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On the Accuracy of Predicting Breakthrough Technologies

By

Jeffrey L. Funk

Associate Professor

National University of Singapore

Division of Engineering and Technology Management

9 Engineering Drive 1, Singapore 117576: EA-5-34

etmfjl@nus.edu.sg

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Significance

Predicting “breakthrough technologies” is an important part of managing R&D at the firm,

country and even global level. Technologies such as smart phones, cloud computing, and tablet

computers have emerged from seemingly nowhere in the last 10 years and with global sales

greater than $50 Billion they provide large value to users and have created huge winners and

losers at the individual, firm and country level. These and other breakthrough technologies can

also help solve global problems such as sustainability and thus predicting them is an important

task for decision makers. For example, the Intergovernmental Panel on Climate Change’s

recommendations are partly based on their predictions of which technologies have the largest

chance of becoming economically feasible and of diffusing. Nevertheless, there has not been a

concerted effort to study the accuracy of predictions on breakthrough technologies. This study

analyzed predictions made over a span of 5 years by a respected publication on technology that

is sponsored by a leading US engineering university. The findings reveal a low-level of

accuracy and we suggest a better method of predicting breakthrough technologies.

Abstract

The accuracy of breakthrough technology predictions between 2001 and 2005 by a

respected publication on technology that is sponsored by a leading US engineering university

is analyzed. Recent market sales data on the predicted breakthroughs show that few of the

predicted breakthroughs became large markets within ten years of their predictions.

Furthermore, we also note that many successful breakthroughs such as smart phones, cloud

computing, and tablet computers were not predicted. These results are contrasted with more

successful social science forecasts and analyzed with theories from cognition. Second, we show

that rates of improvements in new technologies or in the technologies that form the basis of

new systems are a much better predictor of “breakthrough” technologies than are the expert

opinions used by the publication to predict breakthrough technologies.

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1. Introduction

Breakthrough technologies change the world. Often termed “creative destruction” by

economic, management and other social science scholars such as Joseph Schumpeter (1),

breakthrough technologies both destroy an existing system and create a new one. The new

system provides significantly higher economic value to users than does the old one, enables

dramatic improvements in economic productivity and thus living standards (2), creates winners

and losers at the individual, firm, and country level, has a large impact on our ecological and

social environment (both positive and negative), and proposed solutions for climate change and

other global problems imply positive predictions about breakthrough technologies. For

example, the Intergovernmental Panel on Climate Change’s recommendations (3) are partly

based on their predictions of which technologies have the largest chance of becoming

economically feasible and of diffusing and other analysts have forecast the future costs of these

technologies (4)(5)(6)(7)(8). Thus, predicting those technologies that are likely to become

breakthrough technologies is an essential task for policy makers, managers, university

professors entrepreneurs, universities, and even students whose future careers depend on these

breakthrough technologies.

However, attempts to assess predictions or forecasts of breakthrough technologies are few

in spite of the many magazines, books, and websites who make predictions and the availability

of techniques for eliciting expert opinion (9) such as the Delphi method (10). This is partly

since technology forecasting is dismissed by many including the management and economic

journals (11) who instead focus on other issues such as cognitive biases (12) and capabilities

(13). Others may not want to assess forecasts for fear of angering those who made the forecasts.

One exception is an analysis (14) of Herman Kahn’s and Anthony Wiener’s well-read 1967

forecast for the year 2000 (15). In 2001 a committee of experts concluded that more than 40%

of the forecasted innovations occurred before the year 2000 and that the highest success rates

were for forecasted innovations with rapidly improving underlying technologies (14).

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The lack of assessments and the perceived problems with technology forecasting are

consistent with the psychological, economic and management literature on cognition.

Assessments of Delphi forecasts have concluded that they are a “specious” consensus in which

there is strong group pressure for conformity (16)(17) and thus they are strongly impacted by

individual biases. People have biases partly because they use heuristics to deal with a

complicated world (18) and they are often over confident and miscalibrate their degree of

confidence (19). For example, people assess the relative importance of issues, including new

technologies, by the ease of retrieving them from memory (20), this causes them to be

optimistic about technologies that are regularly discussed by their peers or the mass media.

