Why Copying AI Code Is Killing Your Career

You’ve probably heard that overused phrase: "If you’re not using artificial intelligence today, you’re falling behind." Well, it’s a phrase I’d love to use... if I believed it. But I don’t. In fact, I’ve been observing something quite different: most people are using AI in a way that, far from helping, is actively harming their professional development.

The Curious Case of the Developer Who Doesn’t Code

Let’s dive deeper into this topic, especially focusing on the use of ChatGPT and similar tools in software development. And I’ll tell you why this current approach might be hurting you more than helping you.

First, let’s talk about something curious in the world of development. When you ask a developer what part of the job they enjoy the most, the answer is usually "programming, writing code." And when you ask what they like the least? "Ah, meetings and code reviews." Funny, isn’t it? The only part of the job they say they love is precisely the one they’re outsourcing to AI. There’s something odd there, don’t you think?

The Myth of Loving Code: The LeftPad Case

Actually, I have my doubts about whether developers really love writing code that much. Let me show you something interesting: do you know the famous leftpad case? Look at these 11 lines of code that literally broke millions of JavaScript applications:

module.exports = leftpad;
function leftpad (str, len, ch) {
    str = String(str);
    var i = -1;
    if (!ch && ch !== 0) ch = ' ';
    len = len - str.length;
    while (++i < len) {
        str = ch + str;
    }
    return str;
}

Yes, just that! A simple function that adds characters to the left of a string until it reaches a certain length. And why did this break so many things? Because developers "loved" writing code so much that, instead of implementing this simple function, they preferred to import an external library. And when that library was removed from npm... well, chaos ensued.

And it doesn’t stop there. In the JavaScript world, we also have the "is-odd" library—yes, an entire library just to check if a number is odd, which implies that developers don’t know how to work with the modulo operator! If this isn’t proof enough that these folks aren’t that passionate about writing code, I don’t know what is.

But here’s the crucial point that many people don’t understand: our job isn’t to be "code typists." We’re not code writers, we’re software programmers. And developing software goes far beyond simply generating lines of code. If it were just that, you could just open GitHub and start copying and pasting—after all, there’s already enough code in the world, right?

ChatGPT and Quick Sort: A Tale of Two Codes

Let’s talk about the wrong way to use ChatGPT, which unfortunately has become very common. It’s that story: the person goes there, asks "please, make me a quick sort function in Python" (yes, I always say please to AI, just in case they take over the world someday), and gets something like this:

def quicksort(arr):
    if len(arr) <= 1:
        return arr
    pivot = arr[len(arr) //2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x == pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

At first glance, it looks like a nice piece of code, doesn’t it? But there’s a serious problem here. This implementation, although it works, is terribly inefficient in terms of memory. With each recursive call, three new arrays are created (left, middle, and right). In a large array, this means a significant amount of memory being unnecessarily allocated.

When I pointed this out to ChatGPT, it provided me with an improved version:

def quicksort(arr, low, high):
    if low < high:
        pivot_index = partition(arr, low, high)
        quicksort(arr, low, pivot_index - 1)
        quicksort(arr, pivot_index + 1, high)

def partition(arr, low, high):
    pivot = arr[high]
    i = low - 1
    for j in range(low, high):
        if arr[j] < pivot:
            i += 1
            arr[i], arr[j] = arr[j], arr[i]
    arr[i + 1], arr[high] = arr[high], arr[i + 1]
    return i + 1

This version is much more efficient because it performs the sorting "in-place," meaning it modifies the original array instead of creating new arrays at each step. But here’s the problem: if you don’t understand the quick sort algorithm, how would you know that the first implementation was bad? How would you suggest improvements? How would you understand the performance implications for your system?

The Four Horsemen of Generated Code

And this brings us to the four major problems with this "copy and paste" approach to AI-generated code:

• First, you’re potentially introducing low-quality code into your codebase. If you don’t understand what you’re copying, how will you know if it’s good or bad?

• Second, the code generated by AI rarely fits perfectly into your project’s standards and conventions. Every codebase has its peculiarities, its design patterns, its rules. ChatGPT doesn’t know your project, it doesn’t know these conventions.

• Third, code you don’t understand is hard to maintain. It might seem like you’re saving time now, but wait until you need to make modifications or fix bugs in this code later. Have you seen those PHP files with 100 thousand lines? Yeah, they usually start like this.

• And the fourth problem, which I consider the most serious: you’re not learning. You’re creating a dependency on AI without developing your own skills. And that will hurt you a lot when you face more complex problems, the ones AI can’t solve on its own.

The Speed Trap: Fast Today, Stuck Tomorrow

Imagine you’re trying to clone a complex app like Uber Eats using only ChatGPT. At some point, you’ll run into problems that AI can’t solve. And then what? If you haven’t developed a deep understanding of what you’re doing, you’ll be completely stuck.

It’s like using a crutch when you’re not really injured—it seems like a help, but it’s actually preventing you from developing your own strength. Yes, using ChatGPT to generate code might seem faster in the short term. You get that quick sort implementation in seconds, instead of spending some time studying and implementing it on your own. But it’s a false victory—you win the battle of the moment, but you’re losing the war of your professional development.

The True Cost of AI Dependency

True software development isn’t about writing code quickly—it’s about understanding problems, designing solutions, maintaining scalable systems, and creating code that other developers can understand and maintain. It’s about growing as a professional and being able to tackle increasingly complex challenges.

Using AI as a crutch to generate code is preventing you from developing these fundamental skills. Every time you copy and paste without understanding, you’re losing an opportunity to learn, to grow. You’re condemning yourself to being an eternal junior, dependent on external tools to do your job.

Code you don’t understand is like technical debt that you’ll have to pay back with interest in the future. Every piece of code copied without understanding is a small commitment to mediocrity, a small surrender of your growth potential.

Artificial intelligence is an incredible tool, but like any tool, its value depends on how we use it. Instead of using it as a crutch, we should learn to use it as a support for our professional development, always keeping the focus on deep understanding and continuous growth.

At the end of the day, what will determine your success as a developer isn’t your ability to generate code quickly, but your ability to understand, design, and maintain complex systems. And that, my friend, no AI can do for you.