Writing code should be a process of understanding, reasoning, and making trade-offs. Copy-paste tells you 'it works,' but not 'why.'
The emergence of GPT and similar code generation tools in recent years has had a huge impact on us, affecting every aspect of our lives and even the people around us.
Have you noticed—
Back when GPT first came out, you could still learn something from it
We almost all used it with a problem-solving and learning mindset
Looking at its thought process, asking questions when we didn't understand,
But now? Everyone relies on it, copy-pasting blindly.
And then there's the so-called 'Coding Vibe,' 'Coding Agent'
Don't read, don't modify, don't test
If it runs once, ship it. Three months later, you don't even know what you wrote, or the overall project structure and package references.
When debugging, your mind is full of question marks:
“Who wrote this code?”
“Oh my god, it was GPT.”
I Strongly Oppose Handing All Coding Work to GPT
Writing code has become a process of copying answers; you stop thinking.
Understanding requirements? Code design? Project maintainability? You skip all of that.
It might look efficient, but you're constantly losing the basic judgment and growth space that a programmer should have.
Writing code isn't just about writing; it's about learning.
Bugs Don't Decrease; They Get Harder to Fix
The project code you paste in might never have been tested.
Why would GPT consider edge cases for you? Why would it care about your project context?
They just need to make the project work and write the code correctly.
You see 'Wow, the feature works!'
But there's a bunch of landmines inside. Even if they trigger, debugging is even harder.
Because you didn't write it, you don't understand it.
When an error pops up, you have no idea where the problem is, so you go back to GPT
and ask it to fix the code it generated.
The More You Write, the More Blurry Code Quality and Style Become
Today you paste in some async code, tomorrow an old-style callback, the day after a weird, more abstract approach.
The project looks like a patchwork quilt, and maintainers are doing a freestyle debugging passion project.
Every piece of code runs, but nobody wants to maintain it.
Outsourcing Your Project to an LLM Is Fooling Yourself
I still don't understand why so many people respond
'Oh, our Coding AI tool has a new feature!'
Back when people said 'directly pasting GPT code is harmful,' others would argue:
We are the reformists. We have Cursor, Trae, Tongyi, Copilot Chat... Full automation for writing projects, automated debugging.
They claim one-click bug localization. The truth is, the tool takes your error log and keeps guessing the source of the anomaly,
giving you a patch that looks decent and might be useful.
But then fixing A breaks B.
Auto-Refactoring Isn't Magic
So-called refactoring = Find & Replace.
It doesn't understand the project's complete testing flow, or any implicit conventions. Merging into old code often leaves a trail of hidden problems.
It seems beginner-friendly, completely hiding the learning cost.
Low barrier to entry: a beginner clicks twice and 'fixes' a bug without understanding the root cause.
Next time they encounter the same problem, they'll just run to the tool for help.
Time Savings Are an Illusion; the Real Cost Is High
Sure, it saves time in some situations—no argument there. But what about later?
You spend time reading its diffs, running your private regression tests, and fixing new issues it introduces.
In the end, the total time often exceeds what a novice would spend debugging manually.
Relying on Tools ≠ Solving Problems
Now a bunch of people just say, 'Just use Cursor, problem solved.'
That's a bait-and-switch. It's not solving the problem; it's just expanding the dependency from writing code to debugging, supercharging the dose.
These tools create a pure illusion, making some beginners think, 'As long as I have Cursor, I don't need to understand debugging or code; I can still become an engineer.'
When it goes live and something breaks, what do you do as a beginner? Keep Cursor-ing your way out?
Short-term progress is fast, but long-term maintenance is a nightmare.
Those chaotic project structures, interface references (even duplicate identical interfaces)
Whether it's your company team collaboration or an open-source community,
the person taking over feels like they're eating garbage.
You have to first understand which parts were written by humans and which are AI 'seasoning,'
then roll back or refactor one by one.
In short, on top of the existing wound of 'CPDD (Copy-Paste Driven Development),' you slap on a so-called 'auto-debugging band-aid,' making the code mutate faster and scarier for anyone to touch.
Beginners: Before, they just 'couldn't write.' Now they 'can't debug' either, all in one package.
One-click generation + one-click patching = 'autopilot toward a cliff.'
Beginner generates - pastes - auto-fixes. One round runs, and they think everything's fine, never forming a debugging mindset: set breakpoints, check the stack, verify hypotheses...
The project grows bigger and bigger, and the further along, the more hopeless it gets.
The Final Choice: Trust Yourself, Stay Proactive
I don't even need to think about it. After reading this long rant, with its repeated points, you'll say 'makes sense' but when you go back to writing features, you'll still unhesitatingly open GPT, open an LLM, and keep pasting and fixing,
pasting in a brand new piece of 'magic code.'
I have to admit, I use them too. Blindly rejecting new technology without enjoying it would be pure stupidity.
But using it wisely versus using it numbly leads to two completely different outcomes.
You can ask, you can reference, you can even have it write tests and documentation for you.
But the code in your project must ultimately be worthy of your own understanding, not just GPT's output capability.
Finally: GPT is not you, and it doesn't serve only you.
The person who writes good code should be you.
Don't let the tool think for you, or you'll gradually lose your ability to solve problems.
Trust yourself. Stay proactive.