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Prime Minister Narendra Modi’s recent roundtable with a dozen Indian AI startups signalled the maturation of an ecosystem that has been simmering for years, poised to transform sectors from agriculture to healthcare and position India as an 'AI superpower, a report said on Saturday. 

By 2030, AI is projected to add $500 billion to India’s GDP, creating millions of jobs in data annotation, model training, and deployment, the report from India Narrative said.

Held weeks before the India AI Impact Summit in February, the meeting showcased homegrown models and applications spanning healthcare, agriculture, education and regional?language services.

With over 900 million internet users generating petabytes of data daily, India is a fertile ground for training robust AI models, the report said.

Indian startups are pioneering AI with multilingual capabilities to understand Hindi, Tamil, Bengali, and more, making technology accessible to non-English speakers.

This linguistic diversity is a competitive edge, enabling exports to other Global South nations facing similar barriers, the report noted. Startups at the roundtable demonstrated tools for diagnosing diseases like tuberculosis and diabetes with unprecedented accuracy, it said.

Some startups demonstrated crop yield prediction, pest detection systems and adaptive learning, useful for solving farmers' problems and mitigating the impacts of climate change.

The summit, scheduled for February 15–20, featuring keynotes, policy panels, and expert roundtables will bring international stakeholders together to discuss governance, standards and collaboration. It will showcase 12 qualified Indian startups in areas like healthcare and multilingual tech. This exposure could attract foreign investment and talent inflows, accelerating India’s goal of a $1 trillion digital economy by 2030, the report noted.

The India AI Mission, launched in 2024 with a $1.2 billion funding, democratised access to computing resources, datasets, and talent development. Further the National AI Portal provided open-source datasets, while partnerships with tech giants ensured high-performance computing infrastructure.