DoorDash launches a new ‘Tasks’ app that pays couriers to submit videos to train AI

2026-03-19 · Source: AI News & Artificial Intelligence | TechCrunch · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Data Science & Analytics · Depth: Fundamental Awareness, quick

Summary

DoorDash has launched a new, stand-alone "Tasks" app and integrated similar features into its existing Dasher app, allowing delivery couriers to earn money by completing assignments that improve AI and robotic systems. These tasks include filming everyday activities, recording speech in other languages, and capturing specific footage like washing dishes with a body camera. The collected audio and video data will be used to evaluate DoorDash's in-house AI models and those developed by partners in retail, insurance, hospitality, and technology sectors. This initiative, which mirrors a similar program by Uber, aims to digitize the physical world and provide Dashers with flexible earning opportunities beyond traditional deliveries. The Tasks app and in-app features are currently available in select U.S. locations, excluding California, New York City, Seattle, and Colorado, with future expansion planned.

Key takeaway

For AI/ML engineers developing systems that interact with the physical world, consider integrating human-powered data collection directly into existing gig economy platforms. This approach can provide diverse, real-world data at scale, accelerating model training and validation, especially for tasks requiring nuanced human interaction or environmental context. Your team could leverage such platforms to gather specific visual or auditory data sets that are difficult to simulate.

Key insights

Gig economy platforms are leveraging their workforces for AI data collection and system training.

Principles

• Data collection improves AI/robotics.
• Upfront pay based on task complexity.

Method

Couriers perform specific physical or digital actions (e.g., filming, photographing) to generate data for AI model training and system evaluation.

In practice

• Film specific actions for AI training.
• Photograph menu items for digital display.

Topics

• AI Data Collection
• Robotic Systems Training
• Machine Learning Models
• Gig Economy Platforms
• Computer Vision Data

Best for: Machine Learning Engineer, NLP Engineer, Computer Vision Engineer, AI Engineer, AI Product Manager, Tech Journalist

Related on AIssential

• YY Group (NASDAQ: YYGH) Unveils Scalable AI Training Data Strategy to Power Next-Generation Robotics and Artificial Intelligence — YY Group is leveraging its global workforce network to generate real-world human activity data for AI and robotics training.
• I Stopped Sending My Team AI Tutorials. Here’s What Actually Worked — Direct, integrated AI demonstrations on real tasks drive adoption more effectively than passive education.
• Porn, dog poo and social media snaps: the ‘taskers’ scraping the internet for AI firm part-owned by Meta — AI training relies on a vast gig workforce performing ethically questionable data collection from public and private sources.
• AI Platform for Data Science & Machine Learning | H2O.ai University — H2O.ai offers an integrated platform for the full ML lifecycle, from data to production, with unified governance.
• [D] Data scientists - what actually eats up most of your time? — Understanding real-world data science workflows is crucial for building effective support tools.
• I Tried DoorDash’s Tasks App and Saw the Bleak Future of AI Gig Work — DoorDash's "Tasks" app expands gig work into household chores, requiring video verification of user actions.

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by AI News & Artificial Intelligence | TechCrunch.