mit's poor predictions about technology
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MIT’s Poor Predictions:
Isn’t there a better way to make
predictions and search for
opportunities?
Jeffrey FunkAuthor of Technology Change and the Rise of New Industries
(Stanford University Press, 2013)
jeffreyleefunk@gmail.com
The Context
MIT’s Technology Review produces a list of 10 breakthrough
technologies each year (2001, 2003-2014)
“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”
“The mission of MIT Tech Review is to equip its audiences with the
intelligence to understand a world shaped by technology”
These lists are based on conversations with academic
experts from a variety of scientific disciplines
predictions were based on the “educated predictions of our
editors (made in consultation with some of the technology’s top
experts)”
The Context (2)
In October 2016, I gathered market sales data for the
40 predictions done in 2001, 2003, 2004 and 2005 (ten a
year)
Reports by market research companies were major
sources of data
Reports were found 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
The Basic Conclusion
1 of 40 has greater than $100 Billion in sales: Data mining (i.e.,
big data)
3 others have sales greater than $10 Billion: power grid control
(i.e., smart grid), biometrics, distributed storage (i.e., cloud storage)
1 has sales between $5 and $10 Billion: micro-photonics (photonic crystals)
6 have sales between $1 and $5 Billion, 8 have sales between
$100 million and $1 Billion, 14 have sales < $100 million
Data could not be found for 7 technologies probably because
the names were too broad to gather data
Some important technologies were also missed………
MIT’s Technology Review Missed Some Big
Markets that Have Emerged in the 21st Century
Technology Global Market Size, 2015
Smart Phones $400 Billion
Cloud Computing $175 billion
Internet of Things $130 billion
E-commerce for apparel $65 billion
Tablet Computers $60 billion
Social Networking $24 billion
Fintech $20 billion
eBooks $15 billion (only U.S.)
Wearable Computing $14 billion
Technology Review’s
Predictions
Only one (Big Data) has
sales larger than $100
billion
No others have sales
larger than $50 billion
Only three others have
sales larger than $10
billion (biometrics,
cloud storage, smart
grid)
Technologies Chosen in Place of Missed 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
Nanoprintlithography
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
MicrofluidicsOrange: <$100 Million sales
Blue: too broad and vague to gather data
Green: Over $10 Billion sales
Predictions/Forecasts Matter
Every successful business (and good decision) implies a successful forecast
Apple’s introduction of MP3 players, smart phones, tablet computers
Amazon’s introduction of cloud services and ebooks
Most companies would have financial problems if they made as bad of forecasts
(and thus product introductions) as did MIT’s Technology Review
Many funding agencies, companies and students follow MIT Technology Review
Thus, Tech Review’s poor predictions have adversely affected many funding, startup, and
career decisions
How could MIT, the leading authority on technology, have made such bad
predictions (and not fix their method of predicting)?
Isn’t there a better way to predict and thus search for opportunities?
MIT is the Leading Authority of
Technology in the World
According to Chronicle of Education, MIT is one of the top ten
universities each year
Recipients of patents, research money, and licensing income
Sources of startups
More than 80 Nobel laureates have been connected in some
way to MIT at some moment in their careers
Its professors are big promoters of techno-optimism: Race
Against the Machine, The Second Machine Age, Andrew McAfee
and Erik Brynjolfsson
As the world’s leading authority on technology, how could
MIT’s Technology Review made such bad forecasts?
Shouldn’t it take responsibility for the predictions and fix the
method?
Possible Reasons for the Bad Forecasts
Lack of accountability?
Major issue in forecasting literature (e.g., Philip Tetlock)
These slides can provide feedback and thus accountability to MIT’s
Technology Review
Not enough time has passed?
Perhaps, but missed markets is bigger issue
Different definition by Tech Review?
Breakthrough science or ideas and not technologies?
Profound impact on economy means something other than money?
If so, then why didn’t Tech Review use different term?
Let’s assume Tech Review meant what it wrote
Cognitive Biases is more Likely Explanation
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
assume they are quickly diffusing
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 their existing
economics or whether they are improving
Cognitive biases are also biggest reason given by management scholars for
incumbent failure during technological change
Source: Daniel Kahneman, Thinking Fast and Slow, 2011. Kahneman received Nobel Prize for Economics in 2002
How Might Cognitive Biases Apply to Predictions?
MIT’s Technology Review didn’t pay attention to popular media when it
made predictions
But it used a network of engineers and scientists, who may be smarter
than popular media but nevertheless biased. Leading academic engineers
and scientists usually
research elemental technologies that follow “science-based model of tech change”
emphasize new scientific disciplines or ideas, consistent with “science-based model
of technology change”
optimistic about their technologies or those of their colleagues
and thus ignore products and services such as smart phones, tablet computers, and
cloud computing
Upshot is that MIT’s Technology Review chose a wide variety of science-
based technologies many of which will never become big markets
Most of These Technologies Sound More Like Scientific
Disciplines Than Products and Services2005
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
Nanoprintlithography
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
MicrofluidicsOrange: <$100 Million sales
Blue: too broad and vague to gather data
Green: Over $10 Billion sales
Is there a Better Way to Find Opportunities?
