The summit marks India’s rise as a pivotal AI market, signaling a roadmap for policymakers, investors, and industry leaders to collaborate on AI cooperation and technology investment as the country aims to become a global hub for AI activity.

Gen Loop is developing lightweight language models for all 22 scheduled Indian languages, including base, instruction-tuned, and moderation variants for education, social platforms, and enterprise use.

OpenAI expanded its footprint in India with a New Delhi office opened in 2025 and rolled out price-sensitive incentives, such as a ChatGPT Go plan under five dollars and a year of free access to spur adoption.

Privacy remains central, with quantisation techniques enabling locally run models, unlocking enterprise and hospital use cases without data leaving local environments.

The report notes that exchange withdrawals do not necessarily imply long-term storage and may serve various on-chain or off-chain activities.

Data sovereignty is a policy challenge; experts warn that fully localising data could waste compute resources and stress the need for programmable security controls and interoperable policies.

Tiny AI offers lower latency and stronger privacy on resource-constrained devices but faces hardware and performance limits compared to larger models.

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Scalability and competitiveness beyond document recognition and speech synthesis remain open questions, underscoring the need for independent benchmarking and third-party adoption.

Generative AI should avoid high-precision, high-stakes contexts like pricing, manufacturing, or healthcare procedures due to non-deterministic outcomes.

Altman cautions that heavy usage does not automatically yield broad economic impact and highlights challenges around affordability and infrastructure.

Regulation should be minimal but effective, with governments prioritizing cybersecurity research and targeted investments over sweeping rules.