Why Startups Fail: The Most Common Causes, According to Research

Startups fail for a small set of recurring reasons: they build something the market doesn't need, they run out of money before they find out, the founding team breaks down, or they grow faster than their evidence justifies. The official cause of death is often cash. The underlying cause is usually one of the other three.

This article walks through what "failure" actually means, the main cause categories, the warning signs that tend to show up first, and what the research can and can't tell you. It's about failure only. For the positive mirror image, see startup success factors. For the basics of what a startup is, start with what is a startup.

What "failure" means (it isn't one thing)

Before any statistic about failure means anything, you have to ask which kind of failure it measures. The word covers at least four different outcomes.

• Shutdown. The company stops operating, pays what it can, and closes. This is the cleanest definition, and the one most people picture.
• Failing to return capital. The company may still exist, or may have been sold, but investors got back less than they put in. This is how venture investors tend to judge outcomes, and it's a much broader definition than shutdown.
• Missing the plan. The company is alive but far behind the targets in its business plan or pitch. Whether that's "failure" depends on who set the targets.
• Acqui-hire. A larger company buys the team, not the product, and the product is shut down. Founders and early employees may land well. Investors may get little. Whether it counts as a failure depends on the lens.

A business can also be a poor investment and a fine business at the same time. A company that grows to a profitable few million in revenue is a success for its owners, even if it never returns a venture fund. The difference matters most for bootstrapped vs venture-backed startups, where the bar for "worked" sits in very different places.

So when you read that "most startups fail," ask: fail at what, measured against whose goal, over how many years?

How many young businesses survive

The cleanest public data isn't about startups specifically. It's about all new private-sector establishments in the United States, which includes corner shops, restaurants and contractors alongside technology companies. The US Bureau of Labor Statistics publishes survival data for private sector establishments by opening year.

For the cohort of establishments that opened in the year ended March 2013, 79.6% were still operating a year later, 50.6% after five years, and 34.7% after ten. For the newest cohort with a full first year of data, the year ended March 2024, 77.9% were still operating a year later.

Two things are worth taking from this. First, the first-year drop is the steepest in the series, roughly one in five establishments gone within twelve months. Second, the later years show a steady, gentler attrition: the share of each year's survivors that make it to the next year rises into the 90s.

The caution is the one you'd expect. These are all new establishments, not venture-backed technology startups. A new plumbing firm and a new software company don't face the same odds or the same risks, and an establishment closing isn't always a business failing (some are merged, moved or sold). Use these numbers as a baseline for how hard it is to keep any new business alive, not as the failure rate for startups.

You may have seen a specific headline figure, such as "90% of startups fail." It doesn't have a clean primary source, and the definitions behind the versions in circulation vary so much that none of them should be treated as a measured finding.

The common causes of startup failure

Researchers, investors and founders tend to sort failure causes into a similar handful of buckets. The labels differ, and real failures usually involve more than one, but the pattern is consistent.

1. No market need

This is the cause investors and operators name most often. Marc Andreessen puts it flatly: "The #1 company-killer is lack of market." He argues that in a great market, the market pulls the product out of the startup, and that product/market fit means being in a good market with a product that can satisfy that market.

A company can have a good team and a well-built product and still fail here. Paul Graham describes the classic setup in his essay The Hardest Lessons for Startups to Learn: "a couple of founders who have some great idea they know everyone is going to love, and that's what they're going to build, no matter what." The antidote is talking to real customers before and during the build. The customer discovery article covers how, and product-market fit explained covers how you'd know you have it.

2. Running out of cash

Almost every failed startup runs out of money, which is why cash is so often named as the cause. But it's usually the last event in a chain, not the first. Paul Graham notes in How Not to Die that "the official cause of death is always either running out of money or a critical founder bailing." He argues the deeper cause is usually that the founders became demoralized.

Practically, this means cash problems are a timing question. A company that finds its market with six months of runway left may live. One that finds the same answer with none left may not. Watching burn rate against runway is the early warning, and it works only if the numbers are updated every month.

3. Team problems

Founders disagree, one leaves, or the team lacks a skill the business needs. Graham lists internal disputes, inertia and ignoring users as the three main ways startups hurt themselves, and writes that "way more startups hose themselves than get crushed by competitors."

Co-founder conflict is especially damaging early because there's no HR function, no process, and often no clear decision rights. The founding team article covers how teams get structured, and solo founder vs cofounders covers the tradeoffs of going alone.

4. Competition

Competitors do kill startups, but less often than the stories suggest. Graham's view, quoted above, is that most failures are self-inflicted. Where competition does matter, it's usually because the startup entered a crowded market with no clear difference, or because an incumbent copied the one feature that mattered and bundled it for free.

