Have University incubators already eclipsed business plan competitions in terms of student startup outcomes?

While I do not have formal data on the subject, my sense of the historic landscape would be that university incubators, accelerators, and even startup weekends — relatively recent phenomena — may have already spun out a larger pool of surviving startups than that pool spun out over the entire history of university business plan competitions. If true, than the predominance of “Garages” (my word for incubators) on college campuses may need to be taken far more seriously.

There are some people out there who consider incubators a hot trend, if not a cliché. In the minds of these folks, incubators are simply playgrounds where monkey bars, see -saws, and slides have been replaced by computers, bean bag chairs, and whiteboards. The undeniably high failure rate of startups from incubators simply adds fuel to the argument that these new venture zones may be more akin to McDonalds playlots.

In my opinion, this “new” phenomenon of shared startup space is actually a reflection of something quite old. Incubators simply bring together and into and single location and clear sight those startup activities that were previously taking place in dorm rooms, apartments, coffee shops, and — well — garages. Furthermore, these community workzones encourage the sorts of behaviors common to vibrant startup communities: e.g., open innovation, tinkering, fluid teaming, demoing, and more.

If you see startup activity from an evolutionary (or natural selection) perspective, than the seemingly random attempts (and failures) of a large numbers of startups is what we need in order to happen upon that small number of surviving ideas and teams. In essence, the purpose of the incubator space is to accelerate natural selection — to curate an ecosystem within which both failure and the glimmer of success surface sooner rather than later, at as little cost as possible. In fact, this accelerated selection is more like evolution than incubation. Most chicks survive incubation. Most variations fail through selection.

From a purely pragmatic perspective, there are a number of more basic reasons for incubators having the potential to trump plan competitions, when it comes to raw startup development:

Building what you can build versus Pitching what you can’t build.

Incubators tend to require of the individuals therein either(a) the ability to build what they are imagining or (b) the tenacity to find someone, right now, to help build what they have imagined. As a result, the teams in these workspaces tend to be in position to act on their ideas, almost immediately.

Plan competitions, however, tend to be populated by ideas presented by teams who lack the capacity to pursue the idea at the moment. Instead, these people are pitching for the capital to acquire those things that early teams truly require — the portfolio of talent required to get through the most uncertain phases of the startup: proof of concept and customer #1.

Learning through mentoring versus Winning by judgment.

The best startup workspaces are more than just walls with whiteboards and power outlets. These spaces aggregate a community of individuals and, importantly, mentors. Building out a startup is ultimately a craft, and mentoring has been the method over the millennia through which craft is developed and refined.

Competitions are guided by judgement, and often conclude with that judgement. Winners are chosen, and that moment can often be the least time the team and the judges ever communicate again.

Furthermore, competition selection is wholly reliant upon the judgment of a small set of individuals. As a result, we significantly limit the population of new firms based upon the impressions of people who, while intelligent and experienced, may not have all of the information or experience that is needed for the best decision at the time.

The best of both worlds

When you think about it, what we really need is the best of both worlds: the market research, feasibility analyses, and capital that go into business plan competitions combined with the tinkering, teaming, and mentoring activities that are common among incubators. Certain universities have already begun to merge these two phenomena, and this combination may very well result in a compelling new cohort of student startups.


A random walk down Sandhill Road

What if startup success might be best modeled and even navigated if it were imagined to be akin to a random walk down Sandhill Road? This question is the one that has recaptured my interest lately.

Simply stated, a random walk is a process that can be understood as a series of steps or stages, wherein each step or stage can be described according to some probabilistic set of outcomes (e.g., a coin toss, or a normal distribution). One of the classic examples of a random walk involves mapping the circuitous walk home of an individual who has had too much alcohol. 

At any corner on their walk home, this inebriated individual might turn left or right, go forward or turn back. While a map of this drunken walk betrays the underlying randomness of the clouded mind, the probability that the individual will make it home is actually pretty good — even when each decision at each corner is understood as simply a 1 in 4 chance of heading in the right direction.

If startup success were a random walk, we might find that attempts to predict success in this market would be foiled. Investment returns would be highly volatile, with low success rates but (potentially) very high returns. Investors in this sort of market would be very likely to embed “options” and other downside protections into their deal terms (e.g., liquidation preferences, follow-ons, anti-dilution, etc.).

Furthermore, and from a sociological perspective, actors in the market would be very likely to imitate the behaviors and characteristics of successful individuals (e.g., hoodies), given the reliable attributes of and mechanisms for success would be hard to predict. For example, since Google instituted a 20% rule for employee projects, other firms would emulate this rule just in case it had something to do with Google’s success.

By and large, we tend to associate startup success with some indispensable personality trait (e.g., gambler, gunslinger, genius, or guru, according to my research), defendable market position, inimitable technology, or simply dumb luck. In this context, asserting that startup success is a function of something “random,” would likely be thought to suggest that all of those other factors are trumped by the last … dumb luck.

In truth, however, each of these factors — whether realistic or foolhardy — could play a role in navigating a random walk. When affiliated with risk, random walks have been shown to be navigated and managed through either (a) a set of “options” or ways to minimize downside risk (i.e. loss) amidst uncertainty (i.e., not knowing what will happen), or (b) a large enough portfolio of random items, such that all of the randomness (positive and negative) is absorbed into a single “market” profile.

Choice (a) above basically supports why we even have options markets and why investors tend to stage investments. Choice (b) above basically explains why index funds have replaced mutual funds and stock-picking as the dominant strategy for long-term investors.

From this random walk perspective, a phenomenon that may be random, at its core, can be managed successfully through a portfolio of options and other strategies that accomplish two interconnected goals: (1) limiting exposure to negative outcomes (aka, downside), while  (2) opening if not increasing exposure to positive outcomes (aka, upside).

For example, the premise of affordable loss emerging from the effectuation school in entrepreneurship research can be seen as simple rule to manage downside risk in a random walk; limit the earliest investments in a venture to the smallest amount possible, even zero.. This rule limits downside risk by simply limiting the size of the investment if not mitigating the personal risk by relying upon the contributions of others.

Similarly, the development (and even popularity) or the MVP — the minimal viable product — can be seen as a risk-mitigating strategy in the context a random walk. If you don’t know what is going to happen, target the most important uncertainty (e.g., the existence of real demand) and confirm or deny that uncertiunaty through committing as little capital as possible.

Importantly, the odds of success are not really changed with these strategies, all other factors held equal. Instead, the firm’s exposure to both desirable and undesirable outcomes is altered. In other words, adopting these strategies may not really change the probability of survival in a set of firms, all in the same market, all with the same initial investment, etc.

However, we would see that the payoff profile of the more enterprising firms — those that manage risk in this asymmetrical manner — would be quite different from the payoff profile of the less enterprising firms. The more enterprising firms would commit leas time, money, or other resources before failing.

Ironically, these uncertinaty mitigating firms could even fail more often. In aggregate, however, these firms would provide a better payoff than a portfolio of firms adopting an “all or none” strategy.
What if startup success were best understood as a random walk down Sandhill Road?