Our Man in Nirvana

Rate of Innovation Part II

As a child I was a keen chess player but I have not played regularly for over 20 years.  That has changed recently with technology giving me two new ways to play and practice.  First, I have bought a chess computer game on my Sidekick II.  I can now play a game in 5 minutes against a computer during idle times such as waiting for MUNI.  Second, Auren Hoffman invited me to play online at Red Hot Pawn where you can play multiple asynchronous games against other people over the web.  I am still playing my first game with Auren which started on February 19th.  I must admit I am finding the lack of rhythm a challenge with a game spread out over several weeks but it's good to be playing a human being rather than a computer

Speaking of human beings vs computers, as I have been laid up in bed for a couple of days trying to shake off a virus, so in between sleeping and drinking lots of water I took the opportunity to watch the DVD Game Over about Gary Kasparov's chess match against IBM's Deep Blue in 1997.  The premise of the documentary is intriguing - IBM cheated to beat Kasparov to bolster their flagging image - but the execution and evidence presented is circumstantial and hearsay at best.  Putting aside the premise and just examining the trends - Kasparov beats computer in 1996, Kasparov loses to computer in 1997, Kasparov draws to computer in 2003 - the seemingly slower rate (backwards?) of innovation is in marked contrast to that shown in the DARPA Grand Challenge.  Why is this?

The reason that first comes to mind is that the tasks differ in their complexity.  My gut instinct is that the task of getting a car to autonomously drive from A to B on a known course is less complex than defeating the world's greatest chess player with more playing permutations than there are atoms in the universe.  However, upon further reflection it's not clear that the number of known permutations is the best measure of complexity.  The number of chess permutations is enormous (10 to the power of 120) but there is no element of randomness, unlike the DARPA Grand Challenge where the cars have to deal with other cars, weather conditions, changing gradients, etc.

I shall noodle on this some more, but it's a cautionary tale on extrapolating from just a few data points to a wider conclusion.

Sundance: Due Diligence Reminder

I enjoyed my annual pilgrimage to Sundance Film Festival a couple of weekends ago to get a sneak preview of thought provoking edgy movies.  I saw 7 movies (The Matador, Brothers, Why We Fight, 3 Iron, On A Clear Day, Forty Shades of Blue and a collection of shorts) and there were only 2 bad movies which is actually better than the 50/50 hit rate that I have typically had over the past 7 years.

This got me thinking about whether there were lessons I could learn from film making and apply them to venture capital.  Many observers more erudite than I have commented on the similarity between Hollywood and Silicon Valley.  Kevin Laws for example has written some thoughtful posts on this subject.  Suffice it to say that I believe, as with any analogy, there are insights to be gained from understanding what is similar AND what is different.  Overreaching the analogy leads to simplistic and erroneous conclusions (e.g. in Hollywood there are actors who guarantee box office success, while there is no CEO/founder who can guarantee success in a startup).

This year's Sundance experience raised two questions for me.  First, have I become a better picker after 5 visits to/30+ movies at Sundance?  Second, surely when a movie is SO bad someone associated with the movie must recognize this during the making? 

After some reflection I believe I have become a better picker because I have developed a better process.  I go for the second weekend, not the first, so I can benefit from the "buzz".  I search for films on the basis of director, actor, screenwriter, subject.  I remember movies I liked and disliked and match against similar traits.  I remember reviewers I agree and disagree with.  It's not always at a conscious level but I have developed some level of pattern recognition, eventhough I cannot always articulate it.  Interestingly the two movies I did not like where 1) the grand prize winner (so I did not really pick it, the critics did) and 2) the collection of shorts (which was an experiment).  Of course the acid test shall be if I continue to pick better than 50/50.  Gives me a good excuse to keep going to Sundance to indulge my craving for new movies.

The second question was a timely reminder for me of the importance of always doing due diligence with the people in the middle of the action.  The CEO/director will always tend to present a rosy picture.  But the engineer/cameraman always knows that the product/film is going to be a disaster.  The trick is 1) finding this person 2) asking them the right questions (they will rarely volunteer information) and 3) understanding their motivation. 

P.S.  If you get a chance to see Why We Fight don't miss it.  It's Fahrenheit 911 without the polemic and a focus on the system (military-industrial complex) rather than on individuals (the Bush family).