Since the launch of ChatGPT in 2022, artificial intelligence in India has moved rapidly from the margins to the mainstream. What began as a limited tool for select business applications has become a pervasive force across the economy. With India accounting for around 13.5 per cent of ChatGPT’s 700 million weekly users, AI adoption today reflects both scale and depth. This rapid diffusion has created a complex AI ecosystem — one that is reshaping labour markets, governance frameworks and digital infrastructure, even as it opens new avenues for India in the global technology race.
From niche technology to economy-wide adoption
AI usage in India has expanded sharply over the past three years. A recent survey by “” and the “” found that nearly 70 per cent of organisations now deploy AI-enabled products or services. Innovation indicators mirror this trend. Between 2019 and 2025, over 83,000 AI-related patents were filed in India, compared to fewer than 4,000 in the preceding eight years.
This pace of adoption means AI is no longer a standalone technology. It is embedded in everyday economic decisions, shaping productivity, organisational structures and even consumer behaviour.
Understanding the AI ecosystem, not just AI tools
Globally, it is increasingly recognised that AI does not operate in isolation. Its effectiveness and impact depend on the surrounding ecosystem — chiefly the labour market, governance systems and digital infrastructure. These elements are not only essential individually but also deeply interconnected. AI alters labour demand, governance responses shape innovation incentives, and infrastructure constraints influence how widely AI can be deployed.
In India, this interconnectedness is especially important because AI adoption is widespread but uneven. Differences in income, education, digital access and regional capacity mean the benefits — and costs — of AI are distributed unevenly, demanding constant policy vigilance.
Labour markets under the greatest strain
The most immediate and intense challenges lie in the labour market. AI has revived fears of job displacement, skill erosion and employment insecurity, particularly in the IT sector. Research increasingly suggests that while AI boosts efficiency, it can also degrade human skills over time through overreliance and task automation.
Supporters often counter that AI will create new jobs in development, maintenance and oversight. While true in principle, this view underestimates structural barriers. Reskilling requires time, money and institutional support — resources many workers lack due to family responsibilities, limited employer training or financial constraints.
Coding, skills erosion and inequality risks
Software development illustrates these tensions vividly. AI systems already assist with coding and debugging, raising fears of widespread displacement. Yet AI does not truly reason; it extrapolates from patterns in existing data and struggles with genuinely novel problems.
This creates a paradoxical outcome: elite, high-skill roles become more valuable, while entry-level and routine coding jobs shrink. Over time, this could produce a polarised labour market — fewer opportunities, higher skill thresholds and deeper inequality if reskilling access remains limited.
Why AI challenges traditional growth thinking
AI also forces economists to rethink growth paradigms. Conventional models assume technology enhances labour productivity without replacing labour entirely. Advanced AI challenges this assumption by potentially substituting for human labour while output continues to rise.
If production becomes less labour-intensive, metrics such as GDP per capita may no longer reflect broad economic well-being. Wealth could concentrate among firms and technical elites, while participation in growth narrows — exposing the limits of traditional measures of prosperity in an AI-driven economy.
Governance dilemmas: privacy versus innovation
Beyond labour, governance poses another major challenge. Data privacy and ethical use of AI remain unresolved. Many users are unaware how their data may have been absorbed into AI training models since 2022, often without explicit consent. Even if safeguards are introduced now, much of this data cannot be practically retrieved.
Regulators face a persistent trade-off: strong privacy protections can constrain AI performance by limiting data access, while lax rules risk misuse and public distrust. Striking this balance is central to sustaining AI adoption.
Digital infrastructure and environmental stress
AI’s infrastructure footprint is often overlooked. Data centres powering large models consume vast amounts of electricity, much of it still generated from fossil fuels. They also require enormous quantities of water for cooling, intensifying stress in water-scarce regions.
As AI usage scales, these environmental costs become macroeconomic concerns, linking technology policy directly with energy security and climate resilience.
Policy priorities as India looks ahead
Securing India’s AI ecosystem is therefore not about a single regulation or mission. It requires adaptive policymaking that accounts for continuous feedback between AI and its users. Immediate priorities include reforming education and skilling systems, lowering socio-economic barriers to reskilling, and strengthening governance frameworks without stifling innovation.
At the same time, AI presents India with a rare opportunity: to move beyond replication and create original use cases suited to its scale, diversity and development needs. How India manages this balance will determine whether AI becomes a driver of inclusive growth or a force that deepens existing divides.
What to note for Prelims?
- Scale of AI adoption in India and key usage statistics
- Components of an AI ecosystem: labour, governance, infrastructure
- Environmental costs of AI-driven data centres
- Role of patents and innovation indicators
What to note for Mains?
- Impact of AI on labour markets and inequality
- Limits of traditional growth models in an AI-driven economy
- Data privacy versus innovation trade-offs in AI governance
- Policy strategies for inclusive and sustainable AI adoption in India
