Technology Change and the Rise of New Industries

Jeffrey L. Funk

• Publisher: Stanford Business Books
• Year: 2013
• ISBN:
  9780804784924
• Edition: 1
• Topic: Business
• Subtopic: Business generale
• Index: Business

Technology Change and the Rise of New Industries explores why new industries emerge at specific moments in time and in certain countries. Part I shows that technologies which experience "exponential" improvements in cost and performance have a greater chance of becoming new industries. When "low-end" discontinuities incur exponential improvements, they often displace the dominant technologies and become "disruptive" innovations. Part II explores this phenomenon and instances in which discontinuities spawn new industries because they impact higher-level systems. Part III addresses a different set of questions—ones that consider the challenges of new industries for firms and governments. Part IV uses ideas from the previous chapters to analyze the present and future of selected technologies.

Based on analyses of many industries, including those with an electronic and clean energy focus, this book challenges the conventional wisdom that performance dramatically rises following the emergence of a new technology, that costs fall due to increases in cumulative production, and that low-end innovations automatically become disruptive ones.

1

INTRODUCTION

The U.S. and other governments spend far more money subsidizing the production of clean energy technologies, such as electric vehicles, wind turbines, and solar cells, than they do on clean energy research and development (R&D).¹ Why? A major reason is that many believe that costs fall as a function of cumulative production in a so-called learning or experience curve, and thus stimulating demand is the best way to reduce costs. According to such a curve, product costs drop a certain percentage each time cumulative production doubles as automated manufacturing equipment is introduced and organized into flow lines.² Although such a learning curve does not explicitly exclude activities performed outside of a factory, the fact that learning curves link cost reductions with cumulative production focuses our attention on the production of a final product and implies that learning gained outside of a factory is either unimportant or is driven by that production.

But is this true? Are cumulative production and its associated activities in a factory the most important sources of cost reductions for clean energy or any other technology for that matter? Among other things, this book shows that most improvements in wind turbines, solar cells, and electric vehicles are being implemented outside of factories and that many of them are only indirectly related to production. Engineers and scientists are increasing the physical scale of wind turbines, increasing the efficiencies as well as reducing the material thicknesses of solar cells,³ and improving the energy storage densities of batteries for electric vehicles, primarily in laboratories and not in factories. This suggests that increases in production volumes, particularly those of existing technologies, are less important than increases in spending on R&D (i.e., supply-side approaches)—an argument that Bill Gates⁴ and other business leaders regularly make. Although demand and thus demand-based subsidies do encourage R&D,⁵ only a small portion of these subsidies will end up funding R&D activities.

Should this surprise us? Consider computers (and other electronic products such as mobile phones⁶). The implementation of automated equipment and its organization into flow lines in response to increases in production volumes are not the main reasons for the dramatic reduction in the cost of computers over the last 50 years. The cost of computers dropped primarily for the same reason that their performance rose: continuous improvements in integrated circuits (ICs). Furthermore, improvements in the cost and performance of ICs were only partly from the introduction of automated equipment and its organization into flow lines. A much more important cause was large reductions in the scale of transistors, memory cells, and other dimensional features, where these reductions required improvements in semiconductor-manufacturing equipment. This equipment was largely developed in laboratories, and these developments depended on advances in science; their rate of implementation depended more on calendar time (think of Moore’s Law) than on cumulative production volumes of ICs.⁷

NEW QUESTIONS AND NEW APPROACHES

We need a better understanding of how improvements in cost and performance emerge and of why they emerge more for some technologies than for others, issues that are largely ignored by books on management (and economics). While most such books are about innovative managers and organizations, and their flexibility and open-mindedness, they don’t help us understand why some technologies experience more improvements in cost and performance than do others. In fact, they dangerously imply that the potential for innovation is everywhere and thus all technologies have about the same potential for improvement.

Nothing can be further from the truth. ICs, magnetic disks, magnetic tape, optical discs, and fiber optics experienced what Ray Kurzweil calls “exponential improvements” in cost and performance in the second half of the 20th century, while mechanical components and products assembled from them did not.⁸ Mobile phones, set-top boxes, digital televisions, the Internet, automated algorithmic trading (in hedge funds, for example), and online education also experienced large improvements over the last 20 years because they benefited from improvements in the previously mentioned technologies. A different set of technologies (e.g., steam engines, steel, locomotives, and automobiles) experienced large improvements in both cost and performance in the 18th and 19th centuries. An understanding of why some technologies have more potential for improvements than do others is necessary for firms, governments, and organizations to make good decisions about clean energy and new technologies in general.

We also need a better understanding of how science and the characteristics of a technology determine the potential of new technologies. Although there is a large body of literature on how advances in science facilitate advances in technology in the so-called linear model of innovation,⁹ many of these nuances are ignored once learning curves and cumulative production are considered. For example, improvements in solar cell efficiency and reductions in material thicknesses involve different sets of activities, and the potential for these improvements depends on the types of solar cells and on levels of scientific understanding for each type. Lumping together the cumulative production from different types of solar cells causes these critical nuances to be ignored and thus prevents us from implementing the best policies.

Part of the problem is that we don’t understand what causes a time lag (often a long one) between advances in science, improvements in technology that are based on these advances, and the commercialization of technology. And without such an understanding, how can firms and governments make good decisions about clean energy? More fundamentally, how can they understand the potential for Schumpeter’s so-called creative destruction and new industry formation? A new industry is defined as a set of products or services based on a new concept and/or architecture where these products or services are supplied by a new collection of firms and where their sales are significant (e.g., greater than $5 billion). According to Schumpeter, waves of new technologies (which are often based on new science) have created new industries, along with opportunities and wealth for new firms, as they have destroyed existing technologies and their incumbent suppliers.

