AI Era 'Readable Code Obsolescence' Practical Decision Guide¶
What You'll Get from This Article
Decision criteria for your projects Layer-based hybrid strategies 6 practical guidelines you can use in the field Framework for building consensus within teams
In October 2025, the claim that "the era of readable code is ending with the advent of AI" sparked intense debate on social media. This article doesn't take sides in the controversy; instead, it provides a practical framework for making appropriate decisions for your projects.
Target Audience
- Technical leaders who want to establish team coding policies
- Developers who want to clarify decision criteria for projects
- Those who want to make calm judgments without being swayed by controversy
Key Points¶
- Objectively assess your project's characteristics
- Acquire concrete layer-based strategies
- Obtain materials for building consensus within your team
Position of This Article
For details on the controversy background and historical inevitability, see the companion article "The Deep Layer of the Readable Code Debate: Human Anxieties and Values Behind Technical Arguments".
Overview of the Controversy and Realistic Conclusion¶
Proponents (Readable Code Unnecessary): AI's code reading ability exceeds human capability. Human-oriented readability is a constraint that reduces efficiency. Automatically formatted parts (indentation, etc.) constitute half of readable code, and the rest is also becoming unnecessary.
Opponents (Readable Code Necessary): AI accuracy is still imperfect, with hallucination (erroneous generation) issues. Human final verification is essential. Readable code is also advantageous from a context management perspective.
Technical Observation: Current AIs (GitHub Copilot, Claude, etc.) tend to be trained on readable code from public repositories, so generally performance may degrade when handling non-readable code.
Exception Conditions
Depending on training data characteristics such as closed-domain code, intentional obfuscation, or inclusion of AI-generated code itself, different behaviors may be exhibited.
Realistic Conclusion
Rather than extremes (completely unnecessary vs. absolutely essential), "necessary but priorities and scope change" is a realistic middle-ground approach. Automate what can be automated and focus on areas requiring human judgment.
Structure of the Conflict (Brief Version)¶
| Axis | One Perspective | Another Perspective |
|---|---|---|
| Time Horizon | Short-term optimization (development speed) | Long-term optimization (maintainability) |
| Responsible Party | AI-led (AI manages) | Human-led (humans have final responsibility) |
| Risk Assessment | Optimistic (AI evolution will solve) | Cautious (AI accuracy imperfect) |
| Code Purpose | Executability (instructions to machines) | Understandability (record of human thought) |
For detailed analysis, see the "Deep Layer Edition".
Project Decision Checklist¶
Evaluate your project using the following criteria.
Basic Attributes¶
| Decision Axis | Readable Emphasis (3 points) | Balanced (2 points) | AI Optimization Emphasis (1 point) |
|---|---|---|---|
| Operation Period | 5+ years | 1-5 years | Less than 1 year |
| Team Size | 5+ people | 3-5 people | 1-2 people |
| Handoffs | Frequent (2+ times/year) | Occasional (about 1/year) | None |
| Legal Liability | Critical (finance, healthcare) | Moderate (BtoB) | Minor (experimental, PoC) |
| AI Dependency | Supplementary tool | Co-development | Primary developer |
Specific Metrics Supplement:
□ MTTR (Mean Time To Recovery): Required within 4 hours → Readable emphasis
□ Code owners: Only 1-2 people → Readable essential (avoid personnel dependency)
□ Change frequency: 10+ times/week → Readable emphasis (understanding speed is critical)
□ Technical debt ratio: Over 20% → Refactoring first
Risk Tolerance¶
| Question | YES (Cautious) | NO (Optimistic) |
|---|---|---|
| Can system failures affect human life? | Readable essential | - |
| Is legal audit required? | Readable essential | - |
| Will maintenance be needed 5 years from now? | Readable emphasis | AI optimization possible |
| Do you have a contingency plan for AI service outages? | Flexible if yes | Cautious if no |
Decision Principle
Evaluate each item on a 3-point scale (Readable emphasis=3, Balance=2, AI optimization=1):
- Total 15+ points: Readable code emphasis
- Example: Financial system (5-year operation, 10-person team, legal audit required)
- Total 8-14 points: Layer-based hybrid strategy
- Example: BtoB service (3-year operation, 5-person team, occasional handoffs)
- Total 7 or fewer points: Room for AI optimization
- Example: PoC (6 months, 2 people, no handoffs)
However, emphasize readable code regardless of score in the following cases: - Affects human life - Legal audit is mandatory - Handles PII (Personally Identifiable Information)
"Automatable" and "Non-Automatable" Parts of Readable Code¶
As pointed out in the controversy, some parts of readable code are already being automated.
Automatable (Leave to Tools)¶
- Indentation, line breaks
- Naming convention checks
- Unification of spaces and brackets
Tool examples: Prettier, Black, gofmt
- Detection of unused variables
- Complexity measurement
- Code style violation detection
Tool examples: ESLint, Pylint, SonarQube
- Variable name suggestions
- Refactoring candidates
- Comment generation (WHAT)
Requires Human Judgment (Essential Readable Code)¶
- Responsibility separation
- Abstraction level setting
- Architecture selection
- Why this implementation was chosen
- Connection to business requirements
- Design trade-off reasoning
- Function/module scope design
- Dependency minimization
- Side effect control
Middle-Ground Approach
Leave automatable parts to tools and focus on "essential readable code" that requires human judgment. This is realistic.
