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Finding Gold in an Ocean of Hype: Using Patterns of Technology Change to Find Valuable Opportunities and Rethink America’s System of R&D
There is so much hype in the world of science and technology from professors, academic journals, university press offices, science writers, technology suppliers, and techno-optimists and this hype goes far beyond simple models such as Gartner's hype cycle. There is a constant drumbeat of press releases on scientific papers each claiming an imminent breakthrough and each spread through a vast ecosystem of professors, writers, journals, funding agencies, and potential suppliers. The problem is that very few of these papers are read and even a smaller number of them lead to new products and services. Hype also comes from management theories such as S-curves, learning curves, and most of all from Clayton Christensen's theory of disruptive innovation, all of which are replaced with better theories in this book.
This book helps readers find real opportunities in an ocean of hype and rethink America’s System of R&D. Entrepreneurs can find valuable opportunities by focusing on the right process of technology change and the right aspects of that process. They are much more likely to find opportunities by focusing on changes in technology (e.g., Moore's Law) than on changes in science particularly those that are described in scientific papers. These results also have implications for university research and teaching and America’s system of R&D. Universities need to focus research and teaching more on technology than on science. The U.S. needs to move away from encouraging and monitoring the number of papers or citations to them and move towards a mission-based approach to R&D, which can return the U.S. rate of productivity growth back to pre-1970 levels.
This book builds from my research and consulting in mobile phones, other digital technologies, and many science-based technologies over the last 25 years as a professor in the US, Japan, and Singapore. I have written more than 50 academic articles and 6 books (Stanford University Press in 2013); consulted with many electronic firms; taught courses on technology change and business models to thousands of students; and managed hundreds of Master student group projects on technology change and business models over the last five years. I received the NTT DoCoMo mobile science award for my research on mobile communication, which has also involved consulting with Nokia, Bouygues Telecom, NTT DoCoMo, Panasonic, and Huawei. My research is often reported in the media, including the Financial Times (Robin Harding, Relax about robots but worry over climate change, July 2016) and op-eds in the Singapore Straits Times (Prof no one is using your ideas; Making University Education More Practical).
Book Outline –
Chapter 1. Introduction (4,500 words)
Part I. How Does New Value Emerge? (20,000 words, 3 figures, 11 tables)
Chapter 2. Two Processes of Technology Change
Chapter 3. The iPhone and iPad
Chapter 4. Billion Dollar Start-up Club: What are their Value Propositions?
Chapter 5. Billion Dollar Start-up Club: How did the Opportunities Emerge?
Part II. Myths of Technology Change (5,000 words, 2 figures, 4 tables)
Chapter 6. S-Curves and Take-Offs
Chapter 7. Learning and Experience Curves
Part III. Implications (15,000 words, 4 figures, 10 tables)
Chapter 8. Techno-Optimists vs. Techno-Pessimists
Chapter 9. Ride Sharing and Driverless Vehicles
Chapter 10. University Education
Chapter 11. University Research
Chapter 12. Closing Words
Using analyses of the Wall Street Journal's billion-dollar start-up club, MIT Technology Review's poor predictions of breakthrough technologies, and many other data, this book shows that there are large differences between the technologies emphasized by universities and policy makers and the technologies being commercialized by the free market, including entrepreneurs and incumbents. Universities and policy makers emphasize science and a science-based process of technology change and entrepreneurs emphasize technology and a technology-based process of technology change, which I call the Silicon Valley process of technology change.
These processes are illuminated by analyzing the emergence of the iPhone and iPad, the products and services commercialized by 143 members of the Wall Street Journal’s billion-dollar start-up club (and by the world’s most valuable companies), and 40 predictions on breakthrough technologies by MIT’s Technology Review between 2001 and 2005. It finds that many more opportunities emerged from the Silicon Valley process than from the science-based process of technology change. This is in spite of the fact that managers, policy makers, and professors (including academics and most social scientists) largely focus on the second process of technology change in which university research along with science and engineering journals are heavily emphasized. Building from this empirical analysis, it discusses in detail the issues involved with finding the opportunities that are emerging from the Silicon Valley process of technology change.
After analyzing data and examples for both processes in Part I, Part II explores the myths of technology change that are proven wrong by data in Part I and Part III discusses the implications of Parts I and II for managers, policy makers, and professors. By destroying the myths and hype of S-curves and learning curves in Part II, Part III can explore the implications of the much greater output from the Silicon Valley than from the science-based process of technology change for many important issues. This includes the debate between techno-optimists and pessimists, the future of driverless vehicles and ride sharing (example of optimism), and the future of university education and research.
