The Entrepreneurial Bible's Self-Collapse: The More You Know, The Faster You Die

Everyone is using the same approach, so everyone is failing.

Author: Colossus

Translation: Deep潮 TechFlow

Deep潮 Guide: This article uses U.S. government data to reveal an uncomfortable truth: over the past 30 years, all the bestselling startup methodologies—Lean Startup, Customer Development, Business Model Canvas—have shown no statistical evidence of improving startup survival rates.

The problem isn’t necessarily that the methodologies are wrong; it’s that once everyone adopts the same approach, it loses its advantage.

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

Full text below:

Any method for building a startup, once widely adopted, will lead founders to converge on the same answers. If everyone follows the same bestselling startup techniques, ultimately they will build similar companies with no differentiation, and most of these companies will fail. The fact is, whenever someone insists on teaching a specific way to build a successful startup, you should do something different. Once you understand this paradox, it’s obvious, but it also points the way forward.

Before the rise of the new wave of “startup evangelists” twenty-five years ago, the advice they replaced was, frankly, worse than useless. It was a naive mix of Fortune 500 strategies and small business tactics—long-term planning alongside daily operations. But for high-growth startups, long-term planning is pointless—future is unpredictable—and focusing on daily operations exposes founders to faster competitors. The old advice was suited for a world of incremental improvement, not for fundamental uncertainty.

The new generation of startup evangelists offers different advice: intuitive, seemingly well-argued, providing founders with a step-by-step process for building companies amid real uncertainty. Steve Blank’s “Four Steps to the Epiphany” (2005) introduced Customer Development, teaching founders to treat business ideas as falsifiable hypotheses: go out, interview potential customers, validate or disprove assumptions before writing code. Eric Ries’s “Lean Startup” (2011) built on this, proposing Build-Measure-Learn cycles: release a Minimum Viable Product, measure real user behavior, iterate quickly instead of wasting time perfecting a product no one wants. Osterwalder’s Business Model Canvas (2008) gives founders a tool to sketch out nine core components of a business model and pivot quickly when something doesn’t work. Design Thinking—promoted by IDEO and Stanford—emphasizes empathy for end users and rapid prototyping to identify issues early. Saras Sarasvathy’s Effectuation theory suggests starting from founders’ skills and networks rather than reverse-engineering a plan to achieve grand goals.

These evangelists aim to establish a science of startup success. By 2012, Steve Blank claimed that the U.S. National Science Foundation was calling his Customer Development framework “the scientific method of entrepreneurship,” asserting “we now know how to reduce startup failure.” The Lean Startup website claims it offers a “scientific approach” to creating and managing startups, quoting IDEO CEO Tim Brown that Ries “proposed a learnable, replicable scientific process.” Osterwalder’s thesis states that the Business Model Canvas is based on design science (the precursor to Design Thinking).

Academic entrepreneurship research also studies startups, but their science resembles anthropology: describing founders’ cultures and startup practices to understand them. The new evangelists have a more pragmatic vision—echoing what natural philosopher Robert Boyle articulated at the dawn of modern science: “I do not dare to call myself a true naturalist unless my skills can grow better herbs and flowers in my garden.” In other words, science should seek fundamental truths but also be effective.

Its effectiveness, of course, determines whether it deserves to be called science. And regarding startup evangelism, one thing is clear: it has not worked.

What Have We Really Learned?

In science, we determine whether something works through experiments. When Einstein’s relativity gained acceptance, physicists invested time and money designing experiments to test its predictions. We learn in elementary school that the scientific method is science itself.

But, due to a human flaw, we tend to resist the idea that “truth is discovered this way.” Our minds seek evidence, but our hearts want a story. An ancient philosophical stance—discussed brilliantly by Steven Shapin and Simon Schaffer in Leviathan and the Air-Pump (1985)—argues that observation cannot give us truth; true knowledge can only be derived logically from what we already accept as true, starting from first principles. While standard in mathematics, in fields with noisy data or shaky axioms, this can lead to seemingly plausible but absurd conclusions.

Before the 16th century, doctors treated patients based on Galen’s writings from the 2nd century Greek physician. Galen believed disease was caused by an imbalance of four humors—blood, phlegm, yellow bile, black bile—and recommended bloodletting, emetics, and cupping to restore balance. These treatments persisted for over a millennium, not because they worked, but because the authority of ancient scholars seemed to outweigh current observations. Around 1500, Swiss physician Paracelsus noticed that Galen’s therapies often didn’t help; some, like mercury for syphilis, were nonsensical within humoral theory but actually effective. Paracelsus began listening to evidence rather than authority: “The patient is your textbook, the bedside your laboratory.” In 1527, he publicly burned Galen’s works. His ideas took centuries to be accepted—nearly 300 years later, George Washington died after a radical bloodletting—because people preferred the neat, simple stories of Galen over the messy reality.

