How big is the Internet of Things? (Part 1)
This two-part article discusses the value of the Internet of Things. We take a non-crystal ball approach to measure IoT’s tangible and intangible value, and make an estimate of its long term value based on real market data.
In the first post, we explore why IoT needs to make the shift from a technology to a value driven business to meet its growth expectations.
Moore’s law has been driving IoT evolution
In 1965, Intel co-founder Gordon Moore predicted that transistor density (and thus the performance) of microprocessors would double every 2 years. Take for example today’s iPhone 6s, which is 3.5 times faster than the iPhone 1, and has 8X more RAM memory, while its (on contract) price is 40% less than the first generation 7 years ago.
Figure 1: Moore’s law
Note: “doubling every 2 years” suggests a parabola-shaped curve, but Moore’s growth function is almost always represented in a straight line ― complemented by a logarithmic scale on the Y-axis.
Although recent articles (such as the one in Nature) and chip-maker announcements (like the one by Intel) are suggesting that the evolution of silicon technology may be reaching its physical limit, Moore’s prediction has been driving Information and Communications Technology (ICT) evolution for the past 5 decades.
Figure 2: processor evolution is following Moore’s law
While being Interviewed by the New York Times on the 50th anniversary of the law that was named after him, Gordon Moore admitted his amazement about the preciseness of his 1965 forecast:
“The original prediction was to look at 10 years, which I thought was a stretch. This was going from about 60 elements on an integrated circuit to 60,000 — a thousandfold extrapolation over 10 years. I thought that was pretty wild. The fact that something similar is going on for 50 years is truly amazing. You know, there were all kinds of barriers we could always see that [were] going to prevent taking the next step, and somehow or other, as we got closer, the engineers had figured out ways around these. But someday it has to stop. No exponential like this goes on forever.”
Mobile devices and machine-to-machine (M2M) communication modules are following similar performance and cost curves. According to Machina Research, NB-IoT efforts will result in sub-10$ “Category Zero” LTE modules by 2018. It has to be noted, however, that the total cost of ownership (TCO) of M2M connectivity depends on more than just the hardware module element, which is only about 15% of the cost. It also includes design, integration and device-network certification costs.
Metcalfe’s law adds value to the equation
Several years after Gordon Moore’s famous observation, another technology pioneer, 3Com co-founder Bob Metcalfe, stated that the value of a network grows by the square of the number of network nodes (or devices, or applications, or users) while costs follow a more or less linear function. Take for example a wireless network: if you have only 2 mobile devices, they’re only able to communicate with each other. However, if you have billions of connected devices and applications, opportunities rise dramatically.
Figure 3: Metcalfe’s law
So, Metcalfe’s law is really about network growth, value creation, and customer acquisition rather than about technological innovation.
A good example of the above is the revenue growth of Facebook, which has been following an almost perfect Metcalfe trajectory.
Figure 4: Facebook growth is following Metcalfe’s law
The combination of Moore’s and Metcalfe’s laws explains the evolution of communication networks and services, as well as the rise of the Internet of Things. The current IoT growth is enabled by hardware miniaturization, decreasing sensor costs, and ubiquitous wireless access capabilities that are empowering an explosive number of smart devices and applications (with a predicted CAGR of over >50% for the next 5 years.)
Unfortunately, general availability of state-of-the-art technology is not always a recipe for success. Some of the designated IoT “killer” devices and apps, mainly in the consumer domain with smart watches and connected thermostats as notorious examples, are still struggling for broad user adoption.
Figure 5: Worldwide wearable device shipments by the Top 5 vendors in 2015 (in million units)
Crossing the chasm
To explain the rather slow take-up of connected devices, we have to take a look at the technology adoption lifecycle, and more specifically at the “chasm theory” that was developed by management consultant Geoffrey Moore, based upon Everett Rogers’ Diffusion of Innovations curve.
In his book “Crossing the Chasm: Marketing and Selling High-Tech Products to Mainstream Customers,” Moore writes about the gap, a.k.a. the chasm, that product marketers have to bridge for the take up of new technology by early enthusiasts and mass market adoption.
Figure 6: technology adoption curve with the chasm
According to the author, early adopters are technology enthusiasts looking for a radical shift, while the early majority wants a productivity improvement.
“What the early adopter is buying, is some kind of change agent. By being the first to implement this change in their industry, the early adopters expect to get a jump on the competition, whether from lower product costs, faster time to market, more complete customer service, or some other comparable business advantage. They expect a radical discontinuity between the old ways and the new, and they are prepared to champion this cause against entrenched resistance. Being the first, they also are prepared to bear with the inevitable bugs and glitches that accompany any innovation just coming to market.
By contrast, the early majority want to buy a productivity improvement for existing operations. They are looking to minimize the discontinuity with the old ways. They want evolution, not revolution. They want technology to enhance, not overthrow, the established ways of doing business. And above all, they do not want to debug somebody else’s product. By the time they adopt it, they want it to work properly and to integrate appropriately with their existing technology base.”
But there's more behind the chasm than consumers being slow (or conservative) in adopting new technologies, products, and services.
As Clayton Christensen suggests in “The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail”, successful companies put too much emphasis on customers' current needs, and fail to adopt new technology or business models that will meet their customers' unstated or future needs. In other words, they need to deliver disruptive products and services. For example, the iPhone was a disruptive product, but the Apple Watch wasn't.
A shift is happening within the chasm
By combining the three preceding charts, and admittedly visually cheating with axes, scales, and representations, we can come to the conclusion that the chasm is actually the point where the shift from a technology driven model to a value and customer experience driven business needs to take place. If this doesn’t happen, any new product or technology introduction is doomed to fail.
Figure 7: the tipping point of technology and value
In a more recent article, “The Nature of the Firm – 75 Years Later,” Geoffey Moore writes:
“Smartphones and tablets are reengineering whole swaths of the consumer economy, from information access (Google) to communication (Facebook and Twitter) to media and entertainment (YouTube) to transportation (Uber) to hospitality (Airbnb) to dining (OpenTable and Yelp), and beyond.” [...] “At the same time, the big data analytics and cloud computing that enabled consumer IT to scale are now also being coopted by enterprises to help them scale their reach and increase their efficiency and effectiveness. […]
“Not surprisingly, transaction costs decrease—dramatically! All the overhead, all the delays, all the errors, all the confusion created by complex systems and well-intentioned but imperfectly informed human beings—all that sludge is being flushed from the system,” [...]
“as transaction costs decrease, the value of services relative to products increases. That is because one of the key selling points of a product is that it eliminates future transaction costs once it has been purchased.”
Which leads us to Jeremy Rifkin’s “zero marginal cost model”, which we will add to the equation in part 2 of this article.