Data shows that popular startup methodologies fail to improve survival rates.

Author: Colossus

Compiled by Deep潮 TechFlow

DeepChao Summary: This article uses U.S. government data to debunk an uncomfortable truth: over the past 30 years, all popular entrepreneurial methodology books—Lean Startup, Customer Development, Business Model Canvas—have shown no statistical benefit in improving startup survival rates.

The issue isn't necessarily that the methodology itself is flawed, but that once everyone is using the same approach, it loses its advantage.

This argument applies equally to crypto and Web3 entrepreneurs, especially those reading various “Web3 startup guides.”

The full text is as follows:

Any method for building a startup, once widely disseminated, causes founders to converge on the same answers. If everyone follows the same popular startup advice, everyone will end up building the same kind of company, and without differentiation, most of these companies will fail. The truth is, whenever someone insists on teaching you a method for building a successful startup, you should do something different. Once clearly understood, this paradox becomes obvious—but it also contains within it a direction forward.

Before the new wave of "entrepreneurial evangelists" emerged twenty-five years ago, the entrepreneurial advice it replaced was, frankly, worse than useless. It was a naive blend of Fortune 500 corporate strategy and small-business tactics, combining five-year plans with day-to-day operations. But for startups with high growth potential, long-term planning is meaningless— the future is unpredictable, and focusing on daily operations leaves founders exposed to faster-moving competitors. The old advice was designed for a world of incremental improvement, not one of fundamental uncertainty.

The advice from a new generation of entrepreneurial evangelists differs: intuitive and reasonable, with seemingly solid reasoning, providing founders with a step-by-step process for building a business amid real uncertainty. Steve Blank introduced the Customer Development method in The Four Steps to the Epiphany (2005), teaching founders to treat business ideas as a set of falsifiable hypotheses: go out, interview potential customers, and validate or invalidate your assumptions before writing a single line of code. Eric Ries built on this in The Lean Startup (2011), introducing the Build-Measure-Learn loop: release a minimum viable product, measure real user behavior, and iterate rapidly—rather than wasting time perfecting a product nobody wants. Osterwalder’s Business Model Canvas (2008) gave founders a tool to map the nine core components of a business model and quickly pivot when one element fails. Design Thinking—promoted by IDEO and Stanford’s d.school—emphasizes empathy for end users and rapid prototyping to surface problems early. Saras Sarasvathy’s Effectuation theory advises starting from the founder’s own skills and network, rather than reverse-engineering a plan to achieve a grand goal.

These evangelists consciously sought to establish a science of entrepreneurial success. By 2012, Blank stated that the National Science Foundation was referring to his Customer Development framework as the "scientific method for entrepreneurship" and claimed, "We now know how to make startups fail less." The Lean Startup website asserts that "The Lean Startup provides a scientific method for creating and managing startups," and the book’s back cover quotes Tim Brown, CEO of IDEO, saying Ries "has proposed a scientific process that can be learned and replicated." Meanwhile, Osterwalder claimed in his doctoral thesis that the Business Model Canvas is grounded in design science—the precursor to design thinking.

Academic entrepreneurship research departments also study startups, but their science is more akin to anthropology: describing the cultures of founders and the practices of startups to understand them. The new generation of evangelists holds a more practical vision—one articulated by the natural philosopher Robert Boyle at the dawn of modern science: “I would not call myself a true naturalist unless my skills enabled my garden to grow better herbs and flowers.” In other words, science must pursue fundamental truths, but it must also be effective.

Whether it works, of course, determines whether it deserves to be called science. And when it comes to startup preaching, one thing we can be certain of is that it doesn’t work.

In science, we use experiments to determine whether something is effective. When Einstein’s theory of relativity gradually gained acceptance, other physicists invested time and money to design experiments testing whether its predictions were accurate. We learned in elementary school that the scientific method is science itself.

