文章围绕“90%的初创公司会失败”这一常见说法，分析其数据来源，并梳理2026年初创企业失败的主要原因，帮助读者更理性地理解创业失败率。
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Startups promise freedom, innovation, and life-changing upside, but most don’t make it.
The numbers in 2026 are as brutal as ever: the majority of startups fail, often faster than founders expect. Markets shift, funding dries up, and the same patterns keep repeating.
Understanding why startups fail is a survival skill. In this article, we’ll break down the latest failure rates, the most common reasons behind them, and what today’s founders can learn to avoid becoming another statistic.
Startup Failure Statistics
- 9 out of 10 startups fail (source: Startup Genome - the 2019 report claims 11 out of 12 fail).
- 7.5 out of 10 venture-backed startups fail (source: Shikhar Ghosh).
- 2 out of 10 new businesses fail in the first year of operations (source: Bureau of Labor).
- Only 1% of startups become unicorn firms like Uber, Airbnb, Slack, Stripe, and Docker (source: CB Insights).
- The success percentage for first-time founders is 18% (source: Exploding Topics).
These are some of the most common statements on startup failure. While those stats could certainly be helpful, if you put them in the wrong context, they could also be misleading.
In this article, we’ll try to go to the source of the data and Failory’s unique experience of talking directly to hundreds of successful and failed startup founders to shed light on the question of how many startups fail and the startup failure rate.
What Is a Startup And Why Is It Prone to Failure?
In its broadest sense, it is a new business in its earliest stages of development.
This definition is too general, however, and as a result - misleading. A new hairdresser salon is also a new business in its early developmental stages, but most people in the startup community would tell you a hairdresser salon isn’t a startup.
A startup usually has two important characteristics:
- Innovation: A startup is testing assumptions that haven’t been tried before – sufficiently new technologies, products & services, or markets.
- Growth: A startup has the potential to grow exponentially rather than linearly. It is scalable. This usually happens because technology provides leverage (usually, a marginal cost of production close to 0).
So, a startup is in essence, a business experiment with potential. This means that real startups are prone to failure by definition.
They are testing assumptions, and it’s very likely these assumptions are wrong. The more innovative the startup, the riskier the assumptions and the more likely it is to fail.
When you put this new kind of risk on top of the traditional risks of starting a business (finance/cash flow risks, operational risks, team risks, marketing risks, etc.), it’s no surprise most startups fail.
Example: New Startup vs Non-startup Projects
Imagine you have a new IT consultancy that builds software for your clients. Even though you are a new business and you work with technology, you are not a startup because:
- You are not innovative by definition. You’re providing the same service other IT consultancies all over the world are providing.
- You can grow linearly – you are getting paid per hour, so growth would require hiring new developers, and increasing your costs at a similar rate to your revenue.
One day, you notice that all your clients have a similar problem, so you decide to invest some time in developing your own software to solve that particular problem.
This is a startup project because:
- It’s innovative – it is solving a problem in a new way (your software solution).
- It’s scalable – gaining new users of the software doesn’t increase the costs of running the software linearly.
The likelihood of your consultancy business failing is lower than that of your new software product because the software project is still trying to find product-market fit. Once validated, however, the software project could have more significant returns because of its potential for exponential growth through leveraging technology instead of human capital.
How Many Startups Fail?
So, when you talk about startup failure rates, it’s essential to understand one thing:
- Are you talking about the failure rates of new businesses in general (traditional businesses like the new hairdresser salon included)?
- Or are you only talking about the failure rates of innovative and scalable business ideas?
Failure Rates of All New Businesses
Statistical sources from government institutions are largely concerned with the failure rate of new businesses. This is useful if your project is closer to a traditional business.
In this case, your baseline failure rate would be lower than 90%. One of the most quoted statistics, in this case, is the Business Employment Dynamics report coming from the Bureau of Labor:
- 20% failure rate until the end of the 1st year
- 30% failure rate until the end of the 2nd year
- 50% failure rate until the end of the 5th year
- 70% failure rate until the end of the 10th year
Most newly registered businesses aren’t true startups, so you shouldn’t assume your likelihood to fail in the 1st year is only 20% if you’re trying to do something innovative.
