India Urges Domestic AI Development Amid Concerns Over Global Tech Firms Harvesting Data

Abhishek Singh, CEO of the India AI Mission and additional secretary in the Ministry of Electronics and IT, has raised a significant concern for India's digital future. He cautioned that global technology firms offering 'free' Artificial Intelligence (AI) tools, such as OpenAI's ChatGPT, are actively harvesting massive amounts of Indian user data to train their proprietary AI models. Singh emphasized the principle that 'When a product is free, you are the product,' highlighting the hidden cost of using such services.

To counter this, India is pushing for the development of indigenous AI models to ensure domestic control over datasets and foster innovation. The IndiaAI Mission is actively supporting Indian startups like Sarvam AI, Gnani, and Soket, which are working on foundation models trained on Indian languages and data. The mission is also scaling up compute infrastructure, with over 38,000 GPUs currently available and plans to add more.

Singh noted that while GPU access is not a barrier, funding and scaling remain challenges. The government plans public-private investment for AI compute centers, which can cost between INR 500 Cr to INR 800 Cr each. He also flagged the potential risk to India's IT workforce from foreign AI code generators like GitHub Copilot, proposing that major Indian IT firms like Tata Consultancy Services and Infosys collaborate on a national Indian code generator.

Furthermore, the government is integrating AI and data science education from Class 5 onwards and expanding the IndiaAI Fellowship. The overarching goal is for India to become the 'use case capital' of AI.

Impact This news is highly significant for the Indian technology sector, IT services, and startups. The government's proactive stance on data sovereignty and fostering domestic AI capabilities, coupled with substantial investment in compute infrastructure, could drive considerable growth for local players. Policy considerations regarding foreign AI firms and the focus on skill preservation for the IT workforce will have a material impact. Rating: 8/10.

Definitions: Data harvesting: The process of collecting large amounts of data, often without explicit consent, from users interacting with digital platforms. AI models: Computer programs trained on vast amounts of data to perform specific tasks, such as understanding language, recognizing images, or generating text. Foundation models: Large AI models trained on broad data that can be adapted to a wide range of downstream tasks. GPUs (Graphics Processing Units): Specialized microprocessors designed for parallel processing, essential for training and running complex AI models due to their high computational power. Compute infrastructure: The combination of hardware (servers, GPUs, networking) and software required to perform computational tasks, especially for AI development. Public-private investment: Funding and resources contributed by both government entities (public) and private companies to undertake large-scale projects.