Leadership of Technological Change
Ten Areas of Strategic Disruption, Opportunity and Threat
World Future Society 2013
July 2013  Chicago, IL
John Smart, President,
Acceleration Studies Foundation
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Foresight Education
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There are now 22 universities where you can get a grad degree in
foresight, and at least six good certificate programs, including SU.
FERNweb.org
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FERN networks foresight
grad students and helps
them find good jobs.
GlobalForesight.org
GlobalForesight is a
wiki of foresight orgs,
resources and people.
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Theory of Change
What Drives Accelerating Change?
Strategic Vision:
What’s Your Theory of Change? Of Progress?
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Good theories of change include values, and an idea of progress.
My bias: I’m in a group of scholars who study complex systems from
• Evolutionary “evo” variation,
• Computational “compu” selection, and
• Developmental “devo” optimization approaches.
More at:
EvoDevoUniverse.com
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Bury, 1920
Evolution, Computation, and Development:
Three Drivers and Two Patterns Found in All Complex Systems
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Adaptation/Selection
Partial predictability/optimization
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“Trees”
“Funnels”
Diversifying, Local
Unifying, Universal
Chance
Necessity
Evolution
Development
Unpredictable/
Not optimized
Predictable/
Optimized
The Structure of Evolutionary Theory, Gould, 2002, p. 1052
The Plausibility of Life, Kirschner & Gerhart, 2005, p. 219
Evo Devo Universe?, Smart, 2008, p. 18
What Technology Wants, Kelly, 2010, p. 123
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The “95/5%” Evo/Devo Ratio:
Most Change is Bottom-Up
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5% Devo
Nearly all (perhaps 95%) of the decisions and
events that create or control complex systems
appear to be bottom-up evolutionary processes.
Only a small critical subset (~5%) are top-down,
hierarchical, developmental processes.
95% Evo
Planning and policy leadership often forgets this.
Roughly 20X More Change is
Bottom-Up than Top-Down
Examples:
▪ Almost all genes in an organism create evolutionary variety vs. a special
subset (3-5%) that form the developmental toolkit.
▪ Almost all thoughts in an organism are unconscious, vs. ~5% conscious.
▪ Almost all behaviors of an indiv. are environmental reactions vs. plans.
▪ Almost all decisions & actions in an org. are “out of control” vs. planned.
▪ Almost all social innovation occurs in economic markets vs. by govt policy.
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▪ Almost all new IT prods & services empower network nodes vs. hierarchies.
(personal computers, email, web, smartphones, wearables)
3P’s Model: Possible, Preferable, and Probable Futures
Three Types of Foresight Management
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Forecasting, Risk Mgmt,
& Prediction
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ILP Model: Innovation, Learning, and Protecting:
Three Basic Leadership Challenges
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Any system can be analyzed as either:
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1. A Learning (“Adaptive”) System
2. A Innovating and Protecting (“Sustainable Innovation”) System
3. An Innovating, Learning and Protecting (“ILP”) System
Evo Devo Universe?, Smart, 2008, p. 10
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IMF Model: Innovation, Management,
Foresight: Three Leadership Toolsets
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Emerging Tech MS Curriculum Framework, U. of Advancing Technology, Smart, 2011.
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Developmental Foresight
What Can We Anticipate?
