The first time I used AI seriously for coding, I didn’t feel replaced.
I felt exposed.
Not because the AI was smarter than me — but because it kept asking questions my own code couldn’t answer.
That experience changed how I see both AI and software development.
AI doesn’t replace developers. It reveals how clearly we think about our work.
“It Works” Isn’t the Same as “It’s Understood”
Many of us evaluate code with a simple metric:
Does it work?
If the answer is yes, we move on.
But working code doesn’t always mean well-understood code.
When you involve AI — asking it to refactor, optimize, or extend an existing system — it immediately asks questions like:
- What is the goal here?
- What constraints should be respected?
- What tradeoffs were intentionally made?
If those answers aren’t clear, the limitations surface quickly.
AI Fills the Gaps We Leave Undefined
A common criticism of AI tools is that they “hallucinate.”
In practice, what often happens is more subtle.
When we give vague instructions like:
- “Refactor this”
- “Make it scalable”
- “Improve performance”
…without defining intent or constraints, we’re asking the AI to make assumptions on our behalf.
AI doesn’t struggle with ambiguity — it responds to it.
The output reflects the clarity (or lack of it) in the input.
Prompting Is Mostly About Thinking Clearly
There’s a lot of discussion around “prompt engineering,” but in day-to-day development, good prompts usually come down to clear reasoning.
Effective prompts tend to answer:
- What problem are we solving?
- What must not change?
- What constraints matter?
- What tradeoffs are acceptable?
If writing a prompt feels difficult, it’s often because those questions haven’t been answered yet — not because the wording isn’t clever enough.
AI Encourages Better Explanations
One unexpected benefit of working with AI is how often it forces clarification.
When extending or modifying code, AI naturally pushes back with questions:
- Why is this structured this way?
- Why is this state shared?
- Why is this synchronous or asynchronous?
- What assumptions does this depend on?
Answering those questions improves the codebase — whether the AI is involved or not.
Why This Is a Positive Shift
Used thoughtfully, AI encourages better habits:
- Clearer intent
- Explicit constraints
- Better documentation
- More deliberate design decisions
It discourages vague, assumption-heavy development and rewards clarity.
In that sense, AI works less like a replacement and more like a continuous review loop — one that responds immediately to how well we articulate our thinking.
The Skill Gap AI Makes Visible
The biggest gap AI highlights isn’t about:
- Syntax
- Framework familiarity
- Memorizing APIs
It’s about:
- Systems thinking
- Understanding tradeoffs
- Explaining decisions clearly
- Knowing why something exists
Those skills compound. AI simply makes them more visible.
Final Thought
AI didn’t make me a better developer.
It made me more aware of the difference between code that works
and code I actually understand.
And that awareness has been far more valuable than any autocomplete.
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