Paracelsus started from what worked and traced back to causes. First principles thinkers, on the other hand, assume a cause and then claim it’s effective regardless of outcome. Are modern startup thinkers more like Paracelsus, driven by evidence? Or more like Galen, maintaining elegant stories? Let’s look at the evidence.

Below is official U.S. government data on startup survival rates. Each line shows the probability that a company founded in a given year survives to that year. The first line tracks one-year survival, the second two-year, and so on. The chart shows that from 1995 to today, the proportion of companies surviving one year has remained flat. Two-year, five-year, ten-year survival rates are similar.

The new evangelists have been around long enough and are well-known—millions of copies sold, most university entrepreneurship courses teach their ideas. If these methods worked, the data would reflect it. But over the past three decades, there has been zero systemic improvement in making startups survive.

The government data includes all U.S. startups—restaurants, dry cleaners, law firms, landscaping companies—not just high-growth tech startups supported by venture capital. The evangelists do not claim their methods are only for Silicon Valley-type companies, but these techniques are most often tailored for environments where founders are willing to accept extreme uncertainty for the chance of large returns. So we use a more targeted metric: the proportion of VC-backed startups that, after completing their initial funding round, go on to raise subsequent rounds. Given how venture capital works, we can reasonably assume that most companies that fail to raise follow-up rounds do not survive.

The solid line shows raw data; the dashed line adjusts for recent seed-stage companies that might still raise Series A.

The sharp decline in the proportion of seed-funded companies that go on to raise further rounds does not support the idea that VC-backed startups have become more successful over the past 15 years. If anything, they seem to fail more often. Of course, VC deployment is influenced by factors beyond startup quality: COVID shocks, the end of zero-interest rates, the capital-intensive nature of AI, and more.

Some argue that the growth in total venture funding has flooded the market with less qualified entrepreneurs, offsetting any gains in success rates. But in the chart below, success rates decline both during periods of increasing and decreasing startup numbers. If over-qualification of founders was dragging success down, then success rates should rebound when funding drops after 2021. They do not.

But isn’t an increase in the number of founders itself a kind of success? Try telling that to entrepreneurs who followed evangelist advice and still failed. These are real people risking their time, savings, and reputation; they deserve to know what they’re facing. Top VCs may have earned more money—more unicorns now than before—but that’s partly because exits take longer, and the power-law distribution of exits means more startups lead to huge successes as more companies are launched. For founders, that’s cold comfort. The system may produce more big wins, but it doesn’t improve individual odds.

We must face the fact: the new evangelists have failed to make startups more likely to succeed. Data shows that, at best, they have had no effect. We have spent countless hours and billions of dollars on a fundamentally flawed mindset.

Moving Toward a Science of Entrepreneurship

Evangelists claim they are giving us a science of entrepreneurship, but by their own standards, we have made no progress: we still don’t know how to make startups more successful. Boyle would say that if your garden hasn’t grown better herbs or flowers, then there’s no science. Disappointing and confusing. Given the time invested, widespread adoption, and the apparent intellectual level behind these ideas, it’s hard to believe they are useless. Yet the data shows we’ve learned nothing.

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

Maybe the theories are fundamentally wrong

If these theories are wrong, then their spread should decrease overall startup success. Our data shows that this is not the case; failure rates for VC-backed companies have actually increased for other reasons. Setting aside the data, these theories don’t seem obviously wrong. Talking to customers, running experiments, iterating—these all seem beneficial. But Galen’s theories in 1600 also didn’t seem wrong to physicians of that time. Unless we test these frameworks as we do other scientific hypotheses, we cannot be sure.

This is Karl Popper’s criterion for science: a theory is scientific if and only if it can, in principle, be falsified. You have a hypothesis; you test it. If the evidence contradicts it, you discard it and try something else. An unfalsifiable theory isn’t a theory at all, but a belief.

Few have applied this standard to startup research. There are some randomized controlled trials, but they lack statistical power and often define “effectiveness” as something other than real startup success. Given that venture capital invests billions annually, and founders spend years trying their ideas, it’s strange that no one seriously tests whether the techniques taught to entrepreneurs actually work.

