Not long ago, in 1992, Intel CEO Andy Grove shared a remark about the future of the smartphone, “The idea of a personal communicator in every pocket is a pipe dream driven by greed.” Like him, many great thinkers made a blunder in predicting the unfolding future. The underlying cause has been our failure in forecasting technology and figuring out consumer preferences in Getting jobs done with technology. Technology forecasting is a challenging task. But it’s vital for dealing with pervasive uncertainties. Due to the failure of technology forecasting, giant firms overlook the unfolding future, give away the opportunity to new entrants, and suffer from the burn of disruptive effects. For this reason, the history of technology evolution is littered with carcasses of once-dominant firms, the rise of Startups, and deserted once-prosperous towns.
Technologies, like living things, have dynamic lifecycles. During the early stage, the potential remains in embryonic form. With an adequate supply of knowledge and ideas, the lifecycle keeps reaching different stages of maturity. Along with the lifecycle progression, innovations start unfolding, often causing destruction to incumbent products, jobs, and firms. Despite having a familiar S-curve-like shape, the lifecycle of technologies varies. Besides, Innovation dynamics created by each technology are also unique. Hence, technology forecasting is paramount for predicting the unfolding future.
Technology forecasting attempts to predict the growth characteristics of valuable technologies. Such forecasting exercises are far more complicated than collecting past data and extrapolating or relying on the intuitive insights of experts. There appears to be no simple, straightforward approach to perform this essential job. But there are multiple means to get insights. The challenge is to leverage each of them in a complementary way to increase our accuracy of technology forecasting.
Ray Kurzweil’s Future technology predictions:
Twenty honorary doctorates recipient, Ray Kurzweil is a rare personality in predicting technologies. Among many achievements, he is the chancellor and co-founder of Singularity University. He was right to predict that a computer would defeat a world chess champion by 1998. Mr. Kurzweil was also right that PCs would be capable of answering queries by accessing information wirelessly via the Internet by 2010.
Despite many past successes, Ray’s prediction that by the 2020s, most diseases will go away as nanobots become smarter is questionable. But with the given helpless situation of humans in dealing with COVID-19, perhaps, this prediction runs the risk of failure with a significant margin. Recent oscillating performance of deep learning capability also indicates that his prediction about self-driving cars to take over the roads in the 2020s also runs the risk of failure. He has also predicted that people won’t be allowed to drive on highways in the 2020s.
His prediction that non-biological intelligence will be a billion times more capable than biological intelligence by the 2040s demands the definition of what we mean by intelligence. Indeed, weight matrix-based memorization in imitating human intelligence does not indicate that machines will take over humans’ creativity and imagination.
The underlying rationality of Ray’s predictions is based on the power of Moore’s Law. But the growth of computational capability does not necessarily make proportionate progress in imitating human intelligence. For leveraging accelerating returns from exponential growth, computational capability demands a scalable mathematical model of human intelligence.
Technology forecasting: Next big things
According to MIT technology review, the next big things are (i) Unhackable internet, (ii) Hyper-personalized medicine, (iii) Digital money, (iv) Anti-aging drugs, (v) AI-discovered molecules, (vi) Satellite mega-constellations, (vii) Quantum supremacy, (viii) Tiny AI, (ix) Differential privacy, and (x) Climate change attribution.
Contrary to the unhackable internet, experts predict the increased use of AI by hackers. There are predictions about the advancement of digital twin technology, optimization for hybrid working, and increased workplace automation. There are also optimistic predictions about blockchain and cryptocurrency. In predicting the future of intelligent machines, Elon Musk warned people about the dangers of AI-powered robots. He has also expected “scary outcomes” like in “The Terminator.” Despite having such predictions and developing “Tesla Bot”, his promise of supplying 1 million robotaxis by 2020 has fallen through the crack.
Similarly, his prediction made in 2026 about full self-driving vehicles by 2018 did not materialize. Perhaps, like Mr. Musk, many experts have grossly failed to predict the emergence of AI-powered machines. What could be the underlying reasons? Why does not Moore’s law work here?
Technology forecasting methods:
Should we just rely on a few extraordinary persons to make technology forecasting? Although it helps, that is not sufficient for organizations to make decisions about making an investment. Hence, we need a systematic approach for progress monitoring and technology forecasting.
Monitoring publications for assessing science base growth:
The flow of knowledge determines the growth of technology, not an arbitrary law, like Moore’s law, set up by a genius. For example, the miniaturization journey of silicon chips continued due to the availability of knowledge of quantum mechanics and its application. It happens to be that before the invention of the Transistor in 1947, scientists worked for 50 years in developing the quantum science knowledge base. Similarly, it took 50 years to develop enough science base to support electricity invention. In the absence of it, the scalability of silicon chip forming Moore’s law could have been history.
For example, despite the invention of the steam engine in 50 AD, the industrial revolution started out of it in the 1700s. Why did it not keep growing just after its invention? Due to a lack of science base, it remained stunted for as high as 1700 years. The formation of Newtonian mechanics and thermodynamics became the fuel for its growth, unfolding the 1st industrial revolution in the 18th century. To predict the growth of any technology, we should monitor the buildup of the science base. Without it, any amount of hype or expert speculation will not lead to expected growth. Hence, we should monitor publications and detect the development of scientific knowledge supporting technology advancement.
Tracking patent portfolio:
Tracking patent portfolios is also a valuable means for technology forecasting. In addition to counting numbers, we should carefully look into how far those patents indicate the progression of technology capability. Patent portfolio monitoring should be linked with the formation of a relevant science base for increasing technology forecasting.
Seeking expert opinion—Delphi:
Delphi method focuses on collecting insights from a group of experts in a structured manner. However, there have been examples of the failure of expert opinions in technology forecasting. For instance, although Kodak was the first to get a digital camera patent, Kodak management failed to predict its future. Similarly, experts of GE have made an error in the uprising of renewable energy sources.
The consequence of the failure in technology forecasting could be catastrophic. In some instances, dominant firms disappear. For example, due to the failure in predicting the future of PC, Digital Equipment Corporation (DEC) went out of business. In fact, technology forecasting failure is the root of Prof. Clayton’s Disruptive innovation theory. Despite its tremendous importance, still to date, we rely on expert opinions. Due to it, there have been gross mistakes in predicting the future of work. To reduce the error in technology forecasting, we should fuse multiple complementary means, as explained here.
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