The first, execution risk, has been well covered - I especially like this old post from Martin Tobias. In a nutshell, execution risk covers the risk involved with successfully doing those things that are under the company’s direct control. I think many entrepreneurs have a tendency to underestimate execution risk in the same way that drivers who correctly understand the population’s risk of having an auto accident underestimate their personal risk, but that will have to wait for another post.

The second category is what I think of as ‘assumption risk,’ the risk that the world does not actually end up working the way the entrepreneur believes it does. Market risk is one type of assumption risk, but it is by no means the only one.

Since I focus on ventures with a big technology component, I’ve noticed that technical assumption risk is often misclassified as execution risk. This may be due in part to the problem of modeling complex systems - many of the systems we work on are sufficiently complex that it is not feasible to prove the validity of a solution without some measure of real world trial.

The misclassification may also be due in part to the roles in a startup; often, the people thinking about risk in a business planning context are different from those thinking about technical assumptions. When senior management is relatively non-technical, there are a couple of ways to deal with technical risk:

1. Think about all technical risk in terms of “the chance that our technical team will pull this off.” Essentially, this is an intentional classification of all technical risk as execution risk.
2. Work with the technical team to distinguish execution and assumption risks, and plan accordingly

It likely goes without saying that well-run startups choose the 2nd approach.

When we first started building the TurnTide product, we faced plenty of technical risk from both categories. There were a couple of primary assumption risks: how effectively would the application of our traffic shaping techniques to email streams control spam? would good email get through even from nodes that also sent spam?

No amount of modeling or analysis could tell us with absolute certainty what would happen in the real world. The only way to deal with these assumption was to get a beta product deployed in a large, real-world mail stream and test.

There were also execution risks from every direction: would we build a stable network appliance? would the traffic shaping implementation work as designed? would the analysis system accurately determine how much of the network resources to allocate to each sending node?

Execution risk is certainly quite different, in that we know from the outset that a correct solution is possible. We knew that we could do all of these things successfully; in order to mitigate our execution risk we needed to figure out how to maximize the chances that we would do so.

In a sense, the elimination of assumption risk could be considered the creation of value, in the same way that the discovery of mineral deposits would be. The elimination of execution risk, then, is the realization of value - analogous to the process of extracting and refining the buried minerals. Both are clearly necessary, but the require different investments and deliver different returns.

The process for technical startups is nowhere near as linear as I’ve just implied, but since the amount of risk is inversely related to the valuation of a startup company during its various rounds of funding, it seems to be well worth thinking about these issues when planning funding rounds and the progress made between them.

Looking at the technically innovative startup pitches I’ve heard from this perspective, I can break them down into two basic categories.

Horse before the cart: we have built this really incredible technology. Here’s what it is and how it works. Take a look at our demo, which proves that we have built this technology. Here’s a list of amazing things we can do better with this new technology.

Cart before the horse: we are doing this amazing thing. it wasn’t possible before, and doing this thing enables an awesome new business. Take a look at our demo, which proves that are doing this amazing thing. It works because of this really incredible technology we have built.

Yahoo, and other big companies, can afford to occasionally build and launch a new horse without putting a clear, focused application of the technology out in front. In fact, doing so can help them maintain their influence and claim leadership in new areas. Some successful startup companies have built technology first without a narrow application guiding the business, but I’d argue that these are the exception rather than the rule.

From the founder perspective, I think that focusing on a technology, however cool, can lead to some problems. When the technology comes first in my thinking, I can fall into a trap of not diligently evaluating even one of the many potential market opportunities. Focusing on one little application of the cool new technology means thinking about things like who, exactly, will buy it… what will they pay for it… how will they use it… how will it change their lives. Cool technology is really exciting to a few people, but the thing it does is what makes it exciting to lots of people.