Nevertheless, some predictions are better than others. Many note that meteorologists (21),

bridge players (22), and more recently strategic intelligence forecasters are well calibrated in

terms of confidence. A recent study of intelligence forecasts found that their forecasts explained

76% of the variance in geopolitical outcomes (23), a much better outcome than previous

research (24). The better forecasts are purportedly due to greater accountability (23). This

suggests that breakthrough technologies can also be predicted if accountability is implemented

and better techniques are developed.

Others imply or argue that it is more reasonable to ask experts to make certain types of

forecasts than others. For example, one experienced elicitor of expert opinion argues that it is

more reasonable to ask climate scientists about future global temperatures than to ask experts

about future gas or stock market prices (9). We agree that some things are easier to forecast

than others but assessments are needed to understand the levels of difficulty in making

predictions and to develop and test better predictive techniques.

Here we report the findings of an analysis of predictions made by a leading publication on

technology (Technology Review) that is sponsored by a leading US engineering university

(MIT). In this publication’s words, “the mission of (this publication) is to equip its audiences

with the intelligence to understand a world shaped by technology” (25). MIT is one of the top

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recipients of patents and licensing income and one of the top sources of startups among

universities each year (26). A total of 80 Nobel laureates were connected in some way to MIT

at some moment in their careers (27).

Technology Review produces a list of 10 breakthrough technologies each year (2001, 2002-

2014) that are based on conversations with academic experts from a variety of scientific

disciplines. In the publication’s words in 2001, “We have chosen 10 emerging areas of

technology that will soon have a profound impact on the economy and on how we live and

work” (28). Although there are many ways to assess a “profound impact on the economy,” we

do this by estimating the current market size for the predictions done in 2001, 2003, 2004 and

2005 (Table 1); this assumes that the predicted breakthroughs should bear fruit within

approximately 10 years.

The market sales data were gathered by Googling market, size, and sales for each

technology, sometimes changing the name of the technology or broadly defining it in order to

increase the chances of finding data. When definitions were uncertain, we either errored on the

side of larger market sizes or we excluded the technology from the analysis (7 were excluded).

Reports by market forecasting companies were the major sources of data in which we were

careful to distinguish between historical data on markets and forecasted data. After organizing

the predictions by market size, we then checked whether the market sizes were significantly

higher for the older than newer predictions and thus whether markets for the newer predictions

might grow rapidly in the near future.

We also looked for data on successful breakthrough technologies that did not exist in 2001

and thus should have been predicted by the publication. Again we used Google to find data on

market sizes for these technologies. Finally, building from the analysis (15) of Herman Kahn’s

and Another Wiener’s 1967 predictions (16), we assess another technique for breakthrough

technologies. By using reported rates of improvements for a wide variety of technologies

(29)(30)(31)(32) and their impact on a variety of higher-level systems (31)(32), we show that

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rates of improvement are a better predictor of breakthrough technologies than are the expert

opinions of the leading publication on technology.

2. Results

The results of our analysis of Technology Review’s predictions for breakthrough

technologies are summarized in Table 2. One (power grid control, smart grids) has greater than

$10 billion in sales, two have sales between $5 and $10 Billion (micro-photonics, personal

genomics), 11 have sales between $1 and $10 Billion, 5 have sales between $100 million and

$1 Billion, and 14 have sales less than $100 million. We could not find data for seven

(Mechatronics, Enviromatics, software assurance, universal translation, Bayesian machine

learning, untangling code, bacterial factories) of the technologies probably because they were

too vague or broad in their scope.

One question is whether ten years was enough time for these predicted breakthrough

technologies to become large markets and might these markets grow in the future? One way to

answer this question is to check whether older predictions have larger market sizes than do

newer technologies. The data shown in Table 3 suggests that this is the case. Other than the

breakthrough technology having more than $10 billion in sales (which is from 2004), the older

predictions have slightly higher market sizes than do the newer predictions. The number of

technologies with market sizes over $1Billion fell from 7 in 2001 to 2 in 2003, 5 in 2004 and

zero in 2005 while the number of technologies with market sizes less than $100 million

increased from 1 in 2001 to 4 in 2003, 3 in 2004 and 6 in 2005. However, even for 2001 and

2003, excluding the three predicted breakthroughs that had too vague or broad of meanings to

gather data, 8 of the 17 remaining predicted breakthrough technologies had less than $1 Billion

in sales.

