I Pitted Two AI Coding Assistants Against Our Legacy Codebase—One Actually Understood It

Last weekend, I did something that felt vaguely unethical: I fed our most cursed legacy module to two different AI coding assistants and watched them fight it out. The results? Honestly surprising. And I'm still not sure whether to be excited or terrified about what they mean for our tooling budget next quarter.

Here's the backstory. My team hit that nightmare scenario every engineering leader secretly dreads: a 4-year-old payment processing service that nobody fully understood anymore. The original authors? Long gone. Documentation? Maybe three paragraphs in a Notion doc from 2022. Yet business needed us to refactor it for a new pricing model—with zero downtime, naturally.

So we needed AI help. But which tool could actually handle this level of mess?

I decided to run a controlled experiment. OpenAI's Codex (via API, gpt-4-turbo) versus Cursor's integrated AI. Same refactoring job. Same 12,000-line legacy codebase. Same prompts. Same expectations.

Here's what happened.

The Test Setup

I isolated a payment calculation module. Here's what we were dealing with:

• 12,347 lines of Python
• 47 interconnected functions
• Deep inheritance chains—5+ levels in some places
• Database calls scattered across 18 files
• Zero type hints. Classic startup code.

The task: Extract pricing logic into a separate service without breaking existing integrations.

I measured three things:

1. Context retention – How much of the codebase could the tool "remember" at once?
2. Dependency awareness – Could it trace ripple effects of changes?
3. Accuracy rate – How many AI suggestions actually compiled and passed tests?

Round 1: Context Retention

Codex (128K context window):

• Successfully ingested ~8,000 lines before responses got... weird
• Started hallucinating function signatures after the 15th file
• Lost track of variable scope when functions exceeded 200 lines

I remember staring at one suggestion where it confidently referenced a parameter that didn't exist anywhere in the codebase. Just... invented it. Like that one contractor we had in 2019 who nodded along in every meeting and then delivered something from a parallel universe.

Cursor (with codebase indexing):

• Indexed all 12,347 lines in under 3 minutes
• Maintained awareness of cross-file dependencies throughout
• But—and this matters—its suggestions became overly conservative after 20+ interactions

Actually, wait—I should clarify what I mean by "conservative." Cursor started refusing to suggest changes that touched more than 3 files at once. It would say things like "this modification may have unintended side effects" even when the change was straightforward. Helpful at first. Annoying by hour three.

Key insight: Cursor's indexing gave it a massive advantage in large codebases. Codex felt like working with a brilliant contractor who occasionally forgot what we discussed 10 minutes ago. Which, I mean... I've worked with that person. It's exhausting.

Round 2: Dependency Tracking

This is where things got interesting.

I asked both tools: "If I rename this calculate_tax method, show me every file that needs updating."

Codex correctly identified 14 of 18 affected files. It missed:

• 2 dynamic imports (understandable, honestly)
• 1 monkey-patched function in a test file
• 1 dependency hidden in a string-based getattr call

Cursor found 17 of 18 files. The miss? That same getattr call.

So neither tool caught it. That string-based metaprogramming trick—the kind of thing a senior dev writes at 11pm and forgets to document—remained invisible to both.

The real differentiator: Cursor showed me a dependency graph visualization. I didn't even know I needed that. My team spent 15 minutes verifying Cursor's output versus 45 minutes tracking down Codex's misses.

For a team of 5 engineers billing at $150/hour average, that's... let me do the math... roughly $375 saved per refactoring session. Not life-changing. But it adds up over a quarter.

Round 3: Accuracy Under Load

I measured suggestion accuracy across 50 refactoring tasks. Here's the raw data:

Neither tool reached what I'd consider "trust without verification" territory. Not even close.

The most common failure mode for both? Edge cases in error handling blocks. The AI would simplify try-catch logic in ways that looked cleaner—more "Pythonic," whatever that means—but would silently swallow critical exceptions.

I think what bothered me most was how confident the suggestions looked. Clean code. Good variable names. And completely wrong about which errors mattered.

My takeaway: These tools accelerate the 80% of work that's tedious—boilerplate, simple extractions, test generation. But the last 20%? That still requires senior engineers who understand the business logic. The messy stuff. The "we added this at 2am during the Black Friday outage" stuff.

What This Means for Engineering Leaders

After this experiment, I implemented three rules for AI-assisted refactoring on my team:

1. Use Cursor for codebase-wide changes – The indexing is genuinely superior for large-scale work. Our refactoring velocity increased about 40% in the first month. (Though I'll admit—that number is probably inflated by novelty effects. Ask me again in Q3.)

1. Use Codex for isolated, well-defined tasks – When I need a single complex algorithm written or optimized, Codex's focused approach often produces more creative solutions. Sometimes too creative. But usually fixable.

1. Never skip code review for AI-generated changes – I don't care if the tests pass. Our senior engineers still catch logic errors in ~15% of AI suggestions. That number hasn't budged since we started tracking it in January.

I also started tracking a new metric: AI-Assisted Cycle Time. It's the time from ticket assignment to production deployment, comparing tasks completed with AI assistance versus without.

Early data shows a 35% reduction for refactoring tasks, but only a 12% reduction for net-new feature work.

The tools excel at understanding existing code. They struggle with understanding business intent.

Well... that's complicated. They don't "understand" anything, obviously. But you know what I mean.

The Real Limit Isn't Technical

Both Codex and Cursor failed most dramatically not because of context windows or indexing algorithms—but because they couldn't ask clarifying questions.

Midway through the refactoring, I realized we had conflicting business rules in the payment module. A human engineer would have flagged this immediately. Probably during the first planning meeting. The AI just faithfully refactored the contradictions into cleaner code.

Cleaner. More efficient. Still wrong.

As Martin Fowler wrote in Refactoring, "Before you refactor, make sure you have solid tests." I'd add: "Before you AI-refactor, make sure you have solid understanding."

The tools are force multipliers for comprehension—not replacements for it.

TL;DR

• Cursor won on large-scale refactoring thanks to codebase indexing and dependency visualization
• Codex was better for isolated, creative problem-solving but lost context on big codebases
• Neither tool reached production-ready reliability—both hovered around 52-61% for complex changes
• The biggest failure mode: AI can't ask clarifying questions about conflicting business logic
• Bottom line: These tools save time on the boring 80% of refactoring. The hard 20% still needs human judgment.

Anyway. I'm curious: Has your team established any "rules of engagement" for AI coding assistants? We're still figuring this out, and I'd love to hear what's actually working in the wild—not just in blog posts.

Drop your experience in the comments. I read every one. Even the ones telling me I should've used Copilot instead.

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