Why Laws Break When Context Changes
You've mastered growth tactics in B2B SaaS. Cold email sequences work brilliantly—20% response rates, consistent pipeline. The formula is proven across dozens of clients. It's practically a law: "Use this sequence format → get these results."
Then a client in B2C e-commerce tries the same approach. Response rate: 0.3%. Complete failure. The "law" broke.
What happened? Context changed. The principle that worked in one environment (B2B: small audience, high-value deals, relationship-based, professional email culture) failed in another (B2C: huge audience, low-value transactions, impersonal, spam-saturated inboxes).
The pattern repeats everywhere: Management practices that work in startups fail in large enterprises. Diet strategies that work for young athletes fail for sedentary elderly. Economic policies that work in growth periods backfire in recessions. Military tactics that work in open terrain fail in cities.
Understanding why principles and laws break when context changes—and how to recognize when context has shifted enough to invalidate familiar approaches—is critical to avoiding systematic failures.
What Makes Principles Context-Dependent
The Foundation: Every Principle Rests on Assumptions
No principle exists in a vacuum.
Every principle implicitly assumes certain conditions:
- Scale (small vs. large)
- Environment (stable vs. volatile)
- Resources (abundant vs. scarce)
- Actors (rational vs. emotional, cooperative vs. competitive)
- Time horizon (short vs. long)
- Constraints (what's possible, what's fixed)
When assumptions hold: Principle works
When assumptions violated: Principle breaks
Example: "Economies of scale"
Principle: Larger production volumes → lower per-unit costs
Assumptions:
- Fixed costs can be amortized across units
- Process can be replicated
- Market can absorb volume
- Quality doesn't degrade with scale
- Coordination costs don't offset gains
Where it works: Manufacturing, software (zero marginal cost)
Where it breaks:
- Craft production (quality degrades with scale)
- Consulting (coordination costs offset gains)
- Services requiring personal attention (doesn't scale)
Context determines validity.
Types of Context Changes That Break Laws
Context Change 1: Scale
What works at one scale often fails at another.
Scale changes properties, not just quantities.
Example: Management
| Scale | What Works | Why Previous Approach Breaks |
|---|---|---|
| 5 people | Direct communication, everyone knows everything, informal coordination | Overhead minimal |
| 50 people | Teams with leads, some formal process, mostly direct communication | Can't know everyone, need structure |
| 500 people | Formal hierarchy, documented processes, systems for coordination | Information overload, need abstraction |
| 5,000 people | Multiple layers, standardized processes, metrics-driven | Direct communication impossible |
Law that breaks: "Just talk to everyone directly"
Works at 5. Impossible at 5,000.
New context requires new approaches.
Example: Biology—surface area to volume ratio
Small insects:
- High surface-area-to-volume ratio
- Can breathe through surface (no lungs needed)
- Fall from height without injury (air resistance dominates)
Large mammals:
- Low surface-area-to-volume ratio
- Need lungs, circulatory system (surface insufficient)
- Fall from height is fatal (momentum dominates)
Physics changes with scale. "Breathe through your skin" works for ants, not elephants.
Context Change 2: Environment Stability
Principles optimized for stable environments fail in volatile ones, and vice versa.
Example: Strategy
Stable environment:
- Long-term planning works
- Optimization pays off
- Efficiency is paramount
- Specialization is advantageous
Volatile environment:
- Long-term planning becomes guessing
- Optimization for wrong scenario is waste
- Flexibility is paramount
- Generalization enables adaptation
Law that breaks: "Optimize for efficiency"
In stability: Correct.
In volatility: Dangerous (over-optimization creates fragility).
Example: Taleb's Antifragile distinction
Fragile systems (work in stability, break in volatility):
- Optimized for single scenario
- No redundancy
- Tight coupling
- Break when shocked
Antifragile systems (work in volatility):
- Benefit from stressors
- Redundancy built in
- Loose coupling
- Improve when shocked
Context (stability vs. volatility) determines which approach succeeds.
Context Change 3: Resource Availability
Scarcity and abundance create different dynamics.
Example: Capital availability
Capital-scarce context (bootstrapped startup):
- Law: "Focus on profitability from day one"
- Every dollar matters
- Can't afford experiments
- Must reach profitability to survive
Capital-abundant context (venture-backed startup):
- Law: "Focus on growth, worry about profitability later"
- Runway measured in years
- Can afford experiments
- Must reach scale to justify investment
Same industry, different resource contexts, opposite optimal strategies.
