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A double-edged sword in the AI race

Voice and Data

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February 2025

While DeepSeek’s open-source nature triggers privacy risks, India accelerates the development of its own Al framework to harness local datasets for growth.

- BY VERNIKA AWAL

A double-edged sword in the AI race

On 20 January this year, a very little-known Chinese upstart named DeepSeek released what it claimed to be a foundational Artificial Intelligence (AI) model called R1-seemingly designed at the same scale as the world's largest, most complex commercially released foundational large language model (LLM) until now, the OpenAl of Pro. In what now feels like seconds, DeepSeek became the most sought-after worldwide for the way it handled data and restructured the needs.

Before anything else, some added context: foundational LLMs are massively complex AI algorithms trained on vast troves of data by a company. This database includes as diverse a range of data as possible-US firm OpenAl is notoriously embroiled in an industrial argument and legal battles around the world for using 'copyrighted' data scoured from across the Internet.

This diversity of data gives LLMs a lot of context to understand most things around them in a natural language format. No wonder, then, that ever since the public release of the now-famous ChatGPT, people have become wary and predicted that AI would soon "take away our jobs." Case in point: Generative AI (GenAl), which is the application layer sitting atop the LLMs, uses the foundational models to understand, think, and speak like humans.

So far, most Gen AI applications require mammoth models that use vast amounts of computing to retrieve all of their training data. DeepSeek, on the other hand, transformed this approach. As many stakeholders highlighted, DeepSeek would use a simple algorithm to understand a query's broad topic and then retrieve only the specific dataset required for the query.

By DeepSeek's own whitepaper admission, this can reduce the cost of compute required for GenAl applications by 90% and produce similarly deep results as, say, OpenAl's 01 Pro.

WHAT DOES IT MEAN FOR INDIA?

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