N.B. Some articles out there are quoting those statistics in the context of startups, which is misleading, so be careful!
What about the failure rate for companies that went over Shark Tank? Only 6% have shut down.
Failure Rates of Scale-Ups
Statistics coming from Venture Capital funds are primarily concerned with real, innovative, scalable startups. However, venture funds invest mainly in growth-stage startups, AKA scale-ups.
They are true startups, but most have gotten past one of the most significant risks for startups: the search for product-market fit. They have tangible proof that people want what they are offering (this proof is how they attract venture capital).
This means their failure rates would be lower than those of early-stage startups. Harvard Business School lecturer Shikhar Ghosh says in a WSJ article that 75% of venture-backed companies never return cash to investors and in 30-40% of the cases, investors lose their initial investment (he works with a dataset of 2000 venture-backed startups).
That said, only 0.05% of startups get VC funding (Source: Fundable), so this statistic does not apply to the vast majority of new businesses, especially if they are in the early idea stage.
Failure Rates of All Startups:
Early-stage (idea stage) startups bear the highest risk and failure rates. It’s hard to claim accuracy about failure rate statistics for those kinds of projects because a large chunk fly below the radar.
They don’t raise capital from funds or other entities who maintain a dataset - most early-stage businesses are funded by the founders, their families, and friends.
Many early-stage startup projects don’t even register a legal entity – you don’t need one to test an idea. You need one once you start making money.
The regularly quoted number when searching how many startups fail is that 9 out of 10 startups fail, and it seems to originate from the Startup Genome project (in some of their more recent reports, however, they even say only 1 in 12 entrepreneurs succeed).
The exact accuracy of the statistic is beside the point for most people. The fact remains that startups are extremely risky, as seen by our growing collection of interviews with failed startup founders and our Startup Cemetery. But equally rewarding, as seen in our startup success story interviews.
Failure Rate Implications
For Startup investors
So why can investing in startups be profitable even with the abysmal failure rate?
It’s because successful startups make up for the unsuccessful ones.
If a startup fund has a portfolio of 100 companies, most of its returns would come from one investment (ideally, a unicorn), followed by the nine successful-but-not-huge companies. The 10 successful startups more than compensate for the 90 failures.
The implication is that startup investors are searching for the home run and are willing to lose money on most of their investments to find that company.
As a founder, you’re unlikely to get funding from startup angels and VCs if you don’t show a lot of ambition and scalability.
This doesn’t necessarily mean that your idea isn’t worth pursuing if it doesn’t fit the investment criteria of VCs. Being a successful founder of a lifestyle business is way better than being an unsuccessful founder of a traditional go-huge or go-home startup.
For Entrepreneurs
If you’re doing anything remotely innovative, you need to accept that you are likely to be wrong. The world is very complex, and most ideas (and the assumptions they carry) turn out to be bad.
A great example is when Twitter acquired Vine to disrupt the video-sharing and social network ecosystem and ended up shutting the app down only a few years later (here's why Vine shut down).
That said, simply accepting that you have a 90% chance to fail doesn’t seem like a healthy mentality. There are plenty of ways you can maximize your chances of success. The fact that the average is 90% doesn’t mean you can’t nudge this number in your favor.
Some of the concepts that would help you the most:
For Idea-Stage Startups
You are searching for a product-market fit. The principles of the Lean Startup are extremely important at this stage. The goal is to validate your assumptions as quickly and cheaply as possible and to give yourself time to pivot if necessary.
Get a good grasp of the meaning of MVP, validation experiments, and validated learning. Get used to the agile project management principles when you are in the process of building. Learn to prioritize and change your priorities based on customer feedback.
Here are some findings from the Startup Genome Project:
- Startups need 2-3 longer to validate their market than most founders expect. (The implication here is that cashflow/availability problems can kill the project before you are able to properly test the waters.)