However, due to a certain flaw in our human nature, we also tend to resist the idea that "truth is discovered this way." Our minds crave evidence, but our hearts need to be told a story. There is an ancient philosophical stance—brilliantly explored by Steven Shapin and Simon Schaffer in Leviathan and the Air-Pump (1985)—which holds that observation cannot give us truth; true truth can only be derived through logical principles from other things we already know to be true, that is, from first principles. While this is standard in mathematics, in fields where data is slightly noisier or axiomatic foundations less solid, it can lead to conclusions that seem appealing but are in fact absurd.

Before the 16th century, doctors treated patients using the writings of the second-century Greek physician Galen. Galen believed diseases were caused by an imbalance of four bodily fluids—blood, phlegm, yellow bile, and black bile—and recommended therapies such as bloodletting, vomiting, and cupping to restore balance. Doctors followed these treatments for over a thousand years, not because they worked, but because the scholarly authority of the ancients seemed to outweigh the value of contemporary observation. However, around 1500, the Swiss physician Paracelsus noticed that Galen’s therapies did not actually improve patients’ conditions, and some treatments—such as using mercury to treat syphilis—were effective despite having no basis within the humoral theory. Paracelsus began advocating for listening to evidence rather than obeying long-dead authorities: "The patient is your textbook, the bedside is your library." In 1527, he even publicly burned Galen’s writings. His vision took centuries to be accepted—nearly three hundred years later, George Washington died after an aggressive bloodletting treatment—because people preferred the neat, simple stories of Galen over the messy complexity of reality.

Paracelsus started with what worked and traced it back to the cause. First-principles thinkers, by contrast, assume a "cause" upfront and insist it is valid regardless of the outcome. Are our modern entrepreneurial thinkers more like Paracelsus—driven by evidence—or more like Galen, sustained by the elegance and self-consistency of their own narratives? In the name of science, let’s examine the evidence.

Here are official government statistics on the survival rates of U.S. startups. Each line represents the survival probability of companies founded in a given year. The first line tracks the one-year survival rate, the second line tracks the two-year survival rate, and so on. The chart shows that, from 1995 to the present, the proportion of companies surviving one year has remained largely unchanged. The same holds true for two-year, five-year, and ten-year survival rates.

The new generation of evangelists has existed long enough and become well-known enough—combined book sales have reached millions, and nearly every university entrepreneurship course teaches their methods. If they were effective, this would be reflected in the statistics. Yet, over the past thirty years, there has been zero systematic progress in making startups more likely to survive.

Government statistics cover all U.S. startups—including restaurants, dry cleaners, law firms, and landscape design companies—not just venture-backed tech startups with high growth potential. Startup evangelists have not claimed that their methods apply exclusively to Silicon Valley-type companies, but these techniques are most often tailored to the extreme uncertainty that founders are willing to endure only when the potential returns are sufficiently large. Therefore, we adopt a more targeted metric: the proportion of U.S. venture-backed startups that secure follow-on funding after completing their initial financing round. Given how venture capital operates, we can reasonably assume that most companies failing to secure follow-on funding did not survive.

The solid line represents the original data; the dashed line adjusts for recent seed-stage companies that may still complete their Series A financing.

The proportion of seed-funded companies that go on to complete subsequent rounds of financing has declined sharply, undermining the claim that venture-backed startups have become more successful over the past 15 years. If anything, they appear to be failing more frequently. Of course, venture capital deployment is not determined solely by startup quality: factors such as the impact of the COVID-19 pandemic, the end of the zero-interest-rate era, and the highly concentrated capital demands of AI, among others, also play a role.

Some might argue that the growth in total venture capital has flooded the market with less qualified entrepreneurs, offsetting any gains in success rates. However, in the chart below, the decline in success rates occurs during both periods of growth and contraction in the number of funded companies. If an oversupply of underqualified founders were dragging down the average, success rates should have rebounded after 2021, when the number of funded companies decreased. Yet they did not.

