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DISRUPTIVE INNOVATION

STEVE JURVETSON
DFJ Global Network
Evermore Moore

Electronics, April 19, 1965
Moore’s Law — A Recurring Catalyst

Source: Ray Kurzweil (each dot is a computing machine)
Why does technology accelerate?
“All technologies are combinations of technologies that already exist.” —
• Combinatorial ...
Global Network Effects Further Accelerating Change
Users

Months from Launch
Disrupting Many Industries
robots & AI

explosive
post-CMOS era

syn bio

agriculture

manufacturing

new space
New Space
The Information Systems of Biology
 Proteins crystallized
 Imaging resolution

 Genes mapped
Carlson’s Curve

Gene Synthesis
WRITE

Gene Sequencing
READ

$10,000 genome

$100 genome
Eve Biomedical

Data: Rob Carlson...
Eve Bio: a possible path to ubiquitous sequencing

Sources: “DNA sequencing via motion”, Stanford U. , US Pat No: 7,556,92...
Synthetic Biology: Gen9 Gene Printer

Chip2Gene

• Parallel Chip synthesis

• Pooled Assembly

• Automated QC

• Picoliter...
What to build?
GenBank Release 194
 260,000 Organisms
 190 Gb (billion base pairs)
 Doubling every 1.5 years

Human Dru...
Pleistocene Park
GMOs for Food, Chemicals, and maybe Fuels

“To support humanity’s needs,
we will have to grow more crops
in the next 50 ye...
Design, engineer, evolve

Sugars

1,4-Butanediol
(BDO)
Building Complex Systems
• Nanotech
• Synthetic Biology
• Computer Science
• Innovative Organizations

…whether to design ...
The Problem with Evolution
• Subsystem Inscrutability
- Black box defined by its interfaces
- No “reverse evolution”

• No...
Robots & AI
Google’s First Quantum Computer
“We actually think
quantum machine
learning may provide
the most creative
problem-solving
...
Rose’s Law

1,000

100

Number of Qubits

10,000
512q Vesuvius 4

Faster than the universe

128q Rainier 4

Faster than al...
Beyond Engineering

Danny Hillis, The Pattern on the Stone

“The greatest achievement of our technology
may well be the cr...
Summary

Efficiency

Accelerating Technological Change
- Interdisciplinary Renaissance
- IT innervates $T markets
- More B...
THANK YOU
Steve@DFJ.com
Iterative Algorithms & Evolved Systems
• Consider:
- Cellular automata

- Genetic programming
- Analog Circuits & Antennae...
The Problem with Evolution
• Subsystem Inscrutability
- Black box defined by its interfaces
- No “reverse evolution”
- Twe...
The tradition of design…
Design
• Simple Problems
• Control
• Brittle
• Subsystem Clarity
- Portable, Modular

• Top Down
...
Dichotomy
Design
• Simple Problems

Evolution
• Complex
- Transcendence proof

• Control
• Brittle

• Out of Control
• Rob...
Innovation Management

• Cognitive Diversity > Ability
• Disagreement > Consensus
• Voting Policy > Coherence or Comprehen...
AI Implications
• Cut & Paste Portability?
• Locus of learning: Process, not Product
- Would we bother to reverse engineer...
Steve Jurvetson - Disruptive Innovation
Steve Jurvetson - Disruptive Innovation
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Steve Jurvetson - Disruptive Innovation

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Steve Jurvetson's presentation on Disruptive Innovation at TTI/Vanguard's [next] in San Francisco on December 10, 2013. http://www.ttivanguard.com

