Breaking the Laws
Can traditional technology foresight models predict IoT value growth?
Ziyon Amram |1.12.2017 |
For the last few decades, key technology industries such as the computer and communications industries were driven by various prediction models that were set by industry pioneers. Some of these models – justly or not – became industry cornerstones or “laws” that every industry executive had to follow to keep up with the industry’s rapid growth pace.
In 1965, Intel co-founder Gordon Moore formulated his prediction that transistor density of computer chips - and thus the performance of microprocessors - would double approximately every 2 years. Although some recent chip-maker announcements suggest that the evolution of silicon-based chip technology may be reaching a physical limit, Moore’s Law – as it was dubbed - has been driving computer and information technologies evolution for the past 5 decades, living up to its prediction (see chart).
In the early 80’s, several years after Moore’s observation, another technology pioneer, 3Com co-founder Robert Metcalfe, often called the “Father of Ethernet”, formulated his observation that the value of a communication network grows proportionally to the square of the number of network nodes (e.g. users, devices, applications, etc.) while the network growth itself - hence its costs - follow a more or less linear function.
Originally, Metcalfe’s “Law” referred to landline telecommunication networks with the nodes being telephones, fax machines, modems etc., as shown in the diagram below.
Later, as other types of networks such as mobile networks, the internet and social networks emerged, Metcalfe’s Law was extended to suggest these types of networks follow the same value model, counting the end users as the network “nodes”. Take for example Facebook - having X users with its network valued at Y$, if the number of users grows to 2X, the value – per Metcalfe’s Law - will grow to 4Y$.
On social networks, such as Facebook, the square value of the network can be explained by the principle of Virality: Unlike a communication network in which interaction is always “node to node” and every node is always accessible - a FB user has only few hundreds (or thousands) of “friends” to directly interact with, which seem to contradict the square value. However, every such “friend” has other friends, friends of friends and so on - so at any given moment the FB user is indirectly interacting with a huge portion of the FB network, hence the commercial potential is indeed square rather than linear. This also explains the huge amount (approx. 20B$) Facebook paid for instant message service WhatsApp, which is essentially a global communication network hence its commercial value has a square proportion to its user base size (approx. 500 million at the time).
Metcalfe’s Law shows us that expanding the network significantly increases its value. Moore’s Law shows that while capabilities steadily increase, their cost continuously drops. Combined, they explain the evolution of internet communication networks and services as well as the rise of the Internet of Things (IoT).
Smart sensor technologies such as SCADA and RFID have existed for decades, but due to their cost and complexity had limited use and very little growth. IoT growth is now enabled by hardware miniaturization and decreasing sensor costs, coupled by practically unlimited wireless network access capabilities. These enable an explosive number of smart devices and applications to interact, hence creating huge non-linear commercial potential.
However, like any new technology – disruptive or not – IoT technology also has its adoption (or diffusion) lifecycle. To assess where we stand on the adoption timeline we need to take a look at technology adoption models, such as the “Chasm theory” that was developed by another Moore: Business consultant Geoffrey Moore took Everett Rogers’ Innovation Diffusion model from the 1960’s – suggesting a normal (bell curve) distribution from early adopters to laggards – another step forward in his 1992 book Crossing the Chasm.
Moore viewed Rogers’ bell curve also as an adoption timeline and defined “the Chasm” - a gap that product marketers must bridge for the take up of new technology from early enthusiasts into mass market adoption. It may as well be that as far as IoT technology adoption is concerned - the chasm has not been crossed yet.
By various estimates, the IoT is expected to connect 25-30 billion “things” to the Internet by 2020, reaching a total of possibly up to 50 billion “connected devices” to the internet. It may still be difficult to predict how IoT networks will evolve over time and which business model will work best as their “diffusion” into day-to-day life grows. However, as the growth in computer processing power and network communications capabilities created pivotal societal changes in the last few decades, it’s safe to assume that the continued convergence of Metcalfe’s Law and Moore’s Law into the Internet of Things will produce another pivotal societal change in just the next few decades, and the associated products and services will carry market values of hundreds of billions and possibly trillions of dollars. In a 2015 report, consulting firm McKinsey states [1]: “If policy makers and businesses get it right, linking the physical and digital worlds could generate up to $11.1 trillion a year in economic value by 2025.”