The low output from the science-based process suggests that few sectors will experience rapid improvements in productivity over the next 30 years, which is consistent with techno-pessimists such as Robert Gordon (The Rise and Fall of American Growth) and Tyler Cowen (The Great Stagnation). Much of this techno-optimisms come from MIT professors such as Erik Brynjolfsson and Andrew McAfee, who conveniently help MIT and other engineering universities attract more research dollars.
University teaching and research must also be changed to match the reality of the Silicon Valley process of technology change. Students need to learn more about those technologies that are likely to become economically feasible than the science-based technologies being researched by their professors. Professors can focus on science-based technologies in their research, but they must go beyond publishing papers and work more closely with start-ups and other firms to develop prototypes, improve them, and commercialize new products and services. U.S. federal, state, and local governments can support these changes by implementing a mission-based approach to R&D that has been successfully used by the DoD for many years.
First few pages of Introduction:
Every year MIT’s Technology Review chooses ten technologies that it believes will change the world and it calls them breakthrough technologies. It first did this in 2001 and it has been doing this each year since 2003 in consultation with scientists and engineers from MIT and other universities. Not surprisingly, many of these technologies involve advances in science. What is surprising, however, is that few have been successful. For the 40 predictions made between 2001 and 2005, only four currently have sales over $10 billion (one over $100 billion) while eight new technologies not chosen by Technology Review currently have sales larger than $10 billion of which three have over $100 billion and one other has over $50 billion. Furthermore, few of the technologies predicted by MIT’s Technology Review were commercialized by the 143 members of the Wall Street Journal’s billion-dollar start-up club , which are start-ups valued at more than $1Billion.
How could this be? How could a magazine associated with the world’s leading engineering and science university have a highly-flawed search process, particularly when the university is purportedly helping students search for technology-based opportunities and create technology-based start-ups? Furthermore, MIT is certainly not alone in its method of searching for new opportunities and thus this book is not singling out MIT for criticism. University professors heavily emphasize science and engineering journals and thus what I call a science-based process of technology change, or what some call the linear model of technology change . An emphasis on this process in research and teaching causes students to learn about technologies that appear in science and engineering journals and to believe that these technologies will soon be commercialized. This emphasis also extends outside universities and to many policy makers, R&D managers, journalists, and techno-optimists. Journalists for the NY Times, The Economist, BBC News, and CNN often describe technologies being developed in universities as if they are only a few years away. Thus, MIT’s flawed search process has implications for many decision makers involved with R&D.
This is a book about the types of innovations that are emerging, the processes that produce them, and their implications for search and R&D policy. How can we find opportunities that are likely to succeed, i.e., find gold, and avoid the hype that surrounds a search for opportunities? What should managers, entrepreneurs, professors and students monitor or what should they read to find this gold? Should they read science and engineering journals, market research reports, the Wall Street Journal, the Financial Times, or the NY Times? Or maybe it’s a matter of who they talk with? Should they talk with professors, entrepreneurs, journalists, policy makers, or high-level managers? And what does all this mean for R&D policy?
Most management scholars ignore issues of search and instead focus on how entrepreneurs and managers should commercialize a new technology. What should be the business model, method of implementation, or the strategy, including the value proposition, customers, revenue model, alliances, and strategic control? But it didn’t matter what business model or strategy was used for the breakthrough technologies predicted by MIT’s Technology Review. The economics aren’t there for most of these technologies and they may not be there for decades. No business model or strategy would probably work. The problem was that Technology Review chose the wrong technologies, a mistake that many entrepreneurs, managers, policy makers, professors, and students make.
One reason these mistakes are common is because hype about new technologies pervades our world and this hype goes far beyond the timing problem represented by Gartner’s hype cycle (See Figure 1.1). The real problem is that many technologies never experience growth or they only do so decades after young engineers and engineering students have devoted some of their best years to the wrong technologies. Professors hype their science-based technologies in courses and research projects as do universities in general. Although this hype is a natural outgrowth from competition between professors, it overwhelms students who do not have the skills to separate the gold from the hype. Marketing specialists promote a university’s papers, patents, and other activities in a constant stream of press releases that largely overstate what many technologies will likely achieve in the near future. Journalists jump on these bandwagons eager to sell stories and get page views. Social scientists contribute to this hype by focusing on the rising number of patents and scientific papers and implying that this is evidence for large amounts of science-based technology change. Even the policy section of Science and Nature magazines do similar things by focusing on the rising number of Chinese patents and thus the need for more science-based research in the U.S. Techno-optimists bundle this together into a mega-hype that makes it difficult for decision makers to have a fundamental understanding of technology change.
