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I Let AI Rewrite 40% of My Codebase. Here’s What Actually Happened.

AI Summary5 min read

TL;DR

The author let AI handle 40% of a production codebase, finding it boosts speed for repetitive tasks and refactoring but risks wrong code and architectural drift. AI amplifies senior judgment, making strong fundamentals crucial for leveraging its benefits.

Key Takeaways

  • •AI excels at boilerplate tasks, refactoring with clear instructions, and expanding test coverage, but can produce confidently wrong code and cause architectural drift.
  • •The psychological shift involves spending more time reviewing and shaping AI outputs rather than typing code, emphasizing judgment over creation.
  • •AI compresses skill gaps, benefiting those with strong fundamentals in architecture and tradeoffs, while increasing risks for those with weak systems thinking.
  • •A key rule is to never merge AI-generated code without thorough testing, review, and simplification, as AI writes drafts but developers own decisions.

Tags

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AI is no longer a novelty in development.

It’s not a toy.
It’s not magic.
It’s not replacing engineers tomorrow.

But it is changing how we work in ways most developers are underestimating.

Over the last 3 months, I intentionally let AI handle roughly 40% of a mid-sized production codebase I maintain. Not toy scripts. Not demos. Real features. Real refactors. Real bugs.

Here’s what broke.
Here’s what improved.
And here’s what I learned.

The Setup

  • Tech stack: Node, TypeScript, React, PostgreSQL
  • ~60k lines of code
  • 2 production environments
  • CI with tests and linting
  • Moderate technical debt

AI tools used:

  • Code generation for features
  • Refactoring suggestions
  • Test generation
  • Documentation drafting

I did not blindly paste outputs.
I treated AI like a fast but junior engineer.

Where AI Was Surprisingly Good

1. Boilerplate and Repetition

CRUD endpoints

Validation schemas

Form wiring

Data transformation layers

AI handles repetition extremely well. It does not get bored. It does not forget patterns.

What used to take 30 minutes of mechanical typing now takes 3 minutes of review.

2. Refactoring With Context

When given a clear instruction like:

Refactor this service to separate business logic from transport layer

It often produces structurally sound suggestions.

Not perfect.
But directionally correct.

It accelerates architectural cleanup if you already know what good looks like.

3. Test Coverage Expansion

AI is very good at:

  • Writing happy path tests
  • Generating edge case scaffolds
  • Increasing baseline coverage

However, it struggles with:

  • Domain nuance
  • Subtle business invariants

It boosts quantity.
You must guard quality.

Where It Quietly Failed

This is the part people gloss over.

1. Confidently Wrong Code

AI does not say “I’m not sure.”
It outputs something plausible.

Subtle issues included:

  • Incorrect async error handling
  • Overly optimistic null assumptions
  • Performance regressions from naive loops
  • Query inefficiencies

Everything looked clean.
Everything compiled.
Some of it was wrong.

This is dangerous.

2. Architectural Drift

When generating code incrementally, AI tends to:

  • Introduce slightly different patterns
  • Create inconsistent abstractions
  • Duplicate logic under new names

It optimizes locally.
It does not protect global coherence.

If you do not enforce standards, entropy increases.

3. Overengineering

Sometimes AI writes code that looks impressive but is unnecessary.

  • Extra abstraction layers
  • Generic wrappers
  • Overuse of patterns

It mimics what it has seen online.
That includes overcomplicated solutions.

The Psychological Shift

This was the most interesting part.

I noticed:

  • I write less trivial code.
  • I spend more time reviewing structure.
  • I think more about constraints before prompting.

AI did not reduce thinking.
It changed where thinking happens.

Instead of typing:
I evaluate.

Instead of building from scratch:
I shape outputs.

The bottleneck shifts from creation to judgment.

The Real Skill That Becomes Critical

AI amplifies senior judgment.

If you:

  • Understand architecture
  • Recognize tradeoffs
  • Know performance constraints
  • Understand security implications

You get leverage.

If you:

  • Copy paste blindly
  • Cannot spot flawed abstractions
  • Do not understand your stack deeply

You accumulate hidden landmines.

AI increases the cost of weak fundamentals.

What Actually Improved

  • Development speed for standard features: +30 to 40 percent
  • Test coverage: +18 percent
  • Documentation clarity: significantly better
  • Refactor velocity: much higher

What Got Harder

  • Code review intensity
  • Maintaining architectural consistency
  • Ensuring performance integrity
  • Trust calibration

The Biggest Misconception

AI does not replace developers.

It compresses skill gaps.

Junior engineers move faster.
Senior engineers move exponentially faster.

But weak systems thinking becomes more expensive.

The better your fundamentals, the more powerful AI becomes.

The Rule I Now Follow

I never merge AI generated code without:

  1. Running full test suite
  2. Reviewing complexity and duplication
  3. Checking performance implications
  4. Verifying error handling
  5. Simplifying unnecessary abstractions

AI writes drafts.
I own decisions.

Final Thoughts

Letting AI handle 40 percent of a codebase did not make me obsolete.

It made me more responsible.

AI is not a replacement.
It is leverage.

And leverage amplifies both strength and weakness.

If your fundamentals are strong, it is a multiplier.

If they are weak, it accelerates technical debt.

That is the tradeoff nobody talks about.

If you’re experimenting with AI in production systems, I’d love to hear:

  • What surprised you?
  • What failed?
  • What changed about how you think?

Let’s compare notes.

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