SherpaShare helps workers optimize pay from multiple services like Uber, Lyft, and Postmates
An App That Helps Drivers Earn the Most From Their Trips — When Steve Smith began driving for Uber and Lyft several months ago, he concentrated on picking up passengers near his Walnut Creek neighborhood in the San Francisco Bay Area. Tweets: @mikeisaac Tweets: @mikeisaac : rise of on-demand economy+obfuscation of true wages+multitude of income sources = new apps to make sense of it all. http://www.nytimes.com/...
Context & Ripple Effects
In 2015 the on-demand economy's defining feature was wage obfuscation: drivers like Walnut Creek's Steve Smith were stitching together income from Uber, Lyft, and Postmates without any tool showing which trips actually paid. SherpaShare's app is one of the first attempts to give workers the analytics side of the marketplace that only the platforms had.
The story opens a thread that keeps running through this coverage: drivers building their own information layer, first through online forums where ride-hail drivers compare notes, then through dedicated tools like Gridwise that collate earnings across platforms.
First-order effects
- Drivers running Uber, Lyft, and Postmates simultaneously can now route their hours toward whichever service pays best per trip, turning multi-apping from guesswork into optimization.
Second-order effects
- The platforms lose pricing power over their own fleets: when contractors can see real effective wages, opaque pay structures become a churn risk for thin-margin delivery startups already struggling with high operating costs.
- Driver-side analytics create a shared factual baseline that forums and organizer groups can build on, strengthening pushback against the platforms.
Third-order effects
- If the pattern holds, the arc runs from individual optimization tools toward collective vehicles — California driver groups winning labor legislation, and worker-owned alternatives like Up & Go and The Drivers Cooperative designed around maximizing driver pay rather than extracting from it.
The trend: Gig work is evolving from platform-controlled information asymmetry toward a driver-built data and ownership layer that rebalances bargaining power.