TINA Trends:
Irreversible Social, Econ, Political, and Tech Trends
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Pierre Wack, Shell Scenarios Group, 1970’s:
TINA = “There Is No Alternative” (to the Trend, On Average)
Ten Examples To Test:
•
•
•
•
•
•
•
•
•
•
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Decreasing Avg. Violence (Incr. War Cap., Incr. Regul. of Violence)
Increasing Central Government Powers vs. Individual Powers
Increasing Democratization and Global Interdependence
Increasing Indiv. Rights (Women, Child, Relig, Minority, Gay)
Increasing Social Justice and Sustainability (Envir. Justice)
Increasing Total Energy Use/Capita, Saturating Indiv. E. Use/Capita
Increasing Total Wealth, Social Safety Nets, Liesure Time
Increasing Total Information, Comp., Communication, Specialization
Increasing Biological Capacities (Life-Like Abilities) of Tech
Increasing Space, Time, Energy, and Matter Density & Efficiency of Tech
Simon 1996
Pinker 2012
Morris 2013
Reese 2013
Ten Areas of Technological Change
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1. Nanoscience and Nanotech
2. Information Tech
3. Engineering Tech
4. Resource Tech
5. Cognitive Tech
6. Social Tech
7. Health Tech
8. Economic Tech
9. Political Tech
10. Security Tech
LE Drones (Phantom Eye, Scan Eagle)
Disruptive Naval ISR Platforms
Unmanned Surface Vehicle (Piranha)
Naval ISR, Escort, Antipiracy Platform
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Leadership of Technological Change (and 30 Books For Further Reading), Smart, 2013
A “Race to Inner Space” is the Engine
and Steering of Accelerating Change
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Nanoscience/Nanotech - Physical Inner Space – “Engine”
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“There’s Plenty of Performance at the Bottom.”
Photonic crystal lasers 10^6 more E efficient than other microlasers
Programmable synapses 10^6 faster, 10^3 less E/comp. than neurons
Fission 1,000X more E/mass than chem. Fusion 1,000X more E than fission
Fuel cells allow 100,000X more E/mass than chem. batteries (Dan Nocera)
Synthetic catalysts increase reaction speeds and yields 10^3 to 10^6
Single step efficiency jumps in macro (human) space are always far less.
Infotech/Simulation - Virtual Inner Space – “Steering System”
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“As Intelligence Rises, Thinking Becomes More Adaptive Than Acting”
Adult humans no longer act in novel ways, they think in novel ways.
Simulations allow “ephemeralization” (far less mass/energy per action)
Rise of scientific simulations. NSF. IPCC. NASA Solar System Simulator
Telepresence, telerobotics/haptics outcompetes traveling in person
Google maps, sensors, geoweb, parallelized GPUs: visual cortex for the web.
Machine sim data doubles every 2 years. Human sims grow far slower.
Info: Intelligence, Fischler, 1987; Simulation, Ross, 2006; Simulation-Based Engineering Science, NSF, 2006.
Nano: Engines of Creation, Drexler, 1987; Nanotechnology, Ratner, 2002; The Race to Inner Space, Smart,
2012.
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Leaders Must Use the Strongest Levers,
Nanotech and Infotech, and mind the Hype
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"Give me a lever long enough, a fulcrum,
and place to stand and I will move the world."
- Archimedes, 250 BCE
Fenn 2008
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Gartner Hype Cycle
1. Nanoscience and Nanotech
“Nano is faster, stronger, thriftier than humanspace”
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Small-Scale Physics
Nanoenergetics, Fission, Fusion
Nanochemistry
Nanocomputing
Nanodevices
Sloane 2012
Von Weizsacker
2009
Ratner 2002
Battery Energy Densities:
Exponential
Performance Curves
Come from Nano
Lead Acid
NiMH
Li-Ion
Zn-Air
Li-Air
Li-Air Theor.
30 Wh/kg (8X worse)
80 Wh/kg (3X worse)
250 Wh/kg
1084 Wh/kg (4X better)
5200 Wh/kg (20X better)
12000 Wh/kg (50X better)
Leader’s Challenge
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 Where would an 500% (~5X) improvement in efficiency or performance in a
physical process, sensing, computation, or comm system make the most
difference for your team? A 1000% (10X) improvement? How can you get it?
Advanced Energy Materials, 1(1), pp. 34-50, 8 Dec 2010. Figure 1. Courtesy Ramez Naam.
2. Information Technologies
“IT is Becoming Ubiquitous and Brain-Like”
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Computing and Networks
Internet of Things (IOT, MIOT)
Shared Pictures and Simulations
Conversational Interface and Agents
Big Data and Predictive Analytics
Siegel 2013
McAfee 2012
Stibel 2009
In 2006 Watson got 13% of Jeopardy! Q’s correct.
By 2011, Watson got 90% of Q’s correct.
