The Problem with the Current View of Entrepreneurship
The Success Rates of Engineers Compared with Entrepreneurs Remain Worlds Apart
In 1962, United States President John F. Kennedy pledged to land a man on the moon and bring him back safely. The goal presented the National Aeronautics and Space Administration (NASA), the U.S. agency responsible for the space program, with staggering uncertainty (1).
At the time, NASA didn’t know if the moon’s surface was solid enough to land on. The moon was a quarter of a million miles up in space—the highest an aircraft had ascended was 100 miles. They didn’t know if communication systems would work on the moon. They didn’t know how to project orbits to the moon. No one had worked outside a spacecraft. Re-entering Earth’s atmosphere would generate heat and stress magnitudes greater than what existing materials had experienced.
Startups trying to solve big problems and commercialize breakthrough technologies are in much the same position as NASA in 1962. Startups face uncertainty about whether customers will want a new functionality, what features they would value, and how much they would pay. There’s uncertainty about operations: How should it be sold and priced? What’s the best way to do marketing? What should be made in-house versus outsourced? And how can everything be done profitably?
Despite having the same starting point, the success rates of engineers compared with entrepreneurs remain worlds apart.
As we know, NASA’s Apollo 11 fulfilled President Kennedy’s pledge. While a remarkable accomplishment, it wasn’t an outlier. Engineers routinely innovate new, complex systems under conditions of high uncertainty with a high level of reliability. Missions to Mars—a planet lying 140 million miles from Earth—have a 40% success rate.
Today, a startup’s probability of achieving enduring profitability—a business’s measure of success—remains slim. Startup Genome calculates that 90% of startups fail completely, and 1.5% produce a successful exit of $50 million or more (2). A successful exit, however, doesn’t mean the company is profitable. Based on IPOs from the last decade, less than 20% will become profitable. The likelihood of success, in other words, is lower than 1%.
The problem isn’t confined to deep tech, like Google’s failed attempts at rural internet via high-altitude balloons, and using smart contact lenses to measure glucose levels. Startups trying to commercialize novel services are also failing, even after a decade of experimentation. The list includes ventures in a range of new, but still unprofitable, industries, from meal subscription kits, buy-now-pay-later consumer financing, and app-based food delivery, to peer-to-peer car sharing and online fashion resale and rental.
Something is off. Yes, engineers have laws of physics to guide them; but business has a century of research by economists, psychologists, sociologists, and strategists that have revealed basic truths about consumers and markets. Figuring out how to profitably help people put dinner on the table or buy things they can’t afford shouldn’t present hundreds of times the risk of reaching Mars.
Based on two decades researching and building startups, we believe the source of the discrepancy is the different way engineers solve for high uncertainty and complexity compared with today’s entrepreneurs.
(1) Varol, Ozan. (2020). Think Like a Rocket Scientist. Public Affairs. New York.
(2) “The State of the Global Startup Community.” Startup Genome. Accessed at: https://startupgenome.com/article/the-state-of-the-global-startup-economy
Lean startup goes against the basic science of innovating new systems
Based on two decades researching and building startups, we believe the source of the discrepancy is the different way engineers solve for high uncertainty and complexity compared with today’s entrepreneurs.
Engineers invest time researching, modeling, and simulating to first solve for the high-level concept—the basic shape of the solution best able to meet all of a system’s core requirements. The same process is used to turn the high-level concept into a robust, detailed design—one that has a wide margin of safety to absorb uncertainty. Nothing is built until it works on paper. They then prototype and test starting with individual parts, before connecting parts up and testing them together. A live test of the whole system happens at the end when there’s high confidence that the system works.
The science about systems is clear: this way of innovating—which engineers call the V-model method, because the approach traces the shape of the letter V—creates solutions that are more robust, reliable, and economical, and is faster and cheaper.
You see it in the way many engineers-turned-entrepreneurs operate: it was the basis for how the late Steve Jobs built Apple, and Elon Musk’s “algorithm” (3) (4). Renowned architect Frank Gehry ushered in a revolution in architecture by adapting aerospace engineering software based on the V-model to innovating physics-bending buildings. It’s key to his ability to deliver projects on-time and on-budget (5) (6).
Compare that with today’s accepted way of innovating ventures—the lean startup method. Lean startup narrows the focus at the start to one core requirement: coming up with a basic product solution that addresses customers’ needs. As the thinking goes, if customers don’t want the product, solving for profitability and competitive advantage is a moot point. Next, a basic business model to support the product is sketched out, and a company is launched with the understanding that it’s a starting point from which to begin experimenting and searching for a sustainable business model.
Lean startup goes against the basic science of innovating new systems. It’s strapping a spacecraft onto a rocket, blasting off, and trying to figure out how to safely land on the moon while en route. It’s why startup success remains rare today. Losing millions of dollars for years on end trying to discover a profitable business model isn’t the nature of entrepreneurship—it’s the nature of today’s method.
It’s a worthy reminder that Apple, Microsoft, and Adobe generated net profits within two years of their founding and were profitable at the time of their IPOs. Electronics manufacturer 3Com did it in under four years. Nike generated profits throughout a seven-year rapid growth phase leading up to its IPO in 1981 at sales of $450 million.
Lean startup persists because venture capital funds profit even though their portfolio companies are unprofitable. First Round Capital, for example, converted its $1.5 million seed investment in Uber into a $2.5 billion exit even though Uber had $10 billion of accumulated losses and was losing $1 billion annually at the time of Uber’s IPO. Corporate venture teams and self-funded entrepreneurs have faced the TINA problem: there has been no alternative.
Having realized the power of engineering’s V-model method, we set out over a decade ago to translate it into a startup context. In this article, we explain why engineering’s V-model is so effective. We then introduce a methodology called “integrated venture engineering” (IVE). IVE is a V-model for startups. We’ve validated core processes and tools through more than a dozen corporate and entrepreneur-driven ventures across a range of industries. We believe it has the capability to transform startups’ success rates.
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