The most dangerous bug in modern software engineering is not a syntax error. It is a feature that works exactly as requested.
Last Tuesday, an automated background job in our pipeline started chewing through memory until the container ran out of RAM and crashed. No stack trace, no core dump, just a clean SIGKILL from the kernel.
I did what any of us do. I copied the handler code, fed it to an LLM, and asked: "Why is this handler leaking memory under load?"
Ten seconds later, I had my answer.
It gave me an impeccably written breakdown. It pointed out three subtle issues:
- An event listener that might not be cleaning up correctly on socket disconnect.
- A slice allocation that could be retaining references in memory.
- An unbuffered channel that could cause routine leaks under high concurrency.
It even rewrote the entire module for me. Fully typed, idiomatic, beautiful comments, and two edge case tests.
I applied the patch. It looked like the kind of code you put on a slide to teach best practices. I ran the load test again.
The container died at the exact same threshold.
The trap of the eager answer
I spent the next three hours chasing phantom memory leaks. I profiled heaps, inspected GC pauses, and prompted the model five more times. Every single time, it gave me a confident, mathematically plausible explanation of what was "wrong" with the code.
And every single time, it was solving a riddle that did not exist.
The issue had nothing to do with the handler.
Earlier that morning, a separate microservice had quietly updated its payload schema. Instead of sending batches of 50 items, it was dumping a single unbounded array of 80,000 items in one request. The code was not leaking memory. The code was doing exactly what it was written to do: parsing an enormous JSON payload in memory all at once.
The model did not know that. It could not know that.
It looked at the 60 lines of code I handed it, assumed my premise was correct, and hallucinated a believable flaw to satisfy my question.
AI suffers from people pleasing
Here is the quiet truth about using AI as a sounding board:
It never pushes back on your framing.
If you bring a clean function to an LLM and say "find the bug," it will rarely have the courage to say "there is nothing wrong with this code, go check your ingress metrics." It will invent a subtle stylistic or architectural sin, wrap it in senior engineer vocabulary, and hand it to you on a silver platter.
Because the code looks plausible, you believe it. You spend half a day fixing code that was never broken while the real arsonist sits three hops away in an upstream repository.
The shift from coder to investigator
For years, senior developers were defined by recall. You knew the obscure compiler flags, the quirks of the event loop, and the exact database index behavior.
Today, syntax and boilerplate are commodities. The only thing that separates an engineer from an autocomplete engine is scope of skepticism.
The machine only sees the room you put it in. If you lock it in a room with a single function, it will convince you the entire universe is broken inside those 60 lines.
Your actual job is to step out of the room. It is asking:
- Why did the input change?
- Who controls the caller?
- Is this a code problem or an operational boundary problem?
What I changed after Tuesday
I stopped asking models "what is wrong with this code?"
Instead, I treat the model like an eager junior engineer who is terrified of disappointing me.
Now, when I hit a strange issue, I force myself to follow two rules:
1. Never ask the machine to find a bug until I have proved the input is sane. If I cannot verify what walked through the front door, looking at the internal logic is just superstition.
2. Use adversarial prompts. Instead of "find the leak," I ask "assuming this code is completely leak free, what upstream or infrastructure conditions would cause this process to hit an OOM kill?"
The scarce skill in 2026 is not writing the implementation. It is knowing which layer of reality is actually lying to you.
Honest question for the comments: what is the longest you have spent chasing a "bug" that turned out to be an upstream payload or network issue the code had no chance of surviving? 👇
Top comments (1)
The moment I'd flag comes before the input check: the patched build died at the exact same threshold. A real leak fix, even a partial one, moves where the container dies; a crash that doesn't move at all is tracking something outside the code, and that observation was on the table three hours before the answer. Your adversarial prompt helps, though it still hands the model a premise, just the opposite one. Pasting the observation instead ("patch applied, OOM at the identical threshold, what does that rule out?") makes it reason from evidence rather than from whichever assumption you picked.