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Failure Mode Taxonomy

Comprehensive Categories for Systematic Risk Identification

The Two Primary Dimensions

1. Internal vs External

Internal failures:

Under your control (at least partially)
Organizational, execution, resource-based
Can be prevented with better planning

External failures:

Outside your control
Market, regulatory, competitive, acts of God
Can only be mitigated, not prevented

2. Preventable vs Unpreventable

Preventable:

Known risk with available mitigation
Happens due to negligence or oversight
"We should have seen this coming"

Unpreventable (Black Swans):

Unknown unknowns
No reasonable way to anticipate
"Nobody could have predicted this"

Four Quadrants

	Preventable	Unpreventable
Internal	Execution failure, bad hiring	Key person illness, burnout
External	Competitor launch (foreseeable)	Pandemic, war, black swan

Premortem focus: Mostly on preventable failures (both internal and external)

Internal Failure Modes

1. Execution Failures

Team/People:

Key person quits
Co-founder conflict
Team burnout
Cultural toxicity
Skills gap
Hiring too slow/fast
Onboarding failure

Process:

Missed deadlines
Scope creep
Poor prioritization
Communication breakdown
Decision paralysis
Process overhead
Lack of process

Product/Technical:

Product quality issues
Technical debt collapse
Scalability failures
Security breach
Data loss
Integration failures
Performance degradation

2. Resource Failures

Financial:

Ran out of money (runway)
Failed to raise funding
Revenue shortfall
Cost overruns
Budget mismanagement
Fraud/embezzlement
Cash flow crisis

Time:

Too slow to market
Missed window of opportunity
Critical path delays
Underestimated timeline
Overcommitted resources

Knowledge/IP:

Lost key knowledge (person left)
IP stolen
Failed to protect IP
Technological obsolescence
R&D dead ends

3. Strategic Failures

Market fit:

Built wrong product
Solved non-problem
Target market too small
Pricing wrong
Value prop unclear
Positioning failure

Business model:

Unit economics don't work
CAC > LTV
Churn too high
Margins too thin
Revenue model broken
Unsustainable burn rate

Competitive:

Differentiation lost
Commoditization
Underestimated competition
Failed to defend moat
Technology leapfrogged

External Failure Modes

1. Market Failures

Demand side:

Market smaller than expected
Adoption slower than expected
Customer behavior changed
Willingness to pay dropped
Switching costs too high

Supply side:

Input costs increased
Suppliers failed
Supply chain disruption
Talent shortage
Infrastructure unavailable

Market structure:

Market consolidated
Winner-take-all dynamics
Network effects favored competitor
Platform risk (dependency on another company)

2. Competitive Failures

Direct competition:

Incumbent responded aggressively
New entrant with more capital
Competitor launched superior product
Price war
Competitor acquired key talent

Ecosystem:

Complementary product failed
Partnership fell through
Distribution channel cut off
Platform changed terms
Ecosystem shifted away

3. Regulatory/Legal Failures

Regulation:

New law banned business model
Compliance costs too high
Licensing denied
Government investigation
Regulatory capture by incumbents

Legal:

Lawsuit (IP, employment, customer)
Contract breach
Fraud allegations
Criminal charges
Bankruptcy proceedings

4. Macroeconomic Failures

Economic:

Recession
Inflation
Interest rate spike
Currency fluctuation
Credit crunch
Stock market crash

Geopolitical:

War
Trade restrictions
Sanctions
Political instability
Coup/revolution
Expropriation

5. Technological Failures

Disruption:

New technology made product obsolete
Paradigm shift (e.g., mobile, cloud, AI)
Standard changed
Interoperability broke

Infrastructure:

Cloud provider outage
Internet backbone failure
Power grid failure
Critical dependency failed

6. Social/Cultural Failures

Public opinion:

Reputation crisis
Boycott
Social media backlash
Cultural shift away from product
Ethical concerns raised

Demographics:

Target demographic shrunk
Generational shift
Migration patterns changed
Urbanization/de-urbanization

7. Environmental/Health Failures

Natural disasters:

Earthquake, hurricane, flood
Wildfire
Drought
Extreme weather

Health:

Pandemic
Endemic disease outbreak
Health regulation
Contamination/recall

Black Swans (Unpreventable External)

Characteristics

Extreme impact
Retrospectively predictable, prospectively invisible
Outside normal expectations

Examples

9/11 terrorist attacks
COVID-19 pandemic
2008 financial crisis
Fukushima disaster
Technological singularity
Asteroid impact

