The 4 P’s of Silicon Valley Risk Capital
An unrolled tweetstorm of low technicality about the Silicon Valley model of risk capital and its impending disruption by ICO rounds. Get more like this in un-unrolled form at @ecoinomia.
The “4 P’s” of Silicon Valley Risk Capital:
1. Parent shopping
2. Power laws
3. Poker nights
4. Pension funds
People who complain about how badly something is run often simply miss how unlikely it was that this thing came together in the first place, and how many things have to fit together to make it work. There’s been dozens of attempts to copy the SV model, with modest success.
Risk capital is a hybrid model to fund high risk tech R&D before usability is established. It competes against corporate R&D which can marshal more funds and resources. The main drawback of corp R&D is the “single parent” problem.
In a hierarchy, ultimately one person decides over funding for early stage tech. At that point, both false negatives and false positives still abound, so single decision points are single points of failure. Parent shopping biases this upwards by offering a choice between funds.
Collective decision making under uncertainty works quite well under normal distribution, where divergence in opinions should converge to the mean. Everything in risk capital runs on exponential distributions though, or vulgo: power laws.
A tiny number of portfolio picks will yield big money, while most others go bust or drag on. This is a “rigged lottery” model, where you can rig the lottery (increase the chance of a money pick) by competence and due diligence. This is why unfiltered crowd decisions don’t work.
This does not mean VCs always get it right, at that stage the chances of failure are still exorbitant. Even a .300 batter will strike out frequently, and a Mendoza line batter have his day in the sun, but in the long run, small differences in probability matter.
Billy Beane, GM of the Oakland A’s and inventor of “Moneyball”, was not a statistician. But he was a poker player. There is very little real statistical acumen in the VC world but a lot of all-night poker games. Turns out this might be just the right level.
VC requires a fair amount of “unreasonableness” (acc. to GB Shaw) and the search for overlooked and undervalued ideas. The most important task is to sift through large numbers of lottery tickets quickly to zoom in on the few that might work out.
Most of the quant work in VC is utterly utterly pedestrian, but for the task, which requires a mix of quant, technical understanding, bullshit detection and gut instinct, that seems sufficient for most players. Usually the economics is what kills them.
VCs are open-ended risk funnels that stretch out over years. In the end, they turn exorbitant risk into manageable risk for a small group of institutional clients who are looking to add some risk diversification (and spice) to their portfolios.
Because of the power law/lottery/winner-takes-all auction aspects of the market, very few pre PE funds are public. Not having to disclose intermediate results (to reduce meddling) is key to survival.
It’s also key to frequent bouts of collective exuberance when institutional safeguards fail and the gold rush is on. Or the bitgold rush…
So is the VC industry up for “disruption”? Possibly. But not by adding naivité, lack of due diligence, overfunding or roping in maxed out credit carders. The first step to disruption is to understand what you disrupt. 🔚
The original tweetstorm appeared on @ecoinomia. The text has been slightly fixed and it might still evolve into a real essay. Oliver Beige is an industrial engineer turned economist (PhD Berkeley, MBA Illinois, MSIE Karlsruhe) who is focusing on how technologies (machine learning, blockchain) might change the value creation between markets and enterprises.