- Founders overestimate the value of the intellectual property before product-market fit by 255%.
- Startups that pivot 1-2 times have 3.6x better user growth and raise 2.5x more money. Startups that pivot 0 times or more than 2 times do considerably worse. (The implication is that it is prudent to secure sufficient time and resources to attempt up to two pivots.)
For Later-Stage Startups
One of the biggest traps is premature scaling. It means over-investment of resources (in the broadest sense) too early in the startup journey.
The Startup Genome Project breaks the startup stages into four: Discovery, Validation, Efficiency, and Scale. It calls startups that scale prematurely inconsistent. Here are some examples of their findings:
- Inconsistent startups write 3.4x more code in their Discovery phase and 2.25x more code in the Efficiency phase.
- Inconsistent startups raise 3 times more capital in the Efficiency stage and 18 times less capital in the Scale phase.
- The self-reported valuation of inconsistent startups before reaching the Scale phase is $10 mil. Consistent startups report $800k.
- Inconsistent startups have 75% more paid users in the Discovery and Validation phases. Consistent startups have 50% more in the Scale stage.
6 Reasons Why Startups Fail
In the in-depth study of our interviews with the founders of 80+ failed startup projects that you can read in full in our Startup Mistakes article, we found that the most common reasons for failure are the following:
1) Marketing Problems (56%)
Marketing mistakes were the biggest killers, and the biggest problem by far is lack of product-market fit.
Don’t invest a lot of time and resources before you are confident people want what you are offering.
Validate your assumptions quickly and cheaply, and if needed - pivot.
2) Team Problems (18%)
Problems like lack of domain knowledge, lack of marketing knowledge (and plan), lack of technical knowledge, and finally – lack of business knowledge are the biggest killers.
Friction within co-founders, the team, lack of motivation, and lack of availability are also common but less deadly.
3) Finance Problems (16%)
More than 50% of the interviewed founders didn’t have a budget for their project, and 75% were self-funded, yet only 16% pointed to financial problems as the reason for failure.
That’s because you don’t really need a lot of money to test and validate concepts (you need effort). You need money to grow an already validated concept, so financial problems plague mostly exclusively later-stage startups.
4) Tech Problems (6%)
Rarely a big killer, even though the vast majority of the interviewed startups have some kind of technology in their core.
The biggest mistake is over-investment in expensive technology (developer time) before the marketing assumptions have been validated.
5) Operations Problems (2%)
For software startups like most of our interviewees, operational problems are understandably rare. This might not be the case for startups that work with physical products.
6) Legal Problems (2%)
It is largely overestimated and very rarely the reason for failure. That said, heavily-regulated industries like food and finance still present legal obstacles.
Disclaimer: most of the projects we interview are true startups (rather than new traditional businesses) and have some form of technology (usually software) in their core. This means our conclusions might not be that useful for new projects closer to traditional brick-and-mortar businesses. Moreover, we gather the data by interpreting qualitative interviews (rather than surveys), so allow for some error.
Startup Failure Rates by Industry & Sector
When talking about traditional businesses, statistics from the Office of Advocacy show that new business failure rates are very similar across industries (source).
The Statistic Brain Research Institute has other data tracking how many new businesses are dead after four years of operation in different industries:
The highest failure rate is in the Information industry, which might be surprising at first glance. The information industry, however, has a relatively low barrier to entry and includes a large portion of the true high-risk startups, which might be bumping the average failure rates up.
The statistics above should be useful if your idea or business is closer to a traditional business. For true innovative tech startups, there aren’t good sources of failure rates divided by industry.
Nonetheless, this graphic from the Startup Genome 2019 report might prove very valuable. It divides startups into sub-sectors and measures if the sectors are growing, maturing, or declining based on the early-stage funding they tend to receive and the 5-year exits:
Agtech & New Food
Example failed project: The Poultry Exchange
A big challenge Agtech startups are facing is introducing new technologies (especially digital) to a mature, traditional industry that might be short on early adopters.
