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Smart Systems Redux - Harbor Research
Smart Systems Redux - 10 Aug 2022
Smart Systems

catalytic combinations of technologies are multiplying their impacts & creating a new generation of complex adaptive systems

“We shall not cease from exploration
And the end of all our exploring
Will be to arrive where we started
And know the place for the first time.”
-T.S. Elliot, Four Quartets

For quite a few years now, Harbor Research has focused most of its research and consulting on what we call “Smart Systems”— the innovations driven by the convergence of pervasive or embedded computing with the packet-switching “network of networks” called the Internet.

These days, many people often refer to this phenomenon as “digital transformation.”  We prefer “Smart Systems” over other terms in common use because it captures the profound enormity of the phenomenon – something much greater in scope than just leveraging digital technologies to automate existing processes and systems.

But very few people are thinking about smart connected systems on that level. Current technologists are operating with outdated models of data, networking and information that were conceived years ago and do not address the needs of a truly connected world.

When we coined the term Smart Systems our intention was to highlight two important trends driving future information systems innovations: the first trend was that innovations would accelerate exponentially, and the second trend was that interrelated combinations of compute, network, sensor and software innovations would reinforce one another and multiply their impacts.



source: Harbor Research

Smart Systems technologies will radically transform our lives and the global economy, scaling their equity market capitalizations from over $10 trillion today to potentially more than $150 trillion in 2030.


What do we mean by Smart Systems?  This new chapter of innovation lies at the intersection of two concepts: Exponential Technologies and Combinatorial Evolution.

Exponential technologies a term originally coined by futurist Ray Kurzweil in his essay The Law of Accelerating Returns, refers to those technologies for which the power and/or speed doubles each year, and/or the cost drops by half. Robotics, AI, IoT, nanotechnology, renewable energy and gene sequencing are just a small list of examples.



source: Harbor Research

Today, exponential technologies underpin most of modern society. Exponential advances in many technologies are often predictable, such as the rate of growth of computing power that Moore’s Law introduced. Current forecasts for wireless communications anticipate 10X or more performance improvement. Full genome sequencing can be done for a few hundred dollars today versus the millions it cost in 2006, or the nearly one billion it cost to produce the first full sequence in 2003. If we were to calculate the value of a “smartphone” in the early 1960s – a smartphone’s processing power based on the cost of computing it would have cost hundreds of trillions of dollars.

Kurzweil’s critical insight was understanding how difficult it is for a normal human to imagine exponential growth, “an analysis of the history of technology shows that technological change is exponential, contrary to the common-sense “intuitive linear” view. So, we won’t experience 100 years of progress in the 21st century — it will be more like 20,000 years of progress (at today’s rate).

In his book, The Nature of Technology: What It Is and How It Evolves, Brian Arthur introduced the idea of combinatorial evolution. Very simply, each of our technologies is a system assembled from earlier technologies. For example, the GPS and navigation systems we take for granted in smartphones combine the predecessor technologies of satellites, computing, radio receivers, transmitters and atomic clocks into a new unified and infinitely more valuable technology. Hence, the value of a new technology lies not just in what it does, but also in what future technologies it leads to. Every new technology becomes a building block for new innovations.

Evidence of these two concepts surrounds us daily. And yet, we have so much trouble re-imagining different futures. You’ve heard the famous observation by the writer Arthur C. Clarke: “Any sufficiently advanced technology is indistinguishable from magic.”

Whatever we choose to call the next innovation wave – adaptive systems, autonomous systems or otherwise, becoming so advanced they could be mistaken for magic. Smart Systems are evolving to be self-sensing, self-controlling, and self-optimizing—automatically, without human intervention.

How We Got Here

Since the beginning of computing there have essentially been three waves of technology innovations. The first wave in the 1960s to the 1980s was driven by the advent of silicon – microprocessors and microcontrollers that enabled calculations and put computational capabilities into business, society and professionals’ hands on a significant scale.  Engineers could design products, businesses could manage orders and inventories and scientists could model diverse realms like human disease, the weather and natural resources.

The second wave in the 1990s and 2000s, brought us interconnectedness or the “connected economy.”  Everything began to be linked and integrated creating a new virtual world where the Internet and the Web, telco networks and satellites enabled shared computing resources interconnecting machines, software, systems, processes and people that executed everything digitally. This shift involved digitally enabling physical actions and execution.

The third wave, which started in the later 2000s, is based on multiple parallel technology developments which began to accelerate and, more importantly, also began to reinforce one another. Cloud computing infrastructure provided unprecedented computing scale. Mobile computing devices extended the reach of computing itself. Machine learning and AI brought intelligence to diverse things, and embedded systems and IoT technology began connecting and integrating a broad array of physical and digital applications.

Each of these technologies is powerful on its own, but catalytic combinations of these capabilities are multiplying their impacts. Human-connected devices and machine-connected IoT devices enable exponentially more data. The cloud then enables us to capture, analyze and model many phenomena through its computational capacity. This in turn sets the stage for AI and machine learning tools to analyze and capture new insights.


The third wave of innovation, which is still evolving, is enabled by cheap pervasive networked sensors and sensor data fusion. This is driving new innovations that are enabling a whole new generation of “awareness” applications. As networks continue to invade the “physical” world, system developers are seeing the new values that come from the growing interactions between sensors, machines, systems and people and the ability to detect patterns from large-scale sensor and machine data.


Complex Adaptive Autonomous Systems

source: Harbor Research

Applications like computer vision caused the development of systems, based on methods and algorithms that could recognize something and execute an action based on the result. These new capabilities revolve around real-time situational awareness and automated analysis of very large volumes of sensor data. As a result, technology has moved beyond just proposing task solutions — such as executing a work order or a sales order — to sensing what is happening in the world around it, analyzing that new information for patterns, risks and possibilities, presenting alternatives, and automatically taking actions.

These combinatorial innovations are what we call “complex adaptive systems” where multiple technologies converge and reinforce one another.

For example, the development of autonomous vehicles not only depends on advancements in robotics and artificial intelligence to operate vehicles, but also on the maturation of the Internet of Things so an array of sensors can analyze driving conditions and interact with other cars, as well as improvements in lithium and battery technology for cars to be able to efficiently refuel themselves. The interdependence of these technologies has no doubt contributed to their synchronous advancement. For example, many believed a limiting factor in the emergence of driverless cars was the high cost of batteries required to travel long distances. In response, however, battery producers have dramatically increased production to scale down per unit costs.  Over the last ten years, EV battery prices have fallen 90%.

From seemingly disparate innovations powered by self-organizing sensor networks processing voluminous amounts of data, computers are able for the first time to understand and form associations based on statistical methods. Computers can now do what we previously thought only humans could do.

Complex adaptive and autonomous systems have common attributes, including:

  • The ability to infer and reason, using substantial amounts of appropriately represented knowledge.
  • The ability to learn from their experiences and improve their performance over time.
  • The capability of explaining themselves and taking naturally expressed directions.
  • The awareness of themselves and ability to reflect on their own behavior and to respond robustly to surprises.

As these systems evolve, we are setting the stage for numerous “invisible” computer-to-computer interactions (a new generation of “machine-to-machine” transactions):

  • Autonomous machines making decisions about purchases on our behalf or concluding contracts.
  • Multi-agent systems and decentralized autonomous machines enabled to lease themselves out, hire maintenance professionals, and pay for replacement parts.
  • Micro-payments between machines, such as a car looking for a specific spare part, or drones and farming systems negotiating directly with each other for services.

Machine data models and analytics allows not only data patterns but a much higher order of intelligence to emerge from large collections of ordinary machine and device data, similar to the neurons of the brain, ants in an anthill, human beings in a society, or information devices connected to each other. The many “nodes” of a network may not be very “smart” in themselves, but if they are networked in a way that allows them to connect effortlessly and interoperate seamlessly, they begin to give rise to complex, system-wide behavior that usually goes by the name “emergence.” That is, an entirely new order of intelligence “emerges” from the system as a whole—an intelligence that could not have been predicted by looking at any of the nodes individually.


Harbor Research coined the term “Smart Systems” in the early 2000s to describe the emergence of a new generation of complex adaptive systems that are trying to break away from traditional computing and telecom paradigms. As the cyber-physical world continues to dovetail with IoT, machine learning and artificial intelligence, Smart Systems will enable previously unimagined capabilities. The question is whether business leadership really understands the new dynamics driving value and are ready to grasp its potential. ◆

Please fill out the form below to download our Growth Opportunity Insight, “Introduction to Smart Systems.”

Introduction to Smart Systems

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