HomeNewsTrendsIndian-origin CEO says Nandan Nilekani is 'awesome' but also 'wrong' for his stance on AI

Indian-origin CEO says Nandan Nilekani is 'awesome' but also 'wrong' for his stance on AI

Aravind Srinivas expressed his views in a post on X (formerly Twitter), calling Nilekani "awesome" for his unmatched contributions to India's technological advancements through Infosys and UPI. However, he challenged Nilekani’s recommendation for Indian AI startups to focus solely on building practical AI applications rather than training large language models.

January 22, 2025 / 17:28 IST
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nandan nilekani, aravind srinivas
The comments from Aravind Srinivas came in response to Nandan Nilekani’s earlier advice to Indian AI startups.

Aravind Srinivas, the Indian-origin CEO of Perplexity AI, publicly criticised Infosys co-founder Nandan Nilekani's stance on artificial intelligence (AI), asserting that India should prioritise both AI model training and practical applications.

Srinivas expressed his views in a post on X (formerly Twitter), calling Nilekani "awesome" for his unmatched contributions to India's technological advancements through Infosys and UPI. However, he challenged Nilekani’s recommendation for Indian AI startups to focus solely on building practical AI applications rather than training large language models (LLMs).

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“To be clear: Nandan Nilekani is awesome, and he's done far more for India than any of us can imagine through Infosys, UPI, etc. But he's wrong on pushing Indians to ignore model training skills and just focus on building on top of existing models. Essential to do both,” Srinivas wrote.

The comments from Srinivas came in response to Nilekani’s earlier advice to Indian AI startups. At the Meta AI Summit in October, Nilekani had urged startups to avoid the costly endeavour of building large AI models, suggesting that resources should instead be directed towards creating practical AI solutions.

“Our goal should not be to build one more LLM. Let the big boys in the (Silicon) Valley do it, spending billions of dollars. We will use it to create synthetic data, build small language models quickly, and train them using appropriate data,” Nilekani had said. He further emphasised the need for scalable, frugal infrastructure and practical applications tailored to the Indian context.