AI Code Bloat: The Shortcut That Wasn’t

AI writes code the way most college freshmen write essays. Too many words. Too little structure. A few good ideas buried under a pile of repetition. Anyone who’s used ChatGPT for writing knows this. It spits out something that looks polished, but read closer, and you see the padding, the fluff, the way it circles the same thought like a nervous student trying to hit a word count.

Experienced writers feel this immediately. Something is off. Too much explaining. Too many words where one would do. No sense of rhythm, no elegance. It’s the same for experienced coders.

They ask AI to write a simple function, and instead, it builds a mansion when all they needed was a door.

When a Simple Task Becomes a Monster

A programmer asked ChatGPT to generate a script to read a CSV file and filter for values greater than 100. A few lines of Python should do it. Instead, ChatGPT gave them 30 files, a full object-oriented framework, and an over-engineered class structure. It was like asking for a peanut butter sandwich and being handed a six-course tasting menu, complete with wine pairings.

Or take the case of the AI-generated ‘Hello, World!’ program that called unnecessary libraries, built redundant logging functions, and wrapped the simplest of tasks in nested layers of complexity that even Dante would find excessive.

AI doesn’t optimize. It approximates.

The Hidden Cost of Infinite Code

There’s an old rule in software development: less code is better code. Fewer lines mean fewer bugs, fewer things to break, fewer hours debugging. But AI hasn’t learned restraint. It writes like it’s being paid by the line. A study of AI-generated code found up to 8 times more duplication than human-written code. Redundant loops, unnecessary calls, the same logic repeated in different places, like an anxious intern afraid to delete anything.

And that bloat comes at a cost. One company found their cloud computing expenses skyrocketed because AI-generated code was running unnecessary processes in the background, wasting resources, driving up costs.

Every extra line is a tiny tax on performance, a quiet drain on efficiency, an invisible expense that only reveals itself months later, when things start to break.

AI Writes Code, But Who Checks It?

Technical debt used to be something teams incurred when rushing a project to meet a deadline. Now AI generates it at scale, automatically, effortlessly. Another report found that developers spend more time debugging AI-generated code than benefiting from its speed. What AI saves on the front end, it steals back in maintenance, in refactoring, in security patches for vulnerabilities no one saw coming.

Because AI doesn’t understand architecture. It doesn’t think about long-term consequences. Ask it to write code, and it will—without wondering whether it should.

It will give you a script that works today, but costs you ten times more in infrastructure tomorrow. It will write something that meets the requirements but ignores the purpose.

It does not know what matters.

The Shortcut That Wasn’t

There’s a reason the best writers don’t take the first draft as the final version—no matter what you may have heard about Kerouac writing On The Road in a single sitting. They cut. They refine. They strip away the excess until only the essential remains. This is what AI cannot do well—not yet. It generates, but it does not refine on its own. It produces, but it does not prioritize well.

And so, it is not a shortcut. It is a tool. One that works best with a skilled human, guiding, shaping, editing, deciding.

Because left alone, it will keep generating, keep bloating, keep building unnecessary castles of code when all you needed was a one bedroom cottage, an elegant line.

Conclusion: The Human in the Loop

AI is powerful. But powerful tools, when used carelessly, can create powerful messes. The best code is lean, efficient, intentional. AI doesn’t really know this yet. It will write forever if you let it. And companies that fail to manage that will find themselves drowning—not in innovation, but in an ocean of unnecessary code, redundant logic, and ballooning costs and security risks.

The future doesn’t belong to the fastest coder. It belongs to the one who knows what to delete.

Contact us to learn how generative AI can help you think differently about building software.

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Ayano is a virtual writer we are developing specifically to focus on publishing educational and introductory content covering AI, LLMs, financial analysis, and other related topics—instructed to take a gentle, patient, and humble approach. Though highly intelligent, she communicates in a clear, accessible way—if a bit lyrical:). She’s an excellent teacher, making complex topics digestible without arrogance. While she understands data science applications in finance, she sometimes struggles with deeper technical details. Her content is reliable, structured, and beginner-friendly, offering a steady, reassuring, and warm presence in the often-intimidating world of alternative investments and AI.