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AI is fast, powerful, and relentless — but in the real world, experience and human reasoning still matter. Here's what happened when I let my instincts override the AI.
As a senior software engineer, I still debug faster than AI.
One day, after updating a YAML configuration file, my test cases started failing.
As usual, I pasted the exception logs into my AI copilot and let it begin its diagnosis.
It started scanning — meticulously, systematically, line by line.
Meanwhile, I casually reviewed what I had just changed.
Then I realized it: I had updated the API endpoint in the YAML file, but forgot to update the corresponding URL in the test cases.
That was the issue.
The AI was still busy scanning. I canceled the session and fixed the problem myself.
🧠 AI Scans, I Reason
This experience reminded me of a fundamental difference between human engineers and AI copilots.
AI is powerful — it can parse and analyze thousands of lines of code in seconds.
But it lacks the one thing that experienced engineers bring to the table: contextual intuition.
I didn’t need to read every line of code. I knew where the high-risk change was.
I remembered what I just modified.
And based on years of experience, I formed a hypothesis and validated it — all within seconds.
⚙️ AI’s “Full Scan” vs. Human “Hypothesis”
AI approaches each bug like a blank slate. It doesn't know which areas are more likely to be the cause. So it starts with a brute-force scan of the entire codebase.
But as a senior engineer, I don't need to start from zero.
I can zoom in on the most probable cause using:
- Knowledge of recent code changes
- Familiarity with system architecture
- Pattern recognition from past issues
- And above all, the ability to form and revise hypotheses
Debugging isn’t about reading every line — it’s about asking: What’s the most likely root cause?
❌ AI Doesn’t Know When It’s Wrong
AI copilots also tend to commit to a direction and keep going — even when it’s wrong.
In this case, it assumed the problem was inside the code logic.
It didn’t stop to consider: Maybe it’s not the code, but the configuration.
But I did — immediately.
This is what I call the "direction sense" that experienced engineers develop.
It’s not magic. It’s the result of seeing dozens, if not hundreds, of similar bugs.
I knew the AI was digging in the wrong place. And I didn’t waste time letting it continue.
🎯 Experience Is Compressed Intelligence
There’s something deeply human about remembering, “Wait, didn’t I just change that YAML file?”
That memory isn’t in the AI.
It doesn’t have temporal awareness. It doesn’t know what just happened in your IDE five minutes ago.
This kind of “mental diff” — comparing current state with recent changes — is a uniquely human advantage.
It allows us to shortcut the entire diagnosis process, while AI is still loading the full context.
🔄 When I’m Stuck, AI’s Brute Force Becomes an Asset
Of course, this doesn’t mean I always outsmart AI.
There are moments when I hit a wall — when nothing obvious stands out, and my intuition fails to spark.
In those cases, AI’s brute-force approach becomes not just helpful, but necessary.
It scans without fatigue.
It doesn’t get discouraged.
It doesn't care if the problem is obscure, tedious, or buried in legacy code.
And sometimes, in that exhaustive analysis, it highlights things I would’ve completely overlooked.
So yes — while experience can beat AI in many scenarios, AI shines when there’s no shortcut, no hunch, and no recent change to trace back.
It’s not a competition.
It’s a collaboration — and knowing when to rely on which partner is the real skill.
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