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Any method for building a startup, once widely known, causes founders to converge on the same answers. If everyone follows the same bestselling startup techniques, everyone ends up building the same company and, with no differentiation, most of those companies fail. The truth is, anytime someone insists on a method for how to build a successful startup, you should do something different. The paradox is self-evident, once you think about it, but it contains the seed for how to move forward.
Before the wave of New Punditry began 25 years ago, the body of startup advice it displaced was, admittedly, worse than useless. It consisted of a naïve amalgam of Fortune 500 corporate strategy and small-business tactics, of five-year plans and day-to-day blocking and tackling. But for high-growth-potential startups, long-range planning is worthless. The future is unknowable, and focusing on daily operations leaves founders exposed to faster-moving competitors. The old advice was built for a world of incremental improvement, not radical uncertainty.
The New Punditry’s advice was, instead, intuitively rational, apparently well-argued, and offered founders a step-by-step process for building a business amid real uncertainty. Steve Blank’s customer development method in The Four Steps to the Epiphany (2005), for example, taught founders to treat their business idea as a set of falsifiable hypotheses: get out of the building, interview potential customers, and validate or kill your assumptions before writing any code. Eric Ries’ The Lean Startup (2011) built on this with the Build-Measure-Learn loop: Launch a minimum viable product, measure real user behavior, and iterate rapidly rather than waste time perfecting a product no one wants. Osterwalder’s Business Model Canvas (2008) gave founders a tool to map the nine key components of a business model and pivot when something isn’t working. Design thinking, popularized by IDEO and Stanford’s d.school, emphasized empathy with end users and rapid prototyping to surface problems early. Saras Sarasvathy’s Effectuation Theory prescribed starting with a founder’s own skills and network rather than reverse-engineering a plan to meet a distant goal.
These pundits were consciously trying to build a science of entrepreneurial success. By 2012, Blank said that the National Science Foundation was calling his customer development framework “the scientific method for entrepreneurship,” and claimed that “we now know how to make startups fail less.”[1] The official Lean Startup website claims that “The Lean Startup provides a scientific approach to creating and managing startups,” and the back cover of his book quotes Tim Brown, CEO of IDEO, saying Ries “proposes a scientific process that can be learnt and replicated.” Meanwhile, Osterwalder claimed in his PhD thesis that his Business Model Canvas is rooted in design science (the precursor to Design Thinking).
Academics in entrepreneurship departments also study startups, but their science is closer to anthropology: describing the culture of founders and the practices of startups in an attempt to understand them. The New Pundits had a more practical vision, the one that the natural philosopher Robert Boyle articulated at the very dawn of modern science: “I shall not dare to think myself a true Naturalist till my skill can make my garden yield better herbs and flowers.”[2] A science should seek underlying truth, in other words, but it should also work.
Whether it works or not is, of course, what determines whether it deserves to be called a science. And if there’s one thing we know about startup punditry, it’s that it hasn’t worked.
Have we learned anything?
In science, we discover whether something works by running experiments. When Einstein’s theory of relativity was gaining acceptance, other physicists devoted time and money to devising experiments that would test whether it made accurate predictions. We all learned in grade school that the scientific method is science.
Yet through some flaw in our nature, we also tend to resist the idea that this is how truth is found. Our head expects evidence, but our heart demands to be told a story. There is a venerable philosophical position—wonderfully examined in Steven Shapin and Simon Schaffer’s Leviathan and the Air Pump (1985)—that observation cannot give us truth, that we can only find real truth by deriving it through logical principles from other things we know to be true, i.e., from first principles. And while this is the standard in mathematics, in any area with slightly noisier data or a less firm axiomatic base, it can lead to appealing nonsense.
