One of society’s classic mistakes is a belief that people’s success is due to their hard work and determination, when it’s really due to external factors. The startup world lionizes repeat successes like Elon Musk and Steve Jobs as evidence that certain people are more skilled at creating new companies than others, yet the academic literature on startups contains gems like
Human capital variables have limited impact on startup performance, and the few significant effects are split equally between enhancing and impeding performance
In this post, I discuss the extent to which startup success depends on things outside of the founders’ control and what to do about it.
Part 1: The necessary role of luck
If you are like me, you've understood that entrepreneurship is more risky than dependent employment, but you've never really thought about why. There's no law which says that entrepreneurship has to be risky, so what's going on? Are VCs just getting together and deciding to pay entrepreneurs based on a dice roll? The answer is: basically, yes.
If a founder leaves a startup, that company is in much more dire straits than if an entry-level employee leaves IBM. This means that startup founders need to signal their commitment to investors much more strongly than entry-level employees do; that signaling usually takes the form of the founder rejecting a salary and instead gambling their finances on the company success. Because salaries are much more stable than business values, this means that entrepreneurs are forced by investors to accept higher levels of risk.
This is important so I’m going to call it out in very clear terms:
Entrepreneurship must involve an element of risk for the founder (i.e. luck). If it didn’t, no one would invest.
The Importance of Industry Selection
The greatest startups seem to create markets out of thin air: no one cared about search until Google and only petty criminals ran non-medallioned taxis before Uber. But in reality Google couldn't have been successful without the dot-com boom, nor could Uber have gotten traction if smartphone use was low – factors completely outside the founders' control.
This is true more broadly: studies have found that the strongest predictor of a startup’s success is whether they are in a growth industry, rather than the inherent ability of founders (beyond their ability to select a growth industry).
My favorite example of this is a paper entitled “How much does industry matter, really?” In which the authors performed a linear regression on companies’ profitability which included a coefficient for every single company (i.e. it was insanely biased towards finding that individual performance matters). This lead to the following conclusions:
- Your industry’s performance explains 19% of your company’s performance.
- Everything else about your company combined (growth, employees, IP,…) explains 32% of performance.
This means that if you are in a growth industry you can do a lot of things wrong and still end up with a better company than an awesome team with great traction, ironclad intellectual property and a crystal-clear value proposition if that second company's industry happens to take a downturn.
This is not to say that all entrepreneurs are created equal, because clearly they are not. But the point is that skill in entrepreneurship is less about being able to steer your ship through stormy waters and more about checking the weather before you set off.
Amazon’s success is often attributed to the frugality of its founder, Jeff Bezos, including tidbits like that their desks were initially made out of office doors, and that they removed all the light bulbs from their vending machines to save on electricity.
But the actual story of Amazon’s founding is more interesting. Jeff had heard two interesting things in 1994:
- Internet usage was rapidly growing, and
- The Supreme Court had just ruled that online retailers don’t have to collect sales tax
He guessed that this would mean an increase in e-commerce, and his guess was right.
Importantly, he didn’t think that he personally had specific talents in e-commerce (his background was in finance), nor did he start a company to pursue his passion of selling books online. He took the outside view that e-commerce in general was going to be successful, and hitched himself to that wagon.
Conclusion of Part One
Some people invest in market size and market potential and the idea itself. We start with the people first. We think the ideas that entrepreneurs start with evolve and change dramatically from the beginning and sometimes end up unrecognizable, so we believe in investing in the people. - Ron Conway
The idea that there are "superstar" entrepreneurs with a Midas touch is just factually wrong. For a long time, evidence indicated that there was no such thing as entrepreneurial skill at all, and while larger data sets are starting to change that perception, even the most "pro-skill" papers find skillful entrepreneurs to have only modest benefits over their less skilled counterparts (for example, increasing the chance of success from 20% to 30%).
It’s not surprising that people believe individual performance matters even when it doesn’t; this error is so common that it’s known as the “fundamental” attribution error and interested readers can view the Wikipedia page for a list of ridiculous circumstances in which people attribute performance to internal characteristics even when there’s absolutely no way internal characteristics could be involved.
One of the differentiators between entrepreneurs and everyone else is that entrepreneurs have a stronger illusion of control, believing that they can accurately predict things like when competitors will enter the market. This causes the startup community to be obsessed with topics like growth hacking, building "culture", and financing. Those are all important, but there is no question that an hour spent looking at sample investment documents would be better spent looking at market projections.
