India AI Impact Summit 2026: GPU Gap, AI Strategy & Global Governance Challenges

The India AI Impact Summit 2026, recently held in New Delhi, highlighted India’s growing role in the global artificial intelligence landscape while also surfacing critical challenges regarding infrastructure and strategy.

About the Summit

• High Public Engagement: The summit witnessed unprecedented participation, reflecting strong enthusiasm for artificial intelligence among India’s digitally connected population.
• Global Positioning: India has emerged as the world’s second-largest AI user base, after the United States, underscoring its growing digital footprint.
• Rapid Adoption: Generative AI tools such as ChatGPT, Gemini, and Claude are witnessing widespread usage across sectors.

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“Democratisation of AI” Declaration

• Broad International Participation : The summit saw representation from 89 countries, reflecting wide global engagement on the future of artificial intelligence.
• Voluntary Framework : Participants endorsed a non-binding declaration titled “Democratisation of AI,” signalling cooperative intent rather than legally enforceable commitments.
• Core Objective: The declaration seeks to ensure that the benefits of AI are not confined to technologically advanced or wealthy nations but are equitably accessible to people worldwide.

Infrastructure and the GPU Gap

• Infrastructure Disparity: Despite high AI adoption, core infrastructure such as advanced data centres and high-end computing hardware remains largely concentrated in developed countries, creating structural dependence.
• Role of GPUs: AI systems rely on Graphics Processing Units (GPUs), which enable parallel processing of the massive mathematical computations required for model training, unlike CPUs, which process tasks sequentially.
• Dependency and Cost Constraints: GPUs are costly, dominated by a few global firms like Nvidia, and require significant electricity for operation and cooling, making large-scale AI development in India capital- and energy-intensive.

Three levels of AI Engagement

• Training: Building foundational AI models from scratch requires extensive computational power, large amounts of data, and significant capital investment.
• Fine-tuning: Adapting pre-trained models for specific national or sectoral applications is less resource-intensive but still requires technical capability.
• Deployment: Applying ready-made AI models in apps and services is the least resource-intensive and currently forms the core of India’s AI engagement.

Strategic Concern For India

• Risk of Technological Dependence: An excessive focus on deployment, without investment in foundational model training, risks reducing India to a data provider rather than a technology creator, potentially undermining its long-term IT competitiveness.

Global Positioning and Regulation

• Market-Oriented Approach: India has supported a market-led, innovation-friendly global framework that avoids excessive regulation to foster rapid AI development and competitiveness.
•  Concerns for the Global South: A lightly regulated regime may disproportionately favour large AI corporations, while developing countries remain vulnerable to data exploitation, algorithmic bias, and socio-economic disruptions.
•  The Inference Gap: Beyond the digital divide, disparities in computing power may create an inference gap, in which advanced economies deploy faster, more capable AI systems, leaving resource-constrained nations technologically disadvantaged.

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Conclusion

While India possesses a vast consumer base and digital capacity, long-term leadership in AI will require a strategic shift from mere deployment to indigenous model development and proactive participation in shaping global governance frameworks to ensure AI serves as a net societal good.

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