Applying capital-scarce law in capital-abundant context: Miss growth opportunity
Applying capital-abundant law in capital-scarce context: Run out of money
Context Change 4: Stakeholder Characteristics
Who you're dealing with changes what works.
Example: Persuasion
Rational, expert audience:
- Evidence-based arguments work
- Data and logic persuade
- Nuance appreciated
Emotional, non-expert audience:
- Stories and emotions work
- Data overwhelms
- Simplicity required
Law that breaks: "Just show the data"
Works for first group. Fails for second.
Example: Incentive design
Intrinsically motivated people (researchers, artists):
- Autonomy increases performance
- Extrinsic rewards can decrease motivation (crowding out effect)
- Purpose and mastery matter
Extrinsically motivated tasks (routine, well-defined):
- Clear rewards increase performance
- Autonomy less important
- Compensation matters
Deci & Ryan research: Context (intrinsic vs. extrinsic motivation) determines whether rewards help or hurt.
Context Change 5: Time Horizons
What works in the short term often fails in the long term, and vice versa.
Example: Debt
Short term:
- Leverage amplifies returns
- Access capital now
- Can grow faster
Long term:
- Debt accumulates
- Interest compounds
- Can become unsustainable
Law: "Leverage is good" or "Debt is bad"
Both wrong. Context (time horizon, purpose, cost) determines whether debt helps or hurts.
Example: Learning methods
Short-term (cramming for test tomorrow):
- Massed practice works
- All-nighter gets information into short-term memory
- Pass test
Long-term (retain for years):
- Massed practice fails (information decays)
- Spaced repetition works
- Retrieval practice builds durable memory
Different time horizons require different methods.
Context Change 6: Competitive Dynamics
What works when you're alone fails when everyone does it.
Example: Marketing
Early adopter:
- Novel tactic stands out
- High attention, low competition
- Works brilliantly
Mass adoption:
- Tactic becomes noise
- Saturated channel, high competition
- Stops working
Law that breaks: "Use [tactic X]"
Worked when few did it. Fails when everyone does.
Example: Red Queen hypothesis (biology)
Isolated species: Optimization for environment increases fitness
Competing species: Continuous adaptation just to maintain relative position (arms race)
Context (competition) changes nature of optimization.
How to Recognize Context Shifts
Warning Sign 1: Previously Reliable Principle Stops Working
Your established approach fails unexpectedly.
Response:
Don't assume: "We need to execute better"
Instead ask: "Has context changed?"
Investigation:
Compare current conditions to when principle was established
- What's different?
- Scale, environment, resources, constraints?
Identify violated assumptions
- What did the principle assume?
- Which assumptions no longer hold?
Test whether predictions still match reality
- Does principle's logic still apply?
- Or have fundamentals shifted?
Example: Manufacturing quality control
1950s-1980s principle: "Inspect quality at end (catch defects before shipping)"
1990s onward: Stops working (too slow, too costly, doesn't address root causes)
Context change: Competition intensified, customer expectations rose, production complexity increased
New principle: "Build quality in (prevent defects during production)" - Lean manufacturing, TQM
Recognizing context shift enabled new approach.
Warning Sign 2: Success Elsewhere Fails When You Apply It
Copy proven approach. It doesn't work for you.
Common reaction: "We must have implemented it wrong"
Better question: "Is our context different?"
Analysis:
- Identify contextual differences
- Industry, scale, culture, resources, constraints
- Determine which differences matter
- Some differences irrelevant, others critical
- Adapt principle to your context
- Keep underlying logic, adjust implementation
Example: Toyota Production System
Context (Toyota):
- Manufacturing
- Repetitive processes
- Stable product demand
- Long-term workforce
- Japanese culture (consensus, long-term thinking)
Attempts to copy:
- Many US manufacturers tried in 1980s-1990s
- Often failed despite "following the system"
Context differences that mattered:
- US culture (individualistic, short-term)
- High worker turnover
- Volatile demand
- Quarterly earnings pressure
Success required: Adapt principles to new context, not copy practices exactly.
Warning Sign 3: Principle Works in Theory, Fails in Practice
Logic is sound. Execution fails repeatedly.
Likely cause: Real-world context violates theoretical assumptions.
Example: Efficient Market Hypothesis
Theory: Markets instantly incorporate all information into prices, so you can't consistently beat the market
Assumptions:
- Rational actors
- Perfect information
- No transaction costs
- Infinite liquidity
Reality:
- Bounded rationality (psychological biases)
- Asymmetric information
- Substantial transaction costs
- Limited liquidity in many assets
Result: Theory elegant, but context (real humans, real markets) violates assumptions. Hedge funds consistently outperform (though most don't), arbitrage opportunities exist, bubbles form.
