Writing code should be a process of understanding, reasoning, and trade-offs. Copy-paste tells you ‘it works’, but doesn’t tell you ‘why’.

GPT and similar code generation tools have been around for a few years, and their impact on us is enormous, 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 an attitude of solving problems and learning. We looked at its reasoning, asked when we didn’t understand.

Now? Everyone relies on it, copy-pasting.

And then there’s the so-called Coding Vibe, Coding Agent Don’t read, don’t modify, don’t test As soon as it runs, ship it. Three months later, you don’t even know what you wrote, the overall project structure, or the package references of each part.

When debugging, your mind is full of little question marks:

“Who wrote this **code” “Oh my god, it’s GPT”

Strongly Against Handing Over All Code Work to GPT

Writing code becomes a process of copying answers; you no longer use your brain. Requirement understanding? Code design? Project maintainability? You skip all of these.

It looks efficient, but in reality, you are constantly losing the basic judgment and growth space you once had as a programmer. Writing code is not just about writing; it’s also about learning.

Bugs Don’t Decrease; They Become Harder to Fix

The project code you paste in may never have been tested. Why would GPT consider edge cases for you? Why would it care about your project context? It just needs to complete the task and write the code correctly.

It looks like ‘Wow, the feature works!’ But there’s a bunch of landmines inside. Even if they get triggered, debugging is even harder. Because you didn’t write it; you don’t understand it at all. After an error, you have no idea where the problem is, so you can only 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 async, tomorrow a legacy callback, the day after a weird abstraction. The project looks like a puzzle; maintainers are doing passionate debug freestyle on the code. Every piece of code runs, but no one wants to maintain it.

Outsourcing the Project to LLMs, Fooling Yourself

I still don’t understand why so many people respond ‘Oh, our whatever Coding AI tool has a new feature again!’ Back when people said ‘directly pasting GPT code is harmful,’ some would retort

We are reformists; we have Cursor, Trae, 通义, Copilot Chat... project writing fully automated, debugging automated

They say it’s one-click bug localization, but the truth is the tool takes your error log and keeps guessing the source of the exception, giving you a seemingly decent, possibly useful patch. What happens? Fix A, break B.

Auto Refactoring Is Not Magic

So-called refactoring = Find-&-Replace. It doesn’t understand the project’s full testing process or implicit conventions. Merging into old code often leaves a pile of hidden problems.

It seems friendly to beginners, completely masking the learning cost. Low barrier to use: a newbie clicks a couple of times and ‘fixes’ a bug, but doesn’t understand the cause. Next time they encounter the same problem, they just go headless and rely on the tool to put out the fire.

Time Savings Are Illusory; The Real Cost Is High

Saving time is indeed hard to criticize in some current situations, but what about later? You spend time reading its diff, running that private regression, and fixing new issues. In the end, the total time is often > beginner debugging.

Relying on Tools ≠ Solving Problems

Now a bunch of people just throw out ‘Just use Cursor, problem solved.’ That’s a fallacy; it’s not solving the problem, but extending the dependency from writing code to debugging, with the dose super-sized.

These tools purely create illusions, making some newbies think ‘With Cursor, I don’t need to understand debugging or code; I can still become an engineer.’ When it goes live and an incident occurs, what do you do as a newbie? Keep Cursor-ing your way out?

Short-term progress is lightning fast, but long-term maintenance is hell. Those chaotic project structures, interface references (even duplicate identical interfaces) Whether it’s your company team collaboration or open source community, the person taking over feels like they’re eating shit. You look at this pile of code and have to first understand which parts are human-written and which are AI’s ‘seasoning,’ then roll back or refactor one by one.

In short, on top of the existing wound of ‘CPDD (Copy-Paste Driven Development),’ apply another layer of that so-called ‘auto-debugging patch,’ accelerating the mutation of the code gene until no one dares to touch it.

Newbies: originally just ‘can’t write,’ now also ‘can’t debug’ bundled together. One-click generation + one-click patching = ‘auto-cruise toward the cliff.’ Newbie generates - pastes - auto-fixes; one round runs through and they think all is well, without ever forming a debugging mindset: set breakpoints, check stack, verify hypotheses...

The project grows bigger and bigger, and the later it gets, the more hopeless it becomes.

Final Choice: Trust Yourself, Stay Proactive

I don’t even need to think: after reading this long rant, you’ll nod at the repeated points, saying ‘makes sense,’ but when it’s time to write a feature, you’ll still unhesitatingly open GPT, open an LLM, keep pasting, keep fixing. Paste a brand new piece of ‘magic code.’

I have to say I use them too; blindly rejecting new technology without enjoying it is just pure **. But using with understanding versus using numbly are two different things in the end. You can ask, you can reference, you can even let it write tests and documentation for you.

But the code in your project must ultimately be worthy of your own understanding, not GPT’s output capability.

Finally: GPT is not you, and it won’t serve only you. The one who writes good code should be yourself. Don’t let the tool think for you, or you will gradually lose your problem-solving ability. Trust yourself, stay proactive.