India Trains The World’s Robots, But At What Cost?

As global robotics companies race to build physical AI, India's workers are supplying the human movements that teach machines how to work

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In the 1990s, American hospitals began routing physician dictations to transcriptionists in Bangalore and Chennai. It was unglamorous work, but India was good at it. 

Thirty years on, cameras are being slapped on the foreheads of India’s blue-collar workforce, and as they sew a seam, pack an iPhone or assemble a component, the gadget records — every wrist angle, hand movement and intricacy with utmost precision and detail. The data then travels by cloud to robotics labs in San Francisco, where it trains bots to move like people.

Indeed, India is becoming a training ground for robots capable of doing human-like work.

In April 2026, video clips of textile workers at a factory in Delhi NCR wearing small head-mounted cameras went viral on social media. The assumption was that someone was recording them to train robots.

The headgear was identified as originating from Egolab.AI. Founded by 19-year-old Raghav Samani and 18-year-old Varun Pareek, the startup is a first-person point-of-view (POV) data aggregator. It collects ‘egocentric data’, which is first-person footage from body-worn cameras that captures exactly what a robot’s eyes will eventually need to see: the angle of a wrist, the grip on a tool, the small mid-task corrections that no simulation can replicate. 

Collecting such data is cheaper and more useful than synthetic simulation. And just like the transcription work of the 1990s, India has become a gold mine of egocentric data generation — an opportunity that a lot of startups are trying to cash in on.

So, is India writing a crucial chapter in the future of robotics, or once again just doing the cheapest work (outsourcing) while others continue to make the breakthroughs? Let’s find out in this edition of The AI Shift…

The World’s Back Office Gets A Fresh Job

The reason why egocentric data collection matters today is due to a gap that has held robotics back for years. Large language models (LLMs) had the internet to pre-train on before being fine-tuned for specific applications. 

“Robots, on the other hand, have had no equivalent. A robotic arm can be post-trained for a specific task using simulation or teleoperation (where a human physically puppeteers the robot through a task), but that breaks the moment the environment changes,” said Rushil........

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