Upon developing the first digital camera in 1974, Kodak management envisioned no possibility of finding treasures. But, after seeing the 8×8 charge-coupled device (CCD) image sensor of Bell labs, Sony embarked on the mission of finding treasures. In the end, Sony’s treasure-finding mission created traps for Kodak to fail—creating Kodak moment. There have been many such technology uncertainty stories, creating successes and failures. Technology uncertainty fueled innovators’ dilemma, resulting in the rise of Sony and the fall of RCA and Kodak. Due to the failure of dealing with technology uncertainty, IBM prematurely terminated the first smartphone—Simon. Similarly, upon inventing LED, General Electric suffered destruction from the rise of the LED light bulbs. On the other hand, due to technology uncertainty, after investing billions of dollars, startups are failing to make their innovations around emerging technology cores grow as creative waves of destruction—let alone disruptive innovation.
Technology uncertainty relates to a lack of clarity in the technology lifecycle. It’s a great challenge and a matter of concern for decision-makers. As growth potential remains latent, management faces a dilemma in deciding how far to be serious about nurturing and exploiting it. In addition to offering treasures, technology lifecycle also causes traps. For example, due to the premature saturation of machine learning algorithms, after gobbling more than $80 billion, autonomous vehicles are not capable enough to roll out.
On the other hand, due to the failure of technology uncertainty, wireless broadband WiMax operators suffered from massive loss, as LTE grew far faster than anticipated. Similarly, plasma display technology gave up to liquid crystal displays despite having an early lead. Due to technology uncertainty, the management of incumbent firms suffers from innovators’ dilemma. Consequentially, we witness the rise of startups as giants and the disappearance of once-dominant firms.
Causes of technology uncertainties:
Like living things, technologies start the journey in an embryonic form. They grow through phases, reaching maturity. Although we term them as a typical S-curve-like technology lifecycle, there have been many variations. They neither grow at the same rate nor reach the same height. Underlying scientific genetic code, R&D funding, and competition determine their scalability. Innovations from technologies are not good enough until their performances reach a certain height. They must be amenable to progression for expanding the diffusion and sustaining innovations in the competitive market. Besides, multiple technologies compete to fuel target innovations. Furthermore, in many cases, chosen technologies must grow, reaching a certain height for creating successes out of creative waves of destructions of reinventions.
Primitive emergence of technology possibilities:
All technologies, irrespective of their greatness, show up in primitive form. For example, our smartphones’ 50-megapixel camera sensor was born as an 8×8 charge-coupled device. Similarly, A380 or Boeing 747 began its life as a toy Wright Brothers’ flying machine. Their potential remains latent. In the beginning, how far and how fast they will grow and the feasibility of innovating products and processes would remain unclear. They keep progressively unfolding. But the management faces the challenge of detecting and deciding about the course in exploiting the latent potential. Although we know the growth of many technologies creating success stories, there are many that stop growing before reaching the target level. For example, Honda’s ASIMO failed to reach the required height for powering nursing care robot innovations. Similarly, Fuel Cells have been very slow to progress to reinvent automobiles, losing its early lead to batteries.
Loss-making beginning and uncertainty in reaching profitability:
In the early stage of lifecycles, innovations around many great technologies begin life at a loss. For example, electric vehicles are still at a loss despite huge potential. Similarly, Apple’s personal computer as Apple I and smartphone as IBM’s Simon started the journey at a loss. For turning this loss into profit, the underlying technology cores must grow. But due to technology uncertainty, management faces a decision-making dilemma. As a result, sometimes, they avoid altogether. In other cases, they prematurely terminate or show a halfhearted response. For example, although IBM got into the personal computer, it gave hardware business to Intel and the responsibility of supplying the operating system to Microsoft. Due to technology uncertainty, IBM showed such a lukewarm response, resulting in loss of opportunity and suffering from the burn of the creative waves of PC.
Unclear, unpredictable, and unfolding spillover effects:
No single product can fully exploit the latent potential of most of the technologies. But what other products could benefit from them are not clear initially. The lack of clarity about the innovation spillover effect also contributes to technology uncertainty. For example, the microprocessor was developed for the purpose of calculator innovation. But the greatest business benefit from it came from personal computer innovation.
Similarly, although Sony developed image sensor technology to release digital cameras, the largest business came from smartphone innovations. Ironically, smartphone cameras caused destruction to Sony’s digital camera business. In many cases, innovation spillover effects bring far more economic value than the initial purpose of technology development.
Increasingly long and costly experimentation:
The growing cost of experimentation, taking an increasingly longer time, also contributes to technology uncertainty. For example, through 10,000 experiments over a couple of years, Edison found suitable carbon filament for light bulb invention. But its reinvention as an LED light bulb took 40 years long scientific investigation. Similarly, after almost 20 years of journey, the electric vehicle is yet to prove its profitable potential.
