Steve Jurvetson — The Midas Touch and Mind-Bending Futures
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“Celebrate the childlike mind.” — Steve Jurvetson
Steve Jurvetson (@jurvetson) is an early-stage venture capitalist with a focus on founder-led, mission-driven companies at the cutting edge of disruptive technology and new industry formation. Steve was the early VC investor in SpaceX, Tesla, Planet, Memphis Meats, Hotmail, and the deep learning companies Mythic and Nervana. He also led investments in startups that were acquired for $16 billion, and five that went public in successful IPOs.
In 2016, former President Barack Obama announced Steve’s appointment as a Presidential Ambassador for Global Entrepreneurship. Steve has also been honored as one of “Tech’s Best Venture Investors” by Forbes, and as the “” by Deloitte. Steve will be launching a brand-new venture fund sometime later this year, and you can read about it at future.ventures.
Enjoy!
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Steve Jurvetson — The Midas Touch and Mind-Bending Futures
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Want to hear another conversation with an incredible investor? — Check out my interview with Mike Maples, Jr., a partner at venture capital firm Floodgate, and the the man who taught me how to invest. Stream below or right-click here to download.
The Man Who Taught Me How to Investhttps://rss.art19.com/episodes/a37b219c-ddc8-412e-a344-3aae64dee746.mp3Download
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QUESTION(S) OF THE DAY: What was your favorite quote or lesson from this episode? Please let me know in the comments.
Scroll below for links and show notes…
SELECTED LINKS FROM THE EPISODE
Connect with Steve Jurvetson:
Future Ventures | Facebook | Flickr | Twitter
DFJ
D-Wave
The Fabric of Reality: The Science of Parallel Universes — and Its Implications by David Deutsch
Dream Machine: The Mind-Expanding World of Quantum Computing by Rivka Galchen, The New Yorker
Rose’s Law for Quantum Computers, 33rd Square
Device-Independent Quantum Key Distribution with Single-Photon Sources by Alejandro M·ttar et al.
What Is Shor’s Factoring Algorithm? by Peter Shor, Physics World
Quantum Chemistry, Chemistry Encyclopedia
Quantum Mechanics and the Schrodinger Equation, Professor Dave Explains
An Experimental Chemist’s Guide to Ab Initio Quantum Chemistry by Jack Simons, The Journal of Physical Chemistry
Mythic
Nervana
The Deep Learning Textbook by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, MIT
Looking for Entangled Atoms in a Bose-Einstein Condensate by John Toon, Georgia Tech News Center
Vision Talk: Steve Jurvetson — How Do We Bridge The Accelerating Rich-Poor Gap? by Steve Jurvetson, XPRIZE
B612 Foundation: Protecting Earth from Asteroid Impacts
Armageddon
Deep Impact
Abundance: The Future is Better Than You Think by Peter H. Diamandis and Steven Kotler
Questions and Answers about CRISPR, Broad Institute
Scientists Say Ocean Circulation Is Slowing. Here’s Why You Should Care. by Bob Berwyn, Inside Climate News
The Dot Com Bubble Explained in One Minute, One Minutes Economics
Hotmail
Skype
Tesla
SpaceX
A New Kind of Science by Stephen Wolfram
Cellular Automata — The Nature of Code, The Coding Train
Neural Networks and Deep Learning by Michael A. Nielsen
Developer Resources for Deep Learning and AI, NVIDIA
Hewlett-Packard
Steve Jurvetson Never Sells a Share of a Company He Invests In, Jason Calacanis with Steve Jurvetson, This Week In Startups
Done Deals: Venture Capitalists Tell Their Story — Featured HBS Peter Crisp by Udayan Gupta, Harvard Business School
The Trappist Monk Whose Calligraphy Inspired Steve Jobs — and Influenced Apple’s Designs by Niraj Chokshi, The Washington Post
Master Plan, Part Deux by Elon Musk, Tesla
Here’s Why Investor Steve Jurvetson Saved Elon Musk’s Space Dreams by Julie Bort, Business Insider
