The Pitfalls of AI Coding
When Things Go Wrong
AI-powered coding is unlocking new opportunities for entrepreneurs and problem-solvers, allowing them to build software without deep programming knowledge. But while AI-assisted development is undoubtedly a game-changer, it comes with its own set of challenges—especially when things go wrong.
For those who lack coding experience, troubleshooting AI-generated code can be a minefield. When an application fails to work as expected, debugging can become a frustrating process without the foundational knowledge to understand what’s gone wrong. This raises an important question: is AI making software development more accessible, or is it creating a generation of developers who lack the skills to fix their own creations?
The Limits of AI Coding
AI coding tools are impressive, but they are not infallible. While they can generate functional code quickly, they often produce solutions that are inefficient, contain security vulnerabilities, or simply do not work as expected in real-world applications.
Here are some of the most common pitfalls of AI-generated code:
Lack of Understanding – AI might write the code, but if the user does not understand how it works, they will struggle to modify or debug it.
Hidden Errors – AI-generated code can introduce subtle bugs that are not immediately obvious but may cause failures under certain conditions.
Security Risks – AI does not always follow best practices for security, potentially leaving applications vulnerable to exploits.
Over-Reliance on AI – A new wave of developers may emerge who can build software using AI but lack the foundational coding knowledge to problem-solve when issues arise.
Why Developers Shouldn’t Panic
With AI encroaching on the traditional domain of software engineers, it is natural for developers to feel uncertain about their role. However, rather than replacing developers, AI is reshaping the industry and shifting their responsibilities.
Here’s why developers will remain essential:
Debugging & Troubleshooting – AI can generate code, but developers will always be needed to test, refine, and troubleshoot when things do not work as expected.
Code Optimisation – AI-generated solutions are rarely the most efficient. Skilled developers will continue to refine and improve AI-assisted code for better performance.
Security & Compliance – AI often overlooks security best practices, making human oversight crucial to ensure applications are safe and compliant.
AI Supervision – As AI tools evolve, developers will take on roles similar to architects and supervisors, guiding AI in producing high-quality, maintainable code.
A Shift in Developer Roles
Rather than eliminating jobs, AI is changing what it means to be a developer. Future software engineers may spend less time writing individual lines of code and more time overseeing AI-assisted development, reviewing outputs, and ensuring quality control.
AI is a powerful tool, but it cannot replace human intuition, creativity, or deep technical expertise. The future of coding will likely be a hybrid approach—where AI accelerates development, but experienced engineers provide the structure, strategy, and oversight.
The Bottom Line
AI is making software development more accessible than ever, but it is also creating new challenges. While AI can build software, it cannot yet replace the human problem-solving skills needed to debug, optimise, and secure applications. Developers should not panic about job losses but instead embrace the evolving landscape of AI-assisted development.
Those who learn to work alongside AI—understanding its strengths while compensating for its weaknesses—will be the ones who thrive in this new era of software engineering.
If you’ve built some software and require support, check out Telos A local Wellington tech company doing epic things in the world of Ai and Ai Support.
