There is no single authoritative failure rate for every startup. The best available U.S. statistics measure how many business establishments remain active after a given number of years; a separate 2026 analysis describes reasons reported by a selected group of venture-backed companies that shut down. Those figures answer different questions.
For a recent long-term benchmark, 50.4% of U.S. private-sector establishments opened in the year ended March 2019 were still active in March 2024, according to the U.S. Bureau of Labor Statistics (BLS). For a practical view of common risks, CB Insights found that its analyzed shutdowns often cited running out of capital, poor product-market fit, timing, or unit economics. Neither source establishes a universal startup failure rate or proves that a particular practice guarantees survival.
What is the startup failure rate in 2026?
It depends on what counts as a startup, what counts as failure, and how long the business is followed. Government survival figures generally track establishments—individual business locations or operating units—not whether a founder considers a venture successful. Post-mortem analyses, by contrast, look at companies that shut down and the reasons identified in public accounts.
The BLS reported that 50.4% of U.S. private-sector establishments in the cohort opened in the year ended March 2019 remained active in March 2024. The arithmetic complement is 49.6% not active as establishments at that five-year observation point; it is not a separate BLS measure of why they closed. The BLS table also shows 34.7% ten-year survival for establishments opened in the year ended March 2015, observed through March 2025. BLS establishment cohort survival table
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These are U.S. establishment statistics, not a prediction for every new company or founder. A business can close for many reasons, and establishment survival does not measure profitability, founder wealth, or whether a company was acquired.
What do the latest survival statistics measure?
| Measure | Population and cohort | Observation | What it means |
|---|---|---|---|
| 50.4% five-year survival | U.S. private-sector establishments opened in the year ended March 2019 | Active in March 2024; BLS | Share of that cohort still active as establishments at five years. |
| 34.7% ten-year survival | U.S. private-sector establishments opened in the year ended March 2015 | Active in March 2025; BLS | Share of that cohort still active as establishments at ten years. |
| 77.9% one-year survival | New U.S. employer establishments in 2025 | Active after one year; Kauffman Foundation 2026 report | Share active after one year under Kauffman’s early-stage measure. |
| 57.3% five-year survival | U.S. startup establishment cohort opened in 2018 | Reported in a 2024 BLS feature | A historical example of cohort variation, not the newest five-year cohort in the current BLS table. |
The BLS follows establishment cohorts and reports activity over time. The 2019 cohort’s 50.4% figure is a five-year observation, while the 2015 cohort’s 34.7% figure is a ten-year observation. These figures cannot be compared as if they described the same cohort at the same age. BLS Table 7
How to interpret the one-year figure
The Kauffman Foundation’s 2026 report puts one-year survival for new employer establishments in 2025 at 77.9%. Its measure is the percentage still active after one year. The complement, 22.1%, is the share not active at that point—not a general startup failure rate. The report also gives 79.4% for 2019 and says survival is below its pre-pandemic level. Because the measure compares March to March, it does not capture the full effects of the pandemic in calendar 2020. Kauffman 2025 report
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Why long-term figures describe older businesses
A five- or ten-year survival rate requires time to observe the cohort. A 2026 publication therefore cannot provide a ten-year outcome for businesses founded in 2026. For context, the BLS’s 2024 feature reported 57.3% five-year survival for the 2018 startup establishment cohort; this is a historical illustration, not the newest five-year result in the current table. BLS 2024 feature
Why do startups fail? What shutdown reports say
CB Insights’ 2026 analysis examined 431 venture-backed startups that publicly shut down since 2023. It identified failure reasons for 385 of them. Within that selected group, the reported reasons included:
| Reported reason | Share of analyzed shutdowns |
|---|---|
| Running out of capital | 70% |
| Poor product-market fit | 43% |
| Bad timing | 29% |
| Unsustainable unit economics | 19% |
These categories overlap: a company could cite more than one reason, so the percentages do not add up to 100%. They are reported reasons in a sample of VC-backed public shutdowns, not prevalence estimates for all startups. Public post-mortems and announcements can also be incomplete or selective, and the analysis does not prove that any listed factor caused a shutdown in every case. CB Insights 2026 analysis
One useful way to read the findings is to distinguish the immediate cash endpoint from problems that may accompany it. In this selected sample, capital depletion was frequently reported alongside market-fit, timing, or unit-economics issues. That is a practical interpretation of the reports, not a universal causal rule.
How to compare startup survival statistics
Before treating two percentages as comparable, check what each one counts. The Census Bureau’s Business Dynamics Statistics also reports establishment births and deaths and firm startups and shutdowns, but its firm and establishment series should not be silently mixed with BLS cohort survival or private shutdown samples. The Census Bureau currently lists BDS tables through 2023. Census Business Dynamics Statistics
- Population: Is the measure about establishments, employer establishments, firms, or venture-backed companies?
- Cohort and follow-up: Which birth year is tracked, for how long, and through what endpoint month?
- Geography and industry: Is the figure national, regional, or industry-specific?
- Outcome: Does it report survival, a calculated complement, or reasons cited after shutdown?
Survival varies by industry and location as well as by cohort. For example, BLS regional data show one-year survival of 84.6% for Pacific-division establishments born in 2021, while the series’ historical high and low were 84.6% in the Pacific for 2021 and 71.4% in the South Atlantic for 2008. Those are regional historic values, not a current forecast for a new business. BLS regional survival data BLS also notes that survival rates vary by industry. BLS entrepreneurship measures and definitions
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How to avoid common startup mistakes
No cited survival or shutdown study quantifies how much a particular prevention tactic reduces failure risk. The steps below are practical responses to the risks the sources identify, not guarantees.
1. Validate demand before scaling
Define a specific customer segment, investigate its needs, and test whether people will pay for the proposed solution. Actual payment behavior is a stronger demand signal than expressions of interest or founder enthusiasm alone. This addresses the product-market-fit problem frequently reported in CB Insights’ selected shutdown sample. SBA small-business material also points entrepreneurs toward market research. SBA Office of Advocacy brief CB Insights analysis
2. Track unit economics and runway together
Monitor gross margin, customer-acquisition cost, collection timing, recurring obligations, and the time you expect to need before revenue. Set decision points in advance for slowing spending or revisiting assumptions, rather than waiting until capital is nearly exhausted. CB Insights’ reported capital and unit-economics reasons make these useful areas to watch; the analysis does not establish an optimal runway or measure the effect of monitoring.
3. Recheck timing assumptions against real adoption
Compare customer uptake, costs, and sales cycles with the assumptions behind your plan. If adoption or costs move differently than expected, revisit launch pace and spending. BLS documents cohort survival variation over business cycles, while CB Insights’ shutdown sample includes bad timing among reported reasons. Neither source shows that founders can control broader economic timing. BLS cohort context CB Insights analysis
Is “90% of startups fail” an accurate statistic?
It should not be presented as a universal fact. The official sources described here do not establish one failure rate that applies to every definition of startup, geography, industry, and follow-up period. Use a clearly defined establishment survival measure for a cohort and duration, or describe reported causes within a specified shutdown sample.
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