While Scale AI relied heavily on workers in the Philippines, Nigeria, and Kenya, Uber’s approach distributed work across established transportation networks with existing regulatory compliance and payment infrastructure. “This offered enterprise buyers concerned about data sovereignty built-in advantages that traditional crowdsourcing platforms couldn’t match,” Gogia added.
The broader implications extended beyond data labeling. “Uber’s strategy reflects a larger realignment in the digital economy, where gig platforms are no longer confined to mobility or food delivery but are recasting themselves as distributed labour infrastructures for AI,” Gogia observed. “What Uber has launched is not a sideshow but a blueprint for how other platforms may monetise their dormant workforce cycles.”
For enterprise procurement teams, Uber’s entry forced strategic recalculation. “In areas such as retail, logistics, and consumer technology, where datasets are vast but not highly sensitive, Uber’s model is immediately attractive,” Gogia explained. However, “the challenge lies in extending that appeal to regulated domains” where banks, insurers, and healthcare providers require tightly controlled, auditable environments.
This story originally appeared on Computerworld