Furthermore, we note that the predictions made by Technology Review missed some

important technologies that were not markets in 2001 but have since then become very large

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markets. Table 4 shows a number of technologies that probably now have larger than $10

billion in sales in global markets and these technologies have never made Technology Review’s

list of breakthrough technologies, even after 2005. These include smart phones, cloud

computing, tablet computers, big data, social networking and eBooks/ eReaders.

We also used a different method of predicting breakthrough technologies, which builds

from the conclusions reached by the assessment (14) of Kahn and Wiener’s predictions (15).

Since this assessment concluded that forecasts were better for technologies based on underlying

technologies with rapid rates of improvement, we hypothesize that technologies experiencing

more rapid rates of improvement or systems composed of rapidly improving technologies have

a better chance of achieving growth in market size than do other technologies. Using a data

base on rates of improvement for more than 150 technologies (29)(30)(31)(32) and their impact

on various systems (31)(32), we divide these technologies into those with annual rates of

improvement less than or greater than 10%. For example, smart phones, tablet computers, big

data, power grid control have been positively impacted by rapidly improving technologies such

as integrated circuits and the Internet. Second, we divide MIT’s predicted and missed

breakthroughs into two categories; those that have been positively impacted by technologies

experiencing rapid improvements and those that have not. The breakthroughs that do depend

on technologies experiencing rapid improvements are shown in Table 5 along with the

underlying technologies.

Third, we generate a three-by-two matrix with market size on the vertical axis and rates of

improvement in the horizontal axis. As shown in Chart 1, the left side of the chart is for

Technology Review’s predicted and missed breakthroughs that are positively impacted by

rapidly improving technologies (>10% per year) and the right side is for Technology Review’s

predicted and missed breakthroughs that depend on less rapidly (<10% per year) improving

technologies. The one predicted breakthrough with sales greater than $10 Billion along with

the six successful breakthroughs missed by Technology Review were all positively impacted by

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technologies with rapid rates of improvement. Ten of the 11 predicted breakthroughs with sales

between $1 Billion and $10 Billion in sales were positively impacted by technologies with

rapid rates of improvement. And only 3 of the 16 predicted breakthroughs with less than $1

Billion in sales were impacted positively by technologies with rapid rates of improvement.

This is a very rough analysis and it is done in hindsight. It might be that some of the

underlying technologies for unsuccessful breakthroughs were experiencing rapid

improvements and we were unable to identify them. Or some of the unsuccessful breakthroughs

do depend on rapidly improving technologies in our data base but we missed this connection.

In any case, although this is hindsight, Chart 1 suggests that predicted and missed

breakthroughs that are positively impacted by rapidly improving technologies have ended up

with higher market sizes than have the predicted breakthroughs that have depended on slower

improving technologies.

3. Discussion

In one of the few public assessments of breakthrough technologies, this paper found that

the predictions made by MIT’s Technology Review were not very accurate. Only one of the 40

predicted breakthrough technologies achieved greater than $10 billion in sales; 18 achieved

between $100 million and $10 billion, 14 achieved less than $100 million, and 7 of them were

thought to be too broad to gather sales data. Even the ones (7 of them) thought to be too broad

and vague to gather sales data can be considered a negative aspect of the forecast since good

forecasts should include definable entities.

We conclude that these predictions are not as accurate as the technology forecasts made by

Kahn and Wiener (15) and nowhere near as accurate as the strategic intelligence forecasts that

were recently analyzed in a PNAS paper (14). More than 40% of the predictions made by Kahn

and Wiener were judged to have become successful while only one of the 40 (2.5%) predicted

breakthroughs achieved more than $10 Billion in sales. While it is entirely possible that others

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will achieve sales of greater than $10 billion in the future, it is hard to believe that 40% of them

will.

One explanation for the poor forecasts could be a lack of accountability, which has been

shown to improve predictions in a number of ways (33)(34)(35)(36). Most recently, it has been

concluded that accountability was the key to strategic intelligence forecasts (23). For

Technology Review’s predictions, perhaps the long time frames have made it difficult to analyze

the market growth of the predicted breakthroughs and provide feedback to the predictors. If so,

this paper can provide some feedback.

