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In addition to these autonomous cars scanning the roadside, by 2020, an estimated five privately owned low-Earth-orbiting satellite constellations will be imaging every square meter of the Earth's surface in resolutions ranging from 0.5 to 2 meters.
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Simultaneously, we're also about to see an explosion of AI-operated microdrones buzzing around our cities and taking images down in the centimeter range. Do you want to know how many cars are in your competitor's parking lot in Moscow or Mumbai? Or how about following your competition's supply chain as trucks or trains deliver raw materials to their plant and final product to their warehouses? No problem.

All told, according to a report released by the 2013 Stanford University TSensors Summit, the number of sensors in the world is expected to grow into the “trillions” by 2023.
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And this is merely the
sensor side of the equation.

Trillion Sensor Visions

A Trillion Sensor Future

Source:
www.futuristspeaker.com/wp-content/uploads/Trillion-Sensor-Roadmap.jpg

Both in speed and in the number of connected devices, networks are undergoing a similar explosion. On the speed side, consider that in 1991, early 2G networks clocked in at a hundred kilobits per second. A decade later, 3G networks hit one megabit per second, while today's 4G networks sport up to eight megabits per second.
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But in February 2014, Sprint announced plans for Sprint Spark, a super-high-speed network able to deliver 50 to 60 megabits per second to your mobile phone, with a vision of tripling that over time.
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“Our goal is to support a new generation of online gaming, virtual reality, advanced cloud services, and other applications requiring very high bandwidth,” said Stephen Bye, Sprint's CTO. “In more concrete terms, when deployed, Spark will allow you to download a twenty-megabyte video game in three seconds and a one-hour-long high-definition movie in under two and a half minutes.” And Sprint is already making progress, having demonstrated an over-the-air speed of 1 gigabit per second at their Silicon
Valley lab.

On the connection front, ten years ago, the world had 500 million devices hooked up to the Internet. Today that number is up to 12 billion. “In 2013,” says Padma Warrior,
¹¹
the chief technology and strategy officer of Cisco, “eighty new things were being connected to the Internet every second. That's nearly 7 million per day, 2.5 billion per year. In 2014, the number reached almost 100 per second. By 2020, it'll grow to more than 250 per second, or 7.8 billion per year. Add all of these numbers up and that's more than 50 billion things connected to the Internet by 2020.” And it's this explosion of connectivity that is building the Internet-of-Things (IoT).

A recent study by Cisco estimated that between 2013 and 2020, this uber-network will generate $19 trillion in value (net profit).
¹²
Think about this for a moment. The U.S. economy hovers around $15 trillion a year. Cisco is saying that over the ten-year period, this new net will have an economic impact greater than America's GDP. Talk about the land of opportunity.

Global Internet Device Installed Base Forecast

A Trillion Sensor Future

Source:
http://www.businessinsider.com/decoding-smartphone-industry-jargon-2013-11

“E” refers to “Estimated”, as in estimated size of the market.

So where does that opportunity lie exactly? Well, most researchers feel that there are two critical categories worth exploring: information and automation. Let's start with the former.

Our world of networks and sensors generates enormous quantities of information, much of which is extremely valuable. Take traffic data. A decade back, Navteq built a network of in-road sensors across 400,000 kilometers of Europe (through thirty-five major cities and thirteen European countries).
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In October 2007, mobile phone giant Nokia (now owned by Microsoft) paid $8.1 billion for that network.
¹⁴
Fast-forward five years to mid-2013, when Google paid $1 billion to acquire Waze, an Israeli-based company that generates maps and traffic information, not via electronic sensors, but instead via crowdsourced user reports—i.e., human sensors, generating maps by using GPS to track the movements of some 50 million users, then generating traffic-flow data as those users voluntarily share information about slowdowns, speed traps, and road closures in real time.
¹⁵

Behavior tracking is another fast-growing information category.
¹⁶
Insurance companies putting sensors in cars and pricing policies according to real-time driving behavior is one example. Another is Turnstyle Solutions,
¹⁷
a Toronto-based start-up that uses Wi-Fi transmission from smartphones to follow customers around stores, gathering data on where they linger as they shop. Behavior tracking for health care is also growing. AdhereTech
¹⁸
now makes smart pill bottles with sensors embedded in them to better ensure patient compliance, while CoheroHealth has combined sensor-enabled inhalers and mobile apps
¹⁹
so kids with chronic asthma can track and control their symptoms. These medical applications will keep coming. According to William Briggs, Chief Technology Officer for Deloitte Consulting,
²⁰
“the value of the IoT-related healthcare sector will be a multi-trillion dollar market within the next one to two decades.”