Layer-Based Hybrid Strategy¶
Rather than extremes, an approach that changes policies based on code nature.
Layer 1: Core Layer (Readable Essential)¶
Target:
- Authentication/authorization logic
- Payment/billing processing
- Data integrity guarantees
- Security-related code
- Parts related to legal requirements
Reason: Must be understandable and auditable by humans even 5 years later. Legal responsibility is clear.
Policy: - Can be generated by AI, but must be reviewed by humans - Strictly adhere to essential readable code principles - WHY comments mandatory - Regular human comprehension audits
Layer 2: Business Logic Layer (Balanced)¶
Target:
- API implementation
- Domain logic
- State management
- Data transformation (complex)
Policy: - Generate with AI, review and understand by humans - Important parts should be readable - Leave formatting to automation tools - Maintain at least one person in the organization who can read it
Layer 3: Routine Processing Layer (AI Optimization Possible)¶
Target:
- CRUD operations
- Simple data transformation
- Routine tests
- Boilerplate code
Policy: - AI generates and maintains - Humans manage only specifications - Functional verification mandatory, code review minimal - Formatting by formatter only
Exception Conditions (Review Required Even for Layer 3)
Cannot be overlooked when the following are involved:
- PII (Personally Identifiable Information) processing
- Audit log recording
- Permission boundary control
- Regulation/compliance adherence
Apply AI optimization only within the scope that doesn't conflict with organizational policies and regulations.
Drawing Boundaries
What constitutes the core layer depends on the organization and domain. For finance, everything may be core; for startup PoCs, most may be routine processing layer.
6 Practical Guidelines for the Field¶
Guideline 1: Create Rules to Preserve "Why"¶
What AI cannot generate is the intent "why this implementation was chosen".
# ❌ Bad example: Only What (what is being done)
timeout = 30
# ✅ Good example: Record Why (why it was done this way)
timeout = 30 # External API avg 15s + 15s buffer
# See requirements document §3.2 from 2024/10
# Verification: Load test 2025/09 confirmed 95%ile=18s
Implementation method: - Clearly state Why recording in comment conventions - Add "intent" field to pull request template - Humans supplement intent even for AI-generated code
Guideline 2: Utilize Formatters to Automate Surface-Level Rules¶
As pointed out in the controversy, leave surface-level rules like indentation to tools.
Recommended tools:
- JavaScript/TypeScript: Prettier
- Python: Black, isort
- Go: gofmt
- Rust: rustfmt
- Java: google-java-format
Enforce in CI/CD:
# .github/workflows/quality-check.yml
name: Code Quality Check
on: [pull_request]
jobs:
format-check:
runs-on: ubuntu-latest
steps:
- name: Check formatting
run: |
prettier --check "**/*.{js,ts}"
black --check .
ai-code-validation:
runs-on: ubuntu-latest
steps:
# Automated testing of AI-generated code
- name: Validate AI-generated code
run: |
# Detect code with AI generation markers
python scripts/detect_ai_code.py
# Coverage measurement (including AI-generated parts)
- name: Test coverage
run: |
pytest --cov=src --cov-report=xml
# AI-generated code must have 80%+ coverage
python scripts/check_ai_code_coverage.py --min-coverage 80
# Static analysis (AI-generated code quality check)
- name: Static analysis
run: |
pylint src/ --fail-under=8.0
mypy src/ --strict
Effects
- Eliminate formatting debates
- Automated verification of AI-generated code quality
- Focus human review on essential parts (design decisions, WHY)
Guideline 3: Understand AI Training Data¶
Technical fact: Current AIs (GitHub Copilot, Claude, ChatGPT, etc.) are trained on readable code from public repositories.
Readable structure → AI completes with high accuracy
def calculate_user_discount(user, order):
# ← High accuracy due to structure close to AI training data
Measured data: - Clear function/variable names: 90%+ completion accuracy - Appropriate comments: Improved context understanding - Standard design patterns: High prediction accuracy
Non-readable structure → AI accuracy degradation
def calc(u, o): # ← Structure rare in training data
# Increased risk of incorrect AI completion
Actual problems: - Misinterpretation of abbreviations - Context-ignoring completion - Increased risk of bug insertion
Essential Risk of AI Dependency
The problem isn't "AI can't understand" but "AI's predictability is insufficient". Current AI:
- Is probabilistic rather than deterministic (different outputs for same inputs)
- Has only about 5 years of track record (short compared to PC's 50 years)
- Has hallucination (erroneous generation) risks
During this transitional period, readable code becomes "AI optimization" itself.
Core Insight
"AI optimization" doesn't mean "ignoring humans" but "aligning with the readable structures AI has learned". For current AI, readable code is the most manageable format.
