Uber turns drivers into AI data labelers in India pilot

Uber just turned its transportation network into an AI training ground. The ride-hailing giant’s announcement that Indian drivers could earn some extra money by labeling data for AI systems represents a direct assault on the rapidly expanding data labeling industry.

The move positioned Uber’s over one million Indian drivers as an instant workforce competing against established players like Scale AI and Amazon’s Mechanical Turk platform.

For enterprise technology leaders, this represented a potential paradigm shift in how AI training data gets produced—and priced.

Controlled rollout reveals strategic caution

The pilot program would enable drivers across 12 Indian cities to complete “digital tasks” during downtime — encompassing everything from image classification and text analysis to audio transcription and receipt digitization, according to Megha Yethadka, Global Head of Uber AI Solutions. “Until now, in India and other countries, these tasks, such as labelling work, text classification, object counting, and receipt digitisation, were completed by independent contractors outside the app,” Yethadka told ComputerWorld.

The company reported early traction. “Early engagement has been strong, with many thousands of tasks already completed,” Yethadka noted, adding that the pilot was “about giving drivers more choice, flexibility, and ways to earn” during downtime periods.

However, interviews with multiple New Delhi-based drivers revealed none had access to these features yet, though one mentioned hearing about “extra earning opportunities coming up for Uber drivers.”

This indicated Uber was likely implementing a phased deployment strategy, testing with select driver cohorts before broader activation. These digital tasks feed data directly into AI systems for Uber’s enterprise clients.

Economics of idle capacity

The economics were compelling. While traditional data labeling companies recruited separate contractor pools, Uber leveraged pre-verified drivers already familiar with app-based workflows. According to Yethadka, these tasks for Uber were “completed by independent contractors outside the app” earlier.

“Uber is tapping into idle capacity that already exists within its network, converting minutes between rides into productive cycles of annotation,” said Sanchit Vir Gogia, chief analyst and CEO at Greyhound Research. “This is a clever reallocation of labour and could lower the barrier to entry for enterprises that have struggled with spiralling labelling costs.”

This infrastructure advantage aligned with market trends, showing outsourced data labeling capturing 84.6 percent market share as enterprises sought scalable alternatives.

Market dynamics favor disruption

Uber’s entry came at an opportune moment. The AI data labeling market is expected to reach $5.46 billion by 2030, with large enterprises representing 61.11 percent of spending, yet many remained frustrated with quality inconsistencies and slow turnaround times from traditional providers.

Scale AI, despite approaching $1 billion in annual revenue, faced mounting enterprise complaints about “low quality and high turnaround times,” according to a recent Amplify Partners analysis. Amazon’s Mechanical Turk struggled with quality control and enterprise-grade security requirements.

However, disruption came with trade-offs. “Traditional vendors, while slower and often more expensive, offer the assurance of trained annotators, mature governance frameworks, and long-standing audit processes that inspire confidence in regulated industries,” Gogia noted. “Uber’s strength is speed and neutrality at a time when buyers are wary of entanglements with Big Tech-owned providers, but it must still prove that a transient workforce can deliver the same rigour.”

Uber’s secret weapon was domain expertise. According to Mordor Intelligence, the automotive sector accounted for 23.34% of the data labeling market, driven by the development of autonomous vehicles. Uber’s partnership with Aurora and transportation knowledge created natural competitive advantages in this lucrative segment.

Global platform advantage

Uber AI Solutions already operated in over 30 countries — a sixfold increase from five markets when it launched last November. Yethadka confirmed “worldwide expansion” plans, stating that “insights from the India pilot will inform how Uber scales this kind of work to drivers and delivery partners elsewhere in the world.” This signaled a serious strategic commitment that could appeal to multinational enterprises managing fragmented vendor relationships.

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.

Strategic cautions

Despite the compelling opportunity, Uber’s data labeling venture faces significant challenges. “Uber is well-placed to unsettle incumbents in commoditised, high-volume tasks, but unlikely to dislodge established players in sectors where compliance and precision define the contract,” Gogia warned. “The transition from transportation to knowledge work represented significant operational complexity, and pricing strategy remained undisclosed.”

Manual labeling held a leading market share despite automation advances, but semi-supervised methods were accelerating at 34.23% CAGR, creating opportunities for platforms that could blend both approaches.