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Why Planning is 40% of AI Development
In compound engineering, planning isn't overhead — it's where the real work happens.
6 min read • January 2026
The Inversion
Traditional software development: 10% planning, 80% coding, 10% review.
Compound engineering: 40% planning, 10% work, 40% review, 10% compounding.
That's a dramatic inversion. And it changes everything.
Why Planning Matters So Much
When you write code yourself, you can course-correct as you go. You spot edge cases mid-implementation. You adjust the design when you hit obstacles. Planning can be loose because execution provides feedback.
When an AI writes the code, that feedback loop breaks. The AI can't read your mind. It can't know what you meant when you said "user-friendly" or "performant" or "secure." It needs clear, detailed, unambiguous specifications.
The quality of your output is capped by the quality of your planning.
What Planning Actually Means
In compound engineering, planning isn't writing a brief Jira ticket. It's:
RESEARCH
Understanding the problem domain deeply. Reading documentation. Exploring existing solutions. Talking to users. Building context that you'll pass to the AI.
SYNTHESIS
Taking everything you learned and distilling it into clear requirements. What are we building? Why? For whom? What are the edge cases? What are the constraints?
SPECIFICATION
Creating detailed, unambiguous specs that the AI can execute against. Not just "add user auth" but "implement OAuth 2.0 with Google and GitHub providers, session storage in Redis, 24-hour token expiry, automatic refresh..."
CONTEXT LOADING
Pulling relevant patterns, decisions, and learnings from your knowledge base. What have we learned about auth in previous projects? What security issues did we encounter? What works well?
The Paradox
Here's what feels counterintuitive: better AI requires more planning, not less.
You might think that as AI gets better at writing code, you can be lazier with your specs. The opposite is true. Better AI can execute more complex plans — but only if those plans are well-crafted.
A weak plan with strong AI gives you fast garbage. A strong plan with strong AI gives you production-ready software.
The Planning ROI
Every hour spent on planning saves 3-5 hours of revision, debugging, and rework. In traditional development, you pay for poor planning during coding. In AI development, you pay for it during review and compound — when it's more expensive to fix.
What Good Planning Looks Like
A good plan is specific, complete, and grounded in real understanding. The difference between a weak plan and a strong plan determines whether you get production-ready code or something you need to heavily revise.
❌ BAD PLAN:
"Add a dashboard with user analytics"
✓ GOOD PLAN:
"Create a dashboard showing: (1) Daily active users over 30 days, (2) Feature usage breakdown by percentage, (3) Average session duration. Use Chart.js for visualizations, match design system colors (ref: design-tokens.css), fetch data from /api/analytics endpoint (returns JSON format documented in API.md), cache for 5 minutes, show loading state during fetch, handle error state if API fails, responsive layout with 2 columns on desktop, stacked on mobile..."
Anatomy of a Good Plan
What problem are we solving? For whom? Why now? What have we tried before?
Specific, measurable outcomes. What exactly needs to happen? What are the edge cases?
Which APIs? What data format? Which libraries? What design patterns from our codebase apply?
Performance requirements, security considerations, browser support, accessibility standards.
How do we know it works? What tests should pass? What user flows should succeed?
Becoming a Better Planner
The best AI developers aren't the best coders. They're the best planners.
They know how to research deeply. They ask clarifying questions. They think through edge cases. They write specs that leave no ambiguity. They load the AI with relevant context from past projects.
These are learnable skills. But they're different skills than traditional coding.
Planning Skills to Develop
- → Deep research and context gathering
- → Writing unambiguous specifications
- → Identifying edge cases and failure modes
- → Breaking complex problems into clear steps
- → Pattern recognition across projects
- → Technical writing and documentation
Traditional Coding Skills (AI-Handled)
- → Syntax and language features
- → Boilerplate and scaffolding
- → Dependency management
- → Test generation and setup
- → Code formatting and style
- → Basic refactoring operations
The Mindset Shift
Don't think of planning as "pre-work" that you rush through to get to the "real" coding. Think of planning as the work.
When you plan well, the AI executes flawlessly. When you plan poorly, you spend your review time fixing preventable mistakes.
In compound engineering, planning isn't overhead. It's leverage.
Want to learn compound engineering planning for your team?
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