But isn’t the increase in the number of founders itself a success? Try telling that to entrepreneurs who followed the preachers’ advice and still failed. These are real people who risked their time, savings, and reputations; they deserve to know what they’re up against. Top venture capitalists may have made more money—there are more unicorns now than ever—but this is partly due to longer exit timelines and partly because the power-law distribution of exits mathematically means that the more companies you start, the higher the probability of a massive success. For founders, this is a cold comfort. The system may be producing more big winners, but it hasn’t improved the odds for individual entrepreneurs.

We must take seriously the fact that a new generation of evangelists has failed to make startups more likely to succeed. Data shows that, at best, they have had no impact whatsoever. We have spent countless hours and billions of dollars on a fundamentally flawed framework.

The evangelists claim to be giving us a science of entrepreneurship, yet by their own clearly defined standards, we have made no progress: we still do not know how to make startups more successful. Boyle would say that if our garden has not grown better herbs or flowers, then there is no science here. This is both disappointing and confusing. Given the time invested, widespread adoption, and the apparent intellectual rigor behind these ideas, it is hard to imagine they have no effect. Yet the data shows that we have indeed learned nothing.

If we want to build a true science of entrepreneurship, we need to understand why. There are three possibilities. First, perhaps these theories are simply wrong. Second, perhaps they are so obvious that systematizing them is meaningless. Third, perhaps once everyone uses the same theories, they no longer provide any advantage. After all, the essence of strategy is doing something different from your competitors.

If these theories were fundamentally wrong, we would expect startup success rates to decline as they spread. Our data shows this is not the case for startups overall, and the failure rate of venture-backed companies appears to be rising for other reasons. Setting aside the data, these theories don’t seem obviously wrong—talking to customers, running experiments, and iterating continuously clearly appear beneficial. But Galen’s theories also didn’t seem wrong to doctors in 1600. Unless we test these frameworks as we would any other scientific hypothesis, we cannot be certain.

This is Karl Popper’s standard for science as outlined in The Logic of Scientific Discovery: a theory is scientific if and only if it is, in principle, falsifiable. You formulate theories, test them. If experiments do not support them, you discard them and try something else. A theory that cannot be falsified is not a theory at all—it is a belief.

Few have attempted to apply this standard to entrepreneurship research. There have been a small number of randomized controlled trials, but they often lack statistical power and define "effectiveness" as something different from what truly constitutes success for startups. Given that venture capital invests billions of dollars annually, not to mention the years of time founders dedicate to testing their ideas, it seems strange that no serious effort has been made to verify whether the techniques taught to startups are actually effective.

But preachers have little incentive to test their theories: they profit and build influence by selling books. Startup accelerators make money by funneling large numbers of entrepreneurs into a power-law sieve, reaping a few wildly successful outcomes. Academic researchers face their own distorted incentives: proving their theories wrong results in loss of funding without any compensating reward. The entire industry has the structure of what physicist Richard Feynman called “cargo cult science”: a grand imitation of scientific form without its substance, deriving rules from anecdotes rather than establishing fundamental causality. Just because a few successful startups conducted customer interviews doesn’t mean your startup will succeed if you do the same.

But unless we acknowledge that existing answers are not good enough, we will have no motivation to seek new ones. We need to use experiments to discover what works and what doesn’t. This will be expensive, because startups are poor test subjects. It’s difficult to force a startup to do—or not do—something (can you stop founders from iterating, talking to customers, or asking users which design they prefer?), and keeping rigorous records is typically a low priority when a company is fighting for survival. Each theory also contains numerous subtleties that require testing. In practice, these experiments may simply not be feasible. But if that’s the case, then we must admit what we would unhesitatingly say about any other unfalsifiable theory: this is not science—it’s pseudoscience.