Many products and services emerge from a process different than science-based process of technology change
Rapid improvements in ICs, other electronic components, and Internet services enable new products and services to become economically feasible and thus emerge ***
can be called Silicon Valley Process of Technology Change
academics call these technologies general purpose technologies
This is very different from science-based process in which advances in science
enable new concepts (radios, TVs, LEDs, LCDs, OLEDs)
facilitate improvements in performance and cost
While improvements in ICs depend on advances in science, we are more interested in products and services that they enable
***See my papers (What Drives Exponential Improvements, California Management Review: Funk J and Magee, 2015. Rapid Improvements
without Commercial Production, Research Policy), my recent book published by Stanford University Press (Technology Change and the Rise of
New Industries), and my slideshare account (http://www.slideshare.net/Funk98/presentations).
Rapid Improvements in Integrated Circuits Have Enabled
Many New Types of Hardware to Emerge
Similar Things Have Occurred with
Computers, Internet and Smart Phones
Rapid Improvements in Computers enabled new forms of
Software applications
Rapid Improvements in Internet enabled new forms
Content
Services
Software
Rapid Improvements in Smart Phones enabled new forms of
Apps
Services
Software
For MIT’s Predictions, Missed and Successful (>$10 Billion)
Predictions Emerged from Improvements in Electronic
Components, Internet Services, and Smart Phones
Missed Predictions
Smart Phones
Cloud Computing
Internet of Things
E-commerce for apparel
Tablet Computers
Social Networking
Fintech
eBooks
Wearable Computing
Successful Predictions
Data Mining (Big Data)
Biometrics
Smart Grid Control
Distributed Storage (Cloud
Storage)
Similar Results Found from Analysis of Wall
Street Journal’s Billion Dollar Startup Club
Consists of global start-ups that
have billion dollar valuations, are still private
have raised money in the past four years
have at least one venture-capital firm as an investor
These startups reflect positive forecasts by investors that the
technologies will experience market growth
119 of 143 on WSJ’s list as of May 2015 were Internet-related
Most emerged through improvements in Internet services, cloud
computing, and access devices (smart phones, tablet computers)
7 others emerged through improvements in electronic components
Category Number of
Startups
Internet
Related?
Software 41 Yes
E-Commerce 26 Yes
Consumer Internet 37 Yes
Financial 15 Yes
Hardware 10
BioTech, Bio-
Electronics
8
Energy 2
Space 1
Other 3
Total 143 119
Wall Street Journal’s Billion Dollar Startup Club
Only 11 startups had patents that
cited more than 10 science or
engineering papers
119 of 143 startups exploited
opportunities that emerged from
improvements in Internet services
and smart phones
10 (hardware) of 143 startups
exploited opportunities that
emerged from improvements in
electronic components
For More Information on the
Analysis, see:
http://www.slideshare.net/Funk98/finding-
billion-dollar-startup-club-opportunities-
67270449
We Need Foxes, Not Hedgehogs
Philip Tetlock and Dan Gardner distinguish between foxes and hedgehogs in
their book Superforecasting: The Art and Science of Prediction (2015)
Foxes make better forecasts because they focus on larger number
of factors than do hedgehogs
Hedgehogs make predictions based on “a few fundamental truths”
Foxes draw on diverse strands of evidence and ideas
Those who solely monitor advances in science can be
called hedgehogs
because they focus on a single issue, what is published in science
and engineering journals
Those who monitor Silicon Valley process of technology change can be
called foxes because they draw on a diverse set of factors
rapid improvements in various electronic components, computers, and the Internet
impact of these improvements on the emergence of new products and services
Implications for Debate Between
Techno-Optimists and Robert Gordon
Techno-optimists such as Erik Brynjolfsson*, Andrew McAfee, Ray Kurzweil, Peter Diamandis and others argue that
changes in computers, mobile phones, displays, the Internet, and artificial intelligence have reached an “inflection point” in which dramatic increases in productivity will soon occur
Some of them also argue that these increases are so fundamentally large that they might cause wide-scale unemployment
Robert Gordon
demonstrates that there were fewer improvements in standard of living between 1940 and 2010 than between 1870 and 1940 in the U.S.
Argues that few improvements will occur in near future
Erik Brynjolfsson and Andrew McAffe. The Second Machine Age, The Race Against the Machine
Above Analyses Provides Support for
Robert Gordon’s Argument
Small markets for Tech Review’s predictions and small
number of science-based opportunities exploited by
billion-dollar startup club suggest
few science-based technologies will become economically
feasible in near future
we can’t be optimistic about: carbon-nanotubes, graphene,
other nano-technology, hydrogen vehicles, hyperloop,
superconducting transmission lines, mag-lev trains, synthetic
food, and fusion
They may diffuse, but the probabilities are small
Above Analyses Suggests Robert
Gordon may be Right (2)
If we can’t be optimistic about science-based
technologies, we are left with technologies that emerge
from improvements in electronic components, Internet
services, and smart phones, such as
Internet of Things, Big Data, ride sharing, driverless vehicles,
drones, smart payment, mobile payments, online education,
augmented reality, and virtual reality
But they don’t impact on all sectors of economy
They probably won’t lead to large reductions in cost of homes,
food, electricity, water, gas, and appliances
Items that dominate budgets of low-income people
For more information on the analysis of
techno-optimists vs. Robert Gordon, see:
http://www.slideshare.net/Funk98/has-
technology-change-slowed
Conclusions
Predicting future “Breakthrough Technologies” is very difficult
Predictions made by MIT’s technology Review were not very accurate
MIT missed technologies with big markets while choosing many with small markets
Even the 7 technologies thought to be too vague is evidence of poor forecast (should use definable technologies)
Silicon Valley Process of Technology Change is a better process to monitor than science-based process of technology change
Can better help decision makers find opportunities
Can help us understand what types of technologies will impact on our lives and the design changes that they will likely bring
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