Elon Musk and Craig Venter Want to Print Life on Mars by Jason Koebler, Vice Motherboard
Elon Musk and Other Guests Dispute That a Notorious Silicon Valley Gathering Was a ‘Sex Party’: ‘Nerds on a Couch Are Not a “Cuddle Puddle”‘ by Rob Price, Business Insider
The Women of DFJ Push Back on Claims That ‘Predatory Behavior’ Is Rampant by Connie Loizos, TechCrunch
The Tail End by Tim Urban, Wait But Why
On Grief and Grieving: Finding the Meaning of Grief Through the Five Stages of Loss by Elisabeth Kubler-Ross and David Kessler
The Scientist In The Crib: Minds, Brains, And How Children Learn by Alison Gopnik and Andrew N. Meltzoff
LEGO Mindstorms NXT 2.0
Tribe of Mentors: Short Life Advice from the Best in the World by Timothy Ferriss
Model Rocketry with Steve Jurvetson at TED 2007
Planet Labs
Celebrate the Child-Like Mind by Steve Jurvetson, Wired
Would Air Travel Be Safer Without Pilots? Steve Jurvetson Thinks So by Zoran Basich, The Wall Street Journal
Memphis Meats
Excited for the Future of Meat — The Series A for Memphis Meats by Steve Jurvetson, Flickr
Liver Success Holds Promise of 3D Organ Printing by Hasan Chowdhury, Financial Times
Gˆdel, Escher, Bach: An Eternal Golden Braid by Douglas R. Hofstadter
Out Of Control: The New Biology of Machines, Social Systems, and the Economic World by Kevin Kelly
Coursera and Udemy each have courses on deep learning and neural networks.
Steve’s talk on deep learning and AI at O’Reilly Artificial Intelligence Conference San Francisco
Welcome to the AI Conspiracy: The ‘Canadian Mafia’ Behind Tech’s Latest Craze by Mark Bergan and Kurt Wagner, Recode
Jurvetson ’85 Delivers Address, St. Mark’s School of Texas
The Kristina Talent Stack by Scott Adams
James Watson, Francis Crick, Maurice Wilkins, and Rosalind Franklin: The Codiscovery of the Double Helix, Science History Institute
Reed’s Law
SHOW NOTES
On the power of quantum mechanics and the potential for quantum computers. [09:23]
What is a quantum computer? [11:34]
How big is a quantum computer? [14:20]
An explanation of Rose’s Law. [15:10]
How useful are quantum computers now, and how much more useful can we expect them to be in the near future? [19:50]
What is quantum chemistry, and what problems does it potentially solve? [21:06]
Quantum applications for deep learning. [22:22]
Musings on quantum entanglement. [23:15]
What Steve sees for the future of business as we move from theoretical and experimental exploration in quantum physics toward its practical application. [25:37]
What existential challenges of rapid technological advancement are we most likely to face? First: bridging the accelerating rich-poor gap. [26:54]
Protecting Earth from asteroids. [28:33]
Addressing the increasing ease with which weapons of bioterrorism can be synthesized. [30:09]
How might we cope with the effects of climate change through hibernation? [32:07]
In what ways can we prevent or mitigate social unrest resulting from a widening rich-poor gap? [34:14]
If life-saving drugs are to become cheap and affordable to everyone in the future, how does Steve see the incentives for research and development adapting? [41:00]
How did Steve get through his undergrad at Stanford in two-and-a-half years? [42:28]
Why did time and budgeting become less of a concern when Steve began his master’s program? [44:45]
Why did Steve decide to get an MBA, and would he still make that decision today? [46:00]
How did Steve enter the world of investing? [48:39]
What mistakes does Steve see otherwise smart venture capitalists making often? [49:53]
What helped Steve succeed early in his career? [53:13]
The simple rule Steve began to implement around early-stage investing. [55:26]
When did Steve start to see signs pointing toward a likely dotcom crash circa 2000, and how did his investment strategy change? [56:59]
At the time, why did Steve choose nanotechnology as the next big thing? [59:16]
