These slides empirically analyzes predictions made by MIT’s Technology Review. Technology Review has produced a list of 10 breakthrough technologies for many of the last 10 years (2001, 2002-2014). These predictions are based on conversations with academic experts from a variety of scientific disciplines. To analyze these predictions, I gathered recent market sales data for the predictions done in 2001, 2003, 2004 and 2005. I found that many of these technologies still have small markets (<$1Billion), markets that are smaller than technologies not chosen by Technology Review such as smart phones, Cloud Computing. Tablet Computers. Big Data, Social Networking, and eBooks/eReaders. The slides then use theories of cognition to explain these relatively poor predictions and propose an alternative way of predicting breakthrough technologies

1. Predicting Breakthrough
Technologies:
An Empirical Analysis of Past Predictions
by MIT’s Technology Review
Jeffrey L Funk
Associate Professor
National University of Singapore
July 2014

2. The Context
 MIT’s Technology Review produces a list of 10 breakthrough
technologies each year (2001, 2002-2014)
 These lists are based on conversations with academic
experts from a variety of scientific disciplines
 In July 2014, I gather recent market sales data for the
predictions done in 2001, 2003, 2004 and 2005
 This was done by Googling market, size, and sales for each
technology,
 sometimes changing the name of the technology or broadly
defining it in order to find data
 Reports by market forecasting companies were the major
sources of data

3. The Basic Conclusion
 After excluding 7 technologies that were too broad to gather data, there
were 33 technologies
 1 has greater than $10 Billion in sales
 smart grids (power grid control)
 2 have sales between $5 and $10 Billion
 micro-photonics, personal genomics
 11 have sales between $1 and $10 Billion
 Grid computing, Molecular imaging, Synthetic Biology, Distributed Storage
 RNAi Interference, Brain-Machine Interface, Data mining, Biometrics
 Digital Rights Management, Natural Language Processing, Microfluidics
 5 have sales between $100 million and $1 Billion
 14 have sales less than $100 million

4. How Good were these Predictions?
 Difficult to assess, but
 More than half still have small markets of less
than $1Billion in sales
 Might these markets grow in the near future?
 Or have they been abandoned?
 Did MIT’s Technology Review miss any
technologies that have become big markets in the
21st century?

5. Some Big Markets that Have Emerged in
the 21st Century
 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
 eBooks and readers: >$5 billion just in the U.S. for Amazon
 Adjusting for global markets, these technologies have larger
markets than $10B, which is larger than 33 of the 34 technologies
chosen by MIT

6. What Technologies were Chosen in Place of the Big Markets?
2005
 Airborne Networks
 Quantum Wires
 Silicon Photonics
 Metabolomics
 Magnetic-
Resonance Force
Microscopy
 Universal Memory
 Bacterial Factories
 Enviromatics
 Cell-Phone Viruses
 Biomechatronics
2004
 Universal
Translation
 Synthetic Biology
 Nanowires
 T-Rays
 Distributed
Storage
 RNAi Interference
 Power Grid Control
 Microfluidic
Optical Fibers
 Bayesian Machine
Learning
 Personal Genomics
2003
 Wireless Sensor
Networks
 Injectable Tissue
Engineering
 Nano Solar Cells
 Mechatronics
 Grid computing
 Molecular imaging
 Nanoprint
lithography
 Software
assurance
 Glycomics
 Quantum
cryptography
2001
 Brain-Machine
Interface:
 Flexible Transistors
 Data Mining
 Digital Rights
Management
 Biometrics
 Natural Language
Processing
 Microphotonics
 Untangling Code
 Robot Design
 Microfluidics

7. How could this have Happened?
 Why did MIT’s Technology Review choose these esoteric
sounding technologies in 2001, 2003, 2004 and 2005 in place of
smart phones, cloud computing, tablet computers, Big Data,
social networking services, and eBooks?
 A good question for fields of cognition and behavioral science
 MIT is a leading if not the leading engineering university in the
world
 Clearly there are major cognitive biases in predicting
breakthrough technologies, even for smart people
 Let’s consider one from Daniel Kahneman, Nobel Laureate in
Economics

