Writing Code Has Never Been Easier… Or Has It?

The Developer’s Learning Curve

Back when I was a computer science student, a professor once told us a funny story about the three levels of mastering code:

1. Knowing how to write code. This means understanding syntax, algorithms, variable naming, efficient structuring, and design patterns.
2. Knowing how to read other people’s code. Recognizing that there are multiple ways to solve a problem and understanding complex code, even without comments or explanations.
3. Being able to read your own code three months later. Writing code that’s not just functional but also maintainable and readable for your future self and your teammates.

He made it clear that good code is not written for ourselves — it’s for the people who will work with it months or even years down the road. And mastering these levels takes time and experience.

But that was 2012.

Now, 13 years later, I believe there’s a fourth level:

4. Knowing how to read AI-generated code. AI makes coding easier, but combining and adapting it to real-world scenarios is challenging. It’s not just about accepting what AI suggests—it’s about understanding when it fits and when it doesn’t.

I can’t help but wonder—are there developers today who skipped one or two levels and jumped straight into AI-generated code with total confidence?

After using ChatGPT and GitHub Copilot for a while, I can confidently say that they are incredible tools but not always helpful. In fact, it requires Level 3 coding skills to use effectively.

Read till the end to see the best and worst use cases for AI-generated code.

The Rise of AI in Coding

In just two years, statistics say that AI-generated code usage skyrocketed from 1% to 76% among developers. It’s the most impactful change I’ve seen since the start of my career. Naturally, this led to heated discussions:

“Will AI take our jobs?"

The short answer is no, and there are plenty of articles out there explaining why.

For me, the real question is:

How should we use AI-generated code effectively?

When AI-Generated Code Works Well

I use GitHub Copilot and ChatGPT daily, and they’re fantastic—but only when used correctly.

• They help speed up development by handling repetitive or tedious tasks, like naming variables, functions, and writing comments.
• They generate boilerplate code, HTML, CSS, and database structures effortlessly.
• They’re great for learning new technologies — for example, AI helped me get up to speed with Next.js much faster.
• They make API integrations smoother — I’ve used AI to generate API calls, and it worked seamlessly. And the list can continue.

But there’s a catch.

AI-generated code is useful only if you already understand the underlying technology you’re working with.

For example, I can use AI to generate an API integration, but only because I already know how APIs work — how to handle requests, responses, authentication, and errors. Without that knowledge, AI suggestions might look correct but be completely wrong.

When AI-Generated Code Fails

Despite its advantages, AI-generated code has serious pitfalls that can lead to bad practices, security risks, and unreliable applications.

1. Lack of Context (or Bad Context Given by Humans)

AI doesn’t truly understand your specific project requirements - it only works with the input you provide. If you give incomplete or unclear context, it will generate code based on its best guess, which can be irrelevant or completely wrong.

With GitHub Copilot, things are slightly better since it has access to your codebase, but it still doesn’t fully understand the reasoning behind your decisions. If you don’t specify the right structure or files, it can make assumptions that don’t fit your use case.

Additionally, AI has a limited context window, meaning it can only process a fixed amount of information at a time. This makes it impossible for AI to fully comprehend large projects, leading to inconsistencies and missing key dependencies.

2. Security Issues

AI-generated code often prioritises the easiest or most generic way to solve a problem, without checking whether it introduces vulnerabilities. Edge cases — like error handling, authentication, or input validation — are frequently ignored unless explicitly requested.

It’s still the developer’s job to ensure the code is secure, performant, and follows best practices.

3. Random & Unreliable Output

Each AI-generated response is a guess — it doesn’t actually “know” the correct solution. If you ask the same question twice, you might get two different answers. While some responses might work, others could be completely broken or inefficient.

This is especially dangerous when using AI in technologies you don’t fully understand. Without proper knowledge, you won’t be able to tell if the generated code is good or if it’s just hallucinating.

4. The Human Risk: Overtrusting AI

I’ve noticed a trend—many developers, who once relied on Stack Overflow, now blindly trust ChatGPT and Copilot, believing AI-generated code is always smarter, more efficient, or simply “better” than what they would write themselves without questioning its accuracy.

That’s dangerous because AI doesn’t think. It just predicts what might work. If you don’t critically review its output, you might end up with overcomplicated, inefficient, or outright incorrect code.

• AI doesn’t replace critical thinking. You still need to validate and refine its output.

• AI should be treated as an assistant, not an authority.

The Future: Developers as Code Curators

The future is already here. I’ve recently seen job postings for senior developers specialising in AI code curation. This proves one thing:

AI-generated code is far from perfect.

But it’s improving. Over time, AI coding assistants will get smarter, and developers will need to evolve from writing code to curating and refining AI-generated code.

At Atlas Dev Studio, we’ve embraced AI as a tool to increase efficiency while maintaining the high code quality we’ve always prioritized.

AI helps us move faster, but it’s our experience and best practices that ensure the code remains reliable, readable, and scalable.

Conclusion: The Fourth Level of Coding

Early in my career, I was taught to write quality code. And that’s something AI won’t easily replace. I believe more than ever in the four levels of coding mastery:

1. Learning to write code

2. Learning to read bad code

3. Learning to write good code

4. Learning to evaluate AI-generated code

Only after mastering the first three can AI truly become a valuable tool rather than a potential liability.

So, where do you stand? Have you mastered the fourth level yet? 🚀