To some extent, founders didn’t need to formally learn these techniques. Long before Blank introduced “customer development,” founders were already engaging with customers to develop their offerings. Similarly, they were already building minimum viable products and iterating on them before Ries named this practice. They were already designing products for users long before anyone called it “design thinking.” The fundamental dynamics of business often compel these behaviors, and millions of entrepreneurs independently reinvented these approaches to solve the problems they faced daily. Perhaps these theories are obvious—that the evangelists merely put new labels on old bottles.

This isn’t necessarily a bad thing. Having effective theories—even if they are obvious—is the first step toward better ones. Contrary to Popper, scientists do not simply abandon a promising theory the moment it is falsified; instead, they attempt to refine or extend it. Historian and philosopher of science Thomas Kuhn powerfully articulated this point in The Structure of Scientific Revolutions: more than 60 years after Newton published his theory of gravity, his predictions about the Moon’s motion remained incorrect until mathematician Alexis Clairaut recognized it as a three-body problem and corrected it. Popper’s standard would have led us to discard Newton. But this never happened, because the theory was otherwise well-supported. Kuhn argued that scientists are stubborn within a framework of beliefs he called a paradigm. Because it provides a structure that allows scientists to build upon and improve existing theories, scientists do not abandon a paradigm unless absolutely forced to. Paradigms provide a path forward.

Entrepreneurship research lacks a paradigm. Or rather, it has too many paradigms, none of which are sufficiently compelling to unify the field. This means that those who view entrepreneurship as a science have no shared guidelines to determine which questions are worth addressing, what observation entails, or how to improve theories that are only partially correct. Without a paradigm, researchers simply spin in circles, talking past one another. For entrepreneurship to become a science, it needs a dominant paradigm: a shared framework so compelling that it organizes collective effort. This is a more difficult challenge than simply deciding which theories to test, because for a set of ideas to become a paradigm, it must address pressing open questions. We cannot achieve this out of thin air, but we should encourage more people to try.

Economics tells us that if you are doing the same thing as everyone else—selling the same products to the same customers, using the same production processes and suppliers—direct competition will drive your profits toward zero. This concept is a cornerstone of business strategy, from George Soros’s theory of "reflexivity"—where market participants’ beliefs alter the market itself, eroding the advantages they seek to exploit—to Peter Thiel’s Schumpeterian assertion that "competition is for losers." Michael Porter codified this in his landmark work, Competitive Strategy, as the necessity of seeking uncontested market positions. Kim W. Chan and Renée Mauborgne took this idea further in Blue Ocean Strategy, arguing that companies should create entirely new, non-competitive market spaces rather than fight over existing ones.

However, if everyone builds their companies using the same methods, they typically end up competing head-to-head. If every founder interviews customers, they will all converge on the same answers. If every team releases a minimum viable product and iterates, they will all evolve toward the same final product. Success in a competitive market must be relative, which means effective approaches must differ from what everyone else is doing.

Reductio ad absurdum makes this clear: if there were a flowchart that guaranteed startup success, people would be mass-producing successful startups around the clock. That would be a perpetual money machine. But in a competitive environment, such a flood of new companies would lead to most of them failing. The faulty assumption must be that such a flowchart could exist.

There is a precise analogy in evolutionary theory. In 1973, evolutionary biologist Leigh Van Valen proposed what he called the Red Queen Hypothesis: in any ecosystem, when one species evolves an advantage at the expense of another, the disadvantaged species will evolve to offset that improvement. The name comes from Lewis Carroll’s *Through the Looking-Glass*, where the Red Queen tells Alice, "It takes all the running you can do, to keep in the same place." Species must continuously innovate with diverse strategies to survive amid the innovations of their competitors.

Similarly, when a new entrepreneurial approach is rapidly adopted by everyone, no one gains a relative advantage, and success rates remain flat. To win, startups must develop novel, differentiated strategies and establish sustainable barriers to imitation before competitors can catch up. This often means that winning strategies are either developed internally (rather than found in publicly available publications) or are so unconventional that no one would think to copy them.

This sounds like a difficult thing to establish scientifically...