From Debugging Code to Debugging Products
I once spent three days building a feature. The code worked perfectly. Tests passed. No bugs.
Then users started complaining.
The feature was wrong. Not broken. Wrong. It solved a problem nobody had.
The Old Debugging
Before AI assistants, debugging meant finding why code didn’t work as expected. Null pointer exceptions, race conditions, off-by-one errors. Technical problems with technical solutions.
The feedback loop was slow. You’d spend a week implementing something, another week fixing bugs, and only then ship it to users. By the time you discovered the feature was wrong, you’d invested so much time that pivoting felt expensive.
That slowness was painful, but it had a hidden benefit: it forced you to think carefully before coding. You couldn’t afford to build the wrong thing because building anything took forever.
The New Debugging
With AI writing code, I can prototype a feature in hours instead of days. The technical implementation is fast. Too fast, sometimes.
Now I discover that a feature is wrong on day one, not week three. The bug isn’t a missing null check. The bug is that I misunderstood what users actually need.
This is a different kind of debugging. Instead of asking “why doesn’t this work?”, I’m asking “why doesn’t this matter?”
What Changed
The bottleneck moved.
When code was slow to write, the hard part was translating requirements into software. Now that translation is nearly instant. The hard part is making sure the requirements were right in the first place.
I find myself spending more time on:
- Talking to users before building, not after
- Prototyping throwaway versions to test assumptions
- Questioning requirements that seem too obvious
- Killing features early when the signal is weak
Less time writing code. More time making sure the code is worth writing.
The Skill Shift
Traditional debugging requires technical depth. You need to understand memory, concurrency, system internals. Those skills still matter, but they’re table stakes now.
Product debugging requires different muscles:
- Empathy. Understanding what users actually need, not what they say they want.
- Skepticism. Questioning your own assumptions before investing time.
- Speed to signal. Getting something in front of users as fast as possible.
- Willingness to delete. Throwing away code that works but doesn’t matter.
The best engineers I know aren’t the fastest coders anymore. They’re the ones who waste the least time building the wrong thing.
A Practical Example
I once worked on a recommendation algorithm. The technical implementation was elegant. Vector embeddings, semantic search, the whole stack.
Users weren’t happy.
Not because it was broken. Because it surfaced results that matched explicit criteria but not actual intent. Someone looking to change direction didn’t want more of the same. They wanted something different.
The bug wasn’t in the algorithm. It was in the assumption that matching stated preferences equals matching real needs.
Traditional debugging would never have found this. The code was correct. The product was wrong.
What This Means for Engineers
If you only know how to debug code, you’ll struggle. The easy part is now easy. The hard part is everything else.
Start practicing product debugging:
- Ship something small before building something big
- Watch users use it instead of asking what they think
- Measure behavior not just functionality
- Delete faster when something isn’t working
The code writes itself. Figuring out what to write still takes a human.