Right now, tech industry leaders are starting to conceptualize and release products which are designed to operate somewhere between the Internet of Things and the Social Network of Things.

Nest, the learning thermostat released in 2011, does some very smart and helpful things. It employees machine learning technologies to discover the patterns of users’ heating and cooling habits, and coordinates between multiple thermostats in a network. Nest also allows users to remotely view their heating and cooling usage, and to set smarter patterns for their own use, from their phones and browsers.

As the platform evolved, Nest Protect was released, bringing simple machine learning to smoke and carbon monoxide detection. Unlike most other home smoke detectors, Nest Protect connected to other Nest Protects placed in various locations throughout the house to coordinate responses to emergencies, providing smart information like “there is smoke in the upstairs hallway” rather than a simple alarm. Perhaps most useful was its ability to both learn from users’ kitchen usage (simply by waving a hand under an active Nest Protect, a user could disable it temporarily while also teaching it that that type or smoke—or time of day—might indicate that someone was cooking dinner, rather than a more serious safety threat).  At the same time, alerts on the device would remotely trigger users’ mobile apps, letting them know that an alert was going on even if they weren’t home.

None of those functions, however, goes beyond the potential of the Internet of Things concept.

As it evolved, though, the Nest family got bigger. In 2014, Nest announced “Works With Nest,” a platform and API which allowed products outside the Nest brand to coordinate around a shared purpose with Nest. For example, Whirlpool appliances can coordinate with your Nest to find out if you are home—if you’re not, but your laundry is still in the washer, they can run a ‘refresh’ cycle to make sure the clothes don’t become mildewed or wrinkled. If you wear your Jawbone fitness device to bed, it can tell Nest to heat up the house when it detects that you are moving around more and are thus starting to wake up. LIFX programmable lights can flash red throughout the house if there is a smoke alarm. And Mercedes vehicles can tell Nest when you are leaving or returning to the house so it adjusts its energy consumption accordingly. Even Virgin America announced an integration wherein each and every seat in their fleet will feature Nest thermostats. (Well, alright—that was an April Fool's joke, but it'd be interesting nonetheless…)

By giving the user the ability to write “if this, then that” constructions (such as “if the house is empty, turn down the thermostat and turn off most of the lights”), the Nest ecosystem immediately responds to the user’s needs but also learns from all of its users globally about potential uses for its system—shifting from conceiving of individuals as consumers, instead viewing them as co-creators of the products, services and platform.

Platforms: the Shift to the Social Network of Things

The nature and movement of information in the early years of the SNT will trend towards increased speed, access, and personalization. Systems and data which were once closely guarded and proprietary will become interoperable as platform thinking is adopted by most successful organizations, and the products those companies create will begin to collaborate towards shared purposes. Increased connectivity and infrastructure make information available instantly to entire networks, and users can access real-time data through their personal devices. As people grow accustomed to their tech knowing about their needs and preferences, an expectation for personalized design makes “one size fits all” devices a thing of the past.

Many of the problems and benefits of the Social Network of Things are beginning to have an impact right now, in the transitional period. Our old systems are sharing space and purpose with new systems which operate under entirely different assumptions, and organizations struggle to find solutions which allow them to keep what’s been working and simply integrate the old tech with the new. As the pace of business continues to accelerate, companies can no longer afford to use traditional, inefficient styles of communication and governance, and are turning to outside companies, consultants and thought leaders to help restructure the way decisions get made. Privacy and identity politics are tense issues of discussion and legislation, as the collection and use of data becomes increasingly invisible to individuals, with new roles calling for experts who can manage the ethical questions that arise and manage the impact of reputation and brand that come with security failures. Machines are managing much of their own data collection and exchange now, with the capability to learn automatically, deliver information right when it’s needed, and respond and adapt to one another using feedback loops. While the access to all this data creates serious security challenges, it also makes entirely new value possible, especially in personalization and predictive features. Innovation begins to happen on a platform level, rather than an isolated, product-focused one.

The Waze Example: the Rush to Socialize

The community-based travel and navigation app Waze is an example of what networked GPS looks like in the current, early SNT phase. Waze is a social mapping service. Like Google Maps or Apple Maps, it provides turn by turn navigation, including multiple routes to the same destination, and like them it provides traffic data for route optimization. The 1.1 billion dollar question, then, is “Why did Google buy Waze?”

Waze is a system which does something basic mapping systems couldn’t—and which was definitely not possible in the age of traffic radio and paper maps: it connects various users to each other in real time. Specifically, users can quickly report traffic accidents, police, speed cameras, road hazards and the like—allowing a driver like me to save 15-20 minutes on my average commute. While most navigation systems were designed to help people avoid getting lost, Waze was designed specifically to help people avoid traffic delays. This difference in paradigm led its designers to focus on the users’ need to know about non-permanent objects and events which are out of sight or around the next turn—like the vehicles Waze constantly lets me know are stalled on the roadside or blocking a lane. To accomplish this, Waze connects drivers into a rich, user-augmented web that combines both artificial-intelligence-generated routes and instantaneous edits based on human input into the same system.

Google couldn’t afford to be behind the social mapping curve, so the acquisition made sense for several reasons: clearly, the algorithm helps users, but also, something about the interface and business strategy of Waze meant that they could gather a critical mass of users. Although Google could likely have done either themselves, they chose to acquire Waze instead and secure their position as the leader in mobile mapping.

In this phase (between the IoT and the SNT), we’re making better use of the network effect of coordination between various people and devices. Here, we’ve improved on old functions and devices but we’re also able to do new things—like communicate with other drivers about traffic while we’re on the road, and selectively share information about ourselves with others—in ways which require a more robust network infrastructure, real-time machine learning, and many data points for the best prediction.