STEVE JURVETSON

DISRUPTIVE INNOVATION

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DFJ Global Network

Evermore Moore

Electronics, April 19, 1965

Source: Ray Kurzweil (each dot is a computing machine)

Moore’s Law — A Recurring Catalyst

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

Global Network Effects Further Accelerating Change

Months from Launch

Users

explosive

syn bio agriculture

post-CMOS era manufacturing robots & AI

new space

Disrupting Many Industries

New Space

The Information Systems of Biology

Proteins crystallized

Imaging resolution

Genes mapped

Data: Rob Carlson, company forecasts

Carlson’s Curve

Gene Sequencing READ

Gene Synthesis WRITE

$10,000 genome

$100 genome

Eve Biomedical

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.

Eve Bio: a possible path to ubiquitous sequencing

Synthetic Biology: Gen9 Gene Printer

• Parallel Chip synthesis • Picoliter scale • 250,000 at once

• Pooled Assembly • Mut-S Error

Correction

• Automated QC • Sequence

verified

Oligo Synthesis

Gene Assembly

Quality Control

Chip2Gene

1.5 Gb

Enzyme Homologs

150 Mb

Human Drug Targets (Proteins)

200 Mb

Polyketides (Small Molecules)

GenBank Release 194 260,000 Organisms 190 Gb (billion base pairs) Doubling every 1.5 years

What to build?

Pleistocene Park

GMOs for Food, Chemicals, and maybe Fuels

AgraCast

“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

Design, engineer, evolve

Sugars 1,4-Butanediol (BDO)

Building Complex Systems

• Nanotech

• Synthetic Biology

• Computer Science

• Innovative Organizations

…whether to design or evolve?

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

Robots & AI

“We actually think quantum machine learning may provide the most creative problem-solving process under the known laws of physics.” - Google Blog

Google’s First Quantum Computer

2002 2006 2010 2014 2018

Num

ber o

f Qub

its

1

100

1,000

10 FOR DISCRETE OPTIMIZATION

PROBLEMS

Competitive Performance with Classic Computers

Faster than all computers Faster than the universe

10,000

4q Calypso

16q Europa II

28q Leda

128q Rainier 4 512q Vesuvius 4

“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

Rose’s Law

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.”

Beyond Engineering

Efficiency Summary

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

THANK YOU

Steve@DFJ.com

Iterative Algorithms & Evolved Systems

• Consider:

- Cellular automata

- Genetic programming

- Analog Circuits & Antennae

- Danny Hillis: Sort Algorithms

- Neural networks

- Quantum computers

- Wisdom of Crowds

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

The tradition of design…

• Simple Problems

• Control

• Brittle

• Subsystem Clarity - Portable, Modular

• Top Down - Inheritance of Scale

Design

Dichotomy

• Simple Problems

• Control

• Brittle

• Subsystem Clarity - Portable, Modular

• Top Down - Inheritance of Scale

• Complex - Transcendence proof

• Out of Control

• Robust, Resilient, Adaptive - Co-evolutionary islands

• Inscrutable subsystems

• Bottom Up - Hierarchical subsumption

- Layers of abstraction

- Path dependence

Design Evolution

Innovation Management

• Cognitive Diversity > Ability

• Disagreement > Consensus

• Voting Policy > Coherence or Comprehensibility

• Communication Tuning > Leadership

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