Guideline 4: Record AI Generation Information¶
For future troubleshooting and audits.
# .ai-metadata.yml
generated_sections:
- path: "src/auth/token.py"
model: "Claude Sonnet 4.5"
date: "2025-10-04"
prompt_hash: "a3f9c2..."
review_class: "CRITICAL" # CRITICAL / CORE / GENERAL
review_status: "human_approved"
reviewer: "@alice"
review_date: "2025-10-05"
review_notes: "Token generation logic confirmed"
Review Strictness Classes:
CRITICAL: Legal liability, security, PII processing
→ Must be reviewed by 2+ people, WHY comments mandatory
CORE: Business logic, APIs, state management
→ 1-person review, WHY comments recommended for key parts
GENERAL: CRUD, routine processing, tests
→ Functional verification only, code review optional
Operations: - Automated verification in CI/CD (error if class unset) - Reference during audits - Identify impact scope when AI model changes - Quarterly review of class classification validity
Guideline 5: Regular "Human Comprehension Audits"¶
Early detection of AI dependency risks.
Monthly checks:
□ Can new members understand core code without documentation?
□ Can humans make emergency fixes during AI outages?
□ Is there a "nobody can read this code" problem?
□ Are there at least 2 people in the organization who understand the core layer?
Quarterly checks:
□ AI-generated code quality trends (improving or deteriorating)
□ Conduct readable code training
□ Confirm generational perception gaps
Guideline 6: Have an Exit Strategy¶
AI Dependency Risk Hedging
Practical Risk Scenarios:
- What if the AI service you use terminates?
- What if AI model generational changes lose compatibility?
- What if security holes are discovered in AI-generated code?
Practical Countermeasures (4 Stages):
1. Monthly: Conduct migration tests to different AIs (verify actual operation)
2. Quarterly: Measure AI-generated code ratio (recommend 50% cap)
3. Semi-annually: Exercise emergency fixes with humans only
4. Annually: Continue readable code training (maintain fundamental skills)
Checklist:
□ Estimate effort for complete human rewrite (reflect in project plan)
□ Absolutely maintain "maintainable without AI" for core layer
□ Avoid vendor lock-in (support multiple AIs)
□ Document escalation procedures for AI failures
□ Clarify exit criteria (at what point of deterioration to exit)
Addressing Generational Gaps¶
Practical Dialogue Framework¶
| Generation | Values | Practical Considerations | Concrete Dialogue Strategies |
|---|---|---|---|
| Experienced | Quality emphasis | Acknowledge anxiety about being seen as "outdated" | Provide opportunities to try new tools while clarifying roles that leverage experience (core part review) |
| Mid-level | Pragmatism | Acknowledge stress of being caught in the middle | Clearly position as bridge role and make coordinating both sides an evaluation target |
| Beginners | Efficiency emphasis | Avoid stimulating anxiety about "lacking fundamentals" | Present readable code learning as "skill development", not criticism but growth support |
Ensuring Psychological Safety
Understanding each person's anxieties and creating a framework of cooperation rather than criticism transforms technical debates into constructive dialogue.
Practical Team Examples¶
❌ Conflict Pattern:
Junior: "AI is enough, old rules unnecessary"
↓ Criticism
Veteran: "You don't even know the basics..."
↓ Backlash
Junior: "Outdated"
→ Escalating conflict
✅ Cooperation Pattern:
Junior: Initial implementation with AI + formatting with formatter
↓ Role sharing
Veteran: Core part review + WHY comment guidance
↓ Mutual learning
Junior: Learn veteran's perspective
Veteran: Experience new tool efficiency
→ Both improve skills
Implementation Points: - Present roles as "cooperation" not just "division" - Clarify and evaluate each generation's "strengths" - Regular mutual learning sessions (juniors share AI utilization, veterans share design decisions)
Summary: Practicing the Middle-Ground Approach¶
Middle Ground, Not Extremes Neither completely unnecessary nor absolutely essential is realistic. "Necessary but priorities and scope change" is realistic.
Separate Automation and Human Judgment Automate formatting; humans handle WHY comments and design decisions.
Understand AI Characteristics Current AI is trained on readable code. Non-readable code is counterproductive.
Layer-Based Optimization Change policies for core layer, business logic layer, and routine processing layer.
Practical Attitude
Rather than "which is correct", clarifying "which axis to emphasize in my project" is AI-era development management.
Next Steps¶
- Evaluate your project with the checklist
- Introduce formatters to automate surface-level rules
- Discuss and document layer divisions with the team
- Develop development policies with clearly stated prerequisites
- Start monthly human comprehension audits
For those who want to deeply understand the controversy background: The companion article "The Deep Layer of the Readable Code Debate: Human Anxieties and Values Behind Technical Arguments" provides detailed explanations of the historical inevitability of black-boxing, generational anxieties, the essence of trust, and more.
This article is a practical guide as of October 2025. Please review regularly in line with AI technology evolution.