In its latest push to redefine flexible work, Uber has launched a pilot program in the United States that allows its drivers and couriers to earn extra income by completing “microtasks” designed to train artificial intelligence models — a move that could mark the company’s most ambitious step yet into the AI economy.
The initiative, introduced on Thursday, enables Uber’s vast workforce of independent contractors to perform small, app-based assignments such as recording voice clips, uploading images, or submitting documents in multiple languages, according to The Verge. Some of the sample prompts include “upload images of cars,” “record yourself speaking in your language or local dialect,” or “upload a menu written in Spanish.” Depending on the complexity, these tasks can earn drivers up to a dollar per submission.
Uber CEO Dara Khosrowshahi unveiled the program during an event in Washington, D.C., describing it as part of the company’s mission to “build the best platform for flexible work.” The move positions Uber not only as a ride-hailing and delivery service but also as a potential player in the fast-growing data-labeling and AI training industry — a field currently dominated by specialized firms such as Scale AI and Amazon’s Mechanical Turk.
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The new feature repurposes Uber’s existing workforce into an on-demand digital labor pool, capable of supporting AI companies that require massive datasets to train models. Human feedback remains crucial for developing advanced AI systems, from recognizing accents and dialects to labeling real-world images used in robotics and self-driving cars.
Until now, most of this type of labor has been outsourced to lower-cost markets in Africa, Asia, and South America. By contrast, Uber’s pilot recruits workers already operating in the U.S., effectively giving them a dual role as both drivers and digital annotators.
The company has experience in this space. Uber’s internal AI Solutions Group has previously relied on “human-in-the-loop” systems — processes where human judgment complements machine learning — to refine its navigation and mapping algorithms. Earlier this year, Uber acquired the Belgian startup Segments.ai, signaling its intention to scale up its data-labeling operations.
Similar pilots had already been introduced in India, where drivers were paid small sums to respond to in-app prompts. The U.S. expansion marks a major step toward integrating these digital tasks into Uber’s broader business ecosystem.
A Competitive Pivot Toward AI
Analysts view the move as a significant diversification for Uber. By connecting its flexible labor network with AI development, the company is positioning itself as a hybrid technology and workforce platform. The potential scale is enormous: Uber has over 5 million active drivers and couriers worldwide, representing a distributed workforce that can generate vast amounts of labeled data quickly.
If successful, Uber’s entry could reshape the economics of AI training, which depends heavily on human participation despite automation advances. It could also allow Uber to capture part of a growing industry projected by PwC to exceed $50 billion globally by 2030.
Still, the program’s success depends on participation rates. Many Uber drivers already complain about declining pay and rising expenses, and some may not see the value in taking on additional “micro” assignments for minimal pay. A driver might, for instance, earn less than a dollar for recording a voice clip — a small supplement compared to time spent on rides or deliveries.
Labor advocates warn that these developments could further blur the line between employment and contract work. Uber continues to classify its drivers as independent contractors, which exempts the company from providing benefits such as minimum wage guarantees, overtime pay, or healthcare coverage. Yet critics argue that Uber’s heavy use of algorithmic management exerts employer-level control — from setting fares to determining when drivers are deactivated — undercutting the notion of full independence.
Broader Overhaul of the Driver Experience
Beyond AI microtasks, Uber announced a range of updates aimed at improving the experience and security of its drivers and couriers. The company is redesigning its trip offer cards — the interface where drivers accept or reject rides — to include more time and details before making a decision. Couriers delivering multiple orders will now see clearer instructions, including simplified pickup and drop-off details and alerts for commonly missed items.
Uber also introduced a new “heatmap” to show real-time demand across cities. The map uses colors to indicate where drivers can expect shorter wait times or higher surge pricing. Drivers commuting toward high-demand areas can now choose between a route that maximizes earnings or one that minimizes travel time.
Expanding Safety and Fairness
Uber also announced a wider rollout of its Women Rider Preferences feature, initially launched last year, which lets women drivers accept only women passengers. The feature — now available in Baltimore, Minneapolis, Philadelphia, Seattle, Portland, and Washington, D.C. — has been used in over 100 million trips, with more than half of participating drivers keeping it activated for most of their rides.
The company is also allowing drivers to set minimum passenger ratings and toggle the feature depending on circumstances, such as using it for late-night trips. Combined with ID verification for riders, Uber says the measures give drivers “more peace of mind on every trip.”
On fairness, Uber acknowledged complaints about sudden account deactivations — often triggered by rider reports — and said it would now allow drivers to respond before any final decision. Drivers accused of minor issues, such as disputes over alcohol deliveries, could still access other earning options on the platform.
Uber also announced a Delayed Ride Guarantee, allowing drivers to earn more if a trip is delayed due to the passenger or unforeseen traffic. Additionally, the company is expanding tipping reminders, including real-time notifications through Apple’s Live Activities feature, to encourage more consistent gratuities from riders.
Redefining the Future of Flexible Work
Uber’s push to merge traditional gig work with AI-based digital labor represents a shift in how the company defines flexibility. Uber aims to maintain engagement across a workforce increasingly skeptical of the platform’s pay structure by offering drivers multiple revenue streams — rides, deliveries, and now digital microtasks.
But the move also raises questions about the future of work itself. As Uber integrates AI development into its gig economy model, it blurs the boundary between physical and digital labor, positioning its drivers not just as participants in the transportation economy but as contributors to the infrastructure of artificial intelligence.
Whether this model can sustainably benefit drivers remains unclear. But as Uber moves deeper into AI, the company seems determined to prove that flexible work — from car rides to data labeling — can evolve into a single, seamless ecosystem, where human input continues to fuel the next generation of intelligent machines.