AI Coding

1. Intent & Communication

Why AI cannot infer what you mean — even when you explain it

  1. AI Can’t Read Your Mind — And Design Lives in Your Head
  2. The Hidden Work Behind “AI Generated This”
  3. Why Prompts Are a Lossy Interface for Design Intent
  4. The Difference Between Explaining Behavior and Explaining Shape
  5. Implicit Constraints vs Explicit Instructions
  6. Why “You Know What I Mean” Fails With AI
  7. Design Intent Exists Between the Lines — AI Only Sees the Lines

2. Design Authority & Taste

Why design is about rejection, not generation

  1. AI Has No Taste — and Taste Is the Job
  2. Why Reasonable Designs Can Still Be Wrong
  3. Design Is a Series of “No” Decisions
  4. Why AI Over-Generates Abstractions You Didn’t Ask For
  5. Naming, Boundaries, and Responsibility: The Unspoken Design Layer
  6. Why Consistency Matters More Than Cleverness

3. Code-Level Design Failure Modes

Where AI-generated code “works” but is still wrong

  1. Correct Code, Wrong Shape
  2. Why AI Loves Helpers, Utilities, and Glue Code
  3. The Abstraction Drift Problem
  4. Why AI Breaks Encapsulation While Fixing Bugs
  5. Local Refactors That Destroy Global Clarity
  6. When DRY Becomes Design Debt
  7. Why AI Can’t Hold a Stable Mental Model of Your Codebase

4. System-Level Design Failure Modes

Same problem — bigger consequences

  1. AI Can’t Design Systems It Has to Live With
  2. Why System Design Is Mostly About Constraints
  3. AI Doesn’t Know Which Invariants Matter
  4. Why Generated Architectures Collapse Under Change
  5. Local Optimizations, Global Failures
  6. AI Can Describe Architectures — It Can’t Commit to One
  7. Why Cross-Cutting Concerns Are Where AI Fails Most
  8. System Design Is Temporal — AI Is Snapshot-Based

5. Iteration & Convergence Failure

Why “just iterate with AI” doesn’t converge

  1. Why AI Refactors in Circles
  2. The Prompt–Rewrite Death Spiral
  3. Why Fixing One Thing Breaks Three Others
  4. Context Drift Across Sessions
  5. Why Design Corrections Don’t Compound
  6. The Illusion of Progress in AI Iteration
  7. Why Humans Feel Fatigue Before AI Feels Direction

6. Scale, Time, and Evolution

The dimension AI does not experience

  1. AI Optimizes for Now — SaaS Lives in the Future
  2. Why Design Debt Is Invisible at Generation Time
  3. What Happens at Version 5 That AI Never Anticipates
  4. Schema Evolution and the Cost of Early Shape
  5. Why Scale Exposes Design, Not Performance
  6. The Rewrite Trap AI Accidentally Encourages

7. Why Frameworks Exist

Frameworks as intent compression

  1. Frameworks Exist to Encode Decisions
  2. Design Once, Reuse Forever
  3. Why Structure Beats Freedom at Scale
  4. Constraints Are What Make Velocity Sustainable
  5. Frameworks Reduce Degrees of Freedom — and That’s the Point
  6. Why AI Performs Better Inside Strong Structure

8. The Correct Role of AI

AI as leverage, not authority

  1. AI Is a Force Multiplier — Not a Design Authority
  2. What AI Should Decide vs What Humans Must Decide
  3. Where AI Is Exceptionally Good (And Where It Isn’t)
  4. AI as an Implementer, Not an Architect
  5. Why the Best AI Output Comes From Opinionated Foundations
  6. AI on Top of Structure Beats AI From Scratch

9. SaasEasy

SaasEasy as the substrate, not the star

  1. SaasEasy as an Intent Container
  2. Why SaasEasy Reduces Prompting, Not Flexibility
  3. How SaasEasy Turns AI Into a Reliable Teammate
  4. Design Alignment as a First-Class Feature
  5. Why This Isn’t About Lock-In — It’s About Leverage
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