Uncertain growth path and misleading early progress:
Despite our perception of the technology lifecycle as a typical S-curve, the technology growth path shows a high level of variation. For example, over 30 years, LED technology shows little progress. But a single scientific discovery in the 1990s led to a big jump, making it suitable for the reinvention of the light bulb.
In certain technologies, early progress creates an elusive impression. For example, machine learning-based innovations do not take much effort to demonstrate their potential. But upon reaching 90 percent or so accuracy, they start showing saturation. As a result, many prediction techniques like extrapolations run the risk of misguiding.
Continued progression demand scientific discoveries:
Often, we think that technological invention is a big challenge as it demands scientific discovery. Of course, it does. But invariably, technology lifecycle progression demands continued scientific discoveries. In many cases, the challenge of scientific discoveries for nurturing latent technology potential is far more challenging than the invention itself. For example, the LED invention did not win Nobel Prize; but the scientific discoveries for unlocking its latent potential led to winning a Nobel Prize.
Competing multiple technologies:
Often, multiple technologies keep competing to fuel the same type of innovations. For example, Fuel Cells and Batteries are competing technologies for electric vehicles. Similarly, Plasma and LCD were competing to be a preferred technology core for the reinvention of the display. Such a competing situation creates management decision-making challenges.
Consequences of technology uncertainty:
The consequence of the technology life cycle could be a blessing and catastrophic. Due to technology uncertainty, IBM gave away the PC business to Intel and Microsoft. Due to it, upon burning more than $25 billion, Uber has been still waiting for robot taxies to show up to cause creative destruction to taxi service and car ownership.
Uncertain product lifecycle leads to premature decisions
Progression of the underlying technology core determines the product lifecycle. In the early stage, it gives a misleading signal. For example, transistor radio and television were highly primitive. Who knew that they would grow as far better alternatives to incumbent ones? Hence, many great companies made premature decisions. For example, IBM prematurely terminated the smartphone, Simon.
Technology uncertainty: underlying factor of startup failures and successes
The underlying reason for the high failure rate of startups appears to be due to technology uncertainty. In most cases, startups are on the mission of leveraging emerging technology core to cause disruptive innovation. But in many cases, technologies do not grow as per expectation. Hence, they run out of money and fold up operations. For example, due to the unexpected growth of WiMax technologies, many startups failed.
Similarly, due to the early saturation of machine learning technologies, many AI startups have been failing to roll out their innovations within a stipulated time frame. On the other hand, due to technology uncertainty, incumbent firms overlook the rise of emerging waves, giving startups to grow s disruptive force. Due to that reason, Spotify grew in taking the music streaming business from Apple. Similarly, the growth of Microsoft, Apple, Intel, Sony, and many other startups as giants are due to wrong decisions taken by incumbent firms.
The underlying cause of innovators’ dilemma:
Despite having technology competence, risk capital, brand value, and many other assets, why do managers of incumbent firms suffer from innovators’ dilemma? The underlying cause has been technology uncertainty. Due to it, Kodak did not pursue a digital camera. For this reason, GE lost the light bulb business to a tiny Nichia. Due to the continued profitable business out of an incremental progression of incumbent mature technology core and uncertain growth path of alternative technology core, incumbent firms suffer from innovators’ dilemma. This decision dilemma of whether to keep continuing the current wave or switching to the emerging one causes destruction to giants and gives the path for startups to grow as giants.
Technology uncertainty is the underlying cause of creative destruction and disruptive innovation:
It does not pose many difficulties to comprehend the uprising of reinvention out of emerging technology cores to cause creative destruction. But why do they take the form of disruptive innovation, causing destruction to incumbent firms? Instead of IBM, why did Microsoft, Intel, and other startups lead the PC wave? Does it mean that incumbent firms are inferior to startups? For sure, no. The underlying cause has been technology uncertainty. Due to it, incumbent firms suffer from decision dilemmas, leading to the failure of timely switching to emerging creative waves. Consequentially, they suffer from the burn of disruptive innovation.
Due to primitive emergence and unpredictable lifecycle, technology uncertainty poses both opportunities and threats. As a result, startups rise from the creative waves, while incumbent giants suffer from disruptive effects. It’s also the underlying cause of the failure rate of startups. Hence, we should pay attention to technology forecasting for predicting the unfolding future. Perhaps, we will never be accurate; but a slight improvement of accuracy may lead to attaining high success and avoiding disaster. By the way, conventional means are not very useful due to its unpredictable nature. But the formation of undercurrent provides a useful clue of technology forecasting for dealing with uncertainty. This is an essential exercise, as due to technology forecasting failures, giants disappeared giving due to the uprising of startups.
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