Part Two: The Anatomy of an Innovation Wave. Five Signs of a Coming Disruption
Part Two: The Anatomy of an Innovation Wave. Five Signs of a Coming Disruption
What are the characteristics of disruptive technology waves?
This is Part Two of a Three-Part Series on the Anatomy of an Innovation Wave. For a description of the Anatomy Framework, please see Part One here. In this post, we discuss the most common characteristics of technology waves that are more disruptive than your everyday innovation.
Five Characteristics of Disruptive Technology Shifts
By applying the Anatomy Framework to past innovation waves, five characteristics tend to be associated with innovations that have much more disruptive force. Let’s take a look at each characteristic.
Stack Penetration
The first characteristic of disruptive waves is that innovation occurs at all layers of the technology stack. The ubiquity of innovation at all layers, for example, accompanied more disruptive shifts from personal computing to internet to cloud.
The rise of mobile provides a good example on this characteristic in action. Mobile technology existed for many decades dating back to the launch of the first portable computer in 1981 called the Osborne 1. Throughout the decades to follow, there were various predictions about the rise of mobile (and also, perhaps apocryphal, McKinsey’s underestimate). But it wasn’t until 2007 with the launch of iPhone (and the subsequent launch of Android in 2008) that we now recognise a true platform shift to mobile.
Viewing the mobile revolution through the lens of the different layers provides an explanation for why the mobile efforts starting in 2007 would be different. In the past, mobile innovation in the underlying infrastructure (WAP and mobile internet, camera phones, UI and display improvements) provided incremental improvements to the mobile experience. However, innovations were limited to the underlying infrastructure for the most part, only some touched the core platform layer, and very few found success as applications beyond SMS.
The launch of iPhone and Android marked a significant milestone in the mobile revolution. Why? Not only did innovation occur on the core platform and application layers, but tools and ecosystem enablers developed in a way not seen in the prior mobile era. On the tools side, both Google and Apple provided integrated development platforms to make it easier for developers to code and test. SDKs with APIs and libraries made it easier for developers to integrate with various features of the device such as its camera, display, and keyboard. On the ecosystem enabler layer, perhaps the biggest contributor to the success of the mobile platform starting in 2007 as opposed to prior mobile waves was the success of the mobile app stores.
Counter-examples to innovation permeating each layer include wearables, IoT, crypto and Web 2.0. In each case, there may have been pockets of innovation that occurred on a few layers, but far from the ubiquity on all layers that is found by more disruptive waves. For crypto, for example, the most popular applications were limited for the most part to defi/cefi and NFTs. For what it’s worth, I’m still bullish on the technical potential of crypto and certain other technologies, but applying the Anatomy Framework suggests it is still early in the crypto innovation cycle.
Innovation Rebound
While innovation in the underlying infrastructure often creates a chain reaction of innovation in the other technology layers, we also see how the causal direction can go the other way. The more profound innovations have a “rebound effect” whereby rapid adoption of the new technology often times at the application layer stimulates further innovation at the platform and underlying infrastructure layer to accommodate the demand, thus reversing the direction of causation.
The rise of the internet provides a good example of the rebound effect. Early internet data was transmitted across media such as analog phone lines through the use of modems. That may have been good enough for initial text based applications like email, but growing demand on the application layer caused more innovation on the underlying infrastructure on both the software and hardware side. Telecom companies, for example, invested heavily into fiber optic cables that increased network speed and revolutionised data transmission.
The “innovation rebound” is important because it creates the conditions for a second—and often more profound—wave of innovation. The upgrades on the underlying infrastructure for the internet, for example, made possible bandwidth hungry services like YouTube and Netflix, the proliferation of webcams, and live streaming apps like Twitch, which were not possible in the first internet wave.
Not only were they not possible, in many cases they were not predictable. The dramatic improvement in processing during the 70s and 80s provided the ability in the 90’s for supercomputers to perform real-time processing of satellite images or for simulating molecular models in AIDS research. The rise of gaming demanded high performance parallel processing to render graphics that resulted in the GPU. At the time, no one would have predicted that same technology would be adopted for bitcoin mining as well as provide the underlying infrastructure of the current wave of generative AI.
Innovation Agents and Media
To simplify, I’ve combined the next two characteristics into one discussion. More disruptive technology waves tend to see change initiated by both startups and incumbents and include innovation in software as well as hardware. Cloud is a good example where both startups and incumbents had key contributions to the rising trend of cloud computing. Early cloud startups include Dropbox, Salesforce, and Canva mostly focused on the application layer. Incumbents captured an even greater share of the cloud value chain with AWS, Azure and GCP. Moreover, innovation during the cloud computing era spanned both software, such as virtualization and containerisation, as well as hardware, such as high capacity storage and advanced power and cooling systems.
The PC era also witnessed both startups and incumbents innovating across hardware and software. Hardware innovators included the PC incumbents IBM and Xerox as well as startups (Dell, Compaq, Apple with Lisa then Macintosh). On the software side, incumbents IBM and Bell Labs were developing software, but the biggest gains were made by startups like Novell, Lotus, and of course, Microsoft. Other waves that demonstrate startups and incumbents innovating across software and hardware include the original internet wave as well as the mobile era.
There have been other technology waves that have not measured as strongly on this benchmark. Unlike the original internet wave, the social media, or Web 2.0, wave largely was asoftware-based technology wave. The few hardware efforts were exceptions and not very successful, such as Motorola’s social media phone called Moto Cliq. Also, most of the value from Web 2.0 were created and captured by startups like Facebook, YouTube, and Twitter. Incumbents largely ignored the trend or tried but failed to make anything successful (e.g. Google with Orkut, Wave, Buzz, Google+, do I need to go on).
Crypto is another example of a trend that scores lower on this benchmark. While it is true that the rise of crypto caused both software (L1 protocols, dapps) and hardware (mining and hardware wallets) innovation, the agents of innovation were all startups. As with Web 2.0, incumbents largely ignored crypto or tried and were unsuccessful, such as Facebook’s attempt to launch Libra or AriZona Iced Tea’s stablecoin aptly named USDTea.
Foundational Potential
The final measure of disruptiveness is whether the current wave provides the foundation for future waves. Core platforms in prior eras, for example, can become underlying infrastructure for future technology shifts. Admittedly this is a more delayed measure since it takes time for new technologies to be stacked on top, but it is a good trailing indicator of how meaningful a particular innovation has been.
On this characteristic, the internet has been the single best example of a technology innovation that paved the way for future stacks of innovation. The core platform of the internet became a critical piece of the underlying infrastructure for subsequent technology waves including for Web 2.0, mobile, and cloud computing. Broadband internet and virtualisation, for example, served as underlying infrastructure to create core cloud platform, which in turn became underlying infrastructure for the current wave of generative AI companies.
There are some innovation waves that failed to provide the foundation for future waves so score less favourably on this measure. Web 2.0 companies like Facebook and Twitter started in the application layer and tried but failed to move further towards a foundation layer to become a core platform for developers of new applications. On this particular measure, it’s unclear that mobile, albeit a powerful core platform, has been transformed into underlying infrastructure for a new platform.
In Part Three of the series, I will discuss how the current generative AI wave measures against the Anatomy Framework and the five characteristics of disruptive waves.