Uncertainty is where profit lies, not risk

It is such a pleasure to come across a thought-provoking piece about startup investing. Jerry Newman´s series of posts is just that. Here are my notes from that piece:


‘The Necessity of Uncertainty’

Startups that aim to create value can’t have a moat when they begin, uncertainty is what protects them from competition until a proper moat can be built. Uncertainty becomes their moat.


Both Apple and Google were doing something entirely new, and some vital part of how their innovations would play out–whether a market would exist for Apple’s computers; how Google would make money eschewing the established business model–was completely and fundamentally impossible to predict. Nobody, not their potential competitors and not even the founders, could foresee how much value they would create, or how. The established companies who could have out-competed these startups did not enter because they, like most successful companies, had processes in place to prevent them from investing in projects that had substantial uncertainty. This left the field wide open for Apple and Google.


While hard, there are two ways to make reliable predictions about the future: deduction and induction. When either of these is available, prediction is (theoretically) possible.

Deduction

Many business ideas can be confidently predicted to fail, even complicated ones, because the chain of cause and effect can be analyzed. Their failure can be reliably deduced. In most cases, knowing not all but just the important starting conditions and transition mechanisms will get you a good approximation of the future, or at least a good estimate of the probability of success or failure.

Induction Induction, the second way to predict the future, assumes the future resembles the past. The ancients may not have known why the sun rose every morning but were pretty sure it would, because it had every day previously. New restaurants are similar enough to one another in their most important business aspects that their risk of failure can be induced. But predicting the future of high-growth-potential businesses is much harder, and perhaps impossible, because these businesses:

  • Are often doing something entirely new; and

  • Must create a new system of connections between the company, the rest of their business ecosystem, and society in order to have the resources and support to grow quickly.

The outcome of a high growth potential startups—where neither deduction nor induction is feasible—is inherently unpredictable. This unpredictability is more than the everyday unpredictability businesses often face


This isn’t to say that everything about every startup is uncertain. It is just that the nature of high-growth potential tech businesses means that the most important drivers of their value creation must be uncertain. To succeed, startups must choose to brave uncertainty because most companies, especially established ones, won’t.


Risk can be quantified, uncertainty can not. Both of these lead to unpredictability but, as noted, they are qualitatively different. Frank Knight in his 1921 book Risk, Uncertainty, and Profit who addressed entrepreneurial uncertainty directly:

[A] measurable uncertainty, or “risk” proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all. We shall accordingly restrict the term “uncertainty” to cases of the non-quantitative type. It is this “true” uncertainty, and not risk, as has been argued, which forms the basis for a valid theory of profit.


Keynes was more precise about what uncertainty is (in 1937):
About these matters there is no scientific basis on which to form any calculable probability whatever. We simply do not know.

Risk is insurable but uncertainty is not, because risk is only locally unpredictable while uncertainty is globally unpredictable. This is why established businesses are fine taking risks: it is equivalent to just another cost, so it can be accounted for. No one knows the day and hour of their death. But your life insurance company can sell policies to thousands of people and know very closely how much they will pay out every year [through actuarial research].


But uncertainty can’t be reduced to a cost, so it can’t be included in projections or plans.

The Ellsberg Paradox, as it’s called, imagines there are two urns (statisticians love urns), the first with 50 black balls and 50 red balls and the second with 100 balls that are either red or black, but you don’t know how many of each. You are asked to pick a ball from an urn. If it is red, you win $100, if it is black you get nothing. Which urn would you choose from? Most people prefer to pick from the first urn, the one with the known proportion of red and black balls. They prefer a known probability of winning to an unknown probability.


Uncertainty in process-driven decision making bodies

People working at established companies must anticipate that they will be asked to explain their decisions, especially their bad decisions. These explanations need to follow procedures or frameworks the company adopts to try and avoid bad decisions before they are made. Investment memos, financial models, payback periods, discounted cash flow models, etc. As [Clayton] Christensen says, these all demand quantification of market sizes, financial projections, and financial returns. Decisions made outside of these models and without these quantifications are very difficult to justify as rational after the fact, especially if they go bad.


Now imagine walking into your boss’s office and presenting an investment rife with uncertainty. “How likely is this to succeed?” your boss asks. “I don’t know.” you say. “How big will it be if it works?” your boss asks. “I don’t know.” you say. “Why don’t you know?” “Because the customers may be different than who we think; because the customers may want a somewhat different product; because the other companies we need to produce complementary products may decide not to.” Etc. “Well,” your boss says, “we’ll just have to wait until we know those things before we can make a decision.”


The entrepreneur may decide to act, while the boss will not, because the entrepreneur does not need to explain themselves to anyone. Established businesses are uncertainty-averse far more than they are risk-averse. Risk can be managed by building a portfolio of projects. But managers can’t mitigate uncertainty ahead of time.


That startups can do something without having to compete with better-resourced companies is not a side-effect of entrepreneurship, it is a prerequisite. Uncertainty about the prospects for the new market or product will cause most other companies to spend neither time nor money on it. At their best they will give it lip service, more probably they will ridicule it, but most likely they will simply ignore it or quash it internally.


Uncertainty creates a difficult trade-off for entrepreneurs. Without uncertainty they will immediately face competition from many others, including some who are better resourced. But a business subject to high levels of uncertainty has seemingly intractable management problems.

Fools rush in where angels fear to tread, and founders start companies where experienced managers fear to go. You may be uncertain about how others will react—your competitors, companies whose product you are replacing, the government, and society as a whole. You will have to convince financiers, customers, employees, and yourself that your idea is a good one, even though you can’t really know yet. Meanwhile, you need industry incumbents and other potential startup founders to continue believing your idea is too uncertain to want to compete with you. If you understand where uncertainty comes from and how it affects decision processes you can craft the right strategy for your company’s situation.



My take-aways from the above:

  • Startups need to emerge from uncertainty, not mere risk. Uncertainty is their moat. [i.e. early stage investors should not seek a moat, but should seek why other market players see uncertainty where the founder sees opportunity]

  • A founder needs to learn to overcome deductive and inductive based challenges. Their core business needs to have persuasive arguments as to why their idea and its execution remains uncertain and as yet unexplored. The whole due diligence process should focus on applying deductive and inductive based challenges to the business drivers. Bonus Points for founders who manage to make it seem as though upon its realisation, when one applies hindsight the path taken will appear to have been obvious.

  • The entrepreneur needs to be tackling a highly material problem/solution which deviates significant attention and resources away from the status quo in order to become known. Otherwise, there is a chance that an incumbent is working on the same problem/solution with better resources.

  • Incumbent businesses can put a price on risk, but not on uncertainty. This creates the exit case for a startup if they prove that uncertainty has been reliably replaced by risk.

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