Google DeepMind’s India chiefs on the race to make AI cheaper, safer — and finally good at code
Google DeepMind's India leadership is prioritizing the development of more efficient AI models, specifically focusing on coding capabilities. The team is utilizing techniques like 'Matryoshka-inspired' transformers to reduce compute costs and improve performance for global applications.
Why it matters
As AI demand grows, making models more efficient and cost-effective is critical for scaling technology in price-sensitive markets like India and for broader global adoption.
Alphabet’s CEO Sundar Pichai’s admission in a New York Times podcast that Google is “falling a little behind” in AI coding tools has become one of the more pointed lines to emerge from the search giant’s leadership this year. It is also, it turns out, a fair question to put to the two executives who run Google DeepMind’s operations in India — a market the company increasingly treats as a proving ground for making its Gemini models cheaper, faster and more useful to build with.
Manish Gupta, who leads research for Google DeepMind India, and Seshu Ajjarapu, who heads applied AI for the unit, agree that closing the coding gap is now a top-tier priority internally , ranked alongside the company’s other most urgent work streams.
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