Until the 16th century, doctors used the work of the second-century Greek physician Galen to treat patients. Galen believed sickness was caused by an imbalance of the four bodily humors—blood, phlegm, yellow bile, and black bile—and recommended treatments like bloodletting, purging, and applying heated cups to restore balance. Doctors followed these treatments for more than a millennium, not because they worked, but because the intellectual authority of the ancients seemed to dwarf the value of mere contemporary observation. But around 1500, the Swiss physician Paracelsus noticed that Galenic treatments did not actually make patients better, and that some treatments—like mercury for syphilis—worked even though they made no sense within humoral theory. Paracelsus began to advocate listening to evidence rather than deferring to the authority of the long dead: “The patients are your textbook, the sickbed is your study.” In 1527, he even staged a public burning of Galen’s work. His vision took centuries to take hold—nearly 300 years later, George Washington died after an aggressive bloodletting—because people are more inclined to believe neat and simple stories like Galen’s than to confront messy and complex reality.
Paracelsus started with what worked and followed that to why. First-principles thinkers start with a hypothesized “why” and then insist it works, regardless of the results. Are our modern entrepreneurship thinkers more like Paracelsus, driven by evidence? Or more like Galen, sustained by the elegance of their own story? In the name of science, let’s look at the evidence.
Here is the official government data on U.S. startup survival.[3] Each line shows the survival likelihood of companies started in a given year. The first line tracks one-year survival, the second line two-year survival, and so on. What the chart shows is that between 1995 and the present, the percentage of companies surviving for one year is essentially unchanged. The same is true at two years, five years, and 10 years.
The New Pundits have been around long enough, and are widely known enough, that their relevant books have collectively sold millions of copies and are taught in virtually all university entrepreneurship courses.[4] If they worked, it would show up in the statistics. Instead, there has been zero systematic progress over the past 30 years in making startups more likely to survive.
The government data counts all U.S. startups, including restaurants, dry cleaners, law firms, and landscapers—not just the high-growth-potential tech startups that VCs fund and the press covers. The startup pundits do not claim their methods apply only to Silicon Valley-type companies, but the techniques are most often tailored to the kind of radical uncertainty a founder would generally accept only if there were a potentially large payout down the road. So take a more targeted measure: the percentage of U.S. VC-funded startups that raised an initial round of capital and later went on to raise more. Given how venture capital works, we can safely assume that a large majority of the companies that failed to raise a subsequent round did not survive.
The solid line is the raw data; the dashed line adjusts for recently seeded companies that may still raise their Series A.[5]
The precipitous drop in seed-funded companies raising further capital does not support the idea that venture-backed startups have become more successful over the past 15 years. If anything, they seem to fail more often. Venture capital deployment is shaped, of course, by more than just startup quality: The turmoil of the COVID pandemic, the end of the zero-interest-rate era, the concentrated capital requirements of AI, etc.
One might also argue that the growth in VC overall flooded the market with less qualified entrepreneurs, offsetting any improvement in success rates. But in the chart below, the success rate declines both as the number of funded companies grew and as it shrank. If an oversupply of unskilled founders were dragging down the averages, success rates should have rebounded when the number of funded companies fell after 2021. They did not.
But isn’t an increase in founders a success in itself? Try telling that to the entrepreneurs who took the pundits’ advice and failed anyway. These are real people, staking their time, savings, and reputations; they deserve to know what they are getting into. The top VCs may also be making more money—there are more unicorns now than there were—but this is partly a function of longer times to exit, and partly the mathematical reality that the power-law distribution of exits ensures more companies started means a higher chance of a very large outcome. Cold comfort for the founder. The system may be generating more jackpots, but it is not improving the individual entrepreneur’s odds.
We have to take seriously that the New Punditry has failed to make startups more likely to succeed. The numbers show that, at best, it has had no effect. We have spent countless hours and billions of dollars on an intellectual framework that simply does not work.
Towards a science of entrepreneurship
The pundits claim to be giving us a science of entrepreneurship, but we have made no progress on the explicit criterion they themselves laid out: We do not know how to make startups more successful. Boyle would have said that if our gardens do not yet yield better herbs or flowers, then there is no science. This is disappointing, but also perplexing. Given the time invested, the widespread adoption, and the obvious intelligence of the people behind these ideas, it seems inconceivable that they have not made a difference. And yet the data suggest that we have learned precisely nothing.
