, and deeper inequality if access to reskilling remains limited.

Rethinking growth models in an AI-driven economy

AI’s rise also challenges how economists think about growth. Traditional frameworks assume that technology raises productivity by augmenting labour. Advanced AI complicates this assumption by potentially substituting for labour altogether. If output rises while labour input falls, conventional metrics such as GDP per capita may no longer capture economic well-being accurately.

Wealth could become increasingly concentrated among owners of capital, data and algorithms, while broad-based participation in economic gains weakens. This exposes the limits of traditional growth paradigms and forces policymakers to confront uncomfortable questions about distribution, employment and social security in an AI-intensive future.

Governance dilemmas: privacy, ethics and regulation

Beyond jobs, governance poses another set of challenges. Data privacy and ethical use are pressing concerns. Most users remain unaware of how their data may be absorbed into AI training models. In India, AI systems have been deployed since 2022, even as policymakers continue to debate formal governance frameworks — raising the possibility that vast amounts of personal data have already been irreversibly incorporated without explicit consent.

Regulators also face a persistent trade-off. Strong safeguards can protect privacy but may limit AI performance by restricting access to fresh data. Striking a balance between innovation and rights protection is proving far from straightforward.

Digital infrastructure and the environmental cost of AI

AI’s infrastructure footprint adds another layer of complexity. Data centres that power large models consume enormous amounts of electricity and water, especially for cooling. In a country already facing water stress and a carbon-intensive energy mix, the environmental costs of scaling AI cannot be ignored.

As AI usage grows, pressure on power grids, water resources and urban infrastructure will intensify, making sustainability an integral part of AI policy rather than an afterthought.

What policymakers must prioritise next

Securing India’s AI ecosystem is therefore not a single reform exercise but a continuous balancing act. In the short term, the labour market demands urgent attention — through education reform, accessible reskilling pathways and support for workers displaced by automation. Over the longer term, India may need to rethink how it measures growth and success in an economy where labour is no longer the sole driver of output.

Turning disruption into opportunity

Despite these risks, the opportunity is substantial. India’s scale, digital public infrastructure and growing innovation capacity give it a chance to shape original AI use cases rather than merely adopting imported models. If policies remain adaptive and inclusive, AI could help India overcome long-standing system constraints instead of reinforcing them.

India’s AI future will not be determined by algorithms alone, but by how thoughtfully the country builds and governs the ecosystem around them — ensuring that innovation translates into shared prosperity rather than deeper divides.