5. Pricing and unit economics

A startup can have customers and still lose money on every one of them. If the cost of acquiring and serving a customer is higher than what the customer pays over their lifetime, growth makes things worse, not better. This is a unit economics problem, and it's easiest to hide when cheap capital is available. The tension between growing faster and making money is covered in growth vs profitability.

6. Premature scaling

Premature scaling means spending, hiring or marketing as if the company were further along than the evidence says. Startup Genome's report on premature scaling, published in September 2011, drew on a dataset of over 3,200 high-growth technology startups and found that 70% of startups in its dataset showed the problem. It also reported that no startup that scaled prematurely passed the 100,000 user mark, and that 93% of them never broke $100,000 in revenue per month.

Read that report as a framework, not a precise measurement. It's more than a decade old, it covers one segment (high-growth technology startups) and it was an early version of a methodology. Still, the underlying idea has held up: hiring a sales team or buying traffic before you know who your best customer is just burns runway faster. The riskiest assumption test and the lean startup method are both ways to stop scaling before you've earned it.

7. Timing

Some companies are right too early, and the market isn't ready. Others arrive after the window has closed. Timing is hard to see in advance and easy to see afterward, which is why it tends to be named after the fact rather than planned for. The idea is covered in market timing.

Causes, descriptions and warning signs

The table pulls the categories together with the signals that tend to show up before the failure does. None of these is a guarantee. They're prompts to look harder.

Warning signs that show up first

The categories above describe why companies fail. The signals below are what a founder or investor might actually notice, usually months before the end.

1. Customers use it once and don't return. Flat or collapsing retention is the clearest signal that the product isn't solving a recurring problem.
2. Growth depends on one channel or one person. If a single founder's network or one ad channel drives all sales, the model isn't repeatable.
3. The runway number is a surprise. Founders who can't state their months of cash on hand within a minute are probably not tracking it.
4. The roadmap keeps growing and the customer list doesn't. Building more features is a common way to avoid hearing that the market isn't buying.
5. Arguments about direction repeat. A team that keeps relitigating the same question hasn't resolved who decides.
6. Morale drops. Graham's argument in How Not to Die is that the underlying cause is usually that founders become demoralized. Tracking the team's energy is unscientific, but it's an honest leading indicator.

A pivot is the structured response to several of these. Changing direction while there's still money is very different from changing it with a month left.

The limits of failure data

Most of what's written about why startups fail comes from post-mortems, surveys, and investor commentary. Those sources have built-in problems.

Survivorship bias. We hear from the founders who survived and from those who wrote candidly about failing. The ones who quietly closed and moved on rarely publish. And the successful companies that made the same mistakes as the failed ones don't appear in a failure dataset at all, so you can't tell whether a "cause" is actually more common among failures than among survivors.

Self-report bias. A post-mortem is the founder's own account. People can lean toward blaming the market, timing, competitors or investors over their own decisions. "Ran out of cash" feels factual and blame-free, so it can crowd out the less comfortable causes behind it.

Single causes for multi-cause events. A company usually fails for several linked reasons, and the one a founder names depends on what the form allows. Ask "what's the main cause?" and you get one answer. Ask "list all" and you get three.

Definitions and timeframes vary. As covered above, a study that counts "failed to return capital" will report a much higher failure rate than one that counts shutdowns. Two studies can both be right and still seem to disagree.

Different populations. Venture-backed technology startups, small businesses and solo founders all have different failure patterns, and data for one doesn't transfer cleanly to the others.

The practical conclusion is to treat failure research as a set of well-supported patterns, not a ranked list with decimals. The three themes that recur across independent sources (market, money and people) are more trustworthy than any specific percentage.

Key Facts: Why Startups Fail

• Failure has several meanings: shutdown, failing to return capital to investors, missing the plan, and acqui-hire. Rates differ widely depending on which one is counted.
• For US private-sector establishments that opened in the year ended March 2013, 50.6% were still operating after five years and 34.7% after ten (BLS). These are all new establishments, not only startups.
• For the cohort that opened in the year ended March 2024, 77.9% were still operating a year later (BLS).
• Marc Andreessen: "The #1 company-killer is lack of market."
• Paul Graham: the official cause of death is always running out of money or a critical founder bailing, and he sees demoralization as the deeper cause.
• Startup Genome's 2011 analysis of over 3,200 high-growth tech startups found 70% showed premature scaling (Startup Genome).
• Post-mortem data is limited by survivorship bias, self-report bias and inconsistent definitions of failure.

Related reading

• What is a startup
• Startup success factors
• Product-market fit explained
• Founding team
• What is a pivot