Universities have the most to gain from over-hyping science-based technologies because their strategies depend on maintaining an illusion of rapid change. Universities have promised us a better life from advances in science and the resulting new technologies that these advances bring. Engineering and other faculty use this illusion to emphasize research over teaching, papers over prototypes, science over practical courses, and rising tuitions even as job prospects have fallen and overall salaries have stagnated. Increasing the number of PhD students, even as job opportunities for them has also fallen, is part of this overall hype because they provide the cheap labor that universities need. Universities have been able to do these things because governments at all levels have believed more scientific papers somehow lead to more start-ups and economic growth.
Within universities, business schools are also big pushers of hype. In spite of declining start-up activity in the U.S., Germany, and France , they talk of opportunities being everywhere. Hype also comes from their models of technology change. For example, a technology’s performance purportedly experiences a takeoff during the early part of an S-curve and this supposed take-off makes it easy for proponents of a new technology to claim that a technology will soon experience the improvements necessary for rapid diffusion. Similarly, learning curves that show costs falling or experience curves that show performance rising as cumulative production grows also makes it easy for proponents to claim that costs will soon fall and performance will soon rise . Both theories will be replaced with better ones in this book.
Harvard Business School adds to this hype with Clayton Christensen’s model of disruptive innovation . In his model, low-end innovations naturally displace mainstream products as demand for a low-innovation increases thus resulting in rapid improvements in cost and performance. Proponents of this theory believe that low-end products and services are actually superior to high-end ones because they will purportedly experience rapid improvements once demand for them emerges . I was a victim of these proponents in a previous teaching position where a powerful professor and thus students would use the terms low-end innovation and disruptive innovation interchangeably; this created a perception that inferiority is good, something to be embraced. For students, the hype of disruptive innovations overwhelmed any discussion of economics and the real drivers of improvements.
One reason it is easy for us to fall for hype is that we all have biases, biases that come from trying to deal with a complex and difficult world. For example, as documented by Nobel Laureate Daniel Kahneman, we believe things that we hear a lot. Thus, when we hear a lot about specific new technologies from our peers or from the media (and see some of it in our smart phones), we tend to believe these technologies will soon be a reality. The beliefs become even stronger when we want to believe these technologies will become a reality; psychologists call this confirmation bias. For example, reading academic journals confirm what university scientists and engineers have always believed: there are so many technologies becoming feasible and the only limitation is sufficient funding.
Philip Fernbach and Steven Sloman take cognitive biases one step further in their book, The Knowledge Illusion: Why We Never Think Alone, and demonstrate that we think we understand something because other people understand it and write about it . Thus, the constant announcements about new science and technology makes us not only believe there will be lots of technological change, they also make us believe that we understand this technological change. Fernback and Sloman argue that one solution is to understand the mechanisms by which something occurs. Encouraging people to understand these mechanisms helps them go beyond simple statements of their beliefs and thus leads to fewer disagreements.
For this book, it is the mechanisms by which new technologies become economically feasible, which this book calls processes. Although many technologies become scientifically and technically feasible, addressing whether a technology will likely become economically feasible requires a more detailed understanding of technology change, including the processes by which the economics of new technologies improve over time. These processes involve multiple actors, they occur over long periods of time , and they raise many questions. What enables the cost and performance of some technologies to reach the levels at which they will be purchased by someone? Which technologies are doing this and which are not? Addressing these types of questions is the best way to avoid hype about new technologies.
Understanding technology change to this level of detail also helps us understand the debate between techno-optimists and techno-pessimists, a debate clearly influenced by hype. Led by Ray Kurzweil, Erik Brynjolfsson, Andrew McAfee, Martin Ford, and Peter Diamandis , techno-optimists argue that new technologies will improve productivity to such an extent that wide-spread unemployment may occur. Techno-pessimists such as Tyler Cowen and Robert Gordon argue that large productivity improvements are not occurring and thus changes are needed in the way R&D is managed. Where is the truth? By analyzing the processes by which new technologies become economically feasible, this book helps us better understand the types of innovations that have recently been successfully commercialized, the parts of the U.S. innovation system that are working better than others, and needed changes to the U.S. R&D system.
