I Spent 10x Longer Debugging AI Code Than Writing It

AI wrote the code in 30 seconds

Three lines A simple function I prompted it generated I copied It looked fine Clean syntax Good variable names No obvious errors.

I spent the next 5 hours debugging it.

The bug wasn't in the logic The AI had made a quiet assumption - that a list would never be empty It worked 99% of the time The 1% crashed in production A real user A real failure A very real 5 hours of my life.

30 seconds of generation 5 hours of debugging.

That's not efficiency That's a trade-off nobody is talking about.

This isn't an anti-AI article I use AI every single day It has genuinely changed how I work But I've stopped pretending that speed at write time is the only metric that matters.

Here's what I've learned about the hidden cost of AI-generated code after paying that cost enough times to notice the pattern.

The Myth of Fast Code

We've been sold a story AI makes you faster Prompt copy ship Repeat It's true the writing is faster Dramatically faster What used to take an hour now takes minutes That part is real.

But the story always stops there It doesn't mention what happens after.

The AI writes the code in seconds You ship it You move on Weeks later a bug surfaces - subtle hard to reproduce buried in code you didn't write and don't fully own.

Now you're not debugging logic you understand You're reverse-engineering code from a system that can't explain its own assumptions You're reading it like a stranger's handwriting trying to figure out what they meant.

The fast code isn't free It's borrowed time.

The debt shows up later - and by then you've completely forgotten what the AI assumed when it wrote it.

Three Times AI Code Cost Me More Than It Saved

1. The Invisible Assumption (5 hours)

The AI assumed a list would never be empty Didn't check Didn't add a guard Why would it? It only knows what I asked - not what real users actually do.

The bug showed up in production two weeks after I shipped A user with zero data hit the flow The whole thing crashed.

The fix? One line A simple if not list check.

The debugging? Five hours of confused increasingly frustrated me tracing through logs, adding print statements, questioning my own sanity before I found a single missing assumption.

Ratio: 60x.

2. The Works on My Machine Trap (1 full day)

AI code passed all my tests Ran perfectly locally I was confident I shipped it.

In production? Different story entirely.

The AI had optimized for my test environment - the clean inputs I'd been testing with, the neat data shapes in my fixtures the happy paths I'd written It hadn't thought about real data It hadn't thought about the weird edge cases real users create.

I spent a full day chasing a bug that only existed in the wild.

3. The Naming Trap (3 hours)

The AI named a variable data.

Generic Vague Technically acceptable And a completely reasonable thing for an AI to do it didn't know what mattered.

Three months later I had no idea what data contained Was it the raw user input? The transformed output? The cached result from the database? Something I'd filtered?

I spent 3 hours tracing through code that should have taken 10 minutes to understand because the AI chose convenience over clarity and I didn't catch it.

What AI Code Actually Costs

Beyond the hours there are costs that don't show up on any stopwatch.

Cognitive load. You didn't write the code, so you don't have the mental model Every time you touch it, you have to rebuild your understanding from scratch It's like returning to a codebase you've never seen except you supposedly wrote it.

Confidence erosion. After enough works on my machine moments you stop trusting your own testing You start shipping with low-grade anxiety You add logs just in case You write extra tests not because the code needs them but because you don't trust code you didn't write.

The just in case spiral. Extra checks Extra validation Extra error handling not because the requirements demand it but because you're compensating for uncertainty about code you can't fully vouch for This eats time quietly in small pieces.

Opportunity cost. Every hour you spend debugging AI-generated code is an hour you're not spending on the work that actually requires your judgment your context your experience.

These costs are invisible No ticket tracks them No dashboard measures them No retrospective surfaces them.

But they're real And they add up slowly silently until one day you realize debugging has started to feel like the actual job.

What I'm Doing Differently

I'm not quitting AI That ship has long since sailed and I don't want it back.

But I've made a few small changes that have quietly shifted the ratio:

1. I don't ship code I can't explain.
If I can't walk through the logic not skim actually walk through it line by line I don't ship it. Even if it works in testing This catches invisible assumptions before production does.

2. I treat AI output as a first draft.
The AI writes the structure. I rewrite the parts that matter the edge cases the error handling the variable names the things that someone will need to read at 2am when something breaks It's slower It's also code I actually own.

3. I add the missing assumptions explicitly.
The AI always optimizes for the happy path So I've made it a habit to immediately ask: What does this break if the input is empty Null? Malformed Unexpected? I add those checks myself every time.

4. I budget a debugging tax.
Every AI-generated function gets an extra 30 minutes in my time estimate for review and hardening Not pessimism pattern recognition The tax pays for itself in the first incident it prevents.

None of these eliminate the problem But they've meaningfully reduced my personal 10x ratio Some weeks it's closer to 3x now.

That's progress.

The Honest Trade-Off

AI code is faster to write Slower to debug The ratio varies sometimes 2x sometimes 20x sometimes that one time it was 60x and I questioned my life choices.

The question was never is AI good or bad? That's a pointless debate.

The real question is: what's the ratio for your work on your codebase for your team?

For throwaway scripts? Use AI don't look back.

For core logic that someone will need to debug at 2 AM six months from now? Be careful Be deliberate. Be present.

The trade-off is real It's not going away And pretending it doesn't exist doesn't make it disappear it just means you'll discover it in production instead of before it.

One Question

What's the worst AI wrote it fast I debugged it slow story you have?

How long did the bug take to find? What was the assumption you missed?

I'll go first in the comments - the empty list crash 5 hours a single missing if statement.

Your turn. 👇