By Amit Kapoor and Mohammad Saad
Ever since the launch of ChatGPT in 2022, AI usage in India has evolved significantly in both scale and variety. Initially limited to business applications in a few companies, AI adoption has since expanded sharply, with India accounting for 13.5% of ChatGPT’s 700 million weekly users. A recent TCS‑CII survey found that 69% of organisations now use AI‑enabled products and services. Innovation has surged as well, with 83,059 AI patents filed between 2019 and 2025, compared to just 3,931 from 2010 to 2018.This rapid diffusion of AI is intensifying its interaction with economic agents across the macroeconomy, giving rise to an evolving AI ecosystem. This ecosystem is not only shaping AI’s trajectory but is also being reshaped by AI’s impact on labour markets, governance and infrastructure. While the spillover effects of these interactions will influence the broader economy and will require policy vigilance, they also create significant opportunity areas for India to enter and add value in the global AI race.
Globally, emerging AI ecosystems are essential for the ethical, equitable, and effective adoption of AI. These ecosystems, comprising the labour market, digital infrastructure, and governance; are not only individually necessary but also complementary. Each element both influences and is influenced by AI, shaping the broader macroeconomy and national competitiveness. Securing the AI ecosystem therefore requires more than simply establishing these core components but the policies need to be dynamic, such that they account for ongoing interactions between AI and its users. This is particularly critical in India, where AI adoption is already widespread but uneven across socio-economic groups. Continuous policy vigilance is therefore needed to prevent potential macroeconomic risks that could affect the future trajectory of AI adoption.
The most intense challenges policymakers face in securing India’s AI ecosystem arise from the labour market. The advent of AI has raised concerns about workforce skill erosion, socio-economic barriers to transitioning into AI roles, and job insecurity, particularly in the IT industry. Recent research increasingly supports these concerns, showing that AI can degrade skills over time and displace tasks. At the same time, many advocates argue that AI will generate new jobs in AI development and maintenance to replace the job that it eliminates. While this is true, the statement overlooks significant structural barriers. Although many people could benefit from these new opportunities, existing workers may encounter challenges such as the cost of reskilling, limited access to employer-provided training, and personal constraints including work and family responsibilities, which can make reskilling infeasible for large sections of the population.
Although AI can theoretically replace jobs and create new ones, its effects on the labour market are often unexpected and multifaceted. In coding, for example, fears of job displacement are widespread as AI models can already assist with writing and debugging code. Yet AI does not truly “understand” problems; it generates outputs by learning patterns from existing data and struggles with genuinely novel challenges outside its training set. Coupled with the erosion of human skills due to overreliance on AI, this dynamic may create a highly uneven labour market where a few elite-level jobs emerge & lower-level coding positions disappear. The result is a labour market that may simultaneously demand higher skills, offer fewer opportunities, and deepen inequality if access to reskilling remains limited.
In this context AI compels us to rethink growth paradigms, because traditional economic models may ultimately be insufficient to capture its impact on output. Take the Solow growth model, for instance which posits that long-run economic growth depends on technological progress, which enhances labour productivity rather than replacing labour itself. However, the theoretical possibilities introduced by advanced AI challenge this foundational premise. AI has the potential not only to augment human labour but, in some cases, to substitute for it entirely. If AI reduces labour input while overall output continues to rise, conventional metrics such as GDP per capita may fail to reflect true economic well-being. Wealth may become increasingly concentrated among business owners and technical elites, while broad-based participation in the economy diminishes, exposing limitations in how traditional growth models measure prosperity in an AI-driven world.
Apart from its impact on the labour market, AI also poses significant challenges in the policy and governance dimension of the ecosystem. Data privacy and ethical usage are the most pressing concerns. Users in India and elsewhere remain largely unaware about how their personal data may be incorporated into AI training models. Policymakers are still deliberating over a formal governance framework, yet AI models have been deployed in India since 2022, meaning data may already have been included in AI systems without consent and cannot be practically retrieved. Moreover, regulators face an inherent trade-off between privacy and innovation, as stringent techno-legal safeguards that prevent data leakage can inadvertently limit AI performance by restricting access to new data.
Digital infrastructure, a critical pillar of the AI ecosystem, introduces its own set of challenges. Open access and extensive AI usage exacerbate environmental pressures, as data centres powering large models consume enormous amounts of electricity. With much of this energy still derived from fossil fuels, AI systems contribute significantly to carbon emissions. In addition, AI data centres require vast quantities of water for cooling. This intensifies pressure on freshwater resources, especially in regions already facing scarcity.
Given these dynamic interactions between AI and its ecosystem, securing India’s AI ecosystem is a complex task for policymakers. As the year closes, some areas demand immediate attention, while others require strategic shifts and broader stakeholder consultation. AI’s effects are multifaceted and often unexpected, with the labour market likely to feel the strongest impact. Policymakers must focus on adapting education systems and addressing socioeconomic barriers to ensure a smooth transition for existing workers. Potential inequality arising from AI-driven displacement may also require a rethink of traditional growth paradigms. India’s AI future depends not only on dynamic policy action but also on leveraging opportunities to overcome current system limitations, create original use cases, and add genuine value through innovation.
The article was published with Statesman on December 31, 2025.
