OnStartups

Game Theory and Software Startups: Part II

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Thanks to those that read and played the startup valuation game in one of my previous articles.  If you have not yet read the article, I encourage you to do so.  The information below won’t make much sense unless you do.

 

Also, I received some great responses, but wanted to acknowledge two individuals that both emailed in the correct answer (and an accompanying detailed analysis).

 

  1. Dean Fragnito
  2. Alex Hofsteede

 

There were several other people that had the answer right (but left comments in other Internet forums where the article was discussed, so I have no good way to acknowledge them).  Congrats to you too!

 

Basically, the simple answer is that given that it is an even distribution of valuations and the bottom end of the range is zero, Google should offer nothing.  It cannot win this game.  Basically, what we have here is an “adverse selection” problem.  For any offer higher than zero, Google’s issue is that the only companies that would accept the offer are those that have a lower or equal valuation (and they know it, Google doesn’t).  What we have here is also a case of asymmetric information.  If Google makes an offer, on average, they lose money.  The best companies (with valuations higher than the offer), won’t take it.  The worst companies (with valuations lower than the offer), will take it.  Overall, Google loses if it plays this game.

 

What does this pathetically simple and contrived example have to do with software entrepreneurship?  I’m glad you asked.  The larger message here, for software entrepreneurs is this:  You know more about your company than other people do (asymmetric information).  In many cases, you may by the fact that others are facing the adverse selection issue.

 

Lets take a look at an example:  Lets say you are raising venture capital (or some other type of external capital).  Your job is to then provide credible signals to the investor that you are not one of those 9/10 companies that they know is going to fail.  Here are some tips and ideas that flow from this basic concept:

 

  1. Do not send your 100 page business plan to all the VCs you know.  Reason:  The VCs think:  “Only startups that don’t have any contacts in the industry (and hence will have a hard time getting employees, partners and customers) would do this.  Well connected startups would know someone that knows us.   This is a losing deal”.

 

  1. Do not hire an “investment broker” to help you raise money.  Reason:  Same as above – if you need to hire a broker, there’s a problem.  VCs think:  “The best startups with really cool ideas wouldn’t need to hire a broker.”

 

  1. Find a great co-founder that believes in the company:  Reason:  VCs think:  “Here’s someone that gave up their cushy job in a big company to join this startup as co-founder.  They know the business better than we do.  They wouldn’t take on this risk if the startup were really crappy.  Maybe this is a company worth investing in”.

 

The intent here is not to get into a detailed analysis of how to get VCs to invest in you (I’m not a big believer in venture capital for early-stage software startups anyways).  The point here is a little broader:

 

Moral Of The Story:  There are many situations where people you are dealing with are faced with the “adverse selection” problem.  Though you may think you have the coolest idea since sliced bread, they don’t know that.  Further, their experience indicates that every startup thinks they have a cool idea – and their experience also indicates that 90% of them will die.  Your job is to figure out a way around this problem.  Somehow demonstrate that you are in that 10% of companies that actually will succeed.  This applies to investors, partners, employees and customers.

 

In a future article, we’ll look at the issue of “The Lake Wobegon Effect for Software Startups”.  Why all startups think that they are “above average” (which obviously, cannot be true).