If we are ever to build a true science of entrepreneurship, we need to understand why. There are three possibilities. First, maybe the theories are simply wrong. Second, maybe the theories are so obvious that formalizing them was pointless. Or third, once everybody uses the same theories, maybe they stop conferring an advantage. Strategy is about doing something different from your competitors, after all.
Maybe the theories are wrong
If the theories were flatly wrong, startup success rates should have gotten worse as they spread. Our data shows this is not true of startups overall, and venture-backed failure rates appear to have increased for other reasons. Set the data aside, and the theories don’t seem wrong. Talking to customers, experimenting, and iterating all seem obviously beneficial. But the theories of Galen also probably did not seem wrong to the doctors of 1600. We cannot know for certain unless we test these frameworks the way we test other scientific hypotheses.
This is the standard Karl Popper set for science in The Logic of Scientific Discovery: a theory is scientific only if it can, in principle, be proven wrong. You have theories. You test them. If the 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 faith.
Very few people have tried to apply this standard to entrepreneurship studies. There are a handful of randomized controlled trials, but they tend to lack statistical power and define “working” as something other than a startup actually succeeding.[6] Given the billions of dollars VCs put at risk every year, not to mention the years a founder puts into trying their idea, it seems odd that no one has put serious effort into determining if the techniques startups are taught to use actually work.
But the pundits have little incentive to test their theories: They make money and gain influence by selling books. The startup accelerators profit by running large cohorts through a power-law funnel, collecting a few outlier successes. And academic researchers face perverse incentives of their own: proving their own theories wrong would cost them funding with no offsetting reward. The whole enterprise has the structure of what the physicist Richard Feynman called a “cargo cult science”: an edifice that mimics the form of science without its substance, deriving rules from anecdotes without establishing underlying causality. Just because a handful of successful startups conducted customer interviews does not mean your startup will succeed if you do too.
But unless we acknowledge that our current answers are not good enough, we will not have the motivation to pursue new ones. We need to experiment to find out what works and what does not. It will be expensive, because startups are terrible test subjects. It is hard to force a startup to do something or refrain from doing something (can you stop a founder from iterating, or talking to customers, or asking users which design they prefer?), and keeping rigorous records is usually a low priority when a company is fighting for survival. There are also a great many nuances within each of these theories to test. It might, in fact, be impossible to run these experiments well. But if that is the case, then we need to acknowledge what we would have no problem saying of any other unfalsifiable theory: it is not science. It is pseudoscience.
Maybe the theories are obvious
To some extent, founders do not need to formally learn these techniques. Long before Blank coined “customer development,” founders developed customers by talking to them. They likewise built minimum viable products and iterated on them before Ries gave the practice a name. They designed products for their users before anyone called it “design thinking.” The imperatives of business generally force these behaviors anyway, and millions of businesspeople independently reinvented them to solve problems they faced every day. Perhaps the theories are obvious, and the pundits have merely put old wine in new bottles.
This is not necessarily a bad thing. Having theories that work, even if they are obvious, is a first step toward making better theories. Contra Popper, scientists do not simply discard a promising theory the moment it is falsified; they try to improve or augment it. The historian and philosopher of science Thomas Kuhn made this point forcefully in The Structure of Scientific Revolutions: For more than 60 years after Newton published his theory of gravity, its predictions about the motion of the moon were wrong, until the mathematician Alexis Clairaut realized the calculation was a three-body problem and corrected it. Popper’s standard would have us discard Newton. But that is not what happened, because the theory was otherwise so well supported. Kuhn argued that scientists are stubborn within a framework of beliefs, which he called a paradigm. Because it provides a structure that lets them build on and improve existing theories, scientists will not abandon a paradigm until they have to. Paradigms provide a path forward.
