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These slides present seven myths of technology change and the reality for each of them. These seven myths are: 1) Performance improvements follow S-curve; 2) Slow down in old technology causes search for new technology; 3) Costs fall as cumulative production rises; 4) Demand drives improvements; 5) Media attention means a technology will diffuse; 6) We can’t analyze the timing of new technologies; 7) The market doesn’t work.
Citation preview
Seven Myths About
Technology Change
A/Prof Jeffrey Funk
Division of Engineering and Technology Management
National University of Singapore
More details can be found in
1) What Drives Exponential Improvements? California Management Review, May 2013
2) Technology Change and the Rise of New Industries, book from Stanford University Press, January 2013
3) Presentations on slideshare: http://www.slideshare.net/Funk98/presentations
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
Time
Performance
The Myth: Performance Improvements Follow
S-Curves (Jumps and Diminishing Returns)
Emergence of New Technology
Where’s the S-Curve (e.g., Jumps) in Moore’s Law?
You have seen Moore’s Law, Haven’t You?
Moore’s Law for
Microprocessors
Source: http://www.fgarciasanchez.es/thesisfelipe/node5.html
Where’s the S-Curve in Magnetic Recording Density of Platters for Hard Disk Drives?
Luminosity per watt (lm/W) of lights and
displays
Organic
Transistors
Where are the Jumps?
Co
erc
ivit
y(O
ers
ted
or
Am
ps/
Me
ter)
Figure 2.5 Improvements in Coercivity of Magnetic Materials
0.1
1
10
100
1900 1910 1920 1930 1940 1950 1960 1970 1980
SmCo MaBl
PtCo Ferrites
Alnico Alloys Steel
General Trend Line
Trend LineFor SamariumCobalt Magnets
Computer
Processing Speed
Where are the
S-Curves?
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
The Myth: Slow Down in Old Causes Search for New Technology
Technologies exhibit diminishing returns and perhaps limits
As they experience diminishing returns, new technologies are searched for, found and developed
This causes the new technology to experience improvements, perhaps even jumps in performance
The Reality (1)
For many technologies, limits, diminishing returns, and jumps are not clearly evident in time series (see previous figures and next slide for examples)
This suggests that new technologies are searched for, targeted, and developed long before old technologies exhibit limits or even diminishing returns
One reason is that while physical limits do exist, apparently few have been reached
Organic
Transistors
Where is evidence of limits leading to search for new technology?
The Reality (2)
Even if diminishing returns exist, development
of new technology begins much earlier
Why? There are many technologies and many
potential applications for them
These technologies are targeted and pursued
in decentralized manner
◦ Millions of research scientists and engineers
◦ They independently pursue many technologies long
before commercialization occurs
Look for new types of materials
Consider many applications
The Reality (3)
The result is that improvement curves are relatively independent of each other
Each technology is pursued as opportunity, both individual and organizational opportunity
Rates of improvement reflect ◦ ability to find improvements
◦ perceptions about the potential for improvements
Scientists and engineers pursue these technologies in order to obtain publications, patents, and perhaps fame
The Reality (3)
In any case, if we try to change distribution of research effort ◦ We should understand different technologies, their rates of improvements, and their potential for further improvements
Independent of application, we should fund those technologies with ◦ greatest rates of improvement and ◦ greater potential for improvements than do others
But in doing so we should fund many technologies and many forms of them
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
Costs fall as cumulative production grows in learning or experience curve ◦ One suggested mechanism is that automated
manufacturing equipment is introduced, modified, and organized into flow lines
But learning curve ◦ Can’t be used until production has begun
◦ Assumes all components are unique to new product
◦ Doesn’t help us understand why some technologies experience more improvements than do other technologies
◦ Ignores work done in laboratories
Myth: Cumulative Production Drives Cost Reductions
Creating materials (and associated processes) that better exploit physical phenomena
Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs),
magnetic storage, MEMS, bio-electronic ICs
◦ Increases in scale: e.g., larger production equipment, engines, oil tankers
Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems
◦ Telecommunication systems
Reality: What Drives Improvements?