A second possibility is that the elicitation of the predictions may not have been done in the

best possible way (9). Did the elicitations attempt to understand a wide range of opinions or

did the elicitations place too much emphasis on an inner circle of experts? Since people assess

the relative importance of issues, including technologies, by the ease of retrieving them from

memory (20), this causes them to be optimistic about technologies that are regularly discussed

by their peers. This would cause a small circle of experts to bias the predictions towards the

experts own areas of research and scientific disciplines. Since Technology Review’s predictions

were based on the “educated predictions of our editors (made in consultation with some of the

technology’s top experts),” this may have been the case.

How broadly can we generalize this paper’s analysis to other forecasters and predictors of

breakthrough technologies? Do other magazines and websites do as badly as does this paper

suggests? How about technology-based firms, venture capital firms, and governmental funding

agencies (e.g., DARPA)? On one hand, the strategic management literature suggests that firms

also do badly at technology forecasting (11). On the other hand, accountability may be stronger

in firms, venture capital firms, and organizations such as DARPA than it is in magazines and

websites. It could be that internal forecasts are much better than the public one that has been

analyzed in this paper.

Finally, this paper found that rapid rates of improvement are a better predictor of

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breakthroughs than are expert opinion. The predicted breakthroughs that are based on rapid

rates of improvement had much larger market sizes than those with slower rates of

improvement. The successful breakthroughs that were missed by Technology Review’s

predictions also depended on technologies with rapid rates of improvement. This is relevant

for organizations such as the IPCC who are recommending solutions to climate change and

who emphasize technologies with slow rates of improvement such as wind turbines (2% per

year) and electric batteries (5% per year, (37)). Perhaps they should broaden their search for

solutions and look for how technologies with rapid rates of improvement might provide better

solutions than do wind turbines and electric batteries. Further research is needed on these issues.

Materials and Methods

Predicted breakthrough technologies were acquired from Technology Review, which is

sponsored by MIT, arguably the world’s leading engineering and scientific university. This

publication chose 10 technologies in 2001 and each year between 2003 and 2014. Or in the

publication’s words, “We have chosen 10 emerging areas of technology that will soon have a

profound impact on the economy and on how we live and work” (28). The predictions included

a one page description of the technology and of the leading university researchers in the

technology. These predictions were based on the editors’ discussions with experts in various

technologies. Or in this publication’s words, these predictions were based on the “educated

predictions of our editors (made in consultation with some of the technology’s top experts)”

(28).

The market sizes for the predictions made in 2001, 2003, 2004, and 2004 were analyzed to

determine the impact that the predicted breakthrough technologies are having on the economy.

Market sales data were gathered by googling market, size, and sales for each technology,

sometimes changing the name of the technology or broadly defining it in order to increase the

chances of finding data. We used the one-page descriptions to better understand the predictions

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that were being made by Technology Review and thus to use a different name in Google’s search

engine. When definitions were uncertain, we either errored on the side of larger market sizes

or we excluded the technology from the analysis (7 were excluded). Reports by market

forecasting companies were the major sources of data in which we were careful to distinguish

between historical data on markets and forecasted data. After organizing the predictions by

market size, we then checked whether the market sizes were significantly higher for the older

than newer predictions and thus whether markets for the newer predictions might grow rapidly

in the near future.

Successful breakthroughs that did not exist in 2001 and were not predicted by Technology

Review were also analyzed in order to contrast them with the predictions made by Technology

Review. Again we used Google to find data on market sizes for these technologies. Finally,

building from an analysis (15) of Herman Kahn and Anthony Wiener’s 1967 (16) predictions,

we assess another technique for breakthrough technologies. By using reported rates of

improvements for a wide variety of technologies (29)(30)(31)(32) and their impact on higher-

level systems (31)(32), we show that rates of improvement are a better predictor of

breakthrough technologies than are the expert opinions of the leading publication on

technology.

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Table 1. Breakthrough Technologies Predicted by MIT’s Technology Review Between 2001 and 2005

2001 2003 2004 2005

Brain-Machine Interface: Wireless Sensor Networks Universal Translation Airborne Networks

Flexible Transistors Injectable Tissue Engineering Synthetic Biology Quantum Wires

Data Mining Nano Solar Cells Nanowires Silicon Photonics

Digital Rights Management Mechatronics T-Rays Metabolomics

Biometrics Grid computing Distributed Storage Magnetic-Resonance Force

Microscopy

Natural Language

Processing

Molecular imaging RNAi Interference Universal Memory

Microphotonics Nanoprint lithography Power Grid Control Bacterial Factories

Untangling Code Software assurance Microfluidic Optical Fibers Enviromatics

Robot Design Glycomics Bayesian Machine Learning Cell-Phone Viruses

Microfluidics Quantum cryptography Personal Genomics Biomechatronics

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Table 2. Market Sizes for Predicted Technologies

Market Size Technologies

>$10 Billion (1) Power grid control (Smart Grids)

$5 - $10 Billion (2) Micro-photonics, Personal genomics

$1 - $5 Billion (11) Grid computing, Molecular imaging, Synthetic Biology,

Distributed Storage, RNAi Interference, Brain-Machine Interface, Data

mining, Biometrics, Digital Rights Management, Natural Language

Processing, Microfluidics

$100 Million - $1

Billion

(5) Wireless Sensor Networks, Nanoprint lithography, Metabolomics,

Flexible Transistors, biomechatronics

< $100 Million (14) Quantum cryptography, T-Rays, Quantum Wires, Silicon

Photonics, Universal Memory, Injectable Tissue Engineering, Nano

Solar Cells, Nanowires, Microfluidic Optical Fibers, Airborne

Networks, Magnetic-Resonance Force Microscopy, Cell-Phone

Viruses, Robot Design, Glycomics,

Too Broad to

Analyze

(7) Mechatronics, Enviromatics, software assurance, universal

translation, Bayesian machine learning, untangling code, bacterial

factories

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Table 3. Market Sizes vs. Year of Predictions

2001 2003 2004 2005

>$10B 1 >$10B

$5-10B 1 1 $5-10B

$1-5B 6 2 3 $1-5B

$100M-1B 1 2 2 $100M-1B

<$100M 1 4 3 6 <$100M

unknown 1 2 2 2 unknown

Table 4. Successful Technologies Missed by MIT’s Technology Review

Technology Market Size

Smart Phones $335 Billion in 2013

Cloud Computing $110 billion in 2012

Tablet Computers $61 billion in 2012

Big Data $11.6 Billion in 2012

Social Networking Facebook had revenues of $7.8 Billion in 2013 so the global

market is probably larger than $15 Billion

eBooks and eReaders >$5 billion just in the U.S. for Amazon so the global market is

probably larger than $15 Billion

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Table 5. Breakthrough Technologies and Their “Rapidly Improving” Underlying Technologies

Breakthrough Technology Underlying Technologies

Smart phones Integrated Circuits (ICs), Displays

Tablet computing

eBooks and eReaders ICs, Displays, Organic Transistors

Digital Rights Management ICs

Biometrics

Molecular imaging Computers, Photo-sensors

Microfluidics MEMS

Micro-photonics Photonic ICs

Smart Grids (power control) Internet bandwidth

Cloud computing

Big Data

Social Networking

Data mining Internet bandwidth, Computers

Grid computing

Natural Language Processing Internet bandwidth, Computers

Distributed Storage Internet bandwidth, Mag Storage

Personal genomics DNA Sequencing

Synthetic Biology DNA Synthesizing

Brain-Machine Interface Invasive Neural Interface Techniques

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Chart 1. Current Market Sizes vs. Rates of Improvement for Underlying Technologies

Current

Market Size

Rates of Improvement

Slow, <10% Fast, >10%

>$10

Billion

Smart Grids, Smart Phones, Cloud

Computing, Tablet Computers, Big

Data, Social Networking,

eBooks/readers

>$1 Billion,

<$10

Billion

RNAi Interference

Micro-photonics, Personal genomics,

Grid computing, Molecular imaging,

Synthetic Biology, Distributed Storage,

Brain-Machine Interface, data mining,

Digital Rights Management,

Biometrics, Natural Language

Processing, Microfluidics

<$1 Billion Quantum cryptography, T-Rays,

Quantum Wires, Silicon

Photonics, Universal Memory,

Injectable Tissue Engineering,

Nano Solar Cells, Nanowires,

Microfluidic Optical Fibers,

Airborne Networks, Magnetic-

Resonance Force Microscopy,

Cell-Phone Viruses, Robot Design,

Glycomics, Nanoprint lithography,

Metabolomics

Wireless Sensor Networks, Flexible

Transistors, Bio-mechatronics