Entrepreneurship research does not have a paradigm. Or, rather, it has too many, none compelling enough to be unifying. This means that people who think about entrepreneurship as a science have no shared guide to which problems are worth tackling, what observations mean, or how to improve theories that are not quite right. Without a paradigm, researchers are just thrashing around, talking past each other. For entrepreneurship to become a science, it needs a dominant paradigm: a shared framework compelling enough to organize collective effort. This is a harder problem than simply deciding to test theories, because for a set of ideas to become a paradigm, it must answer some pressing open questions. We cannot wish that into existence, but we should encourage more people to try.
Maybe the theories are self-defeating
Economics tells us that if you are doing the same things as everyone else—selling the same product to the same customers, making it with the same production processes and the same suppliers—direct competition will drive your profit to zero. This concept is a mainstay of business strategy, from George Soros’s theory of “reflexivity”—the idea that market participants’ beliefs change the market itself, eroding the very advantages they are trying to exploit—to Peter Thiel’s Schumpeterian quip that competition is for losers. Michael Porter, in his landmark Competitive Strategy, codified this as the imperative to find a market position that no one else occupied. W. Chan Kim and Renée Mauborgne took the idea further with their Blue Ocean Strategy, arguing that companies should create entirely uncontested market spaces rather than fight over existing ones.
Yet if everyone is using the same methods to build their companies, they will generally end up competing head-to-head. If every founder is interviewing customers, they will all converge on the same answers. If every team is launching minimum viable products and iterating, they will all iterate toward the same eventual product. Success in a competitive market must be relative, which means what works must be different from what everyone else is doing.
The reductio ad absurdum makes this clear: If there were a flowchart that guaranteed a successful startup, people would churn out successful startups around the clock. It would be a perpetual money machine. But in a competitive environment, such a proliferation of new companies would cause most of them to fail. The premise that must be wrong is that such a flowchart can exist.
There is a precise parallel in evolutionary theory. In 1973, the 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, in which the Red Queen tells Alice, “it takes all the running you can do to keep in the same place.” Species must constantly innovate with numerous and varied strategies just to survive the innovative strategies of their rivals.
Similarly, when new startup methods are quickly adopted by everyone, no one gains a relative advantage, and success rates stay flat. To win, startups must develop novel, differentiating strategies and build sustainable barriers to imitation before competitors can catch up.[7] This tends to mean that winning strategies are either built in-house (rather than found in published works that anyone can read), or they are so idiosyncratic that no one else would think to copy them.
This sounds like a hard thing to build a science on. Porter’s Competitive Advantage and Kim and Mauborgne’s Blue Ocean Strategy both tried. Porter said “be different,” and then attempted to put an analytical frame around that imperative. But the analytical frame was essentially one of planning, and planning is just another flowchart. Kim and Mauborgne went further, but they still tried to systematize creativity: They offered a framework for finding Blue Oceans. But if everyone uses the same framework, they all find the same Blue Oceans. The economist Joseph Schumpeter worried that capitalism would eventually bureaucratize innovation and routinize entrepreneurship. The current innovation-industrial complex—with its accelerators, canvas workshops, and lean-methodology boot camps—is precisely what he feared: the heroic entrepreneur replaced by process-following framework-appliers.
The other possible view is Thiel’s, recapitulating a more optimistic Schumpeter, who believed that the entrepreneur is simply someone with a contrarian worldview. Entrepreneurs come up with things that are different from what everyone else is doing, because that is who they are and what they do. Yet this, while certainly true, amounts to claiming that entrepreneurs succeed because they possess good judgment, which the economist Frank Knight, one of the earliest theorists of entrepreneurship, defined as a kind of unquantifiable intuition forged through experience. This leaves us at an impasse: If we cannot specify what makes entrepreneurs successful, we cannot turn it into a science. As Knight put it, “The processes of intuition or judgment, being unconscious, are inaccessible to study.” It seems we are caught between theories that are too widely known to confer an advantage, and a quality of mind we cannot even study.
There is a way to thread this needle. Instead of pundits creating theories about how entrepreneurs can be more successful—spawning methods that, as we have seen, can only confer an advantage for a brief period—we have to step up a level of abstraction and articulate theories about how to generate new methods.
