I didn’t notice the moment my coding brain went quiet. There
was no dramatic crash, no red error banner across my life. It was subtle. One
day I was thinking in loops, recursion, and edge cases; the next, I was
thinking in prompts, tokens, and “best-of-5 completions.” Somewhere between
“write a helper function” and “just ask the AI,” my muscle memory clocked out
early.
At first, it felt like a superpower. Coding LLMs like
Copilot or CodeGPT could spin up boilerplate, generate async workflows, or even
draft full class hierarchies in seconds. No more boilerplate, no more wrestling
with obscure API quirks. Suddenly, I was practicing something that has a name
in the AI developer community: Vibe Coding, writing code not line
by line with deliberate thought, but by feeding a “vibe” or intention into a
model and letting it materialize into code. It’s fast. It’s modern. It’s
addictive.
And that’s exactly where the trouble began.
I realized I was reading code the way people skim terms and
conditions. I wasn’t tracing logic anymore; I was trusting vibes. The AI
sounded sure, so I was sure. When something broke, my first instinct wasn’t to
debug, it was to re-prompt. “That didn’t work, try again.” As if software bugs
were just misunderstandings in tone.
The turning point came during a real incident at work. We
had a Python-based backend microservice handling high-throughput requests for
user recommendations. After a seemingly harmless refactor, mostly AI-assisted, the
service started choking under load. The problem statement was clear: response
times spiked, CPU usage climbed, and autoscaling couldn’t save us.
Here’s the kicker: the AI had suggested replacing a simple
dictionary-based cache with a more “elegant” pipeline that applied nested data
transformations inside each request. On paper, this was idiomatic Python,
readable, well-documented. In reality, it had introduced an O(n²) complexity in
what had been a linear-time operation. The refactor looked polished, but under
load, it was a silent killer.
No compile errors. No exceptions. Nothing screamed “bad
idea.” Just subtle degradation, classic vibe coding failure mode:
elegant output hiding inefficient logic.
The resolution? Humbling. We rolled back the abstraction and
rewrote the logic by hand:
# Naive AI-generated nested transformations
for user in users:
for item in items:
item_score =
transform(item, user)
cache[user.id].append(item_score)
# Manual, linear-time approach
for user in users:
item_scores =
precompute_scores(user)
cache[user.id] = item_scores
The hand-crafted version not only restored performance but
also became easier to reason about in future debugging sessions. The CPU usage
dropped, latency normalized, and autoscaling was no longer needed.
This incident taught me something vital: AI didn’t
kill my coding brain. I outsourced it.
Coding LLMs are brilliant at generating solutions,
refactoring code, and even predicting bugs based on patterns in large code
corpora. But they lack intuition about context-specific performance, system
load, or business-critical trade-offs. Without human oversight, Vibe
Coding can lull engineers into a false sense of security.
I’ve since changed how I work with AI:
- Treat it like a sharp junior engineer: fast, enthusiastic, occasionally brilliant, but prone to subtle mistakes.
- Demand explanations: before merging AI code, I ask it to justify complexity, trade-offs, and performance implications.
- Verify with metrics: profiling and benchmarking are non-negotiable. Let AI generate, but humans validate.
- Stay mentally engaged: writing some code manually keeps the debugging instincts sharp and preserves the deep understanding that no model can replicate.
Ironically, this has made me faster than ever, not because
AI writes more, but because I think smarter about what I allow AI to do. I
generate options, analyze them, test them, and own the logic. The friction, the
questioning, the moments of doubt, these are where real learning happens.
That’s the part worth protecting.
AI didn’t steal our skills. It exposed how willingly we’re
willing to give them up. The trick isn’t to reject AI, it’s to stay
intellectually present while using it. Keep the arguments. Keep the friction.
Keep the moments where your brain goes, “Wait… that doesn’t feel right.”
#AI #CodingLLMs #VibeCoding #SoftwareEngineering #DevLife #TechThoughts #LearningInPublic