Hidden Security and Quality Risks
AI can generate code that works in the most common case—but completely ignores security flaws and edge cases. Think unvalidated inputs (hello, security vulnerabilities) or poor resource management (like database connections left open).
The DORA report found a 7.2% drop in software delivery stability linked to AI usage—suggesting that AI-generated code can introduce vulnerabilities and weaken systems if not carefully reviewed.
The Productivity Illusion
More AI-generated code doesn’t always mean real progress. If a company measures productivity only by commit counts or shipped features, it might incentivize technical debt inflation instead.
A study showed that developers can code up to 55% faster with AI—but that can also mean 55% more poorly structured code that needs to be maintained later.
AI Doesn’t Create Technical Debt—But It Can Supercharge It
AI won’t wreck your codebase by itself, but if there’s no strict oversight, it can accelerate technical debt at an insane pace. The key? Never accept AI-generated code blindly—keep solid review, architecture, and quality processes in place.
Used wisely, AI can be a huge asset—but only if your team stays in control of what actually gets shipped.
AI-Generated Code and Software Quality
AI can speed up coding, but that doesn’t automatically mean high-quality code. There are trade-offs in consistency, readability, reusability, testing, maintenance, and performance. Here’s what you need to watch out for:
Lack of Consistency and Broken Standards
AI doesn’t follow your team’s internal standards. It might suggest variable names, class structures, or coding styles that don’t align with your project’s conventions, breaking uniformity.
Small inconsistencies might seem harmless, but in large codebases, they slow down readability and collaboration. Plus, AI often generates generic solutions without considering your company’s architecture—leading to fragmentation and architectural decay over time.
Readability: Clear Code or Bloated Mess?
When properly guided, AI can generate clean code with useful comments. But often, it spits out unnecessarily long and verbose implementations.
Instead of abstracting common logic, AI might just repeat patterns without optimizing them. This makes code harder to read and adds complexity for no good reason.
Reusability vs. Reinventing the Wheel
Studies show AI-generated code tends to have more duplication and less reuse than manually written code. Instead of suggesting an existing function, AI might just recreate it from scratch.
The result? Unnecessary code bloat, broken DRY principles, and higher maintenance costs.
Automated Test Generation—A Win, But With Caveats
AI can help generate unit and integration tests faster, improving test coverage. But these tests often miss edge cases or complex scenarios.
If the AI-generated code already has flaws, the tests might just replicate those mistakes, creating a false sense of security.
Maintenance Can Become a Nightmare
Sustainable code should be easy to understand and modify. But since AI generates code without a global context, it can introduce unnecessary dependencies, accidental complexity, and make future changes harder.
Teams end up maintaining code that nobody actually wrote—a knowledge debt that can snowball over time.
Performance and Security Risks
AI rarely optimizes for performance or security unless explicitly told to. It might suggest:
- Inefficient SQL queries
- Unnecessary memory consumption
- Security vulnerabilities (like storing passwords in plain text—yikes)
Without proper review, these issues can slip through and cause long-term damage.
AI’s Impact on Software Quality: A Mixed Bag
AI can boost software quality by helping with test coverage, documentation, and enforcing certain patterns. But current evidence suggests a decline in fundamental quality practices.
The key? Use AI wisely—as a tool, not a crutch. Your team still needs to own the code and keep quality in check.
Can AI-Generated Code Make Technical Debt Worse?
Short answer: yes, and fast. Without careful review, AI can turn into a technical debt machine in no time. Here’s why:
Unnecessary Reimplementation
AI has zero awareness of what already exists in your codebase. That means it can reinvent the wheel, generating new code for functionality that already has optimized, built-in solutions.
Classic example: A dev asks Copilot for a function to sort a list, and instead of using .sort(), it spits out an entire sorting algorithm from scratch.
The result? Redundant, overly complex, and less efficient code.
Code That Looks Right, But is Fragile as Hell
AI generates code that seems plausible—until you realize it has subtle logic flaws.
A function might work for common inputs but break on edge cases or fail silently. These kinds of errors usually show up way too late, after the code is already in production. Now you’re in firefighting mode, paying off technical debt with interest.
Unnecessary Dependencies & Bad Choices
Ask AI for a solution to generate PDFs in Node.js, and it might suggest some random package—completely ignoring the fact that your team already uses a different one.
Now you’ve got two solutions for the same problem, adding unnecessary complexity.
The same happens with inefficient SQL queries or outdated libraries—problems that only become obvious when they start wrecking performance and maintainability.
Legacy Code & Bad Patterns Spreading Like a Virus
AI learns from public repos, which means it absorbs bad practices too