After boot camp, they went home to work alone in their bedrooms and kitchens, forbidden from telling anyone what they were working on, which wasn’t really a problem because they rarely knew themselves. Also, he acknowledged, “it is very boring.”īut it was a job in a place where jobs were scarce, and Joe turned out hundreds of graduates. “Some people don’t know how to stay in one place for long,” he explained with gracious understatement. Over half of Joe’s students usually dropped out before the boot camp was finished. They would be asked to categorize clothing seen in mirror selfies, look through the eyes of robot vacuum cleaners to determine which rooms they were in, and draw squares around lidar scans of motorcycles. There seemed to be limitless demand for the work. Every two weeks, 50 new recruits would file into an office building in Nairobi to begin their apprenticeships. Then, in 2019, an opportunity arose: Joe could make four times as much running an annotation boot camp for a new company that was hungry for labelers. A several-second blip of footage took eight hours to annotate, for which Joe was paid about $10. In Joe’s case, he was labeling footage for self-driving cars - identifying every vehicle, pedestrian, cyclist, anything a driver needs to be aware of - frame by frame and from every possible camera angle. AI learns by finding patterns in enormous quantities of data, but first that data has to be sorted and tagged by people, a vast workforce mostly hidden behind the machines. A few months after graduating from college in Nairobi, a 30-year-old I’ll call Joe got a job as an annotator - the tedious work of processing the raw information used to train artificial intelligence.

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