It Works... But Why?
Every developer has done it — copied a Stack Overflow snippet or an AI-generated block, dropped it in, watched it work, and moved on. The code runs. The bug is fixed. But if you can't explain why it works, you've borrowed a solution, not learned one.
There's nothing inherently wrong with using existing code — no one writes everything from scratch, and reusing solved problems is a core part of being productive. The issue isn't the copying itself; it's the silent gap it leaves behind. You solved today's problem, but you didn't gain the understanding that would let you solve tomorrow's variant of it without going back to search again.
Debt That Compounds Silently
Unlike technical debt from messy architecture, this kind of debt is invisible — it doesn't show up in code reviews or slow builds. It shows up months later, when a similar bug appears in a different context and you're stuck, because the understanding was never actually yours.
Architectural debt has visible symptoms: slow builds, tangled dependencies, code nobody wants to touch. Understanding debt has none of that. The code looks clean, the tests pass, the PR gets approved. But the knowledge that should have transferred to you during that fix never did — it stayed in the snippet, not in your head. And because there's no visible symptom, this debt can quietly pile up for years without anyone — including you — noticing.
The real cost surfaces at the worst possible time: during a live incident, in an interview, or when you're the only person on the team who's "worked with this before" and everyone's looking at you to explain something you never actually understood.
The Five-Minute Habit That Fixes It
Before moving on from any code you didn't fully write yourself, spend five minutes asking: what would break this? What assumption is it making? What happens with an empty input, a null value, a huge dataset? That five minutes turns a borrowed fix into real understanding.
A simple framework for that five minutes:
This isn't about slowing down every single task. It's about being selective — spend the five minutes on code that's central to what you're building, and skip it for genuinely boilerplate, low-stakes snippets.
AI Tools Make This Worse, Not Better — Unless You're Careful
AI coding assistants make it faster than ever to get working code without understanding it. The developers who benefit most from these tools aren't the ones who accept every suggestion — they're the ones who treat AI output as a first draft to interrogate, not a final answer to trust.
This is a bigger shift than it might seem. With Stack Overflow, there was natural friction — you had to search, read through several answers, and often adapt code manually, which forced at least some engagement with the logic. AI-generated code removes almost all of that friction. You can go from problem to "working" solution in seconds, with zero forced engagement with the reasoning.
That's not an argument against using AI tools — it's an argument for being deliberate about how you use them. Ask the assistant to explain its own code before you accept it. Ask "what would break this" as a follow-up prompt. Use it to generate a first draft, then apply the five-minute habit above just as you would to any borrowed code. The tools aren't the problem — treating their output as automatically understood is.
The Long Game
None of this is about being a purist who refuses to reuse code — that's neither realistic nor productive. It's about closing the gap between "this works" and "I understand why this works," consistently, in small five-minute increments. Do that enough times, and the code you copy today becomes the pattern you write from memory next year.