Turning our attention to automation—which is essentially the process of gathering all the data collected by the IoT, turning it into a series of next actions, and then, without human intervention,
executing those actions. Already, we've seen the first wave of this in the smart assembly lines and supply chains (what's technically called process optimization) that have enabled things like just-in-time delivery. With the smart grid for energy and the smart grid for water—what's technically called resource consumption optimization—we're seeing the second wave. Next up is the automation and control of far more complex autonomous systems—such as self-driving cars.

There are even further opportunities in finding simpler ways to connect decision makers to sensor data in real time. The aforementioned plants that tweet their owners when they need watering were an early (2010) iteration of this sector. A more contemporary example (2013) is the Washington, DC-based start-up SmartThings, a company that CNN called “a digital maestro for every object in the home.”
²¹
SmartThings makes an interface that can recognize over a thousand smart household objects, from temperature sensors that control the thermostat to door and windows sensors that tell you if you left something unlocked to ways to have appliances automatically shut off before you go to bed.

Of course, any discussion of networks and sensors leads directly to a discussion about how we're going to extract value from all this data. The answer is where we're going next. Welcome to the radical world of infinite computing.

In late August 2013, Carl Bass, CEO of the software and design giant Autodesk, gave me a tour of his newly constructed Pier 9 center, located at the tail end of San Francisco's Embarcadero.
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A self-described big kid from Brooklyn (he's 6 feet 5), Bass is clad in jeans, a T-shirt, and a baseball cap. His facility, meanwhile, is clad in the very latest in 3-D printing equipment, machine shop tools, design stations, laser cutters, and welding machines. It's a maker's paradise. These are the tools
that turn imagination into reality, and they're all guided by Autodesk's design software, which in turn is powered by infinite computing.

Infinite computing
is the term Bass uses to describe the ongoing progression of computing from a scarce and expensive resource toward one that is plentiful and free. Just three or four decades ago, if you wanted to access a thousand core processors, you'd need to be the chairman of MIT's computer science department or the secretary of the US Defense Department. Today the average chip in your cell phone can perform about a billion calculations per second.

Yet today has nothing on tomorrow. “By 2020, a chip with today's processing power will cost about a penny,” CUNY theoretical physicist Michio Kaku explained in a recent article for
Big Think
,
²³
“which is the cost of scrap paper. . . . Children are going to look back and wonder how we could have possibly lived in such a meager world, much as when we think about how our own parents lacked the luxuries—cell phone, Internet—that we all seem to take for granted.”

Microprocessor Cost per Transistor Cycle

Microprocessor Cost per Transistor Cycle

Source:
www.singularity.com/images/charts/MicroProcessCostPerTrans.jpg

It's for this reason that Bass feels that much of our thinking about computing is completely backwards. “We've been treating computing as this precious resource,” he says, “when really it's abundant. If you look at all the trends, computing is decreasing in cost, increasingly available, increasingly powerful, and increasingly elastic. Every year we produce more computing power than the sum of all prior years. This overabundance is the beginning of a new era.”

In the old era, the world of human creation, the so-called designed world was the product of “in the box” thinking—thinking limited by computing scarcity. “In that era,” explains Bass, “a problem that used to take one CPU 10,000 seconds to solve would cost about twenty-five cents. But in the new exponential era, powered by near infinite computing, we can now simultaneously apply 10,000 CPUs to the same problem and solve it in one second. Solving the problem 10,000 times faster still costs twenty-five cents, but for the first time in history we're able to apply infinitely more resources to a problem for no additional cost.”

Companies like Google, Amazon, and Rackspace are facilitating this shift. Each has assembled massive computational facilities, opened them to the public, and called them the cloud. “Before the cloud, starting a tech company was slow and painful,” says Graham Weston,
²⁴
chairman and cofounder of Rackspace. “First you order servers from a vendor like Dell or HP. They show up weeks later. Then you have to configure them, install them, purchase software, load software, then finally connect everything to the Internet. All this took weeks, if not months, and required staff. Today you can go to a provider like Rackspace and minutes later have access to as many servers as you need. And it's massively scalable—vertically or horizontally. It's on-demand capability.”

Yet the goal here isn't to become Rackspace (or Amazon or Microsoft), it's to build your big idea atop their infrastructure. Entrepreneurs no longer have to lay out scarce cash for expensive equipment, spend months to install, configure, and program that equipment, worry
about what happens when they need to scale up that equipment, or worry about what happens when it breaks or becomes obsolete.