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Artificial Intelligence has become a major part of software development. Tools like AI coding assistants can generate code, explain unfamiliar concepts, help debug problems, and significantly speed up certain parts of development. There is no doubt that AI can be useful.

However, I think there is an important discussion that is often missing. What happens when programmers rely too heavily on AI to write their code?

The biggest problem is not that AI writes bad code. Sometimes it writes perfectly good code. The problem is that programming is not just about producing code as quickly as possible. It is about understanding how software works, why decisions were made, and how to maintain and improve a system years later.

The Code Will Still Need to Be Maintained

Most software projects are not finished after the first version is released.

They need bug fixes, new features, security updates, performance improvements, and sometimes complete redesigns. The code written today might still be running five or ten years from now.

This is where excessive reliance on AI could become a serious problem.

Imagine a developer using AI to generate most of a project's code. The application works, the deadlines are met, and everything looks fine. But how much of that code does the developer actually understand?

When a bug appears two years later, someone needs to understand how the system works. When a customer requests a new feature, someone needs to know where and how to implement it. When a security issue is discovered, someone needs to be able to review the affected code.

If developers become dependent on AI for every change, businesses could eventually end up maintaining software that nobody truly understands.

AI might still be available to help, of course. But relying on an external tool to understand your own software is not always a good business strategy.

Programming Is Also a Mental Skill

I also think we underestimate the effect AI can have on the way programmers think.

Programming requires problem-solving. You need to break a large problem into smaller parts, understand the relationships between different components, and decide how a system should work.

When AI immediately provides an answer, it can be tempting to skip this process.

Instead of spending an hour thinking about how to solve a problem, you can simply describe the problem and receive working code within seconds. This feels productive, and sometimes it genuinely is. But if this becomes the default way of working, developers might slowly lose the habit of solving problems independently.

The problem is similar to using a calculator without understanding mathematics. The calculator is useful, but you still need to understand what you are doing and recognize when the result does not make sense.

The same applies to programming. AI can generate code, but a developer still needs to understand whether that code is correct, maintainable, secure, and appropriate for the project.

The Risk of AI-Generated Technical Debt

AI-generated code can also introduce technical debt.

An AI model usually does not have the same long-term understanding of a project that an experienced developer has. It can generate a function that works perfectly on its own while not fitting properly into the rest of the codebase.

Over time, this can lead to duplicated logic, inconsistent patterns, unnecessary dependencies, overly complicated abstractions, and code that technically works but is difficult to maintain.

The dangerous part is that these problems are not always immediately visible.

A project can appear productive in the short term because features are being built quickly. But if developers constantly accept generated code without fully reviewing and understanding it, the maintenance cost may appear later.

For businesses, this creates an important question:

Is software really cheaper if it is faster to build but more difficult to maintain?

I Believe Semi-Manual Coding Is More Important Than It Seems

I am not against using AI in programming. I use AI myself, and I think it can be an extremely useful tool.

But I believe the best approach is somewhere in the middle.

AI should assist developers, not completely replace the development process.

For example, AI can be useful for:

• Explaining unfamiliar technologies
• Generating repetitive boilerplate code
• Helping investigate bugs
• Reviewing code and suggesting improvements
• Writing documentation
• Quickly searching for information
• Exploring different solutions to a problem

But the developer should still understand the architecture, make important technical decisions, and write and review significant parts of the code manually.

This is what I mean by semi-manual coding.

The goal is not to reject AI. The goal is to make sure that the programmer remains actively involved in the thinking process.

A developer should be able to look at their codebase and understand how it works without needing to ask an AI model to explain their own application.

Faster Is Not Always Better

There is currently a lot of pressure in the technology industry to build software faster.

AI makes this possible in many situations, but speed should not be the only metric we care about.

Software engineering is not just about how quickly code can be generated. It is also about how long that software can survive, how easily it can be maintained, and whether developers can understand and improve it in the future.

Writing less code is not always a bad thing. Using AI is not always a bad thing. But completely removing the programmer from the coding process could create problems that only become visible years later.

The Future Programmer Should Know How to Work Without AI

I think AI will remain an important part of software development. Ignoring it would be unrealistic.

But programmers should not lose the ability to work independently.

A good developer should know how to solve a problem manually before deciding whether AI can help solve it faster. They should understand the code they ship and be able to maintain a project even when AI is unavailable or gives the wrong answer.

The future of programming does not have to be a choice between manual coding and AI-generated code.

The best approach may be a combination of both.

Use AI where it genuinely saves time. Use it to automate repetitive work and explore ideas. But continue thinking, designing, debugging, and writing code yourself.

Because in the end, software is not finished when the AI generates the code.

Someone still has to understand it.