By most benchmarks, India lags far behind the United States and China in artificial intelligence (AI) and deep technology. Despite having the world’s second-largest digital population—second only to China and more than 2.5 times that of the United States—India has yet to make a meaningful mark in genuine technological innovation. Lacking China’s strong-willed state or America’s pioneering technology giants, and constrained by the exodus of top technical talent, can India realistically aspire to global technological supremacy?
In a chaotic world that throws up opportunities and threats asymmetrically, this question is largely moot. India should instead focus on evolving from a provider of digital services and software solutions into a sovereign creator of high-value intellectual property. The two critical inputs in this transition are data and talent. At the policy level, the priority must be to raise investment in technology research to ignite and sustain innovation.
Data as a Factor of Production
Let us begin with data. India’s failure to develop a foundational AI model comparable to those of the United States or China is partly attributable to a lack of data sovereignty. Much of India’s digital data—particularly that generated on the internet—resides in data centres operated by American technology firms and remains largely inaccessible to Indian AI startups. This strengthens the case for building a sovereign AI cloud infrastructure, an area in which India already enjoys a cost advantage.
A second data-related challenge is India’s linguistic diversity, which complicates the development of a foundational model with pan-Indian—let alone global—applicability. However, this need not be a limitation as the higher complexity has the potential to catalyse more innovative solutions.
India’s AI strategy must aim to capitalise on its vast digital footprint across telecom, e-commerce, logistics and digital payments. The enormous volumes of data generated on platforms such as Jio’s telecom network, Zomato, and MakeMyTrip are rich fuel for AI innovation. This data is both multimodal and multilingual, reducing the need to crowdsource linguistic datasets or rely heavily on synthetic data.
To monetise user-generated and personally identifiable data, data itself must be treated as a formal factor of production, with regulations clearly delineating ownership rights, permitted usage and commercialisation. The development of “data exchanges” would allow AI innovators access to large, structured datasets for research, algorithm development and product innovation. Examples include China’s state-owned Guian Global Big Data Exchange and Shenzhen Data Exchange. Prominent privately operated exchanges include Amazon Web Services and Databricks in the United States, and Datarade in Germany.
A major constraint on this strategy, however, remains U.S. export controls on advanced semiconductors, which restrict the global supply of high-end GPUs.
Building Sovereign Technological Capabilities
The challenge of nurturing and retaining talent is more complex and cannot be addressed without a thriving technology ecosystem that meaningfully rewards innovation. This requires sustained collaboration among government, industry, and academia. As a starting point, India can draw lessons from Estonia, which has successfully implemented multiple technology-driven reforms in its education system—from the Tiger Leap programme launched in 1996 to the more recent AI Leap initiative in 2025. China, too, introduced AI into its primary-school curriculum beginning in 2025. Yet educational reform alone is insufficient.
For India’s technology sector to transition from “deployer mode” to “creator mode,” substantially higher investment in technology-focused research and development is essential. The United States and China not only released detailed AI R&D strategies nearly a decade ago, but also backed them with sustained increases in funding. While disaggregated data on AI-specific R&D spending is limited, overall public R&D expenditure reveals stark divergence: R&D spending in the United States (3.6% of GDP) and China (2.6% of GDP) has risen over the past decade, while India’s (0.7% of GDP) has steadily declined1.
Government funding of early-stage research through universities and independent agencies is critical for translating ideas into market-ready ventures. Equally important is access to patient capital—public or private—to enable startups to scale. Beyond direct government intervention, India can draw valuable lessons from Israel’s “Startup Nation” model in fostering a vibrant private venture capital ecosystem.
India’s national AI strategy, released by NITI Aayog in 2018, recommends the establishment of an autonomous agency modelled on the U.S. Defense Advanced Research Projects Agency (DARPA) to support research in dual-use deep technologies. Such an institution could serve as a magnet for top-tier technical talent—precisely the segment most vulnerable to brain drain. Separately, the Indian government’s Innovations for Defence Excellence (iDEX) programme, launched in 2018 to support aerospace and defence startups, provides a strong template that could be extended to the broader technology sector.
An AI Identity with Indian Characteristics
India’s AI journey should be guided by tactical self-determination rather than direct competition with global peers—a view articulated by technology pioneer Nandan Nilekani. This implies developing technology-driven solutions to India-specific problems. Internationally, it requires identifying and sharpening India’s comparative advantage—whether in general-purpose deep technologies or sector-specific AI applications with global relevance—and striving to dominate that segment of the value chain.
The government’s role can be modelled on countries such as Estonia, Israel, Denmark, and Singapore, which have built robust digital public infrastructure (DPI). Of the six core DPI pillars, India has made significant progress in digital identity and digital payments, but lags in others—notably data-sharing systems, digital post, digital notification, and base registries.
Progress in data-sharing systems and base registries is particularly consequential for technology startups. Advancements here would entail building secure, consent-based, and interoperable frameworks for private data exchange, alongside authoritative public repositories of “High Value Datasets”.
The speed and effectiveness with which these measures are implemented will ultimately determine how rapidly India moves up the value chain and establishes a distinct identity in the global AI landscape.
Bibliography
Benchmarking government support for venture capital: Israel
Data Marketplaces and Governance: Lessons from China
The Missing Pieces in India’s AI Puzzle: Talent, Data, and R&D
Inside India’s scramble for AI independence
- Data for U.S. and China is as of 2022 and for India is as of 2020 ↩︎
Macrologue 