Part Two: What to Do About It
Humans so frequently attribute success to internal characteristics instead of external ones that this bias is known as the "fundamental attribution error". Because it's so frequent, there's a huge amount of research on how to avoid it and one technique I found useful is known as "reference class forecasting".
At a high level, this involves identifying companies that are similar to yours and then estimating your own likelihood of success based on those companies' success rates.
For example, Amazon's claim was that a new technology (the Internet) and new tax advantages would cause people to buy books in a new way. If previous tax cuts had caused huge spikes in book purchasing, or if other products had seen great sales on the Internet, this would cause us to increase our guess as to Amazon's success. If it turned out that people loved buying books in person so that even when previous technologies like telephones spread no one bought books over the phone, this would decrease our estimate.
(There's a cliché that startups describe themselves as "it's like X for Y." There's even a site which generates these clichés. Reference class forecasting is basically taking that cliché and turning it into an investment strategy.)
I gave empirical evidence before about the importance of industry selection, but reference class forecasting gives a strong intuition for it. Take our standard Amazon example:
- Claim: Amazon was successful because its founder was frugal. Examination: make a reference class of all frugal founders. What fraction of them have billion-dollar companies?
- Claim: Amazon was successful because its industry took off. Examination: make a reference class of all founders whose industries took off. What fraction of them have billion-dollar companies?
My first idea for a company was predictive analytics around code quality. While I was at my previous job I had been able to show that certain coding techniques were associated with a greater number of defects, and I figured I could monetize this.
The outside view didn’t look great though. Static code analysis tools have existed for a long time, but few people use them as a core piece of the development process. There were no recent advances in IDEs or version control which I could use to explain why my tools will be more successful. Even though this idea met many of the standard startup idea criteria (I had domain expertise, it solved a problem I myself had, I was passionate about it), I abandoned it.
My current projects deals with quality measurement in healthcare setting. For those of you unfamiliar with American politics, health care reform is kind of a big deal and quality-based payments are expected to triple in the next three years. The market is clearly growing and while I think I have some advantages that my competitors lack, I think the outside view looks pretty good for all of us.
When I explain my unorthodox views on the importance of teams, people often say something like:
It’s better to have an A team with a B idea than a B team with an A idea.
My point isn’t that this is wrong; it’s that it doesn’t make sense.
Industry selection is one of the most, if not the most, important skills of a founding team. If the founding team did their market research, customer validation, etc. and still didn’t realize that their bad idea was bad, then in what sense are they a good founding team?
Also: reference class forecasting is cool, and people should do more of it.
Lastly: if you want to work for a startup with an excellent reference class, we're hiring.
See “Entrepreneurship: a game of poker, not roulette” for a different spin on roughly the same facts.
I would like to thank Brian Tomasik, Gina Stuessy and especially Ben Todd for comments on earlier versions of this article.
 Baum, Joel AC, and Brian S. Silverman. "Picking winners or building them? Alliance, intellectual, and human capital as selection criteria in venture financing and performance of biotechnology startups." Journal of business venturing 19.3 (2004): 411-436.
 See Wikipedia for the general phenomenon, or e.g. Cumming, Douglas. "Adverse selection and capital structure: evidence from venture capital." Entrepreneurship Theory and Practice 30.2 (2006): 155-183.
 Gartner, William B, Jennifer A Starr, and Subodh Bhat. "Predicting new venture survival: an analysis of “anatomy of a start-up.” cases from Inc. Magazine." Journal of Business Venturing 14.2 (1999): 215-232.
 McGahan, Anita M., and Michael E. Porter. "How much does industry matter, really?." (1997).
 Gompers, Paul, et al. Skill vs. luck in entrepreneurship and venture capital: Evidence from serial entrepreneurs. No. w12592. National Bureau of Economic Research, 2006. For a model which has reasonably accurate predictions but assumes that entrepreneurial success is entirely due to luck see: Kihlstrom, Richard, and Jean-Jacques Laffont, A General Equilibrium Entrepreneurial Theory of Firm Formation Based on Risk Aversion, Journal of Political Economy 87. 1979. 719-748.
 Simon, Mark, Susan M. Houghton, and Karl Aquino. "Cognitive biases, risk perception, and venture formation: How individuals decide to start companies." Journal of business venturing 15.2 (2000): 113-134.
 Of course, you might argue that there are ex-ante good ideas which turn out to be bad, and ex-ante bad ideas which turn out to be good. To which I respond: exactly.