But evangelists have little incentive to test their theories: they profit from book sales and influence. Accelerators make money by funneling many entrepreneurs into the same funnel, with a few outliers succeeding wildly. Academic researchers face their own distortions: proving their theories wrong risks losing funding, with no immediate reward. The entire industry resembles what physicist Richard Feynman called “cargo cult science”: a mimicry of scientific form without substance—deriving rules from anecdotes rather than establishing causal relationships. Just because some successful startups have done customer interviews doesn’t mean your startup will succeed if you do the same.

But unless we admit that current answers are insufficient, we won’t be motivated to seek new ones. We need experiments to discover what works and what doesn’t. This will be costly, because startups are poor test subjects. It’s hard to force a startup to do or not do something (can you stop founders from iterating, talking to customers, or asking users’ preferences?), and keeping detailed records is usually low priority when fighting for survival. Each theory has many nuances to test. In reality, these experiments may be impossible to conduct properly. But if that’s the case, then we must admit that any unfalsifiable theory is not science, but pseudoscience.

Maybe the theories are too obvious

In some ways, founders don’t need formal training. Before Blank’s “Customer Development,” founders were already talking to customers. Before Ries’s “Minimum Viable Product,” they were building and iterating. Before Design Thinking, they were designing for users. The laws of business often push people toward these behaviors; millions of entrepreneurs independently reinvented these practices to solve daily problems. Perhaps these theories are obvious, and evangelists are just repackaging old wine in new bottles.

That’s not necessarily bad. Having effective, even obvious, theories is a first step toward better ones. Unlike Popper, scientists don’t abandon promising theories at the first falsification; they try to improve or extend them. Thomas Kuhn’s The Structure of Scientific Revolutions powerfully illustrates this: Newton’s gravity theory, for example, was wrong in predicting lunar motion for over 60 years until Alexis Clairaut recognized it as a three-body problem and corrected it. Popper’s standard would have led us to discard Newton, but it didn’t happen because the theory was well-supported elsewhere. Kuhn argued that scientists are often stubborn within a paradigm—what he called a “paradigm”—which provides a framework for building and improving theories. They don’t abandon a paradigm unless forced, because it offers a path forward.

Startup research has no such paradigm. Or rather, it has too many, none sufficiently compelling to unify the field. This means that those trying to think of entrepreneurship as a science lack a shared guide on what questions matter, what observations mean, or how to improve imperfect theories. Without a paradigm, researchers are just spinning their wheels, each saying different things. To become a science, entrepreneurship needs a dominant paradigm—a set of ideas that can organize collective effort. This is a harder problem than simply deciding what to test, because a paradigm must answer pressing open questions. We can’t create one from nothing, but we should encourage more attempts.

Maybe the theories are self-undermining

Economics teaches us that if you do exactly what everyone else does—selling to the same customers, using the same production methods, sourcing from the same suppliers—competition will drive profits to zero. This is a cornerstone of business strategy: from George Soros’s “reflexivity”—market participants’ beliefs change the market itself, eroding their advantages—to Peter Thiel’s Schumpeterian idea that “competition is for losers.” Michael Porter’s Competitive Strategy codifies this as the need to find uncontested market space. Kim and Mauborgne’s Blue Ocean Strategy takes it further, arguing that firms should create entirely new markets rather than fight over existing ones.

But if everyone uses the same methods to build their companies, they tend to compete head-on. If every founder interviews customers, they’ll converge on similar answers. If every team releases MVPs and iterates, they’ll end up with similar final products. Success in a competitive market must be relative; effective practices must differ from what others are doing.

Reductio ad absurdum makes this clear: if there were a foolproof startup success process, people would be mass-producing successful startups all day long. It would be a perpetual money machine. But in a competitive environment, the flood of new companies leads to most failing. The false premise is that such a process could exist.

An exact analogy comes from evolutionary theory. In 1973, biologist Leigh Van Valen proposed the “Red Queen Hypothesis”: in any ecosystem, when one species evolves an advantage at the expense of another, the disadvantaged species must evolve to counter it. 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 stay in the same place.” Species must continually innovate with diverse strategies to survive against competitors’ evolving tactics.

Similarly, when new startup methods are rapidly adopted by everyone, no one gains a relative advantage, and success rates stay flat. To win, startups must develop novel, differentiated strategies and establish barriers to imitation before competitors catch up. This often means that winning strategies are either developed internally (not found in publicly available publications) or so unique that no one would think to copy them.

This makes building a scientific foundation difficult…