Vibe Coding Is Destroying Your Codebase

The New Technical Debt

There's a new pattern showing up in every codebase we audit: blocks of code that work but nobody on the team can explain. When we ask "why is this implemented this way?" the answer is increasingly: "Copilot suggested it" or "Claude wrote that part."

Welcome to vibe coding. And it's creating technical debt faster than any human ever could.

What Vibe Coding Looks Like

Vibe coding isn't using AI to write code. It's accepting AI-generated code without understanding it. The symptoms:

• Functions that work but use patterns nobody on the team recognizes
• Error handling that looks comprehensive but misses the actual failure modes
• Abstractions that seem clever but don't match the domain
• Dependencies added because the AI suggested them, not because they're needed
• Tests that pass but don't test the right things

// Vibe-coded: looks sophisticated, but why?
const processOrder = pipe(
  validateSchema(orderSchema),
  tap(logOrderReceived),
  chain(enrichWithCustomerData),
  map(calculateTotals),
  chain(applyDiscounts),
  fold(handleOrderError, persistOrder)
);
 
// What the team actually needed:
async function processOrder(input: OrderInput): Promise<Order> {
  const validated = validateOrder(input);
  const customer = await getCustomer(validated.customerId);
  const totals = calculateTotals(validated, customer.tier);
  return await saveOrder({ ...validated, ...totals });
}

Both work. But when the second one breaks at 2 AM, anyone on the team can debug it.

The Real Cost

We've measured the impact across a dozen codebases:

The velocity gain from AI-generated code is real — but it's being eaten alive by the maintenance cost.

The Five Rules for AI-Assisted Coding

Rule 1: Understand Before You Commit

If you can't explain what the code does to a teammate, don't merge it. This isn't about being anti-AI — it's about being pro-maintainability.

Rule 2: AI Writes, You Architect

Use AI for implementation, not design. You decide the patterns, the abstractions, the interfaces. AI fills in the details:

✅ "Implement this interface using our existing database patterns"
❌ "Build me an order processing system"

Rule 3: Smaller Prompts, Better Code

The bigger the prompt, the more the AI invents. Keep requests focused:

✅ "Write a function that validates email format and returns boolean"
❌ "Write a complete user validation module with all edge cases"

Rule 4: Review AI Code Harder Than Human Code

Human code has intent you can ask about. AI code doesn't. Review it like it came from an anonymous contractor who won't be available for questions.

Rule 5: Delete What You Don't Need

AI over-generates. It adds error handling for errors that can't happen, abstractions for cases that don't exist, and utilities you'll never call. Be aggressive about removing what's unnecessary.

The Organizational Fix

This isn't just an individual problem. It's a team problem:

Code Review Standards

• "AI-generated" label required on PRs with significant AI-written code
• Explanation comments mandatory for non-obvious AI-generated patterns
• Reviewer must be able to explain the code, not just approve it

Architecture Guardrails

• Approved patterns list — AI should use your patterns, not invent new ones
• Dependency review — no new packages without team discussion
• Complexity budget — if AI-generated code is more complex than the manual version, use the manual version

Knowledge Sharing

• Weekly code walkthrough — team explains recent AI-assisted code to each other
• Pattern library — document the patterns AI should follow
• Incident reviews — track whether AI-generated code is disproportionately involved in incidents

AI Is a Power Tool, Not Autopilot

A chainsaw is incredibly productive in the hands of someone who knows how to use it. It's incredibly dangerous in the hands of someone who doesn't. AI coding tools are the same.

The teams that win with AI-assisted development are the ones that use it to go faster on code they understand — not to write code they couldn't write themselves.

Use the tool. Understand the output. Own the result.