Luminosity per watt (lm/W) of lights and
displays
Organic
Transistors
Note the names of materials
Co
erc
ivit
y(O
ers
ted
or
Am
ps/
Me
ter)
Figure 2.5 Improvements in Coercivity of Magnetic Materials
0.1
1
10
100
1900 1910 1920 1930 1940 1950 1960 1970 1980
SmCo MaBl
PtCo Ferrites
Alnico Alloys Steel
General Trend Line
Trend LineFor SamariumCobalt Magnets
Note the names of materials
Figure 2.6 Improvements in Energy Product of Magnetic Materials
Ener
gy P
rod
uct
(Meg
a-G
auss
Oer
sted
s)
0.1
1
10
100
1900 1920 1940 1960 1980 2000
steel
alnico alloys
fine particles
rare earths
General Trend Line
0.01
0.1
1
10
100
1000
1960 1965 1970 1975 1980 1985
Op
tica
l Lo
ss (
db
/km
)
Figure 2.9 Reductions in Optical Loss of Optical Fiber
Critical Temperature
for Superconductors
Note the names of materials
Technology
Domain
Sub-
Technology
Dimensions of
measure
Different Classes of Materials
Energy
Trans-
formation
Lighting Light intensity per unit
cost
Candle wax, gas, carbon and tungsten filaments,
semiconductor and organic materials for LEDs
LEDs Luminosity per Watt Group III-V, IV-IV, and II-VI semiconductors
Organic LEDs Small molecules, polymers, phosphorescent materials
Solar Cells Power output per unit
cost
Silicon, Gallium Arsenide, Cadmium Telluride,
Cadmium Indium Gallium Selenide, Dye-Sensitized,
Organic
Energy storage Batteries Energy stored per unit
volume, mass or cost
Lead acid, Nickel Cadmium, Nickel Metal Hydride,
Lithium Polymer, Lithium-ion
Capacitors Carbons, polymers, metal oxides, ruthenium oxide, ionic
liquids
Flywheels Stone, steel, glass, carbon fibers
Information
Trans-
formation
Organic
Transistors
Mobility (cm2/ Volt-
seconds)
Polythiophenes, thiophene oligomers, polymers,
hthalocyanines, heteroacenes, tetrathiafulvalenes,
perylene diimides naphthalene diimides, acenes, C60
Living
Organisms
Biological
transfor-
mation
U.S. corn output per
area
Open pollinated, double cross, single cross, biotech
GMO
Materials Load Bearing Strength to weight ratio Iron, Steel, Composites, Carbon Fibers
Magnetic Strength Steel/Alnico Alloys, Fine particles, Rare earths
Coercivity Steel/Alnico Alloys, SmCo, PtCo, MaBi, Ferrites,
New Classes of Materials Enable Improvements over Long Time Scale
Creating materials (and associated processes) that better exploit physical phenomena
Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs),
magnetic storage, MEMS, bio-electronic ICs
◦ Increases in scale: e.g., larger production equipment, engines, oil tankers
Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems
◦ Telecommunication systems
Reality: What Drives Improvements?
Figure 2. Declining Feature Size
0.001
0.01
0.1
1
10
100
1960 1965 1970 1975 1980 1985 1990 1995 2000
Year
Mic
rom
ete
rs (
Mic
rons)
Gate Oxide
Thickness
Junction Depth
Feature length
Source: (O'Neil, 2003)
Moore’s Law for
Microprocessors
Reductions in Scale Drive Improvements in Capacity
Magnetic Recording Density
Other Technologies Benefit from Reductions in Scale
MEMS (micro-electronic mechanical systems) for many applications ◦ Gyroscopes, resonators, micro-mirrors
◦ Photonics, ink jet nozzles for printers, micro-gas analyzers
Bio-electronic ICs for many applications ◦ Point-of-care diagnostics, drug delivery
◦ chips embedded in clothing, body, etc.
DNA sequencing
Nanotechnology
Creating materials (and associated processes) that better exploit physical phenomena
Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs),
magnetic storage, MEMS, bio-electronic ICs
◦ Increases in scale: e.g., larger production equipment, engines, oil tankers
Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems
◦ Telecommunication systems
Reality: What Drives Improvements?
Scaling in Production Equipment
We all know about economies of scale ◦ But some products benefit from economies of scale more
than do others
◦ Why? Some products benefit from increases in scale of production equipment more than do others
Largest benefits for ◦ chemicals, other continuous flow equipment
◦ furnaces and smelters
Smaller benefits for discrete parts equipment
But also large benefits for ◦ Semiconductor wafers, liquid crystal display (LCD), and
solar cell manufacturing equipment
Production of Liquids or Gases in a Continuous Flow Factory
Liquids and gases are mixed, separated, heated, cooled, filtered, settled, extracted, distilled, and dried in pipes and reaction vessels
Pipes ◦ Cost is function of surface area (or radius) ◦ Output is function of volume (or radius squared)
Reaction vessels ◦ Cost is function of surface area (or radius squared)
◦ Output is function of volume (radius cubed)
Example of Benefits of Larger Scale: Engines
Diameter of cylinder (D)
Cost of cylinder
or piston is function
of cylinder’s surface
area (πDH)
Output of engine
is function of
Cylinder/piston’s
volume (πD2H/4)
Result: output rises
faster than costs as
diameter is increased
Height
of
cylinder
(H)
1
10
100
1000
10000
0.1 1 10 100 1000 10000
Rela
tive
Pri
ce p
er
Ou
tpu
tRelative Price Per Output Falls as Scale Increases
Steam Engine (in
HP) Maximum scale:
1.3 M HP
Marine Engine
Largest is
90,000 HP
Chemical Plant:
1000s of tons of ethylene
per year; much smaller plants
built
Commercial aircraft
Smallest one had
12 passengers
Oil Tanker:
1000s of tons
Smallest was
1807 tons
Output (Scale)
LCD Mfg Equip:
Largest panel size is
16 square meters
Aluminum
(1000s of
amps)
Electric Power
Plants (in MW); much
smaller ones built
Creating materials (and associated processes) that better exploit physical phenomena
Geometrical scaling ◦ Reductions in scale: e.g., integrated circuits (ICs),
magnetic storage, MEMS, bio-electronic ICs
◦ Increases in scale: e.g., larger production equipment, engines, oil tankers
Some technologies directly experience improvements while others indirectly experience them through improvements in “components” ◦ Computers and other electronic systems
◦ Telecommunication systems
Reality: What Drives Improvements?