Blockchain
Example failed project: 300Cubits
Blockchain has obvious potential. Yet, the reality of the overly volatile and speculative coin market and the unfamiliarity of potential stakeholders with the technology makes it hard to put theoretically sound ideas into practice.
AI, Big Data, & Analytics
Example failed project: Roadstar.ai
One of the industry giants in trouble: MapR
Even though the long-run potential of AI is unquestionable, the technology is in its infancy, and finding economically viable applications for it fast enough has proven to be a hard nut to crack.
Many of the most famous AI startups (e.g. OpenAI) resemble a fundamental science research team more so than a business team. A lot of the investors in the field are playing the long game.
Advanced Manufacturing & Robotics
Not a formal statistic, but industry experts believe the robotics startup failure rate is 99% (!).
There are many reasons why, but it boils down to “robotics startups are tackling an extremely hard technical problem”.
So, are these sub-sectors the best choice for would-be startup founders?
The startup sub-sectors above have one thing in common: they might be some of the best to find funding to get a project going (if you have an impressive team), but they are also some of the hardest to create a self-sustaining business in.
The hot subsectors reveal the philosophy of the startup industry as a whole. They represent the toughest technological challenges, the biggest upside potential, but also the biggest chance for failure.
In other words, becoming a unicorn in Digital Media or Edtech is less likely, and finding sufficient funding could be more difficult. Yet, creating a successful, self-sustaining business in those fields might actually be more realistic.
All of that said, if you are an entrepreneur, choosing your sector should be dictated by your area of expertise rather than industry trends.
Failure Is Not Binary
When talking about startup failure, most people imagine this ending: running out of money, shutting down, or filing for bankruptcy.
In reality, many startups never fully fail, but never truly succeed either.
These are often referred to as zombie startups.
A zombie startup generates some revenue but lacks meaningful growth. It cannot raise follow-on funding or justify venture-scale returns, yet continues operating because costs are low or founders are reluctant to shut it down.
For example, Clinkle raised a big funding round but never translated that into lasting growth, effectively operating in “zombie” mode for years after its launch. Another post-accelerator example, Referly, failed to scale after its initial push and faded without ever hitting meaningful traction. One in six tech ventures end up in similar zombie conditions: alive on paper, but stagnant in practice.
This matters because many startup failure statistics only count formal shutdowns. Startups that stagnate for years without traction are often excluded, even though they have effectively failed to achieve the defining goal of a startup: scalable growth.
From a founder's POV, slow stagnation can be more damaging than fast failure. Fast failure creates clear feedback and frees up time, capital, and energy. Zombie startups delay those lessons while doubling down on opportunity cost.
This is why knowing when to shut down is an essential startup skill, not a weakness.
A startup that does not die is not necessarily a startup that succeeded.
Frequently Asked Questions
What's The Startup Success Rate?
As we have seen, 90% of startups fail, which means the startup success rate is around 10%. This rate is much higher if we also consider other more traditional businesses and not only innovative tech startups.
Why Do Startups Fail?
In order of frequency, these are the most common areas in which startups face problems that lead them to shut down: Marketing, Team, Finances, Tech, Operations, and Legal.
If you want to dig deeper, we covered this in our Startup Mistakes article.
What Percentage of Startups Fail in The First Year?
The Business Employment Dynamics report from the Bureau of Labor states a 20% failure rate in the first year.
Most new businesses aren’t true startups, so you shouldn’t assume your likelihood to fail in the first year is only 20% if you’re trying to do something innovative.
What Happens When a Startup Fails?
Failure is not the end. You’d be surprised how many failed startup founders are currently running a successful venture. Another chunk finds a good job because of the skills acquired in the project. With every failed attempt, your competence and chances of success increase.
Final Remarks
We hope that we succeeded in clearing up some of the confusion about startup and new business failure rates!
Startups are undoubtedly very risky, but great potential comes with great risk. Potential not only for financial returns but for progress and innovation that could improve the quality of life of people all around the world. So, don’t let the risk of failure discourage you! Be audacious!
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