Apple’s Siri on
iPhone 4, 2011
Brain-Like Engrg (Modular, Parallel, Weighted)
• 2800 processors, working in parallel
• ~100 text search methods (10M articles)
• ~100 language processing methods
• 550 predictor variables
IBM Watson Jeopardy Challenge, 2011 • Trained on 5.7M Q/A pairs (to set 550 weights)
• Weights continually adjust with experience.
Leader’s Challenge
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 What type of computers, communication devices, platforms, networks,
sensors, databases, predictive analytics, or pattern recognition would add
value for your team? How can you improve your team’s IT intelligence, COTS
use, R&D, procurement, and performance measurement (ROI) every week?
3. Engineering Technologies
“Cities & Machines are Automating, Dematerializing”
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Smart Cities, Networks, Vehicles
Automation and Robotics
Dematerialization and Efficiency
Greentech and Pollution
Transportation and Logistics
Glaeser 2011 Despommier 2010 Zhang 2013
(City Gardens) (Family Farms)
26K Robot
(Postharvest
Automation)
Walkable Cities
• Hub & Spoke
• Underground Pkg
• Greenbelts
• Bike trails (19mph)
• Vert. Farms
• HS Rail
• Country Dacha
Google’s Robotic Cars
Lit Motors C-1 (Self-Balancing)
Leuven, Belgium
Leader’s Challenge
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 How would you improve the “ethics” of autonomous drones and robots for
urban use? What would a good “ethical architecture” for bots look like? On
your team, who is responsible for safety? For speed & cost to capability?
4. Resource Technologies
“Periodic Crises, Long-Term Abundance”
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Energy
Water
Food
Population
Ecosystem and
Other Resources
CSP+Flash Desalination:
Water for Africa,
Energy for Europe
Diamandis 2012
Amine Scrubbers for CO2 Capture
• 1930’s Technology
• Piloted in Mongstad, Norway in 2012
• 20-30% energy cost (Amine cycling, CO2 comp.)
• 70-90% of CO2 is captured
• Will not be implemented without govt. mandate
(like almost all big safety or envir. upgrades)
Eccles 2010
“Cultured” Meat
Likely Late 2010’s
Leader’s Challenge
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 Who is responsible for resource acquisition and sustainability on your team?
How will you track, report, and reward more sustainable resource ROI?
Promises and Pitfalls of Carbon Capture, Fellet, ArsTechnica, 11.28.12.
5. Cognitive Technologies
“Conversational Interface to the Wearable Web”
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Conversational Interface & CyberTwins
Crowdlearning (Teacherless Ed)
Global English (Workforce Growth)
Wearable Web (Augmented Intell)
Education Reform (Finland Model)
Valiant 2013
Google Glass
(Augmented Intell)
Wristphone
concept, 2007
Leader’s Challenge
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Next IT’s Virtual Agents
$500
Finland is #1 in
FREE STEM and Civics Ed.
(50/50 Ed. Model)
 How do you improve online training processes and performance measurements
on your team? Improve use of desktops, laptops, tablets, mobiles, wearables?
How do you figure out the 50% core, and 50% freedom in job and IT training?
6. Social Technologies
“Values-Mapped Web, Groupnets, and Wikinomics”
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Social Freedoms and Privacy
Evidence-Based Behavior
Values-Mapped Web (Valuecosm)
Groupnets (Learning, Perf., Therapy)
Wikinomics & Gamification (Coll. Intell.)
Lessig 2004
Inglehart-Welzel
Cultural Values Map
Page 2008
Shirky 2011
Nissenbaum2009
Why Groupnets Will Help
Criminals and the Mentally Ill:
There are 50X More Normals than
Those Who Need Help.
Leader’s Challenge
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 How can you facilitate Knowledge Creation, Knowledge Management, Ad-hoc
Teams, Ideation, Strategy, Critique, Innovation, and a DIY, Entrepreneurial ethic
in your org? How do you make it more freedom oriented and evidence-based?