This is essentially what the philosopher of science Paul Feyerabend did in his provocative 1975 book Against Method. Feyerabend argued that the great scientists of history did not follow any single, codifiable method of discovery, and that rigidly enforcing any one scientific method would have caused major advances to be missed. Galileo, for instance, made his most important breakthroughs precisely by violating the methodological rules that constituted the science of his day. Feyerabend articulated a meta-theory of scientific progress: The only rule that holds across all of its history is that there are no fixed rules.
This may sound nihilistic; it certainly does not seem to tell us how to build a science. But the Red Queen hypothesis offers a bridge between Feyerabend’s insight and the kind of theories a science of entrepreneurship could actually advance. We already know that startups must do something different to survive. Feyerabend tells us that scientists, too, must do something different, and they do it by discarding the prevailing rules.
Building a science of entrepreneurship would therefore require breaking the rules of science as traditionally practiced. It would mean encouraging a proliferation of competing theories rather than converging on one. It would mean telling entrepreneurs that if everyone is doing something one way, they need to do it a different way: If all the other founders are running lean experiments or conducting customer interviews, then don’t. It would mean encouraging founders to ignore what the data currently says and instead ask what the data would have to look like for their idea to work, to believe their ideas before they are proven true. And it would mean advising them to hire rule-transgressors—visionary weirdos in the mold of Steve Jobs—if they want to create category-defining wins.
This is a sketch of a true science of entrepreneurship. Elements of it appear in Sarasvathy’s effectuation theory, and in the tradition of evolutionary economics associated with Richard Nelson and Sidney Winter. But the former tries too hard to systematize its insights into a repeatable process, and the latter does not sufficiently account for the agency of individual founders. As a paradigm, this Feyerabendian approach is something that could be followed, tested, and improved.
The New Punditry was right to pursue entrepreneurship as a science. Their mistake was that they tried to make it scientific at the wrong level of abstraction. Blank, Ries, and the others tried to discover specific winning moves and teach them as universal method; as a result, their paradigm was never sufficiently adaptive. You cannot follow a flowchart and expect to win.
A genuine science would embrace the dynamism rather than try to eliminate it. It would recognize that competitive differentiation is the mechanism by which startups create durable advantage, that homogeneous strategies cannot produce differentiation, and that the usefulness of any novel approach has a half-life inversely proportional to how fast it spreads. These are strategic claims, not tactical ones. And unlike a pseudoscience of method, they do not become self-defeating the moment they are accepted.
This science has only one axiom: If you do what everyone else does, you get what everyone else gets. The Red Queen hypothesis is the closest thing entrepreneurship has to a foundational law.
It also insists that theories should be tested and testable; evidence still matters. But those theories must be part of a paradigm that generates new theories and motivates improvement when old ones fail. And that paradigm must hold as its central principle that there can be no fixed rules for consistently producing genuinely new things. A science can be built this way.
There is, however, a tension here. This new science means to honor Boyle’s dictum that science should bear fruit in works: it should make startups more successful. Yet even a Feyerabendian science is subject to Feyerabend’s own objection: any paradigm, once institutionalized, becomes the next thing that needs burning. Perhaps what we really need is a science of creating new sciences. But if so, that meta-science would also be one of rejecting method. It’s turtles all the way down, and the turtles are Feyerabendian.
Of course, no science of entrepreneurship can be a science in the sense most people think of the term. There are no fixed and universal recipes, no ultimate truth. This may be unsatisfying to the aspiring founder, but any science that guaranteed success would bring us right back to the perpetual money machine. The best we can hope for is a science that makes startups meaningfully more likely to succeed and that is honest about the limits of its own prescriptions. And then, when those prescriptions harden into orthodoxy, we try something different. A true science of entrepreneurship embraces the Red Queen dynamic so completely that it rejects any attempt to permanently systematize it.
Including, eventually, this.
Jerry Neumann is a retired venture investor, writing and teaching about innovation.