Published in: Technology, Education

Steve Jurvetson - Disruptive Innovation

  1. 1. [ ] DISRUPTIVE INNOVATION STEVE JURVETSON
  2. 2. DFJ Global Network
  3. 3. Evermore Moore Electronics, April 19, 1965
  4. 4. Moore’s Law — A Recurring Catalyst Source: Ray Kurzweil (each dot is a computing machine)
  5. 5. Why does technology accelerate? “All technologies are combinations of technologies that already exist.” — • Combinatorial Explosion • Creates Economy — “Science quickly became the greatest tool for making new things the world has ever seen. Science was in fact a superior method for a culture to learn.” “Throughout history, the engine of human progress has been the meeting and mating of ideas to make new ideas. The human race will prosper mightily in the years ahead, because ideas are having sex with each other as never before.” • Urbanization • Interdisciplinary Disruption • Globalization —
  6. 6. Global Network Effects Further Accelerating Change Users Months from Launch
  7. 7. Disrupting Many Industries robots & AI explosive post-CMOS era syn bio agriculture manufacturing new space
  8. 8. New Space
  9. 9. The Information Systems of Biology  Proteins crystallized  Imaging resolution  Genes mapped
  10. 10. Carlson’s Curve Gene Synthesis WRITE Gene Sequencing READ $10,000 genome $100 genome Eve Biomedical Data: Rob Carlson, company forecasts
  11. 11. Eve Bio: a possible path to ubiquitous sequencing Sources: “DNA sequencing via motion”, Stanford U. , US Pat No: 7,556,922, Ecoli RNAP data and Optical trap system. W J Greenleaf, S M Block Science 2006;313:801-801, T7-RNAP in Magnetic Trap: SUNY and UMDNJ: McAllister lab: Richard T. Pomerantz et al., Nano Lett., Vol 5, No 9,2005.
  12. 12. Synthetic Biology: Gen9 Gene Printer Chip2Gene • Parallel Chip synthesis • Pooled Assembly • Automated QC • Picoliter scale • Mut-S Error Correction • Sequence verified • 250,000 at once Oligo Synthesis Gene Assembly Quality Control
  13. 13. What to build? GenBank Release 194  260,000 Organisms  190 Gb (billion base pairs)  Doubling every 1.5 years Human Drug Targets (Proteins) Enzyme Homologs 1.5 Gb 150 Mb Polyketides (Small Molecules) 200 Mb
  14. 14. Pleistocene Park
  15. 15. GMOs for Food, Chemicals, and maybe Fuels “To support humanity’s needs, we will have to grow more crops in the next 50 years than in the past 10,000 years combined.” Chris Keely, Monsanto AgraCast
  16. 16. Design, engineer, evolve Sugars 1,4-Butanediol (BDO)
  17. 17. Building Complex Systems • Nanotech • Synthetic Biology • Computer Science • Innovative Organizations …whether to design or evolve?
  18. 18. The Problem with Evolution • Subsystem Inscrutability - Black box defined by its interfaces - No “reverse evolution” • No simple shortcuts across the iterations - Simulation ~ Reality - Beauty from irreducibility • Locus of Learning is Process, not Product • Robust, within co-evolutionary islands
  19. 19. Robots & AI
  20. 20. Google’s First Quantum Computer “We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics.” - Google Blog
  21. 21. Rose’s Law 1,000 100 Number of Qubits 10,000 512q Vesuvius 4 Faster than the universe 128q Rainier 4 Faster than all computers Competitive Performance with Classic Computers 28q Leda 16q Europa II 10 FOR DISCRETE OPTIMIZATION PROBLEMS 4q Calypso 1 2002 2006 2010 2014 2018 “Quantum computers have the potential to solve problems that would take a classical computer longer than the age of the universe.” — Professor David Deutsch, Oxford
  22. 22. Beyond Engineering Danny Hillis, The Pattern on the Stone “The greatest achievement of our technology may well be the creation of tools that allow us to go beyond engineering – that allow us to create more than we can understand.”
  23. 23. Summary Efficiency Accelerating Technological Change - Interdisciplinary Renaissance - IT innervates $T markets - More Black Swans - Perpetual driver of disruption  Virtuous cycle for entrepreneurs  a great time for the new
  24. 24. THANK YOU Steve@DFJ.com
  25. 25. Iterative Algorithms & Evolved Systems • Consider: - Cellular automata - Genetic programming - Analog Circuits & Antennae - Danny Hillis: Sort Algorithms - Neural networks - Quantum computers - Wisdom of Crowds
  26. 26. The Problem with Evolution • Subsystem Inscrutability - Black box defined by its interfaces - No “reverse evolution” - Tweak the process not the product • No simple shortcuts across the iterations - Simulation ~ Reality - Beauty from irreducibility
  27. 27. The tradition of design… Design • Simple Problems • Control • Brittle • Subsystem Clarity - Portable, Modular • Top Down - Inheritance of Scale
  28. 28. Dichotomy Design • Simple Problems Evolution • Complex - Transcendence proof • Control • Brittle • Out of Control • Robust, Resilient, Adaptive - Co-evolutionary islands • Subsystem Clarity • Inscrutable subsystems - Portable, Modular • Top Down - Inheritance of Scale • Bottom Up - Hierarchical subsumption - Layers of abstraction - Path dependence
  29. 29. Innovation Management • Cognitive Diversity > Ability • Disagreement > Consensus • Voting Policy > Coherence or Comprehensibility • Communication Tuning > Leadership
  30. 30. AI Implications • Cut & Paste Portability? • Locus of learning: Process, not Product - Would we bother to reverse engineer? - No hard take off? • Co-evolutionary islands - accustomed environment (differential immunity) • Path dependence - algorithm survival - AI = Alien Intelligence defined by sensory I/O

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