Computer Processing Speed: Driven by Improvements in ICs
Bandwidth/Speeds for Wireline Telecommunication: Driven by
improvements in ICs, optical fiber, lasers, and photosensors
Source: Koh H and Magee C, 20016, A function approach for studying technological progress: application to
Information technology, Technological Forecasting & Social Change 73: 1061-1983.
Computers (e.g., tablet computers) networks of RFID tags, smart dust, and
other sensors Cloud/utility computing Internet content (e.g., mashups, 3D
content, video conferencing) Human-computer interface (touch, gesture,
neural) Mobile phones and mobile phone systems
(e.g., 4G, 5G, cognitive radio) Autonomous vehicles Holographic display systems
ICs Drive Improvements in Many Systems
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
The Myth: Demand Drives Improvements
Demand drives cumulative production
Cumulative production drives improvements ◦ Automated manufacturing equipment is introduced, modified,
and organized into flow lines
◦ Better products and processes are introduced
Implications: stimulating demand will lead to cost reductions. This is one reason why many governments subsidize the introduction of clean energy more than they subsidize R&D spending
Clayton Christensen’s theory of disruptive innovation also implies that increases in demand will lead to reductions in cost and improvements in performance
But……
Many improvements occur without product demand
◦ Scientists and engineers create materials to exploit physical
phenomena long before technology is commercialized
Even with scaling, demand is indirect driver
◦ Demand does provide money for increasing scale of
production equipment or reducing scale of features on ICs
and magnetic storage
◦ But often complementary technologies such as new
equipment are the bottleneck
And for some increases in scale, they reduce rates
of increase in unit cumulative production (e.g.,
engines)
Time
Performance
Many also Argue that Increases in Demand
Lead to Accelerations in Performance During
First Half of S-Curve
Accelerations in Rates of
Improvement
But…..
Few performance curves display an acceleration, i.e., jumps, during the first half of an S-curve
What about the few that do?
Do the accelerations reflect increases in demand?
Let’s look at example of superconductors
Rate of improvement for maximum
critical temperature of
superconductors experienced
acceleration in mid-1980s
But what caused the acceleration?
Was it increases in demand for
superconducting materials?
No!
It was because scientists found
new and unexpected class
(ceramics) of superconducting
materials (later, red, black, green,
purple)
We don’t need more demand! We
need scientists and engineers to
look for and find new classes of
materials!
What Does “Open Innovation” Tell Us About
the Role of Demand?
In the old world of closed innovation, ◦ vertically integrated firms developed components for their systems
◦ thus demand for systems and components were somewhat linked
In the new world of open innovation, ◦ different firms develop systems and components, i.e., vertical disintegration
◦ Most firms develop components for multiple systems
◦ Thus weaker link between demand for specific systems and components
Source: Henry Chesbrough, Open Innovation:
The New Imperative for Creating and Profiting from Technology
What Does Open Innovation Tell Us About
the Role of Demand? (2)
Open innovation in R&D is extending the vertical disintegration backwards into research
R&D is now conducted in a very decentralized world, millions of research scientists and engineers now exist
Firms (and professors) do research even when final product demand doesn’t exist ◦ Government funding, wealthy entrepreneurs, venture capital, patent protection, and development prizes support this research
Source: Henry Chesbrough, Open Innovation:
The New Imperative for Creating and Profiting from Technology
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
The most dangerous “model” is hype
Visibility is not a signal of growth
Many technologies never experience
diffusion, even if they are visible
For Example, Lots of Hype about
Energy storage for vehicles
Smart grid
Wind turbines
Solar cells
Nanotechnology
And many other technologies
But which ones will succeed?
Which technologies will become economically feasible and thus diffuse?