Theoretical principles must be tested against actual context.
Are Any Principles Truly Universal?
The Hierarchy of Robustness
Not all principles are equally context-dependent.
Level 1: Mathematical and logical truths
- 2 + 2 = 4
- Modus ponens (if A→B and A, then B)
- Conservation of energy
Context independence: Complete (within domain of applicability)
Level 2: Physical laws
- Gravity
- Thermodynamics
- Quantum mechanics
Context independence: Very high (within physical universe)
Level 3: Biological regularities
- Natural selection
- Organisms require energy
- DNA → RNA → Protein
Context dependence: Low (apply across life as we know it)
Level 4: Psychological patterns
- Cognitive biases
- Motivation principles
- Social dynamics
Context dependence: Moderate (humans across cultures, but cultural variation exists)
Level 5: Social/economic/business "laws"
- Supply and demand
- Network effects
- Management principles
Context dependence: High (strong patterns, but many contextual factors)
Level 6: Tactical rules
- Specific marketing tactics
- Particular management practices
- Industry-specific approaches
Context dependence: Very high (work only in narrow contexts)
Key insight: The more domain-specific and close to application, the more context-dependent. The more fundamental and universal, the more robust.
But: Even physics has contextual limits (quantum vs. relativistic vs. Newtonian regimes).
How to Adapt Principles to New Contexts
Process 1: Identify the Core Logic
Separate underlying mechanism from surface implementation.
Ask: "Why does this work?"
Example: "Stand-up meetings" from Agile
Surface practice: Daily 15-minute standing meeting
Core logic:
- Frequent synchronization prevents drift
- Time constraint forces conciseness
- Standing keeps it short
- Everyone hearing everyone's update creates shared context
Adaptation for different context:
- Remote team: Daily async written updates (preserves logic: frequent sync, concise, shared context)
- Hospital ER: Hourly huddle (faster-paced environment needs more frequent sync)
- Executive team: Weekly check-in (different scale, different cadence needed)
Keep logic. Vary implementation.
Process 2: Test Assumptions Explicitly
Make implicit assumptions explicit. Verify which hold in new context.
Example: "Fail fast" principle
Assumptions:
- Failure is cheap
- Learning from failure is possible
- Iteration is feasible
- Speed of learning matters
Where assumptions hold (software development):
- Failure is cheap (just code, easily changed)
- Can iterate rapidly
- Learning fast is competitive advantage
Where assumptions break (civil engineering):
- Failure is catastrophic (bridges collapse, people die)
- Can't iterate after building
- Must get it right first time
Principle doesn't transfer because assumptions violated.
Process 3: Look for Analogous Structures
Different domains, similar structures → similar principles may apply.
Different structures → need different principles.
Example: Network effects
Works in:
- Social media (users create value for other users)
- Telephones (more users → more valuable to each)
- Marketplaces (buyers attract sellers, sellers attract buyers)
Analogous structure: Value increases with participants
Doesn't work in:
- Traditional restaurants (more customers can decrease value through crowding)
- Luxury goods (exclusivity matters, mass adoption decreases value)
Different structure → different dynamics.
Designing for Context Robustness
Strategy 1: Build in Optionality
Don't over-optimize for single scenario.
Keep options open for context changes.
Example: Modular architecture
Instead of: Tightly coupled monolithic system (optimized for current requirements)
Use: Modular components (can reconfigure as context changes)
Trade-off: Slightly less efficient now, but adapts better to change.
Strategy 2: Monitor Context Continuously
Don't assume context is static.
Watch for signals that conditions are shifting.
Indicators to track:
- Scale changes (growing or shrinking)
- Environmental stability (more or less predictable)
- Resource availability (constraints tightening or loosening)
- Competitive intensity (increasing or decreasing)
- Stakeholder characteristics (audience changing)
When indicators shift significantly: Revisit principles and approaches.
Strategy 3: Separate Principles from Practices
Principles: Why things work (more robust)
Practices: Specific implementations (context-dependent)
Hold principles firmly. Hold practices loosely.
Example: Amazon's "customer obsession" principle
Principle: Stable (always prioritize customer value)
Practices: Change constantly
- 1990s: Online bookstore
- 2000s: Everything store, marketplace
- 2010s: AWS, Prime, devices
- 2020s: Logistics, media, healthcare
Principle guides. Practices adapt to context.
