Data Is the Biggest Bottleneck

A quiet race is on to give India its own AI voice. From call-centre automation to defence and legal systems, Abhishek Upperwal of Soket Labs and journalist Swathi Radhakrishnan tell us why AI trained only on Western, English-heavy data cannot meet India’s needs. Translation isn’t enough; models must “think” in Hindi, Tamil or Marathi to capture nuance and reduce bias. The government’s IndiaAI mission, with nearly ₹10,000 crore in funding, is catalysing startups to build these Indic models. But their biggest bottleneck is data. Only a sliver of the world’s open datasets are in Indian languages, and even public archives like Doordarshan take time to unlock. Startups are scrambling crowdsourcing voices, licensing publishing-house content, generating synthetic text and negotiating with ministries to reach the 15–20 trillion high-quality tokens needed for a world-class foundation model. In this episode Host Anirban Chowdhury, ET’s Swathi Moorthy and Soket AI’s, founder, Abhishek Upperwal try to answer the following questions:

1. What makes sovereign, Indic-first AI critical for India’s economy and security?
2. How are innovators overcoming the huge shortage of quality language data?
3. Can low-cost, DeepSeek-style methods help India build frugal yet powerful models?
4. Where will the commercial payoffs voice AI, regional apps, enterprise tools arrive first? 

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