India's Urgent Call for AI Sovereignty
India stands at a critical juncture regarding its artificial intelligence future, facing a stark warning from global brokerage Bernstein. A recent report highlights that without developing its own sovereign large language models (LLMs), India risks becoming perpetually dependent on foreign AI technologies.
Bernstein likens the current global AI race to the strategic importance of defense technology, where access to advanced capabilities is tightly controlled. The report emphasizes that AI is increasingly treated as a strategic national asset, not a freely available commodity, making national control over foundational AI models imperative.
Beyond Applications: The Foundational Layer
The report argues that merely building AI applications or data centers is insufficient for India's long-term competitiveness. True autonomy and global standing, according to Bernstein, depend on owning the "intelligence layer" – the foundational LLMs that underpin critical sectors like enterprise software, defense, healthcare, and finance.
Relying entirely on foreign LLMs exposes India to significant strategic risks. Geopolitical tensions could lead to restrictions or delays in accessing cutting-edge AI models, forcing Indian businesses to operate with older technologies while competitors in other nations advance rapidly.
Why India Lags: Structural Challenges
Bernstein identifies India's historical technology development as a key reason for its current position. Unlike countries with large consumer internet platforms that generate vast proprietary datasets essential for training frontier AI models, India has primarily built a services-led technology industry.
Furthermore, India's AI Mission, while comprehensive, spreads resources across multiple priorities including compute, research, and applications. This diffused focus, the report suggests, limits dedicated funding for the development of sovereign foundational AI models, perpetuating dependence on external providers for core intelligence.
A Roadmap for AI Autonomy
To mitigate these risks, Bernstein proposes several strategies. One recommendation is for India to build sovereign, domain-specific LLMs, leveraging its unique proprietary datasets in sectors such as healthcare, manufacturing, and defense. Policymakers could also encourage the localization of AI infrastructure or incentivize domestic foundational model development.
Another option involves requiring foreign AI firms to establish and operate India-based AI stacks, insulated from external geopolitical controls. While these approaches present their own challenges, Bernstein concludes that developing an indigenous AI stack – an Indian equivalent of models like DeepSeek – is no longer just a technological ambition but a strategic necessity for safeguarding India's competitiveness and autonomy in the AI era, leveraging its strong digital public infrastructure and engineering talent.