For one week this summer, Taylor and her roommate wore GoPro cameras strapped to their foreheads as they painted, sculpted, and did household chores. They were training an AI vision model, carefully syncing their footage so the system could get multiple angles on the same behavior. It was difficult work in many ways, but they were well paid for it — and it allowed Taylor to spend most of her day making art.
“We woke up, did our regular routine, and then strapped the cameras on our head and synced the times together,” she told me. “Then we would make our breakfast and clean the dishes. Then we’d go our separate ways and work on art.”
They were hired to produce five hours of synced footage each day, but Taylor quickly learned she needed to allot seven hours a day for the work, to leave enough time for breaks and physical recovery.
“It would give you headaches,” she said. “You take it off and there’s just a red square on your forehead.”
Taylor, who asked not to give her last name, was working as a data freelancer for Turing Labs, an AI company which connected her to TechCrunch. Turing’s goal wasn’t to teach the AI how to make oil paintings, but to gain more abstract skills around sequential problem-solving and visual reasoning. Unlike a large language model, Turing’s vision model would be trained entirely on video — and most of it would be collected directly by Turing.
Alongside artists like Taylor, Turing is contracting with chefs, construction workers, and electricians — anyone who works with their hands. Turing Chief AGI Officer Sudarshan Sivaraman told TechCrunch the manual collection is the only way to get a varied enough dataset.
“We are doing it for so many different kinds of blue-collar work, so that we have a diversity of data in the pre-training phase,” Sivaraman told TechCrunch. “After we capture all this information, the models will be able to understand how a certain task is performed.”
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Turing’s work on vision models is part of a growing shift in how AI companies deal with data. Where training sets were once scraped freely from the web or collected from low-paid annotators, companies are now paying top dollar for carefully curated data.
With the raw power of AI already established, companies are looking to proprietary training data as a competitive advantage. And instead of farming out the task to contractors, they’re often taking on the work themselves.
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