Since the launch of ChatGPT in 2022, India’s engagement with artificial intelligence (AI) has shifted dramatically—from cautious experimentation to mass adoption. Today, India accounts for nearly 13.5% of ChatGPT’s 700 million weekly users, and AI is no longer the preserve of a few technology firms. Surveys indicate that almost 70% of Indian organisations now deploy AI-enabled products or services. This rapid diffusion marks the emergence of a dynamic AI ecosystem that is simultaneously reshaping labour markets, governance frameworks and digital infrastructure. The central challenge before India is to ensure that this transformation translates into broad-based gains rather than deeper economic and social divides.
The scale and unevenness of India’s AI surge
India’s AI momentum is evident in innovation metrics. Between 2019 and 2025, more than 83,000 AI-related patents were filed in the country, compared to fewer than 4,000 in the preceding eight years. AI applications now extend far beyond back-office automation into finance, logistics, healthcare, customer services and even governance. Public-sector uses—from predictive analytics in welfare delivery to AI-assisted grievance redressal—underscore how deeply the technology is embedding itself in everyday decision-making.
Yet this surge is uneven. Urban firms, digitally skilled workers and large enterprises are integrating AI at scale, while many rural areas, small businesses and informal workers face limited access to infrastructure, data and training. If left unaddressed, this asymmetry risks widening existing digital and economic inequalities.
From tools to ecosystems
Globally, the focus has shifted from individual AI tools to AI ecosystems. Such ecosystems rest on three interlinked pillars: labour markets, digital infrastructure and governance. Each pillar shapes how AI is adopted and who benefits from it, and each is transformed by AI’s spread. For India, success lies not merely in deploying advanced models but in governing their interaction with society. Static policies are inadequate for a technology that evolves rapidly and interacts continuously with markets and institutions.
Labour markets: the sharpest fault line
The labour market is where AI’s impact will be felt most acutely. In India, fears of job displacement are particularly strong in the IT services sector, long a cornerstone of white-collar employment. AI-driven automation can boost productivity but also risks deskilling workers over time, as reliance on automated systems erodes human expertise.
While proponents argue that AI will generate new jobs in development, maintenance and oversight, this optimism often underestimates structural barriers. Reskilling is expensive, training opportunities are uneven, and many workers face constraints of time and income. For large sections of the workforce, transitioning into AI-intensive roles may be impractical.
Coding and the hollowing-out risk
Software development illustrates the paradox. AI can already write and debug code, threatening entry-level and mid-tier roles. Yet AI lacks true understanding and struggles with novel problem-solving. This may lead to a polarised labour market: fewer routine coding jobs, alongside high demand for a smaller pool of elite engineers who can frame problems, exercise judgement and supervise AI systems. Without accessible reskilling pathways, this hollowing out of the middle could exacerbate inequality.
Rethinking growth and distribution
AI also challenges traditional economic thinking. Conventional growth models assume technology augments labour. Advanced AI, however, can substitute for labour entirely. If output rises while employment stagnates or declines, GDP growth may no longer reflect improvements in well-being. Wealth could concentrate among owners of capital, data and algorithms, weakening broad-based participation in economic gains. Policymakers may need to rethink metrics of success and strengthen social protection systems in an AI-intensive economy.