LoaderSave StorySave this storyCommentLoaderSave StorySave this storyI 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.)
I selected “take out the trash” as my inaugural bot-training task on Kled. It’s marked as “medium pay.” Getting started was easy, since the app guides users on what to record:
Description: Capture how you take out your household trash to help train real-world robotics workflows.
Task Requirements: Record a continuous in-app video showing: removing the bag, tying it, placing a new liner, and throwing the trash out. Keep the camera steady and avoid filming faces.
I slipped the smartphone strap onto my head and filmed as I tied up the kitchen garbage bag and escorted it to the alleyway bin behind my apartment. I was a little anxious about the potential of bumping into one of our neighbors and having to explain what I was doing. The recording automatically shut off around the two-minute mark, before I was able to reline the can, as the app said I’d reached the limit.
Patel says the most important focus for Kled over the past year has been fraud detection. People often attempt to upload videos downloaded from the internet, as well as blank black boxes. There’s also the issue of privacy: “You have to make sure all data is anonymized and remove personally identifiable information, because labs won’t buy from you if you don’t,” he says. “Same thing for any bad uploads. You just have to filter that all out.” Kled recently pulled out of Nigeria, Patel says, because around 95 percent of user-submitted uploads were either useless duplicates or fraudulent.
I completed nine tasks on Kled, recording off and on during my weekend chores, before realizing that the app requires users to upload 100 pieces of media before they are eligible for any kind of payout. A bit miffed, I decided to upload over 90 photos from my vacation last year to meet the payout threshold. Since Kled takes several days to process the data, I moved on to other platforms collecting robot training data while waiting to get my money.
Luel, a platform that pays users from around the world for data, is quite similar to Kled. Both have young founders: Luel’s William Namgyal was just 18 years old when his company joined Y Combinator earlier this year. Both companies collect a variety of data beyond just self-shot videos. “People are willing to record simple clips of them saying lines in their own language,” Namgyal says of Luel’s interest in language preservation. “Why not expand to egocentric videos and documents?” The app now also pays users to record their computer screens and upload photos of receipts.
During my tests, Luel felt a little clunkier than Kled in its design. The platform doesn’t divvy up jobs by chore type; it simply has a Record Any Hands-On Activity From a First-Person Perspective listing that offers $6.60 for an hour of video. (For comparison, the federal minimum wage in the US is $7.25 an hour.) Luel’s requirements are hyperspecific—head-mounted only, wide-angle camera turned horizontally, minimum 1080p resolution, visible hands 95 percent of the time.
I restrapped my phone to my head and got to work in the kitchen, scrubbing plates and loading the dishwasher. I submitted a five-minute video to Luel’s website; a day later it was rejected. “Your hands were not visible in enough frames,” read Luel’s explanation.
I got paid nothing, at first. Luel sent me an email a few days afterward reversing its initial decision. The message explained that while my “hand visibility came in at 83% across the sampled frames,” I had satisfied the rest of the listing’s requirements and Luel would, in fact, pay out. I was 55 cents richer.
Waffle Video was easily my favorite of the three platforms. Unlike Kled and Luel, it focuses solely on video training data, and the “missions” I saw in the app, like shoelace tying and water pouring, paid $25 per hour of video. Now we’re talking.
Each dataset that users create is custom-built for the companies purchasing the data, so Waffle’s “missions” are available only for a limited amount of time. The app also offers gig workers recurring revenue—essentially a syndication—if their videos are relicensed to additional companies. “I think there’s an amazing opportunity to create a symbiotic relationship between the people that are giving their life, their perspective, their creativity, their data, essentially, to these models,” says Joshua Mesnik, Waffle’s 34-year-old cofounder and COO. “For that to be a reciprocal relationship, instead of just a one-way street.” Mesnik cofounded the startup with CEO Joey Newfield, who’s 33, in 2024.
Waffle is also the most detailed in its guidelines about what the app does and doesn’t want. Before uploading, users are able to see every reason their submissions might be rejected, from blurry recording quality to the inclusion of copyrighted audio. Alongside an example video, the tasks include thorough instructions on how to record each chore, like these deets from “pouring liquids”:
After startups collect these first-person videos from humans like me, the next hurdle is transforming the reams of data into sellable formats. Waffle processes every user video through what’s called MAPLE—Media Asset Processing and Labeling Engine. “Everything is checked for copyright. Everything is labeled, annotated, and structured to be ingestion ready for AI training,” Mesnik says. Companies aren’t as interested in raw, unlabeled information; they want cleanly packaged videos with highly descriptive metadata attached to each file.
I really got into my video data groove while using Waffle. The rate was high enough to actually feel enticing, and I gleefully cavorted around the house like a reality TV show producer hoping to film as much content as humanly possible. Tying my shoes over and over again? Done! Scrubbing the dishes until they’re sparkling? Done! Pouring Diet Coke back and forth between glasses until it's flat? Done!
My smartphone was essentially glued to my forehead all evening as I completed AI tasks on Waffle. Each submission was around 20 seconds long, as dictated by the app for this “mission.” I blazed through 125 approved uploads in a few days of work, earning $20 for my efforts.
Namgyal, Luel’s founder, worries about the future of work, despite running a company that trains robots who could one day replace human workers. “My biggest fear is that unemployment will go up extremely high, more than it already is,” he says. Namgyal sees Luel and gig work more broadly as a quick way for people to make some cash, not as a panacea for labor trends. “Part of our goal is also to create jobs, but that’s obviously a hard thing to do.”
Patel proudly shares that one of the top earners on Kled is a truck driver earning $8,000 a month by filming with his dash cam and submitting pictures of potholes. But this user’s experience is clearly an outlier. The majority of those submitting data for AI model training are not making this kind of cash, even though the hunger for video training data seems almost boundless. Egocentric video gig work is still, well, gig work, which comes with fewer worker protections and less overall stability.
Specialization might be the only way to make a meaningful amount of money doing this in the US. Anyone can record videos of cucumber chopping, but only an experienced sushi chef can really show the best way to slice salmon sashimi. “I’m sure there’s a world where chefs are completely replaced from the planet,” Patel says. “But they’re never really replaced, because they’re at home filming videos, making unique recipes used to train these robots. They’re getting paid.”
It was finally time for me to get paid too. When Kled finished processing my nine egocentric videos and 97 vacation photos, I wasn’t expecting much, but I was still taken aback by the payout: $1.
After getting over my initial pangs of disappointment, I felt a twinge of pride. Perhaps I was helping build a better future—one where my grandkids would be blissfully unaware of the cleaning habits I had to cultivate. My pride quickly dissolved, though, the more I considered how I was potentially training the very humanoid robots that could replace humans at jobs beyond the tedious household chores.
My total earning for the week came to $21.55. For me, this experience was a casual side hustle, a way to pay for a few extra Diet Cokes by completing chores around the apartment. But, for workers around the world, and increasingly in the US, this kind of AI gig work may be accepted under economic pressure and relied on as a means to live—teach the robot how to cook tonight so you can put food on the table tomorrow.
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