The Power Law, Long-Tail and Skewed Market Dynamics of Startup Investing

Alex Paterson-Pochet
J12 Ventures
Published in
2 min readSep 4, 2019

The startup investment ecosystem is entirely unique in its characteristics. Most of you will be familiar with the idea that many startups fail, few succeed, and even fewer succeed spectacularly (the Spotifys, Kings, Mojangs and Izettles of the world, to name just a few of our local tech heroes). Just how much the market is skewed is difficult to grasp. Only 3 companies in a 100 will reach a 100 MEUR valuation. Below an illustration of this fact, as demonstrated by the distribution of exit and write-off values in our simulated data set of 5 000 startups and 15 000+ investment rounds mimicking real world observations. Otherwise known as the Babe Ruth effect, the power law or long-tail distribution.

Actual [venture capital] returns are incredibly skewed. The more a VC understands this skew pattern, the better the VC. Bad VCs tend to think the dashed line is flat, i.e. that all companies are created equal, and some just fail, spin wheels, or grow. In reality you get a power law distribution. -Peter Thiel

In venture capital the long-tail represents about 80% of startups that won’t make it past raising a seed round. Why is the long-tail this long? Some of these startups would probably deserve a shot at scaling and implode prematurely for lack of available funding. In the EU, a total of €23bn in venture capital was invested in 2018, dwarfed by Asia’s (mainly China) $92bn and the US’ $130bn, making certain startup failures a self-fulfilling prophecy. Most however probably lack the drive, team, product or execution skill to make a real difference and are rightly denied funding.

So, in a world of extreme events, low probabilities of success and huge upsides, how should you behave as an investor?

Questions? Ideas? Give us a shout @ j12ventures.com.

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Alex Paterson-Pochet
J12 Ventures

Founding Partner/CFO/Sustainability @J12Ventures. Built 1 fintech startup (IPO 2021), the Stockolm School of Economics angel network and 2 AI/data funds.