Large Language Models (LLMs) like ChatGPT, Copilot, and others are becoming a regular part of software development. Many developers use them to write boilerplate code, help with unfamiliar syntax, or even generate whole modules. On the surface, it feels like a productivity boost. The work goes faster, the PRs are opened sooner, and there’s even time left for lunch.
But there’s something underneath this speed, something we’re not talking about enough. The real issue with LLM-generated code is not that it helps us ship more code, faster. The real issue is liability.
Code That Nobody Owns
There’s a strange effect happening in teams using AI to generate code: nobody feels responsible for it.
It’s like a piece of code just appeared in your codebase. Sure, someone clicked “accept,” but no one really thought through the consequences. This is not new, we saw the same thing with frameworks and compilers that generated code automatically. If no human wrote it, then no human cares deeply about maintaining or debugging it later.
LLMs are like that, but on a massive scale.
The “Average” Problem
LLMs are trained on a massive corpus of public code. What they produce is a kind of rolling average of everything they’ve seen. That means the code they generate isn’t written with care or with deep understanding of your system. It’s not great code. It’s average code.
And as more and more people use LLMs to write code, and that code becomes part of new training data, the model quality might even degrade over time, it becomes an average of an average.
This is not just about style or design patterns. It affects how you:
- Deliver software
- Observe and monitor systems
- Debug real-world issues
- Write secure applications
- Handle private user data responsibly
LLMs don’t truly understand these things. They don’t know what matters in your architecture, how your team works, or what your specific constraints are. They just parrot what’s most statistically likely to come next in the code.
A Fast Start, Then a Wall
So yes, LLMs speed up the easiest part of software engineering: writing code.
But the hard parts remain:
- Understanding the domain
- Designing for change
- Testing edge cases
- Debugging production issues
- Keeping systems secure and maintainable over time
These are the parts that hurt when the codebase grows and evolves. These are the parts where “fast” turns into fragile.
Example: Generated Code Without Accountability
Imagine you ask an LLM to generate a payment service. It might give you something that looks right, maybe even works with your Stripe keys or some basic error handling.
But:
- What happens with race conditions?
- What if fraud detection fails silently?
- What if a user gets double-charged?
- Who is logging what?
- Is the payment idempotent?
- Is sensitive data like credit cards being exposed in logs?
If no one really “owned” that code because it was mostly written by an AI and these questions might only surface after things go wrong. And in production, that can be very costly.
So What’s the Better Approach?
LLMs can be great tools, especially for experienced engineers who treat them like assistants, not authors.
To use LLMs responsibly in your team:
- Review AI-generated code with care.
- Assign clear ownership, even for generated components.
- Add context-specific tests and documentation.
- Educate your team on the why, not just the how.
- Make accountability a core part of your development process.
Because in the end, you are shipping the product. The AI isn’t going to be on call at 2 AM when things go down.
Final Thoughts
LLMs give us speed. But they don’t give us understanding, judgment, or ownership. If you treat them as shortcuts to ship more code, you may end up paying the price later. But if you treat them as a tool and keep responsibility where it belongs they can still be part of a healthy, sustainable development process.
Thanks for reading. If you’ve seen this problem in your team or company, I’d love to hear how you’re dealing with it.
