DoorDash is now incentivizing its delivery drivers—known as “Dashers”—to contribute real-world data for artificial intelligence (AI) development. The company’s new “Tasks” app and a separate pilot program pay users to film themselves performing mundane activities, such as washing dishes or folding laundry. This data will then be used to train AI and robotics systems to better understand the physical world.
How It Works: Real-World Data for AI Training
The move reflects a growing trend of companies leveraging gig workers to provide the large datasets necessary for AI advancement. DoorDash’s Tasks app already offers paid opportunities to help businesses gather “on-the-ground insights,” including tasks like photographing restaurant menus. However, the new standalone app takes this further, directly soliciting recordings of everyday actions from users.
“The goal of Tasks is to help more businesses understand what’s happening on the ground and gather new insights, all while giving Dashers a new way to earn on their own terms,” stated Ethan Beatty, DoorDash Tasks general manager.
This isn’t unique to DoorDash; Uber announced a similar program earlier this year, paying drivers for completing “quick digital tasks” during downtime. The demand for this type of data is high as AI systems need continuous training on real-world scenarios to improve accuracy and efficiency.
The Wider Implications
The practice raises questions about data privacy, worker compensation, and the ethical implications of using gig workers to fuel AI development. While DoorDash frames this as a win-win—extra earnings for Dashers and better AI for businesses—the long-term consequences remain unclear. The company is partnering with industries including retail, insurance, hospitality, and technology, signaling broad demand for this data.
Currently, the programs are limited to select U.S. locations (excluding California, Colorado, New York City, and Seattle), but DoorDash plans to expand both the tasks and the program’s reach over time.
This trend underscores how deeply AI is becoming integrated into daily life, and how companies are now turning to everyday people to provide the data needed to advance it.
