AI-Generated Code and Technical Debt: A Practical Perspective

I’ve been using AI coding tools daily for over a year now — Claude, Copilot, local models. They’ve made me significantly faster. They’ve also introduced bugs and patterns into my codebase that I wouldn’t have written myself, and not always in a good way.

The Over-Engineering Problem

Ask an AI to write a utility function and you’ll often get an enterprise-grade solution for a two-line problem. I once asked for a function to format a date string and got back a class with timezone handling, localization support, memoization, and a builder pattern. I needed new Date(str).toLocaleDateString().

AI models are trained on millions of repositories, including complex enterprise codebases. They default to comprehensive solutions because that’s what the training data rewards. You need to be the editor that strips things back to what’s actually needed.

Patterns That Create Debt

Unnecessary abstractions. AI loves creating interfaces, abstract classes, and factory patterns even when you have one implementation. If you have a single email provider, you don’t need an EmailProviderInterface with a EmailProviderFactory. You might need it later — or you might not. Build it when you do.

Inconsistent style. Each AI generation is stateless. It doesn’t remember that your codebase uses early returns instead of nested ifs, or that you prefer const arrow functions over function declarations. Over time, your codebase becomes a patchwork of conflicting styles.

Dependency creep. “Use lodash for that” or “add moment.js” — AI will suggest libraries for things you can do in three lines of vanilla JavaScript. Each dependency is maintenance burden, bundle size, and a potential security vector.

Copy-paste duplication. When you generate similar functions for different entities, AI doesn’t know you already have a nearly-identical function elsewhere. You end up with three slightly different implementations of the same logic.

The Comprehension Gap

This is the real danger. When you write code yourself, you understand every line because you reasoned through it. When AI generates 50 lines of regex parsing or a complex recursive algorithm, there’s a strong temptation to test it, see it works, and move on.

Six months later when it breaks, you’re debugging code you don’t understand. That’s the worst kind of technical debt — the kind where you can’t even reason about the fix because you never understood the original implementation.

// AI generated this. It works. Do you understand why?
function deepMerge<T extends Record<string, unknown>>(target: T, ...sources: Partial<T>[]): T {
  return sources.reduce((acc, source) => {
    Object.keys(source).forEach((key) => {
      const targetVal = acc[key as keyof T];
      const sourceVal = source[key as keyof T];
      if (Array.isArray(targetVal) && Array.isArray(sourceVal)) {
        (acc as Record<string, unknown>)[key] = [...targetVal, ...sourceVal];
      } else if (
        targetVal && typeof targetVal === 'object' &&
        sourceVal && typeof sourceVal === 'object' &&
        !Array.isArray(sourceVal)
      ) {
        (acc as Record<string, unknown>)[key] = deepMerge(
          targetVal as Record<string, unknown>,
          sourceVal as Record<string, unknown>
        );
      } else if (sourceVal !== undefined) {
        (acc as Record<string, unknown>)[key] = sourceVal;
      }
    });
    return acc;
  }, { ...target });
}

If you can’t explain what this does line by line, you shouldn’t ship it.

Strategies That Work

Review AI code harder than human code. With a human colleague, you trust their experience and focus on logic and architecture. With AI, scrutinize everything — unnecessary imports, over-abstraction, edge cases it might have missed.

Provide context aggressively. Don’t just say “write a form handler.” Share your existing patterns, your error handling approach, your naming conventions. The more context AI has, the more consistent its output will be with your codebase.

Refactor immediately, not later. When AI generates working code that’s over-engineered, simplify it right now while you understand the intent. “I’ll clean this up later” is a lie you tell yourself.

Write tests before generating. If you define the expected behavior first (inputs, outputs, edge cases), AI-generated code has a spec to meet. This also forces you to think about what you actually need before AI starts producing code.

Use AI for the boring parts. Boilerplate, type definitions, test scaffolding, data transformations — these are where AI shines without risk. Complex business logic, security-sensitive code, and architectural decisions should still come from your brain.

The Bottom Line

AI doesn’t create technical debt. Developers who accept AI output uncritically create technical debt. The tool is powerful, but it requires an active, skeptical operator. Treat AI-generated code as a first draft from a junior developer — useful, but always needs review.