Sitting outdoors at the café with another espresso, Levchin looks at the data recorded in his bike computer. He has ridden 60 miles, averaging 20 miles per hour—a solid pace, considering it included stops at traffic lights. His pedaling cadence was 91 revolutions per minute, and he has climbed 2,500 feet. For his combined efforts, his body has burned an estimated 2,500 kilojoules.
“There’s something beautiful and interesting in data itself,” he says, momentarily lost in reverie. Then he begins to probe the raw stats for actionable messages. He explains that he got busy over the summer and wasn’t able to ride as much as he wanted to. “So my goal for the last month and a half has been to return to fitness,” he says. Paradise Loop is ideal for assessing his progress because he has ridden the identical route—and collected the data—more than 100 times before.
With the help of an app called Strava, Levchin learns that the percentage of time he had spent churning out around 250 watts—which represented moderate effort—was no less than it had been on previous rides when he was in peak shape. But the app reveals that he had spent a smaller percentage of his time cranking out 350 watts and above, and this leads to a helpful training takeaway: In future rides, he needs to jack up the intensity of his sprints.
Strava compares Levchin’s past pedaling performances with more than 10,000 Paradise Loop rides recorded by other cyclists—riders who are roughly the same age and weight as he is, including professionals. He can see how they did on the whole loop or precisely by segments.
So how does Levchin stack up? “There are a lot of really good cyclists, professional cyclists even, who ride Paradise Loop,” he says as we finish up at the café. “But I think that some of my times are in the top 10%.”
A few days after his bike ride, Levchin, wearing jeans, a black shirt, and a baseball cap marked with the word Code, shares his vision of the quantified future with me. There’s a reason that more people don’t quantify themselves today, he says. “It’s just not easy enough. The cost of measurement—the cognitive load, the physical load, and the practical financial cost—needs to be very close to zero.” In other words, if you wish to quantify yourself like Levchin does right now, you need multiple expensive sensors that accumulate data and communicate with your expensive smartphone. And many of the current, first-generation mainstream devices that have flooded the marketplace, he says, amount to little more than glorified digital pedometers. In the future, he sees a system that is smarter, faster, cheaper, and more refined in the data it collects. Ultimately, it will require little or no work on the user’s part.
The first attempt by HVF, Levchin’s startup incubator, to make this process easier is an app called Glow, which launched in 2013 and aims to help women who are having trouble getting pregnant. Glow users submit conception-relevant personal data like the timings of their menstrual cycles and daily temperatures. The app uses this and other personal health information to make recommendations about when when fertility will be highest. His grander plan is for subsequent versions of Glow to leverage the intimacy of personal tracking with the power of big data. All of the information submitted by tens of thousands of Glow users is being pooled. The data is being analyzed to detect patterns and make pregnancy predictions that will be shared with users in subsequent versions of the app. “Showing people just their own data is only the first step,” Levchin says.
Glow depends upon active sensors, meaning ones that don’t detect anything unless the user directs them to or manually inputs information. In the future, Levchin hopes that passive sensors, which do their jobs autonomously, will predominate. In the bathroom, for instance, the toilet could have built-in sensors to perform a daily urinalysis; a “magic mirror” could employ a camera linked to visual analysis software to help determine if, say, your eyes are jaundiced. Elsewhere in the house, rugs could have sensors to warn you if they had picked up too much allergy- or asthma-aggravating dust and needed cleaning. And outdoors, athletes like him could wear apparel with so-called “smart fiber” sensing technologies to map muscular activity. “People tout this idea of checking yourself all of the time,” says the Mayo Clinic’s Levine. “The future may hold a world in which you’re actually not checking anything, but that stuff is checking you.”
The first generation of largely automatic tracking devices, in fact, has already arrived—like the BinCam. A camera mounted to the underside of your trash can lid, BinCam takes a picture every time you put something into the garbage and uploads it to the Web so that friends and strangers can judge whether you’re throwing out things that you should be recycling. The HAPIfork automatically counts how many bites you are putting into your mouth and vibrates if you’re eating too quickly. And the Narrative Clip, released in November 2013, is a matchbook-size camera, designed to be worn on the front of a shirt, that takes a picture every 30 seconds—for as long as you and the device both shall live.
The prospect of a world in which all data is monitored, everywhere, alarms some people. In the Dave Eggers novel The Circle, recording and sharing personal data is mandatory; the societal mottos are “Secrets are lies. Sharing is Caring. Privacy is theft.” Levchin acknowledges that personal data tracking introduces significant peril—that the data you collect for your own use may be used by corporations or the government against your wishes. “People who say that data privacy is not a big deal are kidding themselves,” he says.
In fact, the Federal Trade Commission has publicly voiced its concern about wearable-tracker manufacturers selling fitness-and-health data to companies, and using it to relentlessly market more products and services to users. “Many of these companies may not maintain reasonable safeguards to protect the data,” says Jessica Rich, director of the Bureau of Consumer Protection at the FTC, in a speech in January. This spring, Forbes reported that Fitbit—through its lucrative corporate wellness partnerships with major corporations—is selling employees’ personal data to employers so that they can monitor their personnel. Jawbone, its chief rival, is considering a similar program.
Regardless, Levchin believes that the risks are worthwhile and can be appropriately managed. Data is a beautiful thing, after all—the key to unlocking mysteries of the world and to maximizing human potential. He would no sooner abandon quantification than he would burn down a library full of books. “I ultimately don’t think the blind fear of data collection is justified,” he says. “The niceties of the 21st century—living longer and healthier and happier—are ultimately going to be worth it.”