Aggregate stats often result in misleading conclusions. Here’s a good example from @a16z ( that implies Uber’s prices in the US are up significantly more than Lyft. At Uber, we measure this particular metric every single week. But to get to the true signal, we always control for mix effects from trip type and geography. We measure price quotes on a standard product (in this case UberX vs. Lyft Standard) on identical trips (same origin/destination/time of day combinations), and we call this metric NPI (normalized price index). Our philosophy consistently has been to price as affordably and as competitively as possible, with the NPI near 1.0 at all times. On an apples to apples basis, that story remains remarkably consistent in the US. Even though the overall assessment is directionally accurate, in not controlling for mix effects, A16Z/Gridwise come to an inaccurate conclusion. In fact, it shows the success of our deliberate strategy to both innovate with new products and expand our footprint into sparser markets. As a result, (1) Uber skews more toward higher-priced premium products, as consumers are increasingly opting into our newer premium products, and (2) Uber trips skew longer, and the mix of trips >15km has increased meaningfully driven by the faster growth in suburban and sparser markets. In other words, we are serving consumers with better products, and we are serving more consumers than ever before in newer markets that have been significantly underpenetrated for the ridehailing category.