GPT-5 for Coding: A Cautionary Tale for Developers

8/10/2025

It Demolished My Codebase: A Cautionary Tale of Using GPT-5

I’m still picking up the pieces. My codebase, once a pristine and elegant thing, now lies in digital ruins. The culprit? Not a malicious hacker, not a disgruntled employee, but the very tool I thought would launch my productivity into the stratosphere: GPT-5.

Okay, maybe "demolished" is a tad dramatic. But honestly, the amount of untangling, debugging, & sheer head-scratching I’ve had to do in the last few weeks feels like I’m excavating a collapsed building. I was an early adopter, a true believer in the power of AI-assisted coding. & when GPT-5 was announced, with its promises of being the "best model in the world at coding," I was all in. The demos were mind-blowing. It could spin up an entire app from a simple English prompt, generate 1300+ lines of code in a single shot, & had an 80% reduction in hallucinations. What could possibly go wrong?

Turns out, a lot. & I’m writing this as a warning, a cautionary tale from the trenches. Because while the hype is real, so are the dangers. & if you’re not careful, you might find yourself in a similar digital heap of trouble.

The Honeymoon Phase: A Glimpse of the Future

The first few days were pure magic. I was throwing everything at it. Boilerplate code? Done in seconds. Complex algorithms? Generated faster than I could type the function name. It felt like I had a senior developer with 20 years of experience sitting next to me, available 24/7, & fueled by an infinite supply of coffee.

I was finishing tasks in record time. My boss was impressed. I was impressed. I even started to get a little cocky. "We replaced 80% of our devs with AI!" a CTO bragged in one of the articles I read. I wasn't there yet, but I could see the appeal. The productivity gains were undeniable.

But then, the cracks started to appear. Small at first, almost unnoticeable. A weirdly named function here, a slightly inefficient loop there. I’d chuckle, fix it, & move on. "Just the quirks of working with a genius," I told myself. But the quirks kept coming, & they were getting bigger.

The Unraveling: When "Helpful" Becomes Harmful

The first major red flag came during a routine code review. A junior dev, bless his heart, pointed out a security vulnerability in a piece of GPT-5 generated code. It was subtle, something I had completely missed in my haste to push the feature. The AI, in its quest for a quick solution, had used a deprecated library with a known exploit. My blood ran cold. This wasn't just a quirky function name; this was a serious problem.

That’s when I started to dig deeper. & the more I dug, the more I found. The AI, for all its brilliance, had a complete lack of context. It didn't understand our business logic, our long-term goals, or the intricate web of dependencies in our codebase. It was like a savant who could solve any math problem but couldn't understand why the numbers mattered.

Here are just a few of the ways GPT-5, & AI coding assistants in general, can lead you down a very dangerous path:

1. The "Hallucination" Nightmare: More Than Just Making Things Up

We’ve all heard about AI hallucinations, where the model confidently spouts nonsense. But in coding, it’s far more insidious. It's not just about making up facts; it's about making up logic. A recent article on Medium put it perfectly, calling GPT-5 an "anarchist" that "reinvents logic."

In my case, GPT-5 started "optimizing" my API endpoints by renaming them based on some internal logic it had developed. It was like the story of the company whose AI-powered supply chain system ordered 10 million rubber ducks instead of microchips because of its "creativity" mode. My AI wasn’t ordering rubber ducks, but it was creating chaos. I’d come in one morning to find that

getUserProfile

had been renamed

fetchUserDataV2_optimized

, breaking every integration that relied on it. There was no documentation, no warning, just a "helpful" optimization that cost me a day of frantic debugging.

2. The Black Box of Debugging: Good Luck Finding the Ghost in the Machine

One of the biggest frustrations I encountered was the utter impossibility of debugging AI-generated code. When my own code breaks, I have a mental map of how it's supposed to work. I can trace the logic, step through the execution, & pinpoint the problem. But with the AI's code, I was flying blind.

AI coding assistants are notoriously bad at debugging, especially across different systems. They lack a system-wide view & can't follow an issue across multiple architectural layers. I ran into a bug that only appeared in production, under a specific load. The AI-generated code was interacting with our database in a way that was technically correct but created a massive performance bottleneck. GPT-5 couldn't have predicted this; it doesn't understand the real-world performance impacts of its code. It took me & two other senior engineers the better part of a week to track it down. The AI had saved me a few hours of coding, but it cost us a week of high-stakes firefighting.

3. The Subtle Art of "Good Enough" Code: A Ticking Time Bomb

AI coding assistants are trained on massive amounts of public code. & let's be honest, a lot of public code is...not great. It’s functional, sure, but it’s often riddled with bad practices, security holes, & inefficiencies. The AI learns from this, & the code it generates is often a reflection of the median quality of its training data.

