AI Self-Reliance Will Need Strategic Realignment, More Capital

Within days of the White House announcing America’s AI Action Plan, China released a Global AI Governance Action Plan and proposed the establishment of an inclusive governance model to support international cooperation on AI capacity building. The signs of the intensifying AI arms race and ambitious declarations made in spectacle-like AI summits in Washington and Shanghai highlight the differing strategic considerations of the world’s two leading economies.

AI’s role extends beyond boosting innovation and productivity in the economy to creating new vistas for advanced technology exports. This aspect of technological dominance adds strategic heft to international trade and business, reshaping global power dynamics. Each invention in human history has triggered a new phase of the industrial revolution based on the ‘winner takes it all’ phenomenon. Invariably, such invention and technology diffusion have also led to the emergence of global monopolies, controlling technology supply to preferred global partners.

At the onset of the AI-driven industrial revolution, global rivalry in AI advancements exacerbates the rifts and fragmentation of the global economic landscape—already divided into multiple camps. In January this year, the US imposed new restrictions on the export of computer chips for AI systems to prevent its adversaries from accessing this advanced technology. Although the export control for China has been reversed with a levy of 15% on export revenues, restrictions on high-bandwidth memory chips still persist.

Hardened dogmas on global AI standards and governance models create divergences in global collaboration. While some countries promote open, collaborative frameworks for safe and inclusive AI development, others prioritize unobstructed pathways free of regulations and ethical concerns. The Paris AI Action Summit highlighted the widening fissures in global collaboration for an inclusive AI governance construct, with the UK and US avoiding signing the summit declaration.

Self-reliance in AI
Even if a relative latecomer, the IndiaAI Mission’s thrust to build a comprehensive ecosystem for developing indigenous AI capabilities and fostering technology democratisation is exemplary. However, India’s current limitations in producing its own DeepSeek or Blackwell architecture, despite the presence of a strong engineering talent pool and an established IT services industry, highlight the challenges ahead. India’s self-reliant pathway to drive its AI vision requires a focused approach on foundational requisites to emerge as a global AI powerhouse:

GPUs: India’s aim to develop its own high-performance GPUs in the next 3-5 years and reduce dependency on foreign chips requires a multipronged approach that includes utilising open-source architectures, technology transfer, and licensed chip design, besides deepening existing partnerships in chip manufacturing. It requires scaled-up investments for the promotion of R&D, startup financing, and boosting existing chip manufacturing capacity.

Foundational models: Startup-driven sovereign AI model development requires extensive training on local datasets in a wider context, capturing linguistic diversity and cultural nuances free from the biases of foreign models. With a focus on adaptability and transfer learning, vertical-specific models can spawn generalised AI-driven tasks, supporting wide segments of agriculture, healthcare, manufacturing, finance, retail, MSME, and the public sector.

Applications: Combined public and private sector efforts to harness AI innovation across a wide range of industry applications are progressing well. By expanding the ambit of innovation use cases to provide industry-specific solutions, including manufacturing robotics, India can make a big difference in its global positioning.

Skilling: To reap innovation benefits, the creation of a broader AI-skilled workforce pipeline is urgently needed to bridge skill gaps. It demands an overhaul of university programmes focused on AI training, fostering research orientation, and encouraging entrepreneurship through industry partnerships. Industry-academic collaboration for AI talent development, upskilling, and expansion of AI training institutes in non-metro cities can improve talent pipelines.

Safe and responsible AI frameworks: Extending Niti Aayog’s Operationalising Principles, the setting of sectoral governance standards and frameworks can credibly address privacy, explainability, and fairness concerns along with potential employment and societal impacts.

Amid the volatile interplay of national interests, as India prepares for the AI Impact Summit 2026, our AI priorities require strategic alignment and increased budget commitment to develop credible full-stack capabilities. Mass-scale digital inclusion and UPI-based payment initiatives give us confidence to pursue AI self-sufficiency, even if it is a longer development journey. Our success in building ingenious capabilities in defence, space, and nuclear technologies, coupled with our growing credibility in electronic device exports, speaks volumes about our resolve and resilience.

Without pursuing a binary option—i.e., either turning into a camp follower for access to technological resources or an isolationist pursuit of self-sufficiency—India can adopt a pragmatic approach by forging natural coalitions in its AI pathway. Building on India’s Atmanirbhar Bharat initiative, a shift in our positioning to act as a bridge connecting diverse interest groups globally can vitally uplift India’s AI trajectory.