I am no longer a mere human being. I am a conduit of reality, a medium of messages. I hold a knife in my hand and slice into an organic cucumber, hunching so the iPhone strapped to my forehead can capture all 10 fingers. I throw the slices into a salad bowl and end the recording. Somewhere, a baby robot is a tiny bit smarter.
This was my existence for a full week last month as I performed data collection from the comfort of my apartment, teaching humanoids how to scrub dishes, fold laundry, and pour drinks, among other menial tasks. If robots are ever going to live with us and help out around the house, they need to develop fine motor skills. I performed my household chores with pride (I’m not usually contributing to mass datasets when I put away my jockstraps). And I was glad to make some money too.
First-person videos, shot with a camera attached to a person’s head or chest, are a growing need as more companies attempt to build bots and improve their AI models. Even though the internet is full of scrapeable videos, hyperspecific clips—like thousands of close-ups showing hands pouring water into a glass without spilling—can be critical for fine-tuning machines to excel at real-world tasks. This style of recording, called egocentric data by the industry, is in such high demand that some investors estimate leading companies will purchase hundreds of millions of hours from third-party suppliers over the next few years.
“I want every person on the planet to be recording themselves doing the dishes,” says Avi Patel, the 22-year-old founder of data collection marketplace Kled. “That’s going to make a robot so that you never have to do the dishes ever again.” Egocentric data collection is already growing in countries like India where, generally, self-employed workers make around $125 a month on average, and these first-person video gigs can offer similar rates.
As interest swells, more data collection companies are looking to expand in the States, like DoorDash’s stand-alone Tasks app launched earlier this year. Before long, many gig workers in the US may start delivering reality to make ends meet, as well as the typical room-temperature takeout.
Thankfully, I already had a smartphone head mount in my possession from testing DoorDash’s Tasks app. My impression, even then, was that bespoke video data was the dystopian future of gig work, but I wanted to better understand this growing industry. Since Tasks is not available in California, where I live, I signed up for three other platforms: Kled, Luel, and Waffle Video.
The money I made was meager. I essentially trained the robots for close to free and didn’t make a dent into the $2,500-a-month San Francisco rent that I split with my partner. But the gigs did have one unexpected perk: My apartment has never been this clean.
Kled’s breakout moment came when Patel posted a video on X earlier this year, showcasing a sliver of the company’s wide-ranging archive of video data. The clip was quickly viewed more than 4 million times, and data purchasers started blowing up Patel’s phone. “Every major foundational model and lab reached out to me asking for data,” he tells me.
Robot training data is only a slice of what Kled collects from its over 300,000 users—mostly the startup pays people to upload their entire camera roll as AI training data. Patel has seen early adopters latch on to the gig work in Malaysia, and there’s a “special tasks” section to help promote video submissions. Users pick, from a list, which chore they want to film and then capture content directly through the app. An hourly rate is not listed for these; each is labeled low, medium, or high paying, without a specific range. (The company says that in about a month, an update will include rates for many, but not all, tasks.)
