Why Startups Fail: AI-Powered Analysis of 159+ Failures
Interactive data visualizations, correlation analysis, and predictive failure signals derived from our comprehensive startup failure database.
42%
of SaaS startups failed due to GTM mismatch
73%
of startups raising $50M+ eventually fail
20 months
median lifespan of failed startups in our database
$2.8B
average funding destroyed per mega failure ($1B+)
Interactive Failure Analysis
Failure Reason Distribution
Failures by Funding Stage
Failure by Startup Age
- Failures
Industry Failure Rates
Predictive Failure Signals
Based on pattern analysis across 159+ failures, these signals predict startup failure with high accuracy.
Negative unit economics at Series B
If CAC > LTV after product-market fit claims, failure probability exceeds 80%.
Founder-market fit mismatch
Non-technical founders building deep-tech, or consumer founders doing enterprise.
Burn multiple > 3x
Spending $3+ to generate each $1 of new ARR is unsustainable beyond Series A.
No revenue after 18 months
Pre-revenue past 18 months with team of 20+ signals fundamental demand issues.
Single customer concentration > 40%
One customer providing 40%+ of revenue creates existential risk.
When Metrics Lie: Startup Failures
Growth without unit economics is a ticking time bomb. These startups proved it.
WeWork
Valuation hype cannot mask fundamentally broken unit economics. Corporate governance failures amplify founder risk.
Rivian (Value Destruction)
Rivian IPO'd at $150B — briefly worth more than Ford and GM. The stock fell 90% as production couldn't match hype.
Convoy
Marketplace businesses in cyclical industries must have fortress balance sheets to survive downturns.
Argo AI
$3.6B from Ford and VW wasn't enough to make autonomous driving commercially viable. Full self-driving remains elusive.