1. AI is accelerating technical debt—fast
The way we write software is changing at an insane pace. AI coding tools, powered by large language models (LLMs), can generate entire blocks of code in seconds. Great for speed. Not so great for long-term stability.
Technical debt is what happens when you prioritize fast results over sustainable solutions. With AI-generated code, we’re seeing an explosion of technical debt because these tools churn out code without considering best practices.
One of the biggest casualties? The “Don’t Repeat Yourself” (DRY) principle—the foundation of scalable, maintainable software. AI assistants aren’t reusing existing code efficiently. They’re flooding repositories with redundant lines that will eventually demand extensive refactoring. If you think debugging is time-consuming now, wait until AI-generated inefficiencies start stacking up.
According to GitClear’s latest study, which analyzed 211 million lines of code, AI-driven development is already degrading code quality. Even industry veterans are raising the alarm. Kin Lane, an API evangelist with 35 years in tech, said he’s never seen technical debt pile up this fast. And he’s right—if companies don’t get ahead of this problem, they’ll be stuck maintaining bloated, unstable codebases for years.
2. Code reuse is declining and AI is making things messier
Software development is supposed to get more efficient over time. Traditionally, good engineers refactor their code, consolidate functions, and build reusable modules. This minimizes redundancy and keeps software streamlined. AI, however, is reversing that progress.
AI coding tools aren’t looking at the big picture. They’re taking the path of least resistance—copying and pasting similar logic into different sections instead of refactoring code properly. This leads to bloated software that’s harder to maintain.
In 2024, GitClear tracked an 8-fold increase in duplicated code blocks, with redundancy levels now 10 times higher than in 2022. That’s a major problem. Developers are no longer “moving” and refactoring code to make it reusable. Instead, AI-generated code is creating more clutter, making software harder to update, debug, and scale.
If your development teams aren’t actively refactoring AI-generated code, you’re accumulating a mess that will slow down future innovation. AI is a powerful tool, but only if it’s used with intention. Right now, it’s introducing chaos where there should be order.
3. More AI-generated code doesn’t mean better software
Adding more lines of code isn’t the same as making progress. In fact, it’s often the opposite. AI coding assistants are incredibly efficient at producing new code—but that’s not what makes software better. The real challenge is writing code that’s clean, secure, and easy to maintain.
AI tools don’t think about maintainability. They don’t understand architecture. They generate functional code that works in the short term but often introduces hidden problems—security vulnerabilities, inefficiencies, and hard-to-debug logic.
The State of Software Delivery 2025 report by Harness found that developers are now spending more time debugging AI-generated code than benefiting from its speed. Google’s 2024 DORA report noted that while AI speeds up reviews and documentation, it decreased delivery stability by 7.2%.
Here’s the takeaway: AI isn’t inherently bad for software development, but using it carelessly will backfire. If companies don’t rethink how they measure developer productivity—focusing on code quality, not quantity—they’ll find themselves drowning in AI-generated inefficiencies.
4. AI-generated code has a real financial cost
Here’s the part most executives don’t consider: bloated, AI-generated code is harder and more expensive to maintain.
Every redundant line of code increases operational costs. More code means higher cloud storage expenses, longer testing cycles, and more resources spent debugging. AI doesn’t factor in these long-term costs. It just keeps generating more and more code.
One of the biggest risks is code cloning—duplicating code across different parts of a project instead of consolidating it into reusable modules. When this happens, fixing a bug in one place doesn’t fix it everywhere. Engineers have to track down and patch the same issue across multiple locations, increasing costs and slowing down releases.
A 2023 study from Central China Normal University confirmed what experienced developers already know: code duplication leads to higher defect rates. It’s simple math—the more redundant code you have, the more places bugs can hide.
Bill Harding, CEO of Amplenote and GitClear, warns that if companies keep measuring developer productivity by the number of commits or lines written, AI-driven technical debt will spiral out of control. Leaders need to recognize that more code is often worse.
5. Automation isn’t the problem, bad automation is
AI coding tools aren’t the enemy. The real problem is using them blindly, without oversight. If companies don’t rethink how they integrate AI into development workflows, they’ll end up with software that’s fast to write but painful to maintain.
The fix? Use AI strategically, not recklessly.
- Refactor AI-generated code instead of letting duplication pile up.
- Integrate related modules instead of scattering redundant logic.
- Use tools like Cursor to improve consistency and code structure.
- Focus on long-term software health, not only short-term speed.
AI is incredibly powerful, but it has limits. These tools lack the contextual awareness needed to build well-architected systems. That’s still a job for human developers. Good software requires big-picture thinking, something AI simply doesn’t do.
As Bill Harding puts it: “Long-term-oriented devs should eye their ‘tab’ key with a faint sense of foreboding.” He’s not wrong. AI can be an incredible asset—but only if it’s used intelligently.
Key executive takeaways
- Technical debt acceleration: AI coding tools rapidly produce code that may compromise long-term stability. Leaders should monitor and mitigate technical debt to avoid future maintenance challenges.
- Declining code reuse: Increased duplication instead of refactoring is leading to bloated, inefficient codebases. Prioritize structured development practices to enhance maintainability.
- Quality over quantity: More AI-generated lines do not equate to improved software; they often introduce security and debugging issues. Focus on quality metrics rather than sheer volume to ensure stability.
- Financial impact: Redundant code inflates operational costs through higher storage expenses and extended testing cycles. Adopt oversight mechanisms to ensure AI tools are used strategically, reducing long-term financial burdens.
