初创公司资金需求计算器。。。。

In my last post, The Product, Part II: Technical architecture and the innovator’s paradox I talked about the importance of staying in the game and linked to a Wikipedia article on the Kelly criterion.  In the comments, entrepreneur and physicist Max Skibinsky took the idea literally and used the Kelly criterion to calculate the optimal burn of a startup.  I was so impressed with Max’s comment that I imported his Google spreadsheet into Excel and played around with it:  here is an editable copy of [my updated version] Max’s spreadsheet.

Max’s math:

p = probability of success
b = payout odds per kelly
F = funding
V = valuation
M = valuation multiplier on “win”
R = burn rate per time frame
T = time frame units to develop and prove

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What we can learn about optimal burn from the Kelly criterion.

You obviously shouldn’t take this too literally.  But I do find that it is a very interesting reality check.  

Assumptions in the spreadsheet:

+ Capital raised to $2MM on a $6MM post money valuation.  

+ 15% chance of any experiment returning a 10x increase in valuation.

+ 9 months to build and test each experiment in the market.

+ $100,000 per head (includes salary, benefits, rent, computers, marketing).

Using Max’s spreadsheet which is based on the Kelly criterion’s probability of maximizing long-term returns, the optimal monthly burn is $32K, which would cover 4 heads.  This would give you capital for 7 experiments.

A few brief thoughts:

For any hit driven (or wildly innovative) business, you should assume that [at least] your first experiment will fail.  This will remove pressure and allow for maximum flexibility.  It also drives how you should build your product and manage your finances.  It also drives the following recommendations:

1.  Keep burn very low until you have proof of traction. 

Everyone knows this intuitively, but the vast majority of startups spend an order of magnitude greater than their target Kelly burn.  You can reduce burn by hiring fewer people, keeping salaries low, working long hours, and hiring very productive people.  Most people focus on keeping salaries low bit, but my experience is that hiring a few exceptional people at higher salaries is cheaper than hiring more [less productive] people at lower salaries.

2.  Raise more money than you need.

Easier said than done — but if you have the opportunity to raise a bunch of capital, you should seriously consider doing so.  Figure out the optimal number of people needed to run an experiment and use the Kelly burn spreadsheet to impute required capital.  The cost of giving up more equity early on is often more than offset by the increased flexibility to take chances.  There is obviously some equilibrium point in there between loss of present value as a result of taking too much equity capital (for the entrepreneur) versus loss of present value as a result of taking too little capital and putting too much capital into a single bet or few bets.  Many entrepreneurs can’t raise more capital, but those who can should.  

3.  Increase the probability of success on each experiment.

This is clearly the highest leverage point in the model.  You can increase your odds of success by (a) picking a big existing market (rather than trying to invent a market, reinvent an existing one); (b) recruiting a killer team; and (c) picking great investors.

You can also increase your odds of success by building and shipping product quickly, by instrumenting your site / product so that you can run tests and make data-driven decisions, and by killing failed experiments quickly.   

For an entertaining history on mathematics, information theory, economics, gambling, and the mob check out Fortune’s Formula.