Ride-sharing platforms such as Lyft and Uber are among the fastest growing online marketplaces. A key feature of these platforms is the implementation of localized real-time dynamic pricing - where ride prices react to instantaneous system state, and across very small geographic areas. In this talk, we will explore the value of such real-time dynamic pricing, using a queueing model for ride-share platforms, which combines the stochastic dynamics of the platform's operations with strategic models of both passenger and driver behavior. Our analysis suggests that dynamic pricing is not necessarily better than the optimal static price in large-scale settings. However, finding the optimal static price requires exact knowledge of system parameters; we will also show that dynamic pricing is much more robust to fluctuations in these parameters as compared to static prices. Thus, our work suggests that dynamic pricing does not necessarily buy more than static pricing, but rather, it lets rideshare platforms realize the benefits of optimal static pricing with imperfect knowledge of system parameters.