The world has been taken up by a storm of AI tools. Don’t get me wrong, AI is magnificent at doing creative tasks but where-ever there is pin point accuracy involved it fails miserably.
As a developer I have been using a lot of AI tools like Github CoPilot , Cursor the AI Coding IDE and today after so many months of using I want to claim this “AI can’t code shit”. I have tried and trust me it is all hype. Recent trends suggest the same.
The Myth of “Vibe Coding”
A new trend called “Vibe Coding” has emerged, where developers rely on natural language prompts to generate code instead of writing it themselves. On paper, this sounds futuristic and efficient. In reality, it’s an expensive trap.
Imagine asking AI to create a racing car for you. Sure, it might assemble something resembling a car, but what happens when you need to replace a single screw? Without deep technical understanding, AI-generated code quickly turns into a maintenance nightmare.
The Harsh Truth About AI-Generated Code
Here are some of the biggest issues with AI-powered coding:
1. No One Can Maintain AI-Generated Code
AI often produces code that is difficult to decipher, let alone maintain. It lacks the structured thought process of an experienced developer, leading to convoluted logic and unnecessary complexity.
2. AI Connects Dots, But You Need an Expensive Developer to Make Sense of Them
AI-generated code is often fragmented. While AI can spit out functions and modules, integrating them into a coherent system requires a senior developer—someone either already bald from stress or willing to lose their hair in frustration.
3. Reading AI-Generated Code Is More Painful Than Writing New Code
Just like fixing a faulty appliance can sometimes cost more than buying a new one, debugging AI-generated code often takes more effort than simply writing it from scratch. The time spent deciphering machine-generated gibberish could be used to craft clean, efficient code instead.
4. AI-Generated Code Doesn’t Align with Agile Development
One of the core principles of Agile is adaptability. However, AI-generated code often needs full replacements rather than incremental changes. A simple requirement tweak can force you to scrap entire sections and start over—a nightmare scenario for any developer.
5. AI-Generated Code Looks “Finished” But Is Far From Usable
Unlike human developers who build iteratively, AI presents code as if it’s production-ready. But more often than not, it lacks robustness, scalability, and real-world practicality, forcing developers to redo large portions of it.
The Future of AI in Coding: My Predictions
While AI tools will continue to improve, I believe many people are grossly overestimating their current capabilities. Here’s why:
- We haven’t reached the required computational power yet. AI is still far from being able to truly “understand” and connect the dots of complex codebases.
- AI is limited by the tools made by humans. AI doesn’t innovate—it only regurgitates and recombines what already exists.
- People will eventually realize AI coding is not a replacement for skilled developers. The reliance on AI-generated code will lead to costly mistakes, and businesses will shift back to prioritizing real engineering talent.