However, Scale's contract workers, many of whom earn just dollars per day via a subsidiary called RemoTasks, are unlikely to benefit at all from the deal, according to sociologists who study the sector. Typically data workers are not formally employed, and are instead paid for the tasks they complete. Those tasks can include labeling the contents of images, answering questions, or rating which of two chatbots' answers are better, in order to teach AI systems to better comply with human preferences. "I expect few if any Scale annotators will see any upside at all," says Callum Cant, a senior lecturer at the University of Essex, U.K., who studies gig work platforms. "It would be very surprising to see some kind of feed-through. Most of these people don't have a stake in ownership of the company."

On TikTok, Reddit, and elsewhere, posts are popping up from users claiming they're making 20 per hour--or more--completing small tasks in their spare time on sites such as DataAnnotation.tech, As companies have rushed to build AI models, the demand for "data annotation" and "data labeling" work has increased. Workers complete tasks such as writing and coding, which tech companies then use to develop artificial intelligence systems, which are trained using large numbers of example data points. Some models require all of their input data to be labeled by humans, a technique referred to as "supervised learning." And while "unsupervised learning," in which AI models are fed unlabeled data, is becoming increasingly popular, AI systems trained using unsupervised learning still often require a final step involving data labeled by humans.

Jay fell in love with math at boarding school after a supportive physics teacher introduced him to the joy of complex calculus. He went on to study physics and math in college, hoping to one day similarly pass on what he'd learned to a new generation. That chance came in October 2022, when 25-year-old Jay answered a job listing seeking a math expert to grade equations through an online platform. But he would not be inspiring budding young mathematicians like his past self. He would instead be training an artificial intelligence system that may eventually make his expertise obsolete.

IN2018, ON A TRIP to his ancestral homeland, Alexandr Wang listened as China's brightest engineers gave impressive presentations on artificial intelligence. He found it odd that the researchers conspicuously avoided any mention of how AI might be used. Wang, whose immigrant parents were nuclear physicists at Los Alamos National Laboratory, where the first atomic bombs were designed, was unsettled. "They were really dodgy on what the use cases were. You could tell it was for no good," recalls Wang, the cofounder of Scale AI, who has no second "e" in his first name so that it has eight characters, a number associated with good fortune in Chinese culture. Scale was then an up-and-coming startup providing data services primarily to self-driving auto-makers.

Every day for over four years, Ramses woke up in his home in Barquisimeto, Venezuela, turned on his computer, and began labeling images that will help make self-driving cars ubiquitous one day. Through a microtasking platform called Remotasks, he would identify mundane objects that line the streets everywhere -- trees, lampposts, pedestrians, stop signs -- so that autonomous vehicles could learn to notice them, too. Like many Venezuelans, Ramses turned to microtasking when his country plunged into economic turmoil. The gig gave him the opportunity to earn American dollars instead of the local currency, which is subject to extraordinarily high inflation. "I would work Sunday to Sunday," Ramses, who asked to use only his first name for privacy reasons, told Rest of World over WhatsApp.