Why Startups Fail: AI-Powered Analysis of 312+ 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 312+ 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.
Getir
Getir proved that delivering groceries in 10 minutes is technically possible but economically impossible. The company burned $1.8B trying to make ultrafast delivery work across 9 countries before retreating to Turkey.
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.
Getir (Detailed)
Instant grocery delivery requires such massive subsidies per order that even $5.5B in funding can't bridge the gap to profitability.