Predicting Breakthrough Technologies: An empirical analysis of past predictions by MIT's Technology Review
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Predicting Breakthrough Technologies: An empirical analysis of past predictions by MIT's Technology Review

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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 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

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Predicting Breakthrough Technologies: An empirical analysis of past predictions by MIT's Technology Review Predicting Breakthrough Technologies: An empirical analysis of past predictions by MIT's Technology Review Presentation Transcript

  • 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
  • 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
  • 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
  • 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?
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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
  • 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”
  • 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
  • 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
  • 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