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Everyone talks about how AI speeds up coding. I've read a hundred posts about generating entire functions in seconds, scaffolding CRUD apps before your coffee gets cold, shipping features at 3x velocity. Nobody talks about the debugging. Nobody warns you that the real time sink isn't the generation — it's the 45 minutes you spend figuring out why the AI's "perfect" solution silently fails on edge case #17.
I learned this the hard way. Over the past three months, I tracked my time on a side project — a data pipeline that processes real-time stock feeds. I logged every hour: writing code, debugging code, and debugging AI-written code. The numbers were brutal. I spent roughly 8 hours writing my own code, and 62 hours debugging the AI's output. That's not a typo. 62 hours. Almost 10x the time I spent typing my own logic. And that's not even counting the hours I spent rewriting the AI's "optimizations" that were actually just slower, more convoluted versions of what I'd already written. Here's what I learned, the hard way, so you don't have to. The False Confidence Trap
The problem isn't that AI generates bad code. It's that AI generates confident code. It spits out a function with perfect syntax, reasonable variable names, and comments that explain exactly what it's supposed to do. And you read it, and it looks right. So you paste it in, run your tests, and they pass. You ship it.
Then three days later, a user reports that the timestamp on their invoice is off by exactly 4 hours. You dig in. The AI's code handles UTC conversion perfectly — except for one path where it uses .toISOString() on a date that's already a string, which coerces to NaN, which gets caught by a fallback that defaults to new Date(), which uses the local timezone instead of UTC. And the AI wrote a comment saying "// normalize to UTC" right above it.
I've seen this exact pattern play out a dozen times. The AI doesn't know it's wrong. It's predicting the next token, not reasoning about your data. So it writes code that looks correct, passes the happy path, and breaks in the exact place where your domain logic gets weird. The "It Works in My Tests" Fallacy
Let me show you a concrete example. I asked an AI to write a function that deduplicates a list of user objects by email, case-insensitively.
Looks fine, right? I thought so too. My tests passed. Then I ran it on real data and found that two users with the same email but different casing — [email protected] and [email protected] — were being deduplicated, when they were actually different accounts with different permissions. The AI's code was technically correct for the letter of my prompt, but completely wrong for the spirit of my domain.
The fix took me two hours. I had to write a custom key function that only normalizes for comparison, but preserves the original for output. I had to handle the edge case where two users have the same normalized email but different IDs. I had to write a regression test. And I had to explain to my PM why a "5-minute AI task" took half a day.
After that experience, I didn't stop using AI. That would be throwing the baby out with the bathwater. But I completely changed how I use it. Here are the three rules that cut my debugging time from 10x down to maybe 1.5x. Rule 1: AI for Structure, Not for Logic
I stopped asking AI to write the actual business logic. Instead, I ask it to write the scaffolding — the boilerplate, the data transformations, the glue code. When I need a function that loops through a list and applies a transformation, I let the AI write the loop. When I need to decide what transformation to apply, that's on me.
Everyone talks about how AI speeds up coding.