Strategy 4: Embrace Experimentation
In new contexts, you don't know what will work.
Experiment to discover what applies.
Approach:
- Hypothesize: Based on principles from similar contexts, what should work?
- Test small: Pilot before full commitment
- Measure: Did predictions hold?
- Learn: What worked, what didn't, why?
- Adapt: Refine approach based on learning
Don't assume principles will transfer perfectly. Verify through experimentation.
Conclusion: Context Is Not Optional
Principles are powerful tools for thinking.
But they are not magic formulas that work everywhere.
Every principle rests on assumptions about context:
- Scale
- Environment stability
- Resource availability
- Stakeholder characteristics
- Time horizons
- Competitive dynamics
- And more...
When context changes:
- Assumptions may be violated
- Principles may stop working
- Need to adapt
The mistakes:
1. Context blindness: Applying principles without checking context 2. Over-generalization: Assuming principles are universal when they're conditional 3. Cargo culting: Copying practices without understanding context-dependent logic
The wisdom:
1. Make assumptions explicit: What does this principle assume? 2. Check context: Do those assumptions hold here? 3. Adapt thoughtfully: Keep core logic, adjust implementation 4. Monitor continuously: Watch for context shifts 5. Experiment: Test whether principles transfer
Key insights:
- All principles have contextual limits (even physics has regimes where different laws apply)
- Context changes break laws (what worked stops working when conditions shift)
- Recognize context shifts (compare current to original conditions, identify violated assumptions)
- Adapt principles to context (understand core logic, test assumptions, adjust implementation)
- Build robustness (optionality, monitoring, separation of principles from practices)
The path forward:
When learning principles:
- Understand not just what but under what conditions
- Ask about assumptions
- Study contexts where principles do and don't apply
When applying principles:
- Compare contexts (where principle was derived vs. where applying)
- Verify assumptions hold
- Adapt as needed
When principles fail:
- Don't dismiss principle entirely
- Ask: "Has context changed such that assumptions are violated?"
- Refine understanding of contextual boundaries
Context is not a nuisance to be ignored.
Context is the determining factor in whether principles work.
Wisdom isn't knowing universal laws that always apply.
Wisdom is knowing which principles apply in which contexts, recognizing when contexts have changed, and adapting accordingly.
References
Taleb, N. N. (2012). Antifragile: Things That Gain from Disorder. Random House.
Meadows, D. H. (2008). Thinking in Systems: A Primer. Chelsea Green Publishing.
Kahneman, D. (2011). Thinking, Fast and Slow. Farrar, Straus and Giroux.
Deci, E. L., & Ryan, R. M. (2000). "The 'What' and 'Why' of Goal Pursuits: Human Needs and the Self-Determination of Behavior." Psychological Inquiry, 11(4), 227–268.
Christensen, C. M. (1997). The Innovator's Dilemma: When New Technologies Cause Great Firms to Fail. Harvard Business Review Press.
Womack, J. P., Jones, D. T., & Roos, D. (1990). The Machine That Changed the World. Free Press.
Liker, J. K. (2004). The Toyota Way: 14 Management Principles from the World's Greatest Manufacturer. McGraw-Hill.
Spear, S., & Bowen, H. K. (1999). "Decoding the DNA of the Toyota Production System." Harvard Business Review, 77(5), 96–106.
Scott, J. C. (1998). Seeing Like a State: How Certain Schemes to Improve the Human Condition Have Failed. Yale University Press.
Simon, H. A. (1996). The Sciences of the Artificial (3rd ed.). MIT Press.
March, J. G. (1991). "Exploration and Exploitation in Organizational Learning." Organization Science, 2(1), 71–87.
Thompson, J. D. (1967). Organizations in Action: Social Science Bases of Administrative Theory. McGraw-Hill.
Gould, S. J., & Lewontin, R. C. (1979). "The Spandrels of San Marco and the Panglossian Paradigm: A Critique of the Adaptationist Programme." Proceedings of the Royal Society of London B, 205(1161), 581–598.
Van Valen, L. (1973). "A New Evolutionary Law." Evolutionary Theory, 1, 1–30. (Red Queen hypothesis)
Ostrom, E. (1990). Governing the Commons: The Evolution of Institutions for Collective Action. Cambridge University Press.
About This Series: This article is part of a larger exploration of principles and laws. For related concepts, see [What Is a Principle and Why It Matters], [Universal Principles That Apply Across Domains], [Why Principles Outlast Tactics], and [First-Order vs Second-Order Effects].