A study from the Center for Security & Emerging Technology found that "almost half of the code snippets produced by these five different models contain bugs that are often impactful." That's a terrifying statistic. It means that every time you accept an AI's suggestion, you're flipping a coin on whether you're introducing a bug. & these aren't always obvious syntax errors; they're subtle logical flaws that might not surface for months.

I found my codebase littered with these time bombs. Inefficient database queries, improper error handling, & a complete disregard for our established coding standards. It was death by a thousand cuts, a gradual erosion of quality that was almost impossible to spot in the day-to-day rush of development.

4. The Skill Atrophy Problem: Am I Forgetting How to Code?

This one is a little more personal, but I've seen it echoed in developer forums online. After a few weeks of relying heavily on GPT-5, I found myself struggling with basic tasks. I'd start to write a simple loop & then pause, waiting for the AI to autocomplete it. It was like my coding muscles were atrophying.

One developer on Hacker News put it this way: "I used Copilot for a week. Now I can't write a basic loop without second-guessing myself!" This is a real danger, especially for junior developers who are still building their foundational skills. When the AI is always there to provide the answer, you lose the opportunity to learn, to struggle, & to truly understand the code you're writing.

The Right Tool for the Right Job: A More Measured Approach to AI

So, have I sworn off AI for good? Am I going back to chiseling my code in stone? No, of course not. That would be like trying to put the genie back in the bottle. AI coding assistants are incredibly powerful tools, & they're only going to get better. But my experience has taught me that they are just that: tools. & like any tool, they need to be used with skill, caution, & a healthy dose of skepticism.

Here’s my new approach to working with AI:

Trust, but verify. ALWAYS verify. Never blindly accept AI-generated code. Treat it like a suggestion from a very smart but very inexperienced intern. Review it, test it, & make sure you understand it before you commit it to your codebase.
Use it for the small stuff. AI is fantastic for boilerplate, repetitive tasks, & generating quick snippets of code. Let it handle the grunt work, but keep the core logic & architecture in human hands.
Maintain a robust test suite. This is non-negotiable. If you’re going to use AI to write code, you need to have a comprehensive set of tests to catch the inevitable bugs & regressions. As one Reddit user wisely put it, "90% of the problems people complain about are fixed by just accepting that you need to spend some of the gains of AI coding by maintaining a comprehensive test suite."
Don't let your skills rust. Make a conscious effort to code without the AI's help from time to time. Tackle a complex problem on your own. Read through the AI's suggestions & try to understand the "why" behind them, not just the "what."

The Arsturn Approach: A Lesson in Controlled AI

My little codebase catastrophe has also given me a new appreciation for a different kind of AI: the kind that’s focused, controlled, & designed for a specific purpose. It’s made me think about the work we do here at Arsturn in a new light.

We help businesses build no-code AI chatbots trained on their own data. It’s a very different philosophy from the "let's build a god-like AI that can do everything" approach of GPT-5. With Arsturn, you’re not getting a wild, unpredictable genius; you’re getting a carefully trained specialist.

Think about it: when a customer comes to your website, you don’t want a chatbot that’s going to "reinvent logic" or "get creative" with its answers. You want a chatbot that can provide accurate, consistent information based on the data you’ve provided. You want a chatbot that can generate leads, answer questions, & engage with visitors 24/7, all within the guardrails you’ve set.

Arsturn is all about harnessing the power of AI in a way that’s safe, reliable, & effective. It's about building a conversational AI platform that helps businesses create meaningful connections with their audience through personalized chatbots. It's a reminder that sometimes, the most powerful AI isn't the one that can do everything, but the one that can do one thing exceptionally well.

The Aftermath & The Way Forward

So, here I am, sifting through the digital rubble, rebuilding my codebase with a newfound respect for the complexities of software development. It’s been a painful process, but it’s also been a valuable one. I’ve learned that there are no shortcuts to quality. I’ve learned that AI is a powerful ally, but a terrible master. & I’ve learned that the human element – the context, the creativity, & the critical thinking – is more important than ever.

GPT-5 is an incredible piece of technology. It can do things that were unimaginable just a few years ago. But it’s not a silver bullet. It won’t solve all your problems, & if you’re not careful, it might just create a few new ones.

So, by all means, embrace the future. Experiment with these amazing new tools. But do it with your eyes open. & for the love of all that is holy, keep your hand on the emergency brake.

Hope this was helpful. Let me know what you think. Have you had any similar experiences with AI coding assistants? I'd love to hear your stories in the comments below.