The AI revolution's next casualty could be the gig economy (www.businessinsider.com)

Uber and Waymo’s recent moves signal a turning point for the gig economy: instead of driving for fares, riders and drivers are being tapped to build the AI that will replace them. Uber is paying drivers to complete microtasks—taking photos, uploading short audio clips and other data-gathering chores—to improve its machine‑learning systems (training data for perception, mapping and speech models). At the same time Waymo is piloting driverless grocery and meal deliveries with DoorDash in Phoenix (starting with DashMart stores), and already runs autonomous rides in Atlanta and Austin. Customers may still pay delivery fees, but there’s no driver — and no tip. Technically, these developments accelerate supervised and multimodal model training by expanding real-world datasets and edge‑case coverage, shortening the path to robust autonomy. For workers it creates a paradox: short‑term income diversification into low‑paid labeling work could hasten long‑term job displacement. The broader implication for AI/ML is twofold: faster iteration and deployment of autonomy thanks to human-sourced data, and growing ethical, regulatory and economic pressure as labor markets and platform incentives realign. The story highlights an urgent policy and design choice for the community: how to scale and validate AI while mitigating the social costs of automation.