How to Handle

Can't prevent, can:

Increase robustness - Survive the shock
Increase antifragility - Benefit from volatility
Widen confidence intervals - Acknowledge unknown unknowns
Plan for "unspecified bad thing" - Generic resilience

PESTLE Framework for Systematic Enumeration

Use this checklist to ensure comprehensive coverage:

Political

Elections/regime change
Policy shifts
Government instability
Geopolitical conflict
Trade agreements
Lobbying success/failure

Economic

Recession/depression
Inflation/deflation
Interest rates
Currency fluctuations
Market bubbles/crashes
Unemployment

Demographic shifts
Cultural trends
Public opinion
Social movements
Consumer behavior change
Generational values

Technological

Disruptive innovation
Obsolescence
Cyber attacks
Infrastructure failure
Standards change
Technology convergence

Legal

New regulations
Lawsuits
IP challenges
Compliance requirements
Contract disputes
Liability exposure

Environmental

Climate change
Natural disasters
Pandemics
Resource scarcity
Pollution/contamination
Sustainability pressures

Kill Criteria Templates

What is a Kill Criterion?

Definition: A specific event that, if it occurs, drastically changes your probability.

Format: "If [event], then probability drops to [X%]"

Template Library

Regulatory kill criteria:

If [specific regulation] passes, probability drops to [X]%
If FDA rejects in Phase [N], probability drops to [X]%
If government bans [activity], probability drops to 0%

Competitive kill criteria:

If [competitor] launches [feature], probability drops to [X]%
If incumbent drops price by [X]%, probability drops to [X]%
If [big tech co] enters market, probability drops to [X]%

Financial kill criteria:

If we miss Q[N] revenue target by >20%, probability drops to [X]%
If we can't raise Series [X] by [date], probability drops to [X]%
If burn rate exceeds $[X]/month, probability drops to [X]%

Team kill criteria:

If [key person] leaves, probability drops to [X]%
If we can't hire [critical role] by [date], probability drops to [X]%
If team size drops below [X], probability drops to [X]%

Product kill criteria:

If we can't ship by [date], probability drops to [X]%
If NPS drops below [X], probability drops to [X]%
If churn exceeds [X]%, probability drops to [X]%

Market kill criteria:

If TAM shrinks below $[X], probability drops to [X]%
If adoption rate < [X]% by [date], probability drops to [X]%
If market shifts to [substitute], probability drops to [X]%

Macro kill criteria:

If recession occurs, probability drops to [X]%
If interest rates exceed [X]%, probability drops to [X]%
If war breaks out in [region], probability drops to [X]%

Failure Mode Probability Estimation

Quick Heuristics

For each failure mode, estimate:

Very Low (1-5%):

Black swans
Never happened in this industry
Requires multiple unlikely events

Low (5-15%):

Happened before but rare
Strong mitigations in place
Early warning systems exist

Medium (15-35%):

Common failure mode in industry
Moderate mitigations
Uncertain effectiveness

High (35-70%):

Very common failure mode
Weak mitigations
History of this happening

Very High (>70%):

Almost certain to occur
No effective mitigation
Base rate is very high

Aggregation

If failure modes are independent:

P(any failure) = 1 - ∏(1 - P(failure_i))

Example:

P(regulatory) = 20%
P(competitive) = 30%
P(execution) = 25%

P(any) = 1 - (0.8 × 0.7 × 0.75) = 1 - 0.42 = 58%

If failure modes are dependent: Use Venn diagram logic or conditional probabilities (more complex).

Monitoring and Signposts

Early Warning Signals

For each major failure mode, identify leading indicators:

Example: "Key engineer will quit"

Leading indicators (6-12 months before):

Code commit frequency drops
Participation in meetings declines
Starts saying "no" more often
Takes more sick days
LinkedIn profile updated
Asks about vesting schedule

Action: Monitor these monthly, set alerts

Monitoring Cadence

Risk Level	Check Frequency
Very High (>50%)	Weekly
High (25-50%)	Bi-weekly
Medium (10-25%)	Monthly
Low (5-10%)	Quarterly
Very Low (<5%)	Annually

Practical Usage

Step-by-Step: (1) Choose categories (Internal/External, PESTLE), (2) Brainstorm 10-15 failure modes, (3) Estimate probability for each, (4) Aggregate, (5) Compare to forecast, (6) Identify top 3-5 risks, (7) Set kill criteria, (8) Define monitoring signposts, (9) Set calendar reminders based on risk level.

Return to: Main Skill

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