On machine learning, cellular automata, and the difficulties faced when trying to reverse engineer an evolved structure to understand how they work (like a teenager or a human brain). [1:02:15]
A deep dive into deep learning and neural networks — and how GPU technology once designated for video games has pushed the field forward in unexpected ways. [1:06:08]
With an education and background in electrical engineering, why did Steve get involved in product marketing at Apple and NeXT? [1:14:23]
What are the check boxes that help Steve mitigate risk when he’s weighing investment opportunities? [1:18:52]
The question that weeds out “the charlatans and the arbitrage-seeking opportunists.” [1:22:04]
The uncertainty of enormous markets. [1:23:36]
Where did the name for Hotmail originate, and how dedicated to “free” were the founders? [1:24:32]
Wildly successful companies that were initially regarded as bad ideas. [1:25:19]
Why does Steve never sell shares once he’s invested in a company? [1:26:07]
Commonalities and differences observed between Steve Jobs and Elon Musk. [1:31:18]
In what ways does Steve believe Elon Musk is the “most risk-immune person” he’s ever met? [1:36:09]
Elon’s “battle mode” of focus during crises. [1:42:26]
On Steve Jobs’ architecting of the way people communicated, and enforcing the ideal number of people in team sizes and meetings. [1:47:18]
Steve addresses recent bad press. [1:48:54]
What was Steve’s self-talk when these allegations arose? [1:52:15]
Who helped Steve throughout this time, and why was he advised to keep mum about the allegations — even in his own defense? [1:56:59]
What other particularly trying times has Steve endured? [1:59:31]
What helped Steve through the grieving process when his father passed away? [2:00:32]
How Steve prepared to become a parent, and what analytical thinkers can gain by trying to see things from the perspective of a child. [2:04:59]
What Steve would put on his billboard. [2:07:11]
How children are like scientists. [2:07:38]
Steve is so enthusiastic about model rockets that he even gave a TED Talk about them. [2:09:15]
Drones and how to eliminate the TSA. [2:10:36]
As a technology investor, how does Steve budget for regulatory or political opposition from incumbents? [2:15:01]
The current and future science of synthetic “clean” meat and why it’s important. [2:18:27]
Could this technology be adapted to produce human tissue and organs for transplants, or is 3D printing more feasible? [2:28:13]
How might the layman become more scientifically literate? [2:31:13]
How long does Steve estimate it would take for someone to familiarize themselves enough with deep learning to get involved in the field? [2:35:04]
Steve talks about the commencement speech he gave at his old high school, what it covered, and what was most strongly received. [2:36:20]
Personal strengths don’t always come from obvious places, and their combination into “talent stacks” can result in unforeseeable breakthroughs. [2:38:25]
How “every great idea is a recombination of prior ideas,” and the part technology plays in increasing possible pairings of these prior ideas. [2:40:52]
Parting thoughts and what’s next for Steve. [2:42:51]
PEOPLE MENTIONED
Matt Mullenweg
Barack Obama
David Deutsch
Richard Feynman
Albert Einstein
Geordie Rose
Carver Mead
Gordon Moore
Peter Shor
Erwin Schrodinger
Peter Diamandas
Bill Gates
Tınu Jurvetson
Tiiu Jurvetson
John Hennessy
Henry Ford
Tim Draper
Stephen Wolfram
Steve Jobs
Elon Musk
Mark Zuckerberg
Larry Page
Sergey Brin
Naval Ravikant
Sabeer Bhatia
Peter Crisp
Kimbal Musk
Craig Venter
Antonio J. Gracias
Genevieve Lydstone
Tim Urban
Isaac Newton
Kevin Kelly
Matt Ridley
Douglas Hofstadter
Geoffrey Hinton
Yoshua Bengio
Andrew Ng
Marc Andreessen
Warren Buffett
Scott Adams
James D. Watson
Francis Crick
Rosalind Franklin
Maurice Wilkins
Nikola Tesla
Guglielmo Marconi
David P. Reed