It largely depends on the rate of improvement ◦ Fast rates of improvement increase the chances
that a new technology will become economically feasible
Fast rates of improvement reflect ◦ Creation of new materials
◦ Technologies that benefit from changes in scale and the implementation of these changes in scale
◦ Technologies that benefit from reductions in scale have particularly rapid rates of improvement
For Example, Consider Mobile Phones? (1)
In early 1980s, one study concluded there would be about 1 million mobile phones in use by 2000
Some would say we under estimated the need for mobile phones
I say we under estimated the impact of Moore’s Law on the cost of mobile phones
Lesson: pay attention to rates of improvement and not to hype (or lack of hype)
Mobile Phones? (2)
In early 2000s, many believed that location services were a huge market
Until recently no one used these services
Until recently some would say we overestimated the need for such services
I say we ◦ over estimated the impact of Moore’s Law on the cost of such services for short term
◦ under estimated the impact for long term
Lesson: pay attention to rates of improvement and not to hype
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
Myth: We can’t analyze the timing of new technologies, because….
New technologies just appear like bolts of lighting
Someone finds a technology that no one noticed before
Someone comes up with new concept or new business model and “wallah”
Some other form of “unexpected event” occurs
The Reality (1)
Literally thousands of new technologies
are being developed right now
◦ the concepts have been understood for years
◦ champions exist for every one of them
◦ these champions believe these new
technologies will become economically feasible
Some of these technologies are
experiencing more rapid improvements
than others
The Reality (2)
Which ones are experiencing rapid
improvements?
Of course, unexpected events do
occur…but we want to be the ones who
cause these unexpected events
In the end, everything is about
probability, what are the most probable
scenarios?
Analyzing the Timing of New Technologies
If components drive the improvements in performance or cost of new technology, ◦ We must understand the components and their rates of improvement
If we had understood the importance of ICs, even in the 1970s and 1980s, ◦ We would not have been surprised by success of personal computers, mobile phones, and similar technologies
Analyzing the Timing of New Technologies (2)
For technologies that benefit from finding materials that better exploit physical phenomena, ◦ we can use the rates of improvement to better understand the expected changes in performance, cost, and thus economic feasibility over time
◦ if many new classes of materials have been found, this increases the chances that some form of this technology will become economically feasible
Analyzing the Timing of New Technologies (3)
For technologies that benefit from changes in scale ◦ we can use actual rates of improvement and role of supporting components in these changes in scale to better understand expected changes in performance, cost, and thus economic feasibility over time
◦ For new technologies for which little data is available, we can use data from similar systems to analyze the expected benefits from changes in scale
Analyzing the Timing of New Technologies (4)
Finally, demand does impact on costs and prices. Increases in demand can ◦ reduce the development costs per unit ◦ enable larger volumes and thus larger production equipment
But, manual assembly and some production “equipment” benefit little from increases in scale ◦ e.g., assemblers of iPhones!
And increases in demand are the last factor and often the least important
Seven Myths About Technology Change
Performance improvements follow S-curve Slow down in old technology causes
search for new technology Costs fall as cumulative production rises Demand drives improvements Media attention means a technology will
diffuse We can’t analyze the timing of new
technologies The market doesn’t work
The Myth: The Market Doesn’t Work Markets are very short-sighted New technologies will not be developed
unless there is strong government intervention
Governments must target new technologies in response to specific problems ◦ State the problems ◦ List potential solutions ◦ Fund and develop the potential solutions
The Reality
Markets work reasonably well Markets have replaced hierarchies (i.e.,
vertical disintegration) to a large extent even in R&D (i.e., open innovation)
Firms develop systems, components, and materials for those components even when ◦ the system for the components and materials are not clear
◦ and thus potential applications and demand for them are not clear
The Weakness of Markets
They under invest in R&D, particularly research
They under invest because ◦ there are large uncertainties
◦ in addition to uncertainties, information slips out and thus firms can’t appropriate all the benefits from research
What should governments do?
Government policies should support research,
perhaps much stronger than they currently do
These policies should reflect how technology
change occurs and not fall victim to the many
“myths”
The worst policies involve targeting new
technologies in response to specific problems as
if one is the “emperor of the universe”
◦ State the problems
◦ List potential solutions
◦ Fund and develop them
What should governments do? (2)
Government policies should support
technologies that
◦ are experiencing rapid improvements or
◦ have the potential for rapid improvements
Our research helps identify these technologies
◦ Ones that benefit from creating materials to…..
if many new classes of materials are being found,
improvements will probably follow
◦ Ones that benefit from reductions in scale
these technologies experience rapid rates of improvement
Final Words
Myths about technology change reduce our ability to develop new technology
Overcoming these myths can help us ◦ implement better strategies and policies
◦ more effectively find technologies that are or will become economically feasible
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