Manish Gupta, Senior director, Google DeepMind
Image: Nishant Ratnakar for Forbes India
Back in 1983, Manish Gupta began studying computer science at IIT-Delhi despite not having seen a computer before. “I had only heard of computers,” he says. His story is a testament to how far the world has come—from an era of curiosity to one where artificial intelligence (AI) is a central topic of discussion among global leaders.
Since then, Gupta has contributed to renowned organisations like IBM, Xerox and Goldman Sachs, and founded edtech company VideoKen. In 2019, he seized an opportunity to establish Google Research India from the ground up—an experience he describes as “immense privilege”. “Working at a company that is not only pioneering AI advancements but also creating products that impact billions of lives across the globe is truly a privilege,” he reflects.
Impact-led research
Google Research India was founded with a clear mission to develop advanced technologies that address real societal challenges. “We chose the theme of inclusive AI: How do we develop AI in a manner that brings benefits to everyone, especially the billions who have not been touched by AI previously,” says Gupta. India, with its diversity and scale, offers a unique testing ground. “Solutions developed for India are often applicable to many other parts of the world as well.”
One of the lab’s most ambitious projects has been addressing India’s linguistic diversity. “We found that 72 Indian languages had zero known digital presence although these might be spoken by millions of people,” Gupta says.
His team helped build a multilingual model that understands over 1,000 languages, enabling Google Translate to add 110 new languages in a single release, including seven Indian ones. This work goes beyond translation—it’s about access. “People who do not understand English face a big barrier in getting the benefits of technology the way you and I do,” he says.
Recognising that for many Indians their smartphone is their only digital device, Gupta challenged his team to make AI models work within the constraints of mobile hardware.
“This led to a lot of work in making these models efficient, and work with constraints like a certain amount of compute and memory. Now, we have Gemini Nano that is meant for smartphones,” he says. The same principles of efficiency have become critical in the large language model (LLM) era, where the cost of computation has ballooned.
“Gemini Flash is currently the most efficient model in the market, in terms of rupees per watt. I’m proud of our team’s work in contributing to the same.”
Beyond foundational models, Gupta’s team has applied AI to public health and agriculture. In collaboration with the non-profit Armman, they developed models to identify expecting mothers at risk of dropping out of health programmes—improving engagement and outcomes. In agriculture, they built the first AI model to map field boundaries and crops across India using satellite imagery, enabling farm-level insights for loans, insurance and subsidies.
“Manish is a rare, passionate leader who bats for his team, and gives them enormous creative freedom. At the same time, he does not hold back if the work does not have potential impact,” says Shiv Kalyanaraman, CEO of ANRF. “Manish and team have always put impact and helpfulness for a billion people at the heart of their work.”
Gupta’s team has also been at the forefront of addressing bias in AI, particularly in non-Western contexts. “We published one of the first papers identifying bias based on religion and caste in Indian datasets,” he says. “We’ve been publishing papers on how many of these biases also carry over to visual models when you’re trying to understand images. Most of the AI models lack a cultural understanding.”
To address this, the team has been working closely with researchers across India, including institutions like IIT-Madras, to improve Google’s models and support the broader ecosystem of responsible AI development.
Gupta is excited about AI’s role in agriculture, health care, accelerating science and drug discovery, among many others. He cites AlphaFold, which won the 2024 Nobel Prize in chemistry, for its contributions to protein structure prediction and computational protein design. “It can predict the structure of 200 million proteins in under a year—work that once took a PhD student five years per protein. That’s a billion PhD years of work done in less than a year,” he marvels.
What does he hope his legacy will be? “Inspiring and challenging teams to go after ambitious goals—not settling for a modest impact, but solving real-world problems that make a difference to humans.”.