Am I Getting Lazy with AI? A Software Engineer's Honest Reflection
✍️ Introduction
As a software engineer, I know sorting algorithms like the back of my hand. I’ve studied their time complexities, edge cases, and when to use one over another.
But recently, something happened:
Faced with a simple task — sort a list of numbers — I asked AI to write the code. It did. Fast and clean.
There was a small bug; I fixed it. No big deal. But then I paused.
“If I keep doing this… will I forget how to sort at all?”
That question led to a deeper concern:
Am I becoming too dependent on AI? Am I just outsourcing my thinking?
🧪 Two Scenarios That Made Me Think
Let me break this down with two real-world examples:
✅ Scenario 1: Simple Task, AI-Generated Code
I ask AI to write a sorting function. It returns it in seconds.
I tweak something minor, and move on.
Over time, I realize:
I haven't manually written a sorting algorithm in months.
Sure, I still understand it. I can discuss quicksort vs. mergesort, in-place vs. stable, O(n log n) vs. O(n²). But my hands… have forgotten the feel of writing it.
⚙️ Scenario 2: Complex Workflow with AI as a Helper
A real business problem: first do A, then sort, then trigger B and C.
I ask AI to help implement this sequence.
It gives me blocks of code. I connect them. It works. I test and deploy.
Here, AI didn't solve the whole problem. It helped with pieces. I still designed the sequence, validated the flow, and ensured it aligned with the business goal.
But again, I wonder:
Am I letting AI think for me? Am I cutting corners?
🤖 Am I Getting Lazy?
Let’s get honest.
AI is changing how we work. It’s tempting to offload everything — code, test cases, even architecture sketches. But where do we draw the line?
Here’s the key distinction I’ve come to:
It’s not about whether you use AI. It’s about whether you stop thinking.
Let’s break it down:
| Situation | Lazy? |
|---|---|
| Copying code without understanding it | ✅ Yes |
| Skipping testing and assuming AI is right | ✅ Yes |
| Using AI code but auditing it line-by-line | ❌ No |
| Leveraging AI to go faster and solve deeper problems | ❌ No |
| Losing some "hand feel" but keeping conceptual clarity | ⚠️ Maybe — monitor it |
🚗 The Best Analogy I Can Think Of
Using AI as a developer is a lot like driving an automatic transmission car.
- You don’t shift gears manually anymore.
- You might forget how a clutch works.
- But you drive smoother, faster, and farther.
- And if you're paying attention to the road, that’s what matters.
You’ve lost the “gear-shifting hand feel”, but gained speed and freedom to look further ahead.
However, if you stop looking at the road just because the car is doing more — that’s when you crash.
AI helps me move faster. But I still steer. I still decide where to go, when to stop, what to avoid.
And I’ve realized — that’s not lazy. That’s evolution.
🧠 What Really Matters
Here’s what I ask myself now, before labeling anything “lazy”:
- Am I still evaluating the problem with clarity?
- Do I understand the code AI gave me?
- Am I using saved time to work on more meaningful, complex problems?
- Am I growing as an engineer — even if in a different direction?
If the answer is yes, then I’m not outsourcing my intelligence. I’m amplifying it.
✅ Conclusion: Use the Tool. Stay the Driver.
I’ve accepted it: I won’t write sorting functions by hand very often anymore. And that’s okay.
Because now, I’m solving more impactful problems — designing systems, exploring trade-offs, mentoring others, delivering value.
AI is my automatic transmission. But I’m still in control of the wheel.
And as long as I keep steering with intent, I’m not getting lazy — I’m getting smarter.
Have you ever felt like AI is making you a bit “softer” as a dev? Let me know in the comments — I’d love to hear how you're navigating this shift.
Top comments (1)
The big problem is that people use AI as cruise-control. It is a good tool when you are doing long stretches. But it becomes dangerous when you are doing short trips.