7. Health Technologies
“Quantified, Groupnet, Digital Health & Erooms Law”
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Quantified and Groupnet Health
Digital Wellness and Longevity
Public Health and Disease Control
Medical Biotech, Implants, HMIs
Big Pharma and Molecular Medicine
Topol 2011
Angell 2004
Eroom’s Law (Moore’s Backwards): Number of new FDA-approved
drugs per $US Billion of R&D has halved every 9 years since 1950.
Wearable & Implantable Sensors
for Addiction Medicine
• Exist today (insulin pumps)
• Allows State Provision of Illicit Drugs
• Medically Monitored Tapering
• Nausea Option for Behavior Change
Dexcom GlucMonitor2010 • Eliminates an 800B Criminal Industry
Leader’s Challenge
 How digital, quantified and networked is your health and wellness system and
education? What incentives do you provide your team for healthy outcomes?
What are you doing for diseases of affluence (obesity, CHD, addiction, ADHD)?
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Diagnosing
8. Economic Technologies
“Tech Productivity, Wealth, Creative Destruction”
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- Accel. Technical Productivity (TP) is 70% of GDP Growth*
- Labor Productiv. is 20%. Finance is 10% of GDP Growth*
- US Leads in Freedom, Entrepreneurship, Innovation
- US Will Keep Same Ratio (25%) of Global GWP Pie
- If We Moderate Economic Inequality, Have Fair Incentives
Toffler 2006
Lovins 2011
Stack 2013
GDP Per Capita
Western Europe
1000-2000 C.E.
Leader’s Challenge
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 In Econ Downcycle, how can you grow critical product or service performance
while also cutting operating costs 20% this year? How to get everyone knowing
and growing your tech productivity? Labor and finance productivity?
*Robert Solow’s Neo-Classical Economic Growth Model, 1987, Wikipedia.
9. Political Technologies
“Represent., Shrinking Knowl, Access, Wealth Gaps”
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- Adequate Political Capacity & Military Power
- Adequate Political Freedom and Representation
- Fair Rules and Laws, Sound Fiscal Mgmt
- Shrinking Disconnectedness (Barnett’s Gap)
- Shrinking Rich-Poor Divide (Kuznet’s Gap)
Barnett 2005
Democratic-Plutocratic Pendulum
Economic
Kuznets Curve
Acemoglu 2012
Environmental
Kuznets Curve
Leader’s Challenge
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 How can you get your team more represented, invested, and empowered to
make change? How meritocratic and fair are your policies? What is the size of
your internal pay and benefits gap? 10X, 50X? 500X?
10. Security Technologies
“Transparency, Simulations, and Immunity”
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ISR, Reciprocal Transparency (Anti-Corruption)
Physical Security and Cybersecurity (Anti-Crime)
Machine Ethics and Autonomy
Simulations and Red Teams
Redundancy, Resilience, & Immunity (Antifragility)
Brin 1998
“Top Down” vs “Bottom Up” Transparency
Simulations & Red Teams
Herman 2008
Schwartz 2012
Leader’s Challenge
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 How can you empower and increase the bottom up (95%) monitoring and
transparency (sousveillance) by your team and stakeholders? How can you
simulate disruptions, problems, and opportunities ahead? Have strategies
ready to go in case of crisis (danger/opportunity)?
Leadership Strategies
Innovation, Management, Foresight
Good Self-Management
Allows Great People Management
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- Self-Management improves People Management
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Cognitive Diversity: Combat Bias with
Multibiasing, and Open Communication
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“You can’t get an unbiased education, so the next best
thing is a multibiased one.” – Buckminster Fuller
Build cognitively diverse, strengths diverse teams.
Measure for strengths diversity.
Page is Prof. of Complex
Systems, U. Michigan
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Rath is at Gallup
“Don’t expect what you don’t inspect.” – Lou Gerstner
Knowledge Mgmt, Ideation, and
Innovation Platforms Now Critical
Solvers
Problems
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Popular Guides: RMA, Cyberwarfare, LOAC
Benefit-Cost Analysis
to Relatively Rank Ideas
Started in 2005.
3 clearance levels.