Looking at companies from the outside, as an investor or otherwise, I find the technology/application focus question to be even more important. Founders may have a grand vision for an eventual application of the technology, but if they are focused on building a business around a focused, narrow application first then they will make better decisions about how much money to raise, how much to build before launching, and how to measure success. The focus on an initial application makes business decisions, external and internal, quite a bit easier. It also turns the technology into a key asset and competitive advantage, rather than the company’s raison d’être.

Funding or starting this type of company means that the initial, focused application stands on its own as a business that should be built. If that business is successful, the company can leverage its technology assets in tackling larger opportunities.

So, next time you are trying to convince a VC about the merits of your firm, show them how they can make 10x capital on a realistic exit scenario (not how to get a 40% IRR).

reminds me of the importance of the differences in risk model between VCs and entrepreneurs, something I’m going to explore a bit here. One example of a VC model is described in the post, so I’ll use those numbers for the comparison.

Bases loaded

The VC risk model is based on a portfolio of investments, and the VC should choose each investment on the basis that a 40% risk of negative return is justified by a 10% chance of 10x+ return and a 50% chance of a zero to 5x return. This model, along with its countless variations, is designed to accomodate the high risk of failure of startup companies. VC Confidential has this to say on the subject:

In the early stage world, if you target, say a 40% IRR, through assuming a number of 5x wins in a compressed period of time, you will likely be out of the business. Your 5x wins, while possibly generating high IRR’s, don’t return enough multiple to pay for the 4 tube shots and 2 break-even deals.

So, VC-funded startups fail at a high enough rate that the target outcome for each investment needs to be a 10x return in order to pay for the investments necessary to deliver that one return with confidence. In fact, even a high confidence investment in a company targeting a 5x return is a hedge: overall, it decreases the chances of a very low rate of return for the fund but also decreases the chances of an on-target rate of return.

To stick with my tenuous baseball analogy, as a VC you start with no outs and no strikes, so while each swing is unlikely to yield a home run, over all of the swings you will probably have at least one. Also, you only swing for the fences with runners on base, because the value of a successful home run needs to more than cover the outs and strikes used.

Two outs, two strikes

Unlike the VC, the entrepreneur runs one company at a time. This means that the entrepreneur can’t use the same risk model to achieve a high confidence level in a given rate of return. All other things being equal, the entrepreneur can’t target the 10x return desired by the VC risk model without having a lower confidence in the outcome.

From this, it seems like the entrepreneur is incented to target lower returns in order to achieve a higher confidence. Of course, entrepreneurs have a much larger range in their tolerance of risk than VCs. Some entrepreneurs will target a 100x return with a small chance of success, while those at the other end of the spectrum are happy to run lifestyle businesses.

Focusing on businesses that need institutional funding, how does the entrepreneur, with only one swing at the ball, manage risk and still target a return that works in the VC model? One approach is to look for businesses that have a real swing at the fences, but can be turned into triples or doubles along the way. Not every business can work this way: many startups are based on predictions about the future and won’t have exit opportunities until the core assumptions or predictions are validated; at the other end of the spectrum many are really trying for a single or double and could get very lucky with a triple. The ideal situation for this approach, which involves a real risk/reward decision between exit and growth at each stage of funding, doesn’t come along very often.

Wins vs. RBI’s

If a company has the opportunity to decide between exit and growth, the alignment of risk models comes to the center of attention. There has been a lot of discussion lately about founder liquidity as a means for aligning incentives between VCs and entrepreneurs, but in the context of this post, founder liquidity is a mechanism for aligning the risk models by hedging the founders’ risk at the expense of upside.

I think there is value in looking for companies that have a swing at the fences, but can be turned into triples or doubles. Hopefully, recognizing that as a target will help in choosing, and running, businesses that have a real addressable market from the start, can divide market risk over time while decreasing execution risk, and are able to validate their assumptions and predictions early. When it comes to decisions on exit vs. growth, the combination of discount/premium of the exit and alignment of risk models will come into play. Even if those decisions all favor growth, it seems to me that managing toward exit opportunities makes as much sense as managing every startup toward eventual IPO.