This is a book about why specific industries emerge at certain moments in time and how improvements in technologies largely determine this timing. For example, why did the mainframe computer industry emerge in the 1950s, the personal computer (PC) industry in the 1970s, the mobile phone and automated algorithmic trading industries in the 1980s, the World Wide Web in the 1990s, and online universities in the 2000s? On the other hand, why haven’t the personal flight, underwater, and space transportation industries emerged, in spite of large expectations for them in the 1960s?¹⁰ Similarly, why haven’t large electric vehicle, wind, and solar industries yet emerged, or when will such industries emerge that can exist without subsidies?

Parts of these questions concern policies and strategies. When did governments introduce the right polices and when did firms introduce the right strategies? But parts also involve science and technology, and, as mentioned previously, they have been largely ignored by management books on technology and innovation,¹¹ even as the rates of scientific and technological change have accelerated and the barriers to change have fallen.¹² When was our understanding of scientific phenomena or the levels of performance and cost for the relevant technologies sufficient for industry formation to occur? We need better answers to these kinds of questions in order to complement research on government policies and firms’ R&D strategies. For example, understanding the factors that impact on the timing of scientific, technical, and economic feasibility can help firms create better product and technology road maps, business models, and product introduction strategies. They can help entrepreneurs understand when they should quit existing firms and start new ones.¹³ And they can help universities better teach students how to look for new business opportunities and address global problems; such problems include global warming, other environmental emissions, the world’s dependency on oil and minerals from unstable regions, and the lack of clean water and affordable housing in many countries.

Some of the problems that arise when firms misjudge the timing of economic feasibility can be found in the mobile phone industry. In the early 1980s, studies concluded that mobile phones would never be widely used, while in the late 1990s studies concluded that the mobile Internet was right around the corner. Some would argue that we underestimated the importance of mobile communication, but I would argue that these studies misjudged the rate at which improvements in performance and cost would occur. The 1980s studies should have been asking what consumers would do when Moore’s Law made handsets free and talk times less than 10 cents a minute. The 1990s studies should have been addressing the levels of performance and cost needed in displays, microprocessor and memory ICs, and networks before various types of mobile Internet content and applications could become technically and economically feasible.¹⁴

Chapters 2 and 3 (Part I) address the potential of new technologies using the concept of technology paradigm primarily advanced by Giovanni Dosi.¹⁵ Few scholars or practitioners have attempted to use the technology paradigm to assess the potential of new technologies or to compare different ones.¹⁶ One key aspect of this paradigm is geometrical scaling, which is a little-known idea initially noticed in the chemical industries (and in living organisms).¹⁷ Part I shows how a technology paradigm can help us better understand the potential for new technologies where technologies with a potential for large improvements in cost and performance often lead to the rise of new industries. Part I and the rest of this book also show how implementing a technology and exploiting the full potential of its technology paradigm require advances in science and improvements in components.

One reason for using the term “component” is to distinguish between components and systems in what can be called a “nested hierarchy of subsystems.”¹⁸ Systems are composed of subsystems, subsystems are composed of components, and components may be composed of various inputs including equipment and raw materials. This book will just use the terms systems and components to simplify the discussion. For example, a system for producing integrated circuits is composed of components such as raw materials and semiconductor-manufacturing equipment.

TECHNOLOGICAL DISCONTINUITIES AND A TECHNOLOGY PARADIGM

A technology paradigm can be defined at any level in a nested hierarchy of subsystems, where we are primarily interested in large changes in technologies, or what many call technological discontinuities. These are products based on a different set of concepts and/or architectures from that of existing products, and they are often defined as the start of new industries.¹⁹ For example, the first mainframe computers, magnetic tape–based playback equipment, and transistors (like new services such as automated algorithmic trading and online universities) were based on a different set of concepts than were their predecessors: punch card equipment, phonograph records, and vacuum tubes, respectively. On the other hand, minicomputers, PCs, and various forms of portable computers only involved changes in architectures.

Building from Giovanni Dosi’s characterization and using an analysis of many technologies (See the Appendix for the research methodology), Chapter 2 and the rest of this book define a technology paradigm in terms of (1) a technology’s basic concepts or principles and the trade-offs that are defined by them; (2) the directions of advance within these trade-offs, where advance is defined by a technological trajectory (or more than one);²⁰ (3) the potential limits to trajectories and their paradigms; and (4) the roles of components and scientific knowledge in these limits.²¹ Partly because this book is concerned with understanding when a new technology might offer a superior value proposition, Chapter 2 focuses on the second and third items and shows how there are four broad methods of achieving advances in performance and cost along technological trajectories: (1) improving the efficiency by which basic concepts and their underlying physical phenomena are exploited; (2) radical new processes; (3) geometrical scaling; and (4) improvements in “key” components.

In doing so, Chapter 2 shows how improvements in performance and/or price occur in a rather smooth and incremental manner over multiple generations of discontinuities. While some argue that these improvements can be represented by a series of S-curves where each discontinuity initially leads to dramatic improvements in performance-to-price ratios,²² this and succeeding chapters show that such dramatic changes in the rates of improvement are relatively rare. Instead, this book’s analyses suggest that there are smooth rates of improvement that can be characterized as incremental over multiple generations of technologies, and that these incremental improvements in a technological trajectory enable one to roughly understand near-term trends in performance and/or price/cost for new technologies.

GEOMETRICAL SCALING

Chapter 3 focuses on geometrical scaling as a method of achieving improvements in the performance and cost of a technology. ...