If ChatGPT Writes Your Code, What Are You Getting Paid For? The Real Value of Developers in the AI Era

It’s the elephant in the room. Or rather, the chatbot in the IDE.

Since the release of GPT-4 and tools like GitHub Copilot, I’ve had clients, friends, and even junior developers ask me the same question: "If ChatGPT can write the code, what exactly are we paying you for?"

It’s a fair question on the surface. If you view software development solely as the act of typing characters into a text editor until a computer does something, then yes, AI is rapidly devaluing that skill. But as a Top Rated Plus freelancer and developer who integrates AI into my daily workflow, I can tell you that writing code was never the most valuable part of the job.

Here is the reality of software engineering in the age of Generative AI, and why the role of the expert developer is becoming more critical, not less.

1. Syntax is Cheap; Context is Priceless

AI is a statistical probability engine. It knows that function openModal() is usually followed by a specific set of curly braces and logic. It is excellent at syntax. It can write a Shopify Liquid snippet or a Python script in seconds that might take me ten minutes to type out.

But AI lacks context.

It doesn't know that your specific inventory management system has a 5-minute sync delay that breaks the standard API implementation. It doesn't know that your target audience is on older mobile devices that struggle with heavy JavaScript frameworks. It doesn't know that the "standard" solution it just generated conflicts with a legacy plugin you installed three years ago.

I am not paid to type code. I am paid to understand your business constraints, your technical debt, and your user's needs, and then decide which code—if any—needs to be written.

2. The Architect vs. The Bricklayer

Imagine you are building a house. You can buy a robot that lays bricks perfectly, 24/7. Does that mean you fire the architect?

Absolutely not. If anything, the architect becomes more important because the walls are going up faster than ever. If the blueprint is wrong, you just built a disaster at record speed.

In software, AI is the bricklayer. It handles the implementation details. But as a developer, I am the architect. I have to decide:

• Scalability: Will this database schema hold up when you run your Black Friday sale?
• Security: Did that AI-generated snippet just introduce an SQL injection vulnerability because it didn't sanitize inputs correctly?
• Maintainability: Is this code readable for the next person, or is it a convoluted mess that just happens to work right now?

Clients pay for the assurance that the system won't collapse under its own weight six months from now.

3. Debugging the Hallucinations

Here is a secret: AI lies. Confidently.

We call it "hallucination," but in a production environment, we call it a "critical bug." I recently used an AI tool to scaffold a Next.js application. It imported a library that didn't exist and used a function that was deprecated two versions ago. To a non-developer, the code looked perfect. To me, it was broken.

When you hire an expert, you are hiring someone who can verify the output. You are paying for the experience to look at a block of code and say, "That looks right, but it's going to cause a memory leak in the long run."

4. The "Last Mile" Problem

AI gets you 80% of the way there very quickly. It can build the boilerplate, the standard functions, and the basic UI.

But the value of a premium software product—the reason a user converts or a client stays—is usually in that last 20%. It’s the subtle animation that makes the UI feel responsive. It’s the complex edge-case handling in the checkout flow. It’s the integration between your ERP and your Shopify store that requires custom logic no tutorial has ever covered.

That last mile is where generic training data fails and human ingenuity prevails. Getting from "it works" to "it's a great product" still requires a human touch.

5. From Coder to Product Strategist

Ultimately, AI has promoted developers. We are no longer just translators converting human ideas into machine language. We are now Product Strategists.

Because I spend less time fighting with syntax errors, I spend more time thinking about the user experience. I spend more time optimizing conversion rates. I spend more time talking to you about your business goals.

When you hire me, you aren't paying for the lines of code. You are paying for:

• Risk mitigation: Ensuring you don't build the wrong thing.
• Speed to market: Using AI effectively to deliver faster than before.
• Holistic solutions: Connecting the dots between technology and business ROI.

Conclusion

So, if ChatGPT is writing the code, what am I getting paid for?

I'm getting paid to know what code to ask for, how to verify it, and where it fits into the bigger picture of your success. The tools have changed, but the mission remains the same: solving problems. And for that, you still need a human expert.