Returning to the issue of finding valuable opportunities amidst growing hype, one reason the author believes this can be done better is because understanding the future is not as hard as Yogi Berra’s famous quip suggests: “It's tough to make predictions, especially about the future.” Apple’s high market capitalization reflects choices it made about the technologies to introduce, as does the high market capitalization of many other successful companies such as Microsoft, Intel, Amazon, Facebook, and Google. In their book, Five Timeless Lessons from Bill Gates, Steve Jobs, and Andy Grove, David Yoffie and Michael Cusumano concluded that “looking forward and reasoning back” is one critical lesson from managers such as Bill Gates, Steve Jobs, and Andy Grove. Throughout their careers, these three managers have identified trends, used them to define new products and services or changes to existing ones, and then reasoned back to develop appropriate road maps and other strategies for the products and services.
But how should managers look forward, in order to reason back? Whenever someone mentions looking forward, they are referring to some sort of process that is unfolding over time in a specific type of way. Just as there is no spontaneous generation of biological species, there is no spontaneous generation of technologies and instead there is a long-term evolutionary process by which they become economically feasible. But what might this process or processes be, and how can we identify a process or specific types of processes that can be monitored by managers, policy makers, professors, and even students?
Many scholars focus on the short-run dynamics of this process, with an emphasis on post-commercialization activities. Rates of adoption follow an S-curve in which adoption is initially slow and later speeds up. Implementation problems may slow the rate of adoption , particularly in sectors with large barriers to entry or high government involvement such as health care and education. Others focus on the competition between incumbents and new entrants. Clayton Christensen focuses on how low-end innovations displace mainstream products through an interplay between demand, cost, performance, and diffusion.
Still others focus on the large numbers of start-ups and incumbents that pursue new opportunities as they become economically feasible. There is a large academic literature on this subject, which analyzes the patterns of entry and exit, the reasons for the patterns, and thus the best timing for entry. There is a large literature on the evolution of designs for a specific technology including the degree to which new designs build from or deviate from past designs . There is also a large literature on the standards that are set, the alliances that are made, and the regulations that are released for new technologies as they become economically feasible. These literatures tell us that new opportunities are pursued by many entrants, usually in slightly different ways and thus analyzing these new entrants can tells us a lot about which technologies are becoming economically feasible along with when and how.
For this book’s purposes, these literatures suggest that the behavior of a single firm is less interesting than the behavior of multiple firms. Multiple firms pursue opportunities, copying some parts of designs, innovating on others. They also create alliances and set standards as government agencies release rules and regulations. Even if one firm does not pursue a new technology or the right design for a technology, another firm will. Thus, focusing on the overall behavior of firms can call tell a great deal about the process by which new technologies become economically feasible and thus the processes that should be monitored by decision makers.
Basic economics tells us that price and performance are important variables and thus a description of an evolutionary process should illuminate the way in these variables evolve and how they reach a point at which the technology is economically feasible, i.e., it will likely be purchased and diffuse if it is introduced. Performance could include speed, convenience, features, or other variables that for simplicity are subsumed here within the general term, performance. Together performance and price can be used to discuss the economic feasibility of a new technology and when and how this occurs.
It’s important to recognize that both the media and academics largely ignore changes in pre-commercialization price and performance. The media focuses on the “buzz” about a new technology as if lots of talk means a new technology is ready to change the world. Management and economic scholars contribute to this buzz and the hype associate with it by using words such as “discontinuities” as if these technologies popped out of the sky. They also use surrogates such as patents to explain innovation when rising patents are not evidence of imminent commercialization and diffusion (think of nanotechnology). This book will show that price and performance are better variables to emphasize than are patents or scientific papers and the best way to avoid hype.
One way this book will demonstrate the existence of a long-term evolutionary process is to show the degree of improvements in price and performance that are experienced by most technologies before they are introduced and begin to diffuse. These improvements often involve multiple orders of magnitude and typically occur over many decades, thus suggesting a longer and more complex process than is ordinarily thought. In other words, it is not just a matter of looking at the ideas being pursued in university and government laboratories and then attempting to commercialize them. It is more about understanding improvements in price and performance and the processes that they represent in order to more effectively search for, find, and exploit those opportunities that are actually ready for commercialization.
Consider Figure 1.2. New technologies must provide certain levels of performance and price before they will become economically feasible and thus candidates for commercialization. For simplification, Figure 1.2 focuses on price and its relationship with quantity. The intersection between the supply and demand curves defines the price at which users will consider purchasing a new type of product or service that is based on the new technology, e.g., the first iPhone. Economists call this the maximum threshold of price. If performance instead of price is plotted on the y-axis, one can also represent minimum thresholds of performance in Figure 1.2; the performance of a new technology must exceed this performance before users will consider purchasing products based on the new technology .