India’s AI Regulation: Building Through Its Ecosystem
India is increasingly positioning itself as a major hub for Artificial Intelligence (AI) development, infrastructure, and innovation. This ambition was evident earlier this year during the India AI Impact Summit, where policymakers and industry leaders gathered in New Delhi to discuss how nations should govern AI as these technologies become more embedded in economic and social systems.
Emerging AI Hub
With over 900 million internet users and a rapidly expanding digital economy, India is becoming an important centre for AI innovation. Multinational companies are expanding their engineering and data science operations within the country, while domestic technology firms are increasingly integrating AI tools into their platforms and services. Investments in computing infrastructure, data centres, and research capabilities are accelerating in response to growing demand for AI applications.
Governance Challenges
However, the rapid spread of AI technologies raises complex governance questions. Automated systems are now utilized in decisions regarding credit, employment, customer services, and digital platforms. As these tools begin to influence economic and social outcomes, policymakers are examining how regulatory frameworks can address the risks associated with algorithmic decision-making.
Globally, many efforts have focused on governing AI systems themselves, with various jurisdictions introducing frameworks aimed at regulating high-risk applications, particularly in sensitive areas like employment, finance, or public services. These rules typically impose requirements related to transparency, risk assessment, and human oversight.
Beyond Algorithms
However, focusing solely on algorithms captures only part of the governance challenge. AI does not operate in isolation; it relies on infrastructure, computing resources, data systems, and specialized talent. As AI adoption expands, the surrounding policy environment becomes just as crucial as rules governing the technology itself.
Global Capability Centres
One significant driver of India’s AI ecosystem is the rapid expansion of Global Capability Centres operated by multinational firms. India now hosts over 1,500 such centres, which conduct advanced work in artificial intelligence, cloud engineering, and data analytics. These facilities serve as global research and engineering hubs, supporting the development of machine learning systems and managing large datasets utilized across international markets.
Digital Infrastructure Growth
The growth of these centres has been matched by a parallel expansion of digital infrastructure. Modern AI systems demand substantial computing power and data storage capacity, making high-performance computing facilities and data centres critical components of the technology stack. For example, several Indian states have introduced policies to attract investment in this infrastructure. Maharashtra has incorporated incentives for data centre development within its IT policy, offering stamp duty exemptions and electricity duty concessions.
India’s data centre capacity is projected to rise from approximately 1.4 gigawatts today to nearly 9 gigawatts by 2030, potentially accounting for around three percent of the country’s total electricity consumption. Similar concerns about energy usage and environmental impact are shaping regulatory debates in other jurisdictions.
Importance of Semiconductor Supply Chains
Another layer of governance involves semiconductor supply chains. Advanced AI systems depend on specialized chips and high-performance processors. Acknowledging the strategic importance of this sector, India has launched the India Semiconductor Mission, a national initiative offering financial incentives for semiconductor fabrication, packaging, and manufacturing facilities. Companies like Tata Electronics are making investments to bolster domestic capacity in this essential field.
Human Capital Development
Beyond infrastructure and hardware, human capital remains a defining component of India’s AI landscape. With a large base of engineers and software professionals, India has long been a global technology services hub. Universities and technical institutes are expanding programs in machine learning, data science, and artificial intelligence to meet the growing demand for specialized skills. Regulatory bodies like the All India Council for Technical Education are facilitating the launch of dedicated programs in these areas.
AI is also beginning to reshape the technology labor market itself. Several large IT services firms are reporting workforce restructuring and slower hiring as generative AI tools automate certain coding and support tasks. For many companies, this shift represents a transition rather than a contraction, with technology firms investing in workforce reskilling to adapt to AI-driven changes.
The Role of Research Institutions
Research institutions play a vital role in this ecosystem. Universities, public laboratories, and corporate research centres contribute to advancements in machine learning models and applied AI technologies. Sustained investment in research capacity will determine whether countries merely deploy AI systems developed elsewhere or actively shape their development.
Policy Considerations
This rapid expansion raises an important question: How should such rapidly growing AI ecosystems be governed? Currently, there appears to be no dedicated AI legislation on the immediate horizon in India. In the absence of comprehensive legislation, the deployment of AI systems is likely to be shaped by existing regulatory frameworks.
Many of these laws apply directly to digital technologies, including criminal law, consumer protection statutes, intellectual property rules, and contract law, influencing how AI-enabled services operate. Environmental approvals, electricity laws, and land acquisition rules also affect where computing infrastructure can be built and how it operates. Labour and education policies, including initiatives for workforce reskilling, shape the talent pipeline needed for AI development.
However, most of these regulatory frameworks were developed long before the emergence of modern AI technologies. Consequently, they do not fully address the risks associated with automated decision-making, algorithmic bias, or inaccurate outputs generated by AI models. Furthermore, infrastructure, environmental, and labour regulations were not designed with the rapid expansion of AI-driven computing infrastructure in mind.
A Layered Policy Approach
This situation reflects a larger structural issue. AI operates within a complex technological and economic environment that includes computing infrastructure, semiconductor supply chains, research institutions, and specialized talent. The governance challenges posed by AI extend beyond the behavior of algorithms themselves.
For India, this suggests that the future of AI regulation may emerge less from a single comprehensive statute and more from a layered policy approach that addresses the infrastructure, institutions, and talent supporting AI development.
