Uber's strategy involves building a hybrid network of human drivers and autonomous vehicles to optimize resource utilization based on demand.

To prevent drivers from clustering in one area based on demand predictions, Uber provides them with a heat map that shows underserved demand, allowing for better distribution of drivers.

Tesla has real-time information on demand, allowing us to adjust pricing dynamically based on how many orders we receive each day.

We've developed an AI dynamic pricing engine that sets optimal prices based on historical data, market trends, and various external factors.

Our dynamic pricing engine is superior because it absorbs a vast amount of data and learns from past performance to improve predictions.

We launched Uber in June 2010, and we don't own cars or employ drivers; we have 120 employees, most of whom are not in San Francisco, and we're deployed in 17 cities.

Uber is an app on your iPhone that helps you get a car, with the motto 'everyone's private driver.' You can see nearby cars and get notified when your driver arrives.

The future of logistics will involve predictive software that anticipates challenges and allows for quick rerouting to maintain operational effectiveness.

The Uber map exemplifies a psychological breakthrough; it alleviates the stress of waiting for a taxi by providing real-time information, transforming the waiting experience from one of anxiety to relaxation.