8. Cognitive Biases: Nobel Laureate Daniel Kahneman
 People assess relative importance of issues, including new
technologies
 by ease of retrieving from memory
 largely determined by extent of coverage in media
 E.g., media talks about solar, wind, battery-powered vehicles, bio-fuels and thus
many think they have rapid rates of improvement - but only some are
 Second, judgments and decisions are guided directly by feelings of
liking and disliking
 One person invested in Ford because he “liked” their products – but was Ford stock
undervalued?
 Many people “like” some technologies and dislike others without considering rates
of improvement
Source: Daniel Kahneman, Thinking Fast and Slow, 2011

9. How Might Kahneman’s Ideas Apply to MIT
 MIT’s Technology Review didn’t pay attention to popular media
when they made their predictions
 But, they used their own network of engineers and scientists,
who may be smarter than popular media but nevertheless biased
 Leading academic engineers and scientists usually
research elemental technologies
emphasize new scientific disciplines or ideas
optimistic about their technologies or those of their colleagues
and thus ignore system technologies (or markets) such as smart phones,
tablet computers, and cloud computing
 The upshot is that MIT’s Technology Review chose a wide variety of
“ideas,” many of which will never become big markets

10. Preliminary Conclusions
 Don’t ask the experts because they are just as
biased as everyone else…..
 More research on this issue is needed
 A more systematic application of cognitive
biases to predictions about breakthrough
technologies is needed to understand why
predictions aren’t so good
 And a more systematic method of predicting
breakthrough technologies is needed

11. Is their a Better Way?
 Some technologies experience more rapid rates of
improvement than do other technologies
 Do these technologies have a better chance of achieving
growth in market size than do other technologies?
 For example, have integrated circuit-related technologies
(e.g., smart phones) achieved larger market sizes than
have other technologies?
 Rough Analysis: using a data base on rates of improvement
for more than 100 technologies,
 underlying technologies for predicted breakthrough
technologies (and those on slide 5) and their rates of
improvement were identified
See my slideshare account (http://www.slideshare.net/Funk98/presentations), Technology Change and the Rise of New Industries (http://www.sup.org/book.cgi?id=21867) and
Exponential Change: What Drives it and What Does it tell us About the Future http://www.amazon.com/Exponential-Change-drives-about-future-ebook/dp/B00HPSAYEM

12. 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
Technologies DimensionsofMeasure Improvement
RatePerYear
IntegratedCircuits NumberofTransistorsPer
Chip
38%
MEMSChips Dropspersecond 61%
OrganicTransistors Mobility 94%
Photo-sensors Pixelsperdollar 49%
PhotonicICs DataCapacityperchip 39%
Displays Squaremetersperdollar 11%
MagneticStorage Bitsperdollar 39%
Computers Instructionsperunittime 36%
Bitspersecond 49%
InvasiveNeural
Interface
SimultaneouslyRecorded
Neurons
10.1%
DNASequencers Sequencingperunitcost 146%
DNASynthesizers Synthesizingperunitcost 84%
Rates of Improvements for Technologies that Impact on “Breakthrough Technologies”

13. CurrentMarketSize
> $10B
>$1B
<$10B
< $1B
Rates of Improvement
Slow (<10%)
Rates of Improvement vs. Current Market Size
Fast (>10%)
Smart Grids Smart Phones
Cloud Computing Tablet Computers
Big Data Social Networking
eBooks/readers
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
Wireless Sensor Networks, Flexible
Transistors, Bio-mechatronics
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
RNAi Interference

14. Seems to be a Correlation..
 Technologies with faster rates of improvement generally have
larger market sizes
 >$10B: all 7 had rapid rates of improvement
 $1B >, <$10B: 10 of 11 had rapid rates of improvement
 <$1B: 3 of 19 had rapid improvements
 But this is just a rough analysis
 Not easy to identify underlying technologies for all of the
predicted breakthroughs
 Data on all relevant underlying technologies were not found
 Some technologies may be experiencing rapid rates of
improvement even though data for them was not found

15. Conclusions
 Predicting future “Breakthrough Technologies” is very
Difficult
 Predictions made by MIT’s technology Review were not very
accurate
 Missed important technologies while choosing unsuccessful
ones
 Rates of improvement may be a better predictor of future
success
 Understand these technologies
 Then think about the types of new systems that they enable