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Those submitting ideas need:
1. Leadership by Example
2. Manager Support and Incentive (Institutional support can be nonexistent!)
3. Facilitated Exercises, “Innovation Games,” equivalent of Wargames.
4. Benefit-Cost Analysis at the end. Innovation is 95% bottom up.
We are Good at Prediction, and Will Get Even Better
Leaders Need to Do It More
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After convincing ourselves that developmental futures are predictable,
our next prediction problems are deception, bias and understanding probability.
Quantitative models help, but numeracy is no guarantee of accuracy.
We are biased to value confidence over uncertainty.
We need less confidence and more uncertainty for greater accuracy.
National Intell. Council, 2012
“We do not seek to predict the
future – which would be
an impossible feat.”
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Kahneman & Tversky, 2010
Forecasting Bias
Silver, 2012
Forecasting Uncertainty
Thompson, 2012
Prediction Platforms
Prediction Teams:
Good Judgment Project
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Philip Tetlock, UC Berkeley, U Penn, IARPA.
Started with 3,000 forecasters Year 1 (2011).
Second year, took top 60 performers and randomly
assigned them into five teams of 12 each.
These “super forecasters” also delivered a farabove-average performance in Year 2.
Apparently, forecasting skill cannot only be taught,
it can be replicated
Tetlock, 2006
http://www.nytimes.com/2013/03/22/opinion/brooks-forecasting-fox.html
Dator’s “Four Futures”:
Four Classic Growth Scenarios
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Exponential
(Biz As Usual)
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Four Basic Stories/Components of Change
Perspectives in Cross-Cultural Psychology, Jim Dator, Academic Press, 1979
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Innovation:
Procurement Strategies
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Unpopular Truths:
• Small firms are much more Innovative than large firms.
• DoD Acquisitions Programs have been going
backwards in Speed to Capability since 1950’s.
• Tech companies and asymmetric actors have made
exponential gains in Speed to Capability at same time.
Small firms
innovate best.
Lessons:
• “Speed to Capability” is the critical performance measure
for all DoD procurement programs. Lower must die.
• Procurement must include diversity (small firms).
• Diversity needs periodic culling or it gets wasteful.
• DARPA, ONR, SPAWAR, NAVAIR, NAVSEA, etc. need their
own competitions and innovation platforms.
Example: Predator MQ-1. First prototype developed on DARPA
contract (1984) by Leading Systems Inc., Abraham Karen, Israeli Air
Force chief designer and US immigrant. LSI went bankrupt 1990,
bought by Gen. Atomics. LSI did all primary innov. Common story.
Just as true in the
defense industry.
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Won’t Get What We Don’t Measure, Marv Langston, Former US Deputy Asst. Sec. of Defense, Dec 2012.
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How Do You Build Your Best Small, Expert Teams?
How Do You Keep Your Suppliers Competitive?
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Small Teams can:
-- Rapidly innovate and adapt
-- Operate below the radar (stealth)
-- Have superior urgency and purpose
-- Ignore convention and pursue vision
-- Get hand-picked excellence and resources
-- Be expendable, experimental, exploratory
Supply Management Excellence:
-- Learn from Industry Benchmarks
-- Large and Small Suppliers
-- Suppliers Deliver Overlapping Functions
-- Performance-Based Budgets
-- End-Client Feedback Drives Metrics
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-- Balance Supplier Pruning and Redundancy
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DARPA and Google: Client-Centric, Network-Centric
Models for Tech Innovation and Technical Intelligence
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DARPA
• Orientation to Radical Innovation
• Decent Technical Intelligence
• Autonomy and Freedom
• Acceptance and Review of Failure
• Small and Flexible Units
• Flat (3 level) Organization
• Constant Talent Rotation (4-6 yr terms)
Belfiore 2009
Google adds..
• Measurement Culture
• Feedback/Learning Culture
• Analysis/Intelligence Culture
• Client (End-User) Orientation
• Automation Orientation
• Network/Platform-Centric (Tools first)
Girard 2009
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Google’s R&D budget is $6B for 2012, DARPA’s is $3B.
Top 20 IT firms R&D budget >$30B. “It’s a COTS World.”
Discussion
What Do You Think?
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