Download - Myths and Realities of Technology Change
Myths and Realities of Technology Change
by
Jeffrey Funk
National University of Singapore
Chapter 1, Introduction
Technology change is one of the most powerful forces in the world. It has given us dramatic
improvements in economic productivity and standards of living while at the same time creating
winners and losers at the individual, firm, and country level. Technologies such as steam engines,
electricity, internal combustion engines, automobiles, computers, and integrated circuits have had
a particularly large impact on economic productivity and our lives. They have given us marvelous
new products and services, enabled dramatic changes in our life styles, and enabled the substitution
of machines for humans in an increasing number of jobs.
The growing importance of technology means that more is expected from technology than ever
before in human history. People expect technology to give us better food, homes, and vacations,
more time for our families, a cleaner environment, a safer workplace, a happier household, longer
lives, more time to be creative, a better sex life, and a fairer society. We expect scientists and
engineers to effectively develop the right technologies, managers to effectively implement
technologies, policy makers to promote good technological solutions, and universities to help
students deal with an increasingly complex world of technology. These high expectations requires
manager, policy makers, and academics to better understand technology change than is currently
done and to understand it at a much deeper level.
Most technology is embodied in complex systems. Complex systems provide us with food,
shelter, water, manufactured products, entertainment, transportation, and work spaces. These
systems can be subdivided into sub-systems each of which can be further subdivided in what might
be called a hierarchy of sub-systems. For example, manufactured products can be thought of as
systems in which components are processed and assembled in global supply chains. Even the
processing of basic components such as steel pipes is a complex system of basic material processes
that also rely on the mining and transport of raw materials.
These systems are constantly being reconfigured in response to changes and improvements in
technology. Some reconfigurations occur at lower levels and some occur at higher levels. Often
the reconfigurations at higher levels come from improvements in lower-level systems. These
changes percolate up through hierarchies of sub-systems where changes at one level impact on the
next higher level through decisions made by profit-seeking managers.
For example, improvements in integrated circuits made possible better computers, mobile
phones, and set-top boxes while improvements in areal recording density made smaller forms of
hard disks and tape players possible. Some firms responded to these improvements with more
innovative electronic products while other firms responded very slowly. The slow responses by
other firms suggest that they did not understand the implications of these improvements and the
changes in higher-level systems that these improvements made possible.
Sometimes these changes occur over decades and involve not just managers but also policy
makers. For example, the emergence of complex global supply chains occurred as managers and
policy makers adjusted to improvements in information technology, cargo vessels, ports, rail lines,
trucks, containers, and in many other subsystems. These improvements provided managers and
engineers with new choices about how to design products and services and where to purchase
inputs for them. These improvements also provided policy makers with new choices about
regulations, trade rules and their country’s infrastructure for the global supply chains. The falling
cost of information and transportation enabled products to be global designed and manufactured
and the global supply chains emerged from millions of decisions that were made over many years
by managers and policy makers, each responding to the falling cost of information and
transportation.
This book is concerned with how these choices emerge from technology change. How does
technology change create new choices for managers, engineers, and policy makers in the design
of systems? How does it lead to changes in the design of systems or to the emergence of new
systems? What technologies are experiencing significant changes and why and thus what
technologies are impacting on higher-level systems? Can we use this information to more
effectively design higher-level systems and to more effectively allocate resources?
Economists often call this change creative destruction while management scholars often call it
innovation. Both terms provide insights, but neither capture the essence of this change. For creative
destruction, although a new system is often created that destroys an old system, this destruction
occurs over many years and through millions of decisions and we are interested in these micro-
changes and decisions that lead to the replacement of old systems with new ones. Global supply
changes emerged from millions of decisions that were made in response to the falling cost of
information and transportation technology over many years.
Innovation is also too general and abstract for what we are trying to describe. The term does
not help us understand the millions of micro-changes and decisions that led to the emergence of
the global supply chains. While some of these decisions were more innovative than others (e.g.,
container shipping), a focus on such innovations does not help us understand the impact of
improvements in lower level systems on higher level systems. For example, the emergence of
global supply chains primarily came from incremental improvements in information systems,
improvements in information systems primarily came from incremental improvements in
computers, and incremental improvements in computers primarily came from incremental
improvements in integrated circuits. Which improvements were innovations and which were not?
Identifying the impact of improvements in lower level systems on higher level ones helps us better
understand how higher level systems change than does identifying which of the changes qualifies
as an important innovation, a not so important innovation, or not an innovation at all.
Management scholars will usually focus on who captures the most value from innovations and
thus the types of strategies that lead to high profits. This book is less interested in these strategies
and more interested in the front end of this process: how do choices about new systems become
available and thus how can managers, engineers and policy makers find new choices and
effectively consider then? How does technology change cause new systems to become
economically possible and thus provide managers with new choices about designs and strategies?
One premise of this book is that any discussion of new systems and how they emerge from
technology change must focus on performance and cost. They are important because managers and
policy makers are concerned with their absolute levels and because they respond to changes in
them. Managers are concerned with absolute levels of performance and cost and changes in them
because they impact on their value propositions to customers and their profits. One of the ways
they respond to changes in the performance and cost of inputs is by changing the ways systems
are designed and configured.
Policy makers also respond to both changes in performance and cost and to their absolute levels.
Some policy makers are more concerned with the former while others are more concerned with
absolute levels. Absolute levels of performance and cost are important because they directly impact
on productivity, standards of living, and quality of life, issues that often concern voters in the long
term. For example, without increases in crop yield (e.g., bushels of wheat per acre of land) and
labor productivity (output per hour of farm workers) it would be difficult to feed the world’s seven
billion people and to keep doing so as the world population increases. Furthermore, the challenges
of improving these performance and cost indices are exacerbated by water shortages and demands
for lower use of pesticides, insecticides, and fungicides. In other words, meeting these new
challenges require us to increase crop yields and productivity with respect to an increasing list of
inputs.
Almost every problem can be represented by these types of output-to input ratios, of which
examples are shown in Table 1. Better mobile phones have higher faster speeds (bit per second),
higher spectral efficiencies (bits per second and bandwidth), more storage capacity (bits per chip),
better cameras (number of pixels) and faster microprocessors (cycles per second), which may
occur through parallel processing (number of cores). Better computers are measured in terms of
instructions per unit time, per unit-cost, or per kw-hour. More cost effective DNA sequencing and
synthesizing require lower sequencing or synthesizing per unit cost.
Addressing climate change requires improvement in energy output per weight of carbon
emissions along with more efficient and cost effective solar cells. The cost of solar cells is
measured in terms of cost per peak Watt. Energy efficient lighting and displays require higher
luminosity per Watt light bulbs such as LEDs (light-emitting diodes). Economically effective
electric vehicles require higher energy and power storage densities from batteries or alternatives
such as capacitors and flywheels.
A second reason for our interest in performance-to cost ratios is that new technologies and
systems composed from new technologies must reach specific levels of cost and performance for
them to become economically feasible. For example, the cost of solar cells on a per-kw-hour or a
per-peak Watt level must fall to a certain levels before they will begin to diffuse. More generally
speaking, technologies with low carbon emissions must also have much lower costs than they
currently do in order for them to become economically feasible without large subsidies and
contribute towards reductions in carbon emissions. Thus, a major challenge for clean energy
advocates is to find those technologies that are likely to experience cost reductions than are other
technologies and become economically feasible in the near future.
But this raises a number of questions. What drives improvements in a technology’s cost and
performance? Is it improvements on the factory floor, better components, or new product and
process designs? And why do some technologies experience rapid rates of improvement than do
other technologies? Is it because of greater production, demand, R&D, or something else? Unless
we understand the answers to these questions, it is difficult to understand creative destruction and
innovation, to address climate change, and to help engineers and other young entrepreneurs create
Table 1. Examples of Output-to Input Ratios that are Important to Humanity
Technology
Domain
Measure of Performance
or Sub-Technology
Examples of Output-to Input Ratios
Agriculture Productivity of land,
people, water, and
fertilizers
Crop production per acre of land, per person-hour, per
water volume, and per volume or mass of chemicals
and fertilizers
Housing Floor space Area per person
Electricity
Generation
Cost, efficiency Cost per kw hour of electricity, energy per weight of
carbon emissions, energy generated per area of land,
cost per peak Watt of Solar or Wind, efficiency of
solar cells, cost of carbon sequestration of fossil fuels
Electricity
Transmission
Cost, efficiency Cost per distance of transmitting electricity, energy
loss per length of transmission line, current x length or
current x length per dollar for transmission materials
such as superconductors
Electricity
Storage
Cost, efficiency Energy stored (e.g., joules) per mass (e.g., kg), volume
(e.g., liters), or cost; power (watts) generated per mass,
volume, or cost
Transportation Cost, efficiency Cost per weight-mile for transporting cargo, cost per
person-mile of transporting humans, amount of energy
needed to transport cargo (weight) or humans per mile
Lighting and
Displays
Lighting and Displays Luminosity per Watt, lumens per dollar
Displays Square meters per dollar
Lasers Power density, cost per Watt
Health Care Quality of life Longevity and quality of vision, hearing, smell, other
organs, and of mobility
Cost Percentage of GNP devoted to health care, cost of
specific operations and procedures
Information
Processing
Microprocessor ICs Number of transistors per chip, cost per transistor and
cycle time, number of cycles per second
Camera chips Pixels per dollar, light sensitivity
new systems. Evidence of a lack of understanding can be found in the emphasis on wind turbines
and batteries, both of which are experiencing very slow rates of improvement. To do this, however,
we must return to the basics and address the assumptions that form the basis for most management
and economic analyses of technology. In reassessing these assumptions, we have found that many
of them are very wrong and thus we call them “myths.” These myths distort the reality of
technology change and mislead the choices available for engineers, scientists, managers, and
policy makers. Our empirical research has identified six myths that impact strongly on how these
people think about technology change:
#1 Performance vs. time curves resemble an S-curve
#2 Slowing rates of improvement in old technologies drive the development of new
technologies
#3 All technologies have the potential for rapid rates of improvement.
#4 Product design changes drives performance increases and process design changes drives
cost reductions, with product preceding process design changes in a technology’s life cycle.
#5 Costs fall as cumulative production rises in a learning curve
#6 The future of new technologies cannot be analyzed.
These myths are largely based on metaphors and anecdotal evidence that were presented decades
ago and that have not been systematically re-examined. The one exception is the learning curve,
but even here the empirical evidence has been selective, ignoring the improvements that occur for
most technologies before commercial production begins. This book summarizes the empirical
research that proves these myths wrong and it replaces them with much more accurate descriptions
of reality. These more accurate descriptions of reality suggest more appropriate methods of
creating new systems that are very different from the ones based on the myths.
The first two myths prevent us from understanding the shapes of performance or cost vs. time
curves and thus rates of improvement for what Giovanni Dosi calls “technology trajectories.” The
myth of S-curves (See left side of Figure 1), which is explored in Chapter 2, makes us believe that
rates of improvement will strongly accelerate when demand, R&D spending or something else
increases and also that slowdowns and physical limits will quickly emerge. The first half of this
myth facilitates the exaggeration and hype of new technologies and makes it appear as if any
technology is right around the corner as long as governments, firms, and other potential investors
make the right investment and aren’t too focused on the short term. As one of my colleagues said
as I discussed the slow rate of improvement for electric batteries, “but improvements will
accelerate once the demand increases.” In other words, real data isn’t as important as a belief in
the accelerations that form the basis for the S-curve. Purported accelerations have been used to
justify investments in solar cells, wind turbines, and electric vehiclesi.
Time
Performance
Figure 1. Myth vs. Reality of Performance vs. Time Curves on Logarithmic Scale
Slowdown and Limits
Acceleration
Time
Performance(logarithmicscale)
a. The Myth b. The Reality
Note: limits exist but they are often further away than ordinarily thought
The second half of this myth makes us believe that slowdowns and limits will quickly emerge
and thus we must quickly jump to a new technology. Michael Tushman and Philip Andersonii make
the strongest argument for steep accelerations that are quickly followed by slowdowns and limits
in that long periods of incremental change are punctuated by relatively rare innovations that
provide sharp price-performance improvements over existing technologies. Or in their words,
“technology evolves through relatively long periods of incremental change punctuated by
relatively rare innovations that radically improve the state of the art.” Building from this paper and
findings from evolutionary biology, other scholars use the term “punctuated equilibrium” to
describe a long-term process in which stable environments are punctuated by new technologies
that provide jumps in performance and that also instigate organizational transformation.
Punctuated equilibrium is one of the most powerful and often cited theories in management and it
increases the hype of S-curvesiii.
Unfortunately, there is little evidence for punctuated equilibrium or S-curves particularly if one
looks at logarithmic plots of data from engineering and science journals. One must use logarithmic
plots because a straight line on a logarithmic plot means constant rates of improvement over time
while a straight line on a linear plot means a decreasing rate of improvement. Engineering and
science journals plot data for rapidly improving technologies on logarithmic plots and such plots
typically include some combination of best laboratory results and commercialized products.
Without data from best laboratory results, there may be gaps in the data that come from time
periods between product releases and that falsely imply jumps in performanceiv.
Our analysis of performance vs. time curves for 25 technologies and 32 measures show that the
shape of performance-versus time curves more closely resembles a straight line on a logarithmic
plot (See right side of Figure 2) than the classical S-curve and that punctuated equilibrium is a
highly misleading metaphor. Thus, we should not expect or plan for early accelerations or for early
limits. Instead, we should plan for and expect incremental improvements to occur at fairly constant
rates of improvement. These constant rates of improvement allow us to compare rates of
improvements for different technologies and to consider these comparisons when long term plans
for development and implementation are being considered. Firms can and do consider rates of
improvement when they choose research projects and clean energy advocates should consider rates
of improvement when they propose potential solutions.
The second myth, which is explored in Chapter 3, extends the myth of S-curves further into a
fantasy land in which slowdowns in a single old technology are perfectly linked with accelerations
in a single new technology (See left side of Figure 2). In other words, it is the slowdown in an old
technology that causes the development and improvement of a new technology and for an
acceleration in the rate of improvement of this “single” new technology to occur. This myth about
“linked S-curves” suggests that demand is the most important lever for policy and it is the most
important driver for firms when they create R&D strategies. According to this myth, a slowdown
in an old technology represents demand for a better solution and the acceleration in the new
technology represents the response to the slowdown.
Since accelerations were not found in our analysis of the first myth of S-curves, there is little
chance that such accelerations will occur at the same time as slowdowns in the linked S-curves
shown in Figure 2. The best one can hope for with this myth is that a slowdown occurs in an old
technology as a new technology experiences some form of improvements. Chapter 3 analyzes this
possibility using 15 pairs of old and new technologies and finds only two technologies experienced
statistically significant slower rates of improvement after a performance metric was recorded for
a new technology, thus suggesting that slowdowns do not generally cause the development and
improvement of new technologies. Furthermore, Chapter 3 finds that multiple new technologies
are being developed simultaneously as replacements for an old technology (See right side of Figure
2), unlike the single performance-to cost curve that is presented in the linked S-curve theory (left
side of Figure 2).
Time
Performance
Figure 2. Myth vs. Reality of Slowdowns Driving Improvements in New Technologies
Slowdown
Acceleration
Time
Performance(logarithmic Scale)
a. The Myth b. The Reality
Many technologies are simultaneouslybeing developed in a very decentralized manner and their timingdepends on supply-side factors
Chapter 3’s analysis is also more consistent with the “technology push” than of “market pull”
theories of technology change, a debate that has continued for more than 40 years with Clayton
Christensen’s theory of disruptive innovation providing recent evidence for market pull.
Proponents of technology push argue that technology change is driven by universities and other
laboratories that “push” technologies into the marketplace while others argue that technology
change is driven by a market that “pulls” ideas for new technologies from the minds of scientists
and engineers in universities and laboratories through targeted research. One of the persuasive
arguments for technology push in the 1970s was that few of the needs addressed by the important
“innovations” of the 20th century had been recognized before the new technology was developed.
Looking at subsequent innovations such as cellular phones and the Internet, one can also perceive
this lack of early recognition in the needs for these innovations.
Chapter 3’s analysis suggests that performance vs. time curves are being driven much more by
supply than demand-side factors. Although demand becomes more important as a new technology
experiences improvements and thus nears economic feasibility, the initial improvements have
more to do with supply side factors such as advances in science or accidental discoveries than with
a slowdown in an old technology. It is only as new technologies are developed and improved that
the state of the old technology becomes a more relevant driver of the improvements in the new
technologies.
The third myth, which is explored in Chapter 4, is that all technologies have the same potential
for improvements and thus the same potential for rapid rates of improvement. Although this myth
has not been explicitly stated anywhere to our knowledge, it is implicitly stated almost everywhere.
One type of evidence for the existence of this myth is that rates of improvement are rarely
discussed and few people are aware of them, one exception is Moore’s Law. An ignorance of rates
of improvement is particularly common among social scientists even those who specialize in
technology. For example, Clayton Christensen’s books and papers on disruptive innovations do
not mention rates of improvement even though rates of improvement clearly impact on the
replacement of old technologies with new ones and even though his theory is largely based on an
analysis of hard disk drives, which have experienced unusually rapid rates of improvement.
Christensen conveniently ignores such details perhaps in an attempt to promote the universality of
his theory.
Ignoring rates of improvement is also common among advocates of sustainability. Most papers,
books, presentations, and courses on sustainability rarely mention rates of improvement and the
particularly slow rates of improvement for wind turbines and batteries. A notable exception is
Vaclav Smil’s books on energy and sustainability but his books sell far fewer copies than do those
of Thomas Friedman, who does not mention rates of improvement in his books or in his New York
Times columns on clean energy. Even discussions with professors of sustainability or
sustainability-related organizations such as the Intergovernmental Panel on Climate Change or the
Society of Risk Analysis will merely produce blank stares, laughs, and sometimes anger. A
common response is “it’s not just about technology,” as if considering rates of improvement
prevents ones from considering other factors.
Equally troubling, the few analyses of rates of improvement confuse rates of improvement for
each doubling of cumulative production with annual rates of improvement. This causes them to
overestimate the rates of improvement for some technologies since annual rates and those for
doubling will only be the same when cumulative production is doubling each year, which rarely
occurs. Most technologies experience much slower rates of production growth than 100% per year
and thus their annual rates of improvement are 1/10 to ¼ the rates for each doubling of cumulative
production.
The reality is that some technologies have much faster annual rates of improvement than do
other technologies. As shown in Figure 3, 2/3 of the 120 technologies have rates of less than 9%
and 89% of them have rates of less than 15% per year. Furthermore, the empty space between 14%
and 24% and our analysis of chemical vs. non-chemical technologies suggest that there are
multiple distributions in Figure 3, which enable us to draw conclusions about how these
improvements occur. Improvements in chemicals occur through different mechanisms than do
non-chemicals since product innovations are more difficult with fixed-formula chemicals than with
non-chemicals and since chemicals benefit more from increases in production scale than do other
technologies. By analyzing the multiple distributions in Figure 3, we are able to better understand
the design changes that enable improvements. After analyzing other possible reasons for the
differences such as greater production, demand, and R&D, Chapter 4 concludes that some
technologies have greater opportunities for improvements from product and process designs than
do other technologies, a conclusion that was reached by Nathan Rosenberg more than 40 years ago
but contradicts the recent emphasis on demand-based theories (e.g., Clayton Christensen’s theory
0
5
10
15
20
25
-10 -8 -6 -4 -2 0 2 4 6 8
10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 >42
FIgure 3. Number of Technologies by Annual Rates of Improvement
Annual Rates of Improvement
of disruptive innovation) and on demand-based subsidies as a tool for promoting clean energy
technologies.
The fact is that technologies with rapid rates of improvement generally have had and will
continue to have a larger impact on our world than do other technologies. Technologies such as
integrated circuits (ICs), hard disk drives, lasers, and computers have had a large impact on our
world partly because they have experienced one or two orders of magnitude improvements each
decade resulting in 5 to 10 orders of magnitude improvements over the last 50 years. They have
enabled new forms of systems to emerge, one of which is the Internet and another of which is the
global supply chains that were mentioned earlier and that depend on the Internet. They have also
enabled other systems to emerge, many of which are defined as disruptive innovations by Clayton
Christensen. Thus, if we are trying to understand technology change or we are looking for
disruptive innovations, we should be focusing on technologies with rapid rates of improvement as
this will provide us with better ideas than will looking for Christensen’s low-end innovations.
Technologies with rapid rates of improvement in performance and/or cost will also have faster
rates of diffusion than will ones with slower rates of improvement. While rates of diffusion
depends on a variety of factors, the largest factor identified in diffusion studies for the U.S. is the
profitability of a technology for usersv. The users that profit most from a new technology, whether
these users are industrial ones or consumers, are the first adopters and faster rates of improvement
in performance and/or cost mean faster rates of improvement in the profitability for users. One
reason for the large importance of profitability for users in determining rates of diffusion is that
the U.S. and other developed countries have less barriers to entry for “creative destruction” than
do other countries, which is of course why the U.S. and other developed countries are richer than
are poorer ones.
In other words, slow rates of improvement are a type of implementation problem that must be
overcome if a technology is to succeed. Scholars of technology management often focus on
implementation problems and thus argue that rates of improvement are less important than are
organizational or regulatory challenges. This book argues that slow rates of improvement are
probably a larger problem than are organizational and regulatory challenges in developed countries
because these countries have become good at solving organization and regulatory problems
through existing and new firms, venture capital, and changes in regulations by policy makers.
The importance of slow rates of improvement as an implementation problem means that
understanding why some technologies have more rapid rates of improvement than do others is a
critical question. Understanding the answer to this question can help us achieve more rapid rates
of improvement in existing technologies and more importantly identify technologies with a greater
chance of achieving rapid rates than do other technologies. Answering this question about the
reasons behind rapid rates requires us to address two other myths and both these two myths and
the prior two myths also contribute to the myth that all technologies have the same potential for
improvements. When one believes in linked S-curves, accelerations occurring early in a
technology’s lifecycle, the slowdowns that drive these accelerations and two other myths, it is easy
to believe that all technologies have the same potential for improvements.
Fre
qu
en
cy o
f In
no
vati
on
an
d R
ate
of
Imp
rovem
en
t
Figure 4. Myth vs. Reality of Product and Process Innovations
Product Innovation
Process
Innovation
Time
Increases in performance from
product innovations
Reductions in price from
process innovations
Fre
qu
en
cy o
f In
no
vati
on
an
d R
ate
of
Imp
rovem
en
tIncreasing number of
inter-related product and
and process innovations
Time
a. The Myth b. The Reality
Relatively constant rate
of improvements in
cost and performance
The fourth myth, which is explored in Chapter 5, is that product design changes lead to increases
in performance early in the life cycle and process design changes lead to reductions in costs later
in the life cycle (See Figure 4). This myth causes firm strategies and government policies to focus
on product design changes to achieve performance increases and to focus on process design
changes to achieve cost reductions. “Process” in general may include any activities that contribute
to cost and the strategic importance of process innovation for competition increases through the
life cycle of an industry, as the opportunities decline for product innovations that lead to
improvements in functional performance. Many scholars cite some version of this assessment and
some take this assessment further and argue that increasing returns to process R&D lead to a
shakeout, i.e., dramatic decline, in the number of firmsvi.
This myth is addressed by analyzing the cost and performance of 17 different technologies, 22
unique time-series pairs of performance and cost, 358 unique data points and 705 total years of
data for these time-series pairs. Technologies such as chemicals, agricultural products and
materials are excluded since they often have a fixed chemical composition and thus improvements
in performance do not occur and most of their improvements in cost are driven by process
innovations or by increasing the scale of their production equipmentvii. But for other technologies,
our analysis shows that improvements in performance and cost for most products are highly
correlated over many decades suggesting that the same design changes are impacting on
improvements in both cost and performance.
What types of design changes might cause simultaneous improvements in both cost and
performance and also cause very different rates of improvement in different technologies? Some
changes in product design lead to both improvements in performance and cost. For example,
improvements in the efficiency of solar cells, lights and displays and in the speed of electronic
devices, computers and other electronic products also lead to reductions in the cost of these
technologies. Other design changes involve inter-related product and process design changes that
can be implemented by multiple firms in what many call modular design or by a single firm in
what may be called “integral” design. For example, although firms such as Intel design both the
product and process for new microprocessors, improvements in many ICs are achieved through
cooperation between foundries and design houses. However, whether they are implemented via
modular or integral design, is not the issue here, it is the types of inter-related product and process
design changes that enable the improvements and in particular the rapid improvements. Our
analysis of the next myth, the fifth one, helps us better understand the specific types of inter-related
product and process design changes that lead to improvements in cost and performance and
explains why some technologies experience more rapid rates of improvements than do other
technologies.
The fifth myth, which is analyzed in Chapter 6, is that costs fall as cumulative production rises
and as improvements are made to processes on the factory floor (See Figure 5). Since the
publication of Theodore Paul Wright’s analysis of fighter jet costs in 1936, empirical analyses
correlating cost reductions to cumulative production have grown extensively in what some call
learning curves. The early work on learning curves was mostly done on single designs in specific
factories and thus analyzed the impact of factory level changes on factory productivity.
Subsequently, learning curves have been applied to technologies that are manufactured with new
designs and in new factories where the output variable might be cost or performance, albeit these
models are now often called experience curves. For example, the costs of ships, solar cells,
semiconductor memory, chemicals, primary metals, and food have been analyzed using this
approach, across significant design changes and often throughout all global factoriesviii.
Cumulative Production(log scale)
Costs(log scale)
Figure 5. Myth vs. Reality of Cost Reductions (i.e., learning curve)
Time
Costs(logscale)
a. The Myth b. The Reality
Start of Commercial Production
Linking cumulative production to reductions in cost or improvements in performance can lead
to confusion about how the improvements in cost and performance are being achieved. Some
believe that such a linkage suggests most of the improvements are occurring on the factory floor
while others note that cumulative production indirectly leads to improvements in performance.
Increases in production are linked with expected future production and lead to increased incentive
to perform process-related and general R&Dix where the results of the increased R&D spending
lead to improvements in performance or cost. This argument is also implicit in Christensen’sx
analysis of hard disk drives, computers and other “disruptive” technologies in that the emergence
of a low-end product lead to increases in R&D spending and thus rapid improvements in the new
product, which in turn leads to replacement of the dominant technology by the low-end innovation.
Linking cumulative production to reductions in cost or improvements in performance also
makes it easier for policy makers to justify demand-based subsidies for desirable new technologies
such as clean energy ones. It is particularly easy to justify demand-based subsidies if the cost
reductions are coming from activities on the factory floor and thus the subsidies will probably
encourage the cost reductions. However, what if they are not coming from activities on the factory
floor but instead are coming from R&D activities in the laboratories? If it is the latter, then a
different set of policies are needed to achieve cost reductions than the current emphasis on demand-
based subsidies.
Chapter 5 and 6’s analysis suggests a different reality than the myth of increases in cumulative
production lead to cost reductions. The correlation that is found between improvements in cost and
performance in Chapter 5 suggests that costs are falling because of changes in product and process
design that also impact on improvements in performance. Thus, increases in cumulative production
and changes in process design by themselves cannot be the reasons for falling costs. This argument
is taken one step further in Chapter 6 by conducting two forms of analysis that enable us to better
understand how rapid improvements occur.
Thirteen new technologies are analyzed that experienced rapid improvements of greater than
10% per year without commercial production. These include organic materials for transistors, solar
cells, and light-emitting diodes (LEDs), quantum dots for displays and solar cells, new forms of
non-volatile memory integrated circuits such as resistive or magnetic RAM (random access
memory), carbon nanotubes, superconducting materials for both energy transmission and
integrated circuits, and quantum computers. Since there was no commercial production during
most of the time periods in which the rapid improvements occurred, the improvements must be
from factors other than factory floor activities and this chapter examines the specific product and
process design changes that enabled the rapid improvements to occur. Our analysis suggests that
these design changes occur in laboratories and there are primarily two types of inter-related product
and process design changes: 1) creating new materials (and their associated processes); and 2)
reducing the scale of features in the product through process improvements.
Cost breakdowns of higher-level systems in which components have a larger impact on costs
than does the assembly of the system are also analyzed. For example, cost breakdowns of
electronic products such as computers, mobile phones, game consoles, set-top boxes, and eReaders
have found that 95% of their system costs are represented by the cost of components and thus
increases in cumulative production and learning in assembly can only impact on about 5% of the
system costs. Furthermore, since many of the assembly operations are standard ones that are used
across many different electronic products, learning in factories probably has a small impact even
on the 5% of the costs that are represented by the assembly operations. Instead, it is improvements
in components that enable improvements in system cost and performance. Furthermore, since these
improvements in components are also occurring before a new system is commercially produced,
it is improvements in components and not the manufacture of the new system that causes it to
become economically feasible.
Chapter 7 presents our reality of technology change. It uses the analyses from the previous
chapters to summarize our view of technology change. Performance vs. time curves more closely
resemble straight lines on a logarithmic plot than an S-curve. Accelerations do not occur and
physical limits take decades if not longer to emerge. The improvements in the new technology are
not driven by a slowdown in the old technology and in fact multiple technologies are competing
to replace a new technology even without a slowdown in the old technology. This competition
primarily occurs in laboratories where advances in science and accidental discoveries determine
the timing of the first recorded performance metrics and the rates of improvement largely
determine the eventual winning technologies. A lack of S-curves means that pre-production early
rates of improvement can provide an important signal for future rates and thus help us identify the
technologies with the large potential for rapid improvements.
Different technologies have different rates of improvement primarily because of differences in
opportunities for improvements. Simply put, some technologies experience more rapid rates of
improvement because they benefit more from the two inter-related product and process design
changes mentioned above than do other technologies. For the first one, technologies that benefit
from reductions in scale experience very rapid rates of improvement and only some technologies
benefit from reductions in scale. Thus, by itself this type of design change can explain rapid rates
of improvement and help us identify technologies with the potential for rapid rates of
improvement.
It is less clear why some technologies experience more rapid rates of improvement from the
creation of materials (and their associated processes) than do other technologies. Interviews with
several physicists suggest that technologies involving the deposition of thin crystalline films of
less than one micro-meter on other crystalline materials are easier to improve than are other
materials. All of the technologies that benefited from the creation of materials and their associated
processes involve the thin-film deposition of crystalline materials while other technologies with
slower rates of improvement do not involve thin film deposition of crystalline materials.
In any case, rapid improvements increase the chances that a technology will become
economically feasible or enable new forms of higher-level systems to emerge. Thus, if one is
looking for creative destruction, disruptive innovation, or some other new system that might
replace an existing one, one should be looking at technologies that experience rapid improvements.
Finding these systems is a goal of managers, policy makers, university professors and even
students.
Chapter 8 uses this better reality of technology change to addresses the sixth myth: we can’t
analyze the future. This is probably the most controversial and emotional myth because most social
scientists dismiss analyses of the future as useless while failing to recognize that managers, policy
makers, professors, and students are constantly making decisions that imply predictions about the
future. When managers introduce R&D budgets or new product or students choose university
majors, they are making a prediction about the future. The firms are predicting that the R&D
budgets and new products will probably provide greater benefits than their costs. The students (or
their parents) are predicting that the university degrees will provide them with benefits that are
greater than the costs of the education. Even social science professors, who enjoy criticizing
predictions, are implicitly making a prediction when they draw conclusions about specific policies,
strategies, or organizational designs. By concluding that something has worked better than
something else, they are implying that this conclusion will be true in the future.
More importantly, criticizing predictions prevents social scientists from understanding the
challenges of searching for entrepreneurial opportunities, solving global problems such as
sustainability, helping students search for opportunities and address sustainability, and setting
effective R&D policies. Where should young entrepreneurs search for opportunities? How should
they try to address sustainability? Which technologies and markets should they consider? What
kinds of systems should they introduce? These types of questions must be addressed in order to
search for entrepreneurial opportunities, solve global problems such as sustainability, help students
search for opportunities and address sustainability, and set effective R&D policies. Unless social
scientists help their students make such decisions, they cannot help them address these issues and
helping them make such decisions requires them to make predictions about which solutions have
the largest chance of success.
An aversion to helping young entrepreneurs choose the technologies or markets to look for
opportunities reminds me of a joke that economists like to tell. A man walking down the street sees
a man crawling around on his hands and knees under a lamp. The pedestrian asks the crawler, what
are you looking for. The crawler says “my keys. I lost them over there, as he points to a place not
under the lamp.” The pedestrian then asks while pointing at the place where he is crawling, why
are you looking there?” The crawler says, “this is where the light is.”
Analyzing the future requires us to look in places where the light is not very bright. Although
the previous chapters of this book have increased the luminosity of the light by disproving myths
and replacing them with more useful insights into of technology change, the future is still dimly
lit. It is all about probabilities. Which types of systems have the largest probability of becoming
economically feasible? Searching for entrepreneurial opportunities, solving global problems such
as sustainability, helping students search for opportunities and address sustainability, and setting
effective R&D policies requires us to think about the technologies with the greatest chance of
achieving improvement and thus the decisions that will probably lead to the best futures.
Chapter 8 distinguishes between predictions of the future and analyzing the future. After
analyzing predictions made by MIT’s Technology Review, it then shows that this book’s insight
about rapid improvements provide us with a better way to analyze the future than does Technology
Review’s method of expert elicitation. Not only do these rapid rates of improvement provide us
with better predictions than does Technology Review’s method, it also enables us to reduce forecast
error by gathering and analyzing more data. The other chapters reduce forecast error by helping us
better understand technology change. This chapter focuses on reducing forecast error by better
understanding rates of improvement, comparisons between old and new technologies, the
composition of systems, and the extent to which improvements are needed.
This chapter draws on research done by the author for a course on technology change. This
course helps students understand the composition of existing and potential systems, comparisons
between old and new systems, rates of improvement that impact on these systems, and the extent
to which improvements are needed before these new systems become economically feasible.
Effectively analyzing these factors and the uncertainty in them can help students better understand
when new technologies become economically feasible. More specifically, better understanding the
composition of systems can lead to better cost and performance estimates, better comparisons
between new and old systems, and better estimates for when the new systems might become
economically feasible. This is one goal of this book and has been the goal of the author for more
than 10 years.
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