Invisible Business: The Emergence of a Parallel Machine-Driven Economy

Glen Allmendinger
7 min readMar 4, 2021


We increasingly live in two worlds. There is the physical one which is tangible and coherent, and the invisible one which is barely noticed by the average person. In the invisible world, billions upon billions of smart sensors, data, and information systems are creating a “digital nervous system” that interacts autonomously almost as a parallel economy. Its nature and behavior are concerns that have yet to really take center stage, not only in business communities but in most technology communities as well.


People are great, but for many important tasks they’re an impediment. Unless you’re living in ancient Egypt, people are not the proper resource for the excavation and heavy-lifting required by skyscraper construction. We have machines for that now — backhoes, bulldozers, cranes, and so on — and no one seems to lament “the good old days” when thousands of slaves carried pyramid-blocks on their backs.

If you applied this vision in a practical way to business today, you might point to the digital mechanisms that make B2C and B2B transactions, performed by human beings, easier and more convenient. But it amounts to a great deal more than that.

Genuine intelligent systems re-think the whole relationship of people and intelligent machines to business systems and society. These systems are built upon pervasive, across-the-board data and information automation accomplished by enabling virtually all sensors, actuators, machines and everyday electronic devices to communicate with and control each other. The rich, vast streams of meaningful data and intelligence that those devices generate are in turn managed by a new generation of information and communications tools.

The ultimate end state of this technology evolution is what we have always referred to as “Smart Systems,” which create an entirely new portfolio of machine-to-machine applications that transform the way business is done and profoundly change our societies at every level.


Since the beginning of computing there have essentially been three waves of technology innovation. 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 the hands of business and social professionals on a significant scale. Engineers could design products, businesses could manage orders and inventories, and scientists could model and make predictions in realms as diverse as 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 to create a new virtual world where the Internet and the Web, telco networks, and satellites enabled shared computing resources that interconnected machines, software, systems, processes and people. This shift involved digitally enabling physical actions and execution.

The third wave, which started in the early 2000s, is based on the combination of multiple parallel technology developments that reinforce and accelerate 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, with its decentralized computational capacity, enables us to capture, and model many phenomena, which in turn sets the stage for AI and machine learning tools to analyze and capture new insights.

In every age of human history, technology innovation sets the stage for significant cognitive and social shifts — steam engines, railroads, GPS, genetic mapping, and more. The biggest leaps — the ones that fundamentally transform businesses, economies, and societies — have been catalogued and predicted since the 1950s, when such thinkers as Jay Forrester (System Dynamics) and MIT’s Norbert Weiner (Cybernetics and The Human Use of Human Beings) wrote landmark works describing a world transformed by automation, machine intelligence and smart systems.


The third wave of innovation, which is still evolving, is enabled by cheap, pervasive networked sensors and sensor data fusion which are enabling a new generation of “awareness” applications. As networks continue to invade the “physical” world, developers are working with the growing interactions between sensors, machines, systems and people to detect revelatory patterns in large scale sensor and machine data.

Applications like computer vision are based on methods and algorithms that recognize patterns in the real world 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.

Machine data models and analytics allow not only data patterns but a much higher order of intelligence to emerge from large collections of “ordinary” machine and device data — not unlike the workings of neurons of the brain, ants in an anthill, or human beings in a society. The many “nodes” of a network don’t have to be particularly “smart” in themselves. 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.” 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.

You’ve heard the famous observation by the writer Arthur C. Clarke, “Any sufficiently advanced technology is indistinguishable from magic.” There is a distinct magic to emergence, but it happens only if the network’s nodes are free to share information and processing power.

Many ordinary, non-technical people realized the magic of data from Internet-centric companies like Amazon where, in the late 1990s and early 2000s, they first saw statements like, “People who bought this also bought this.” Later it became “People who clicked on this also clicked on this,” and then it moved beyond “people” and started being about you: “The store you made.”

Soon, Amazon was sitting up on the table and having opinions: “We think you’ll like this.” And to an uncanny degree, the suggestions of this intangible edifice were correct. You’d never even heard of half the things Amazon was recommending, but they were right up your alley. Amazon stopped being a “store” and started being an intelligent entity that — to some very real degree — understood who you were and what you cared about.

Obviously, this was all the result of massive data analysis. And so, with further innovations like vision inspection systems powered by self-organizing sensor networks processing voluminous amounts of data, computers were able for the first time to understand and form associations based on statistical methods. Computers could, all of a sudden, do what we previously thought only humans could do.


This cycle of technology development’s most profound impact lies in the integration of smart sensors, machines, information systems and smart algorithms to create a “digital nervous system” that smoothly interacts and makes decisions about a very wide range of situations autonomously.

To repeat: We increasingly live in two worlds — the tangible, coherent, physical one, and the invisible, computed one which is barely seen or noticed by the average person.

And yet, it seems that very few people in technology think about smart connected systems on that level. “Smart Systems” should be understood as networked information and computation that will process a rapidly growing number of situations and decisions without human intervention. Inside such systems, reliable and blindingly fast microprocessors do what they are very good at doing (and what people are very bad at doing): digesting billions of data-points, talking to each other about the data, and controlling each other based upon the state of the data — all in a matter of nanoseconds.

Human beings cannot do this, nor should they. As is always the case with maturing technologies, they “melt” into reality and we stop seeing them. The incessant stream of ongoing data interactions and systems intelligence is increasingly and necessarily “invisible” to people. Intelligence, in many ways, has moved beyond humans to systems.


Whatever we choose to call the next wave on the horizon — autonomous, robotic, self-directed, etc. — intelligent systems will increasingly become self-sensing, self-controlling and self-optimizing automatically, without human intervention. Think of intelligent agents and virtual assistants residing inside systems and making purchase decisions defined by a set of programmed rules. Then think of machines and systems making optimized selections among competing offers based on learning and rules or, ultimately, machines deducing human needs based on rules, context and preferences.

In several industries, invisible commerce is already here — in fact, it’s already the dominant mode. We have machine-to-machine trading of stocks and energy, streaming playlists on Spotify, and algorithm-based traffic optimization. All this give us a preview of what’s coming to the rest of the economy.

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

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

The nature and behavior of truly distributed intelligent system are concerns that have yet to really take center stage — not only in business communities, but in most technology communities and governments as well. Understanding the technologies, markets, companies, and business models that constitute autonomous commerce will soon be a minimum requirement for businesses to compete.




Glen Allmendinger

Founder and president of Harbor Research, a growth strategy consulting and venture development firm with over thirty years of expertise in Smart Systems & IoT.