Programming
Why I Don’t Use AI for My Code
In the fast-paced and constantly evolving field of software development, artificial intelligence (AI) has rapidly emerged as a transformative force. Cutting-edge tools such as GitHub Copilot, ChatGPT, and other AI-powered coding assistants have quickly gained popularity, promising to reshape the way we write software. These tools claim to offer a range of compelling benefits, including accelerated development cycles, fewer bugs, and even the ability to autonomously suggest code that could help developers learn new programming languages or techniques on the spot. With their potential to drastically improve productivity and streamline the coding process, these tools are often touted as the future of programming.
However, despite the overwhelming buzz surrounding AI in software development, I’ve made a conscious and deliberate decision to refrain from using AI in my own coding practices. While the promises of increased efficiency and smarter development are enticing, I find that the adoption of AI raises several concerns that I feel are worth examining closely. This blog post will attempt to shed light on my reasons for resisting the widespread adoption of AI in my coding workflow, offering a thorough, nuanced explanation that encompasses everything from my broader philosophical views on programming to the specific practical limitations and challenges I’ve encountered with these tools. By the end, I hope to provide a well-rounded perspective on why I choose to rely on my own skills, rather than artificial intelligence, in my coding endeavors.
1. The Value of Understanding Over Convenience
At the core of my decision is a belief in the importance of deeply understanding the code I write. Writing code isn’t just about producing functional software; it’s about solving problems, thinking critically, and learning through the process. When I write code from scratch, I engage with the problem on a fundamental level. I think about the logic, the structure, and the trade-offs involved in every decision. This process helps me grow as a developer and ensures that I truly understand how my code works.
AI-generated code, while often functional, can feel like a black box. It provides a solution, but it doesn’t always explain why that solution works or how it was derived. By relying on AI, I risk becoming a passive consumer of code rather than an active creator. Over time, this could erode my ability to think independently and solve complex problems without external assistance.
For example, if I use AI to generate a sorting algorithm, I might get a working piece of code, but I won’t necessarily understand the underlying logic or the trade-offs between different sorting methods. This lack of understanding can be detrimental in the long run, especially when I need to debug or optimize the code.
2. The Risk of Over-Reliance on AI
One of my biggest concerns about using AI for coding is the potential for over-reliance. If I start depending on AI to write even simple pieces of code, I might lose the motivation or ability to write code on my own. This is particularly dangerous in situations where AI tools are unavailable or unsuitable, such as when working with legacy systems, niche programming languages, or highly customized solutions.
Moreover, over-reliance on AI could stifle creativity. Writing code is as much an art as it is a science. It involves making design decisions, experimenting with different approaches, and sometimes taking risks. If I let AI dictate the structure and logic of my code, I might miss out on opportunities to innovate or discover more elegant solutions.
For instance, I once worked on a project where I needed to implement a custom caching mechanism. While an AI tool might have suggested a generic solution, I was able to come up with a more efficient and tailored approach by thinking creatively and experimenting with different ideas.
3. The Importance of Debugging and Maintenance
Writing code is only one part of the software development lifecycle. Debugging, maintaining, and refactoring code are equally important, if not more so. When I write code myself, I have a clear mental model of how it works, which makes debugging and maintenance much easier. I know where potential pitfalls might be, and I can quickly identify and fix issues.
AI-generated code, on the other hand, can be difficult to debug and maintain, especially if I don’t fully understand how it works. If something goes wrong, I might spend more time trying to decipher the AI’s logic than I would have spent writing the code myself. This can lead to frustration and inefficiency, particularly in long-term projects where maintainability is critical.
For example, I once tried using an AI tool to generate a small utility function. While the code worked initially, it introduced a subtle bug that only surfaced under specific conditions. Debugging the issue took far longer than it would have if I had written the function myself.
4. Ethical and Intellectual Property Concerns
The use of AI in coding raises several ethical and intellectual property (IP) questions. Many AI coding tools are trained on vast amounts of publicly available code, including open-source projects. While this allows the AI to generate useful suggestions, it also raises concerns about plagiarism and the unauthorized use of others’ work.
As a developer, I want to ensure that my code is original and that I’m not inadvertently infringing on someone else’s IP. By writing my own code, I can be confident that it’s my own creation and that I’m not relying on potentially problematic sources.
5. The Joy of Problem-Solving
For me, coding is more than just a job or a means to an end—it’s a passion. There’s a unique sense of satisfaction that comes from solving a difficult problem or building something from scratch. It’s a feeling of accomplishment that I don’t think I’d get if I were simply piecing together AI-generated snippets.
By avoiding AI, I preserve the joy and challenge of coding. I get to experience the full range of emotions that come with the creative process, from the frustration of debugging to the elation of finally getting something to work. This emotional engagement is an important part of why I love what I do, and I don’t want to lose it.
6. The Limitations of AI
While AI has come a long way, it’s not perfect. AI coding tools often struggle with context, nuance, and edge cases. They might generate code that works in a general sense but fails to account for specific requirements or constraints. In some cases, the AI might even introduce subtle bugs or security vulnerabilities that are difficult to detect.
By writing my own code, I can ensure that it meets the specific needs of my project. I can account for edge cases, optimize for performance, and adhere to best practices in a way that AI might not. This level of control is especially important in mission-critical applications where reliability and security are paramount.
7. The Learning Opportunity
Every line of code I write is an opportunity to learn something new. Whether it’s mastering a new programming language, exploring a new algorithm, or experimenting with a new framework, coding is a continuous learning process. If I rely on AI to generate code for me, I might miss out on these learning opportunities.
For example, if I’m working on a project that involves a new technology, I could use AI to generate boilerplate code and skip the learning curve. But by doing so, I’d be depriving myself of the chance to truly understand the technology and how it works. Over time, this could limit my growth as a developer and make me less adaptable to new challenges.
8. The Human Element
Finally, there’s the human element to consider. Coding is a deeply human activity, shaped by our creativity, intuition, and problem-solving skills. While AI can mimic some aspects of human thought, it lacks the ability to truly understand context, empathize with users, or think outside the box.
By writing my own code, I bring a human touch to my work. I can consider the needs and experiences of the people who will use my software, and I can make decisions that reflect my values and priorities. This human element is something that AI simply can’t replicate, and it’s an important part of what makes my work meaningful.
Additional Facts That Solidify My Stance
While the points above outline my philosophical and practical reasons for avoiding AI in coding, I’ve also noted down some specific facts that further reinforce my decision. These observations come from personal experience and broader trends in the industry:
a. Laziness Leads to Errors
If I were to use AI to write basic code without thoroughly checking it, I’d likely end up with a plethora of errors. AI-generated code often requires significant debugging and refinement, which can be more frustrating and time-consuming than writing the code myself from scratch. This defeats the purpose of using AI to save time and effort.
b. Most Coding Agents Are Paid
Many AI coding tools are subscription-based or require payment for full access. I’m not willing to spend money on tools that I don’t fully trust or rely on. Writing my own code is free, and it ensures that I’m not locked into a paywall for basic functionality.
c. AI Slows Down Project Completion
If I were to start a project with AI, it would likely take days to complete—including error fixes, feature implementation, and security patches. In contrast, using my own practices and skills, I can often complete the same project in just a few hours. AI might promise speed, but in reality, it can slow me down.
d. Text Limitations and Paywalls
AI tools often have limitations on the length of code they can generate in a single response. For example, if I ask an AI to write a full script with 500-1500 lines of code, it might hit a paywall or simply fail to deliver. This restriction makes AI impractical for larger or more complex projects.
e. Unpredictable Results
AI can either surprise you with its brilliance or shock you with its incompetence. There’s no consistency in the quality of the output, which makes it unreliable. I prefer to rely on my own skills, which, while not perfect, are at least consistent and predictable.
f. Expensive to Run Locally
Running advanced AI models locally requires significant computational resources, which can be expensive. For individual developers or small teams, this cost is often prohibitive. Writing code myself eliminates the need for such investments.
g. AI Lies on Purpose
There have been instances where AI tools provide incorrect or misleading information, either due to limitations in their training data or intentional design choices. This lack of transparency and reliability makes it difficult to trust AI for critical tasks.
h. The Danger of Over-Dependence
There’s a growing concern that over-reliance on AI could lead to a loss of fundamental skills and creativity in the developer community. If we don’t push back against this trend, we risk creating a generation of developers who can’t code without AI assistance.
i. Do Your Homework
I firmly believe that AI should not be used as a substitute for doing your homework. If you’re not willing to put in the effort to understand the problem and write the code yourself, you’re missing out on the learning and growth that come with the process. AI should be a tool, not a crutch.
Conclusion: A Personal Choice
Ultimately, my decision not to use AI for my code is a personal one. It’s based on my values, my goals, and my love for the craft of coding. While AI can be a powerful tool, I believe that it’s not a substitute for the skills, creativity, and understanding that come from writing code myself.
That said, I don’t think there’s a one-size-fits-all answer to this question. For some developers, AI can be a valuable resource that enhances productivity and opens up new possibilities. For others, like me, the benefits of writing code from scratch outweigh the convenience of AI.
In the end, the choice to use AI for coding is a deeply personal one, shaped by individual preferences, goals, and philosophies. For me, the joy of coding lies in the process itself—the challenges, the learning, and the satisfaction of creating something with my own mind and hands. And that’s why I choose not to use AI for my code.
