DoorDash has launched a standalone app for couriers, called Tasks, that allows the company's 8 million U.S. gig workers to earn money by recording themselves doing various tasks. The app features everyday chores like folding clothes, handwashing dishes and making a bed, with each gig offering a payment sum based on effort and complexity.
The data will then be used to help AI and robotics models better understand the physical world, according to the company's announcement. One example of a task involves asking a courier to capture footage of their hands washing at least five dishes while wearing a body camera, holding each clean dish in frame for a few seconds before moving on to the next.
What distinguishes this initiative is its scale and economic logic. Couriers are already out in the world navigating streets, storefronts, and front doors, making them uniquely positioned to capture the messy, real-life data AI systems need but struggle to simulate. Delivery remains a low-margin business, heavily exposed to fuel costs, labour incentives, and competition. Data, by contrast, scales with far higher profitability. If DoorDash can successfully package and sell AI training datasets, it could open a new revenue stream less sensitive to the cyclical pressures of consumer spending.
This is part of a growing ecosystem of gigs that aim to farm AI training data from willing humans. Last year, Uber piloted a similar initiative allowing its U.S. gig workers to perform additional digital tasks for money, including uploading photos and recordings used to train AI.
The programme is not available everywhere. The exclusion of California, New York City, Seattle, and Colorado from the program reflects the regulatory environment in those jurisdictions, where gig worker classification and compensation rules are more stringent. This geographic restriction reveals something important about how companies view gig work economics when regulatory scrutiny increases.
The central tension in this model is straightforward. It raises thorny questions about compensation: if that video helps train an AI model worth millions, is a $5 task payment fair? The strategy introduces new tensions around labour and data ownership. Couriers are effectively producing high-value digital assets, yet are compensated on a per-task basis with no ongoing claim to the downstream value created.
DoorDash has not published detail on consent practices, data retention policies, or the rights that workers retain over footage recorded in their own homes. For a programme that asks couriers to bring cameras into their kitchens and capture their own voices, those are not minor omissions. The footage is intimate by nature; it depicts domestic environments, personal routines, the interiors of private residences.
There is also a forward-looking paradox embedded here. DoorDash has been investing in robotics and autonomous delivery systems, hinting at a future where humans and machines share the workload. Gig workers are effectively helping train the very technologies that could one day reduce the need for human labour. Workers are knowingly or unknowingly training their own replacements.
Real-world data collected at scale from distributed human workers is becoming a meaningful competitive asset. Platforms with large contractor bases, established presence in physical commercial environments and logistics infrastructure to coordinate task-based workflows are positioned to accumulate proprietary training datasets that AI developers and robotics firms cannot easily replicate.
The question for regulators and policymakers is whether the current model adequately protects workers. Since many tasks involve recording personal environments or daily activities, concerns have emerged about how this data will be stored, used, and monetised. Additionally, the long-term implications of gig workers contributing to technologies that could eventually automate parts of their own jobs remain a point of debate.