Just as two people can have similar personalities, two companies can have a remarkably similar approach to business. Google may be more than 15 years old and a search-and-advertising behemoth, and Uber may be a five-year-old startup remaking urban transportation. But they think the same.
Google came of age when search was inefficient and cluttered, and made it simple and easy to find what you wanted online. Then everyone got broadband, and Google could use speed as a strategic weapon. It saved copies of the most-searched pages so it could serve them up even faster. The cost to search in terms of time and effort became infinitesimal. Then Google used data on how and what we searched, and went on to become even better at predicting what we wanted. "I'm feeling lucky" isn't an accident but rather a lot of data used smartly.
Uber, like Google, is taking a highly disorganized business—in its case, private transportation such as taxicabs and private limousines—and ordering it neatly. Just as broadband served as rocket fuel for Google, smartphones and the always-on mobile Internet are powering Uber. CEO Travis Kalanick is playing the speed game as well: Uber has expanded rapidly into more than 90 cities in 34 countries worldwide, adding drivers (and cars) by the thousands because more cars means getting one to pick you up more quickly. The faster that happens, the less likely you are to look elsewhere. As a result, both Google and Uber are hated by those who fear the repercussions of the more efficient worlds they're creating.
Our ubiquitous mobile access has made time and location important data points in how businesses can now be built and managed. The more people who open the Uber app and order car service, the more info Uber has to predict both demand and where demand might come from. So Uber doesn't just collect data; it puts it to work. "What you are aiming for [as a service] is the equilibrium of supply and demand," Kalanick says.
Kalanick has structured Uber to learn from all that data, and to have more of it than anyone else. Uber's algorithms, with help from dozens of in-house data wranglers, try to figure out urban traffic flows—trillions of bits that help them become even more accurate about where and when customers will pull out their phones and open Uber's app, and where its cars are when that happens. "A perfect day," Kalanick tells me, "is when you set an all-time record for trips per hour with zero surges." That means that Uber's algorithms never have to raise prices to try to get more cars on the road to serve customers. A few weeks ago, Kalanick tells me proudly, there was a whole week in New York City when there wasn't even a minute of surge pricing. (Perhaps it's no surprise that days after I spoke with Kalanick, Uber announced a delivery service in New York to take advantage of its optimized platform.)
If Google's primary weapons are relevancy and speed, then Uber's are cost and speed. It not only has to get cars to customers faster, but it also has to do it at the lowest possible price. If Uber can get that right, it will surpass its competition—just as Google's proficiency has kept us from searching elsewhere.
When I ask Kalanick if he agrees that Uber is Google-like in that they're both data-deterministic, he pauses for a moment, and then points out that Uber's task is much harder, because it is about "taking bits and translating them into atoms" and vice versa. Google, in other words, never has to worry about a search result getting stuck behind a trash truck. "The real world is a lot more complicated," he says.
Maybe that's why Google invested more than $250 million in Uber at a rumored valuation north of $3.5 billion. You always see yourself in the mirror.
A version of this article appeared in the June 2014 issue of Fast Company magazine.