India’s recent guidelines on artificial intelligence (AI) governance are garnering attention, positioning the country as a responsible leader in the emerging technology landscape. This framework emphasizes digital public infrastructure and voluntary guardrails over stringent regulations, a strategy that resonates with many developing economies. However, as enthusiasm builds, a more pressing concern emerges: Is India doing enough to cultivate global-scale AI capabilities beyond mere intent?
In a comprehensive analysis, Greyhound Research has explored the implications of India’s governance framework. The ambition and clarity of purpose are evident, yet the focus appears skewed towards deployment rather than fostering innovation, an imbalance that could hinder future growth.
India’s digital public infrastructure, comprising systems like Aadhaar, UPI, DigiLocker, Bhashini, and DEPA, stands out as a global model. These platforms facilitate scalable solutions in identity, payments, and data sharing, providing a competitive edge for developers. By building AI solutions atop this robust framework, India offers a uniquely integrated architecture that few nations can match.
However, the nation faces significant challenges in advancing AI development. A primary concern is the lack of legal clarity surrounding the use of publicly available data for training AI models. The existing Copyright Act remains outdated, lacking provisions for text-and-data mining exemptions. This uncertainty discourages developers from engaging in extensive model training, forcing some to shift operations offshore or rely on fine-tuning existing models instead of creating new ones. Such practices limit India’s sovereignty in technology, as the country produces applications without owning the underlying technologies.
Another issue lies in the ambiguity surrounding liability. The current guidelines do not specify who is accountable if an AI system inflicts harm. Is it the creator, the enterprise that deploys it, or the platform hosting it? This lack of resolution discourages smaller companies, particularly in high-stakes sectors like finance and healthcare, from engaging in foundational development due to the associated risks.
India’s research institutions are also underutilized despite a strong talent pool in AI. While initiatives like AIRAWAT show promise, their operational transparency remains limited, and pathways to access necessary computational resources are unclear. As a result, academic labs face bureaucratic hurdles that slow down experimentation, leaving India lagging behind global counterparts that are aggressively investing in research infrastructures.
Global Context
Internationally, countries are rapidly advancing their AI capabilities. The UAE has introduced Falcon, a government-backed open-source language model, while Singapore is actively defining regulations around AI explainability and user rights. The European Union, despite its stringent regulatory framework, has successfully provided developers with certainty and research exemptions. In contrast, the United States has maintained a broad fair use doctrine that allows innovation to thrive amid regulatory chaos. These nations are not only implementing regulations but are also fostering environments conducive to AI development.
China’s advancements, particularly with DeepSeek, have demonstrated that models can compete with those from OpenAI and Google. However, privacy issues and regulatory constraints have limited their global impact, illustrating that high-quality models alone are insufficient for broader adoption. Trust and transparency are equally critical components of successful AI strategies.
The examples from these countries underscore a shift towards creating AI ecosystems backed by policy clarity, trust frameworks, and sustained public investment. While India has not yet fallen behind in AI deployment, the need for proactive measures is evident to maintain competitive advantage.
India’s strength lies in its ability to deploy AI applications in local languages and integrate them with existing public services. However, as development moves toward deeper layers of AI capabilities, the support infrastructure appears inadequate. Relying on external models creates dependencies that could undermine national competitiveness and control over technological direction.
Fortunately, transformative changes do not necessitate sweeping legislation. Targeted policy interventions could significantly advance India’s AI landscape. Legal clarity regarding the use of publicly available data for AI training would empower researchers and startups, fostering an environment conducive to innovation without legal repercussions.
Establishing safe harbors for developers would mitigate liability risks, encouraging broader engagement in AI development. Additionally, making AIRAWAT accessible through published norms and streamlined onboarding processes would facilitate collaboration across research and industry. Structured sandboxes for AI development in regulated sectors would ensure safe experimentation while providing regulatory guidance.
A lightweight certification regime for models that meet basic standards of fairness and transparency could also enhance trust among users and enterprise buyers. All these measures are feasible and could help India transition from merely safeguarding its achievements to actively building its future in the AI domain.
As the world watches, India possesses the foundational elements needed for a successful AI landscape: talent, data, market scale, and public infrastructure. The pivotal question now is whether the country can leverage these assets to claim a meaningful role in the global AI narrative. If successful, India could set a precedent for inclusive governance models that enable not just safety, but also innovation and leadership.
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