Technology S-curve – oversimplified misleading model

Did LED, Fuel-Cell, Multitouch, Steam engine, electronic image sensor, and many other technologies follow S-curve like typical lifecycle model? If yes, why did many technologically sound companies fail and tiny Startups succeed? On the other hand, while some technologies take a decade or so to reach adulthood, why is artificial intelligence technology still at the infancy of its lifecycle after 70 years of birth? Complexity in technology forecasting has been the root cause of the fall of mega-success stories and the rise of startups. If the technology lifecycle follows a typical S-curve model, technology forecasting could not have been a complex task at all. In reality, the technology lifecycle rarely follows the S-curve-like growth model. In retrospect, inadequate clarity in the relevance of the S-curve-like model has a major contributing factor to technology forecasting complexity.

Technologies are like living things that get birth, grow, and mature. Hence, from the surface, the technology S-curve makes sense. In general, the S-curve pattern lifecycle indicates that technology evolves over time and reaches the maturity stage. In this life cycle, there are three primary stages such as (i) infancy, (ii) growth, and (iii) maturity. During the infancy period, technology capability is faint; the potential is primarily latent. Hence, not much Innovation activity takes place during this phase.

To support innovation, technology must enter the growth stage. For Incremental innovation of existing products, the decision-making challenge about the adoption is not much. But for deciding whether the technology should be adopted for reinventing existing products or innovating completely new ones, the technology should experience an extended growth period. The next challenge is to figure out whether technology reached the maturity level to decide about the next move. As most technologies do not follow typical S-Curve-like patterns, technology adoption and replacement decisions are complex.     

Decision-making challenges—relevance to Technology S-curve 

Innovators derive economic benefit from technology inventions through three different means.  The first one is about incremental advancement of existing products or processes, either through adding new features or improving existing ones with the technology. The next one is about innovating completely new products or processes. And the 3^rd option is about the Reinvention of mature ones.

Apparently, the decision-making challenge with incremental advancement does not pose many challenges. But in some instances, the cumulative effect of incremental upgrades may lead to a significant advantage. Hence, when to start benefiting from emerging technology is also challenging. Exploitable length and height (ExploLifeLen & ExploLifeHeight) highly influence the value extraction possibility. Hence, extraction of economic benefit from emerging technology through incremental innovation depends on ExploLifeLen & ExploLifeHeight.

Similarly, the success of reinvention of mature products by the change of ripe technology core with the emerging one also depends on the unfolding value of these two critical variables: ExploLifeLen & ExploLifeHeight. As both innovation and reinvention begin the journey at a loss, technology must cross the threshold level to produce profitable revenue for the innovators. Unfortunately, innovators cannot afford to wait for the threshold value to decide. If innovators do so, either they will miss the opportunity or suffer from the burn of Disruptive innovation.

Let’s look into underlying reasons for which technology S-curve runs the risk of misleading us. First of all, even if a technology growth path resembles S-curve, the value of two critical variables, such as ExploLifeLen & ExploLifeHeight, is unknown. The next one is that many technologies do not follow such typical patterns. Some of them stop growing after infancy. Others start growing after a specific interval. Some do not cross the threshold level at the end.         

How are technologies born, and how do they grow?  

Technology may be born out of the discovery of scientific knowledge and its systematic exploitation. Or, it may get birth through intuition, tinkering, or accidental observation. For example, the steam engine, the light bulb, and many others were born through tinkering. On the other hand, transistors, laser, charge-coupled devices (CCD), and many others were born as the outcome of systematic scientific investigation. Besides, microwave food heating, x-ray, and a few others got birth out of accidental observation.

However, irrespective of the means of birth, every technology emerges in an embryonic form. Hence, the next question is what it takes for their growth to support innovation. Invariably, intuition-driven tinkering and Craftsmanship run out of fuel very soon, leaving technologies caught in infancy. For development, technologies need a Flow of Ideas. That flow of ideas must originate from the steam of knowledge created by science. Hence, how far we succeed in digging science will determine how fast and how far technologies will grow before reaching saturation.

In retrospect, many technologies get stuck after birth due to a lack of flow of ideas due to scarcity of scientific knowledge. For example, LED remained caught in infancy for almost 30 years before a scientific discovery took place in 1994. Since then, it has been growing, showing an S-curve-like pattern. Similarly, the Steam engine remained caught in infancy for 1700 years, after its invention in 50AD. The development of Newtonian physics gave birth to mechanical engineering, and thermodynamics created the needed flow of ideas for providing an S-curve-like lifecycle.

But, transistors, CCD, hard disks, and many others did not take much time to grow, forming S-Curve.  Because underlying science got shaped prior to their birth. Hence, the underlying science base determines how will the technology S-curve take shape.

Examples of decision-making consequences: Technology S-Curve was of little help or misguidance

Upon observing the demonstration and performing experimentation, RCA, Texas Instrumentation, and others felt that Transistor technology would not cross the threshold level for being a target technology for reinventing Radio, TV, and many other electronic products. On the other hand, Sony believed in it and pursued the journey of reinvention. Interestingly, Transistor technology rapidly grew out of flow ideas stemmed from the knowledge of quantum mechanics. Hence, in the end, Sony succeeded by forming a Creative Destruction force and causing disruption to RCA and many others. RCA could have avoided this disruption if S-curve were the dependable reference model. Among others, Kodak also suffered from similar consequences.     

On the other hand, despite the early superior performance, plasma display technology-saturated far before taking over CRT display. Unlike it, LCD rapidly grew and took over both plasma and CRT in reinventing TV, computer monitors, and many more. Ironically, after 100 years of invention, LCD technology started growing in the early 1980s, forming an S-curve lifecycle.

It seems that technology lifecycles are highly affected by the availability of scientific knowledge and future discoveries. Hence, not all technologies keep growing after birth. Although just after birth, some of them keep growing, taking the S-curve-like shape, many others remain stunted for decades and even a century before starting growing and taking the form of S-Curve. In some instances, some do not grow beyond infancy and get forgotten. By the way, despite the hype, perhaps, artificial intelligence based on deep learning or neural network is still caught in infancy, waiting for new science. Therefore, the technology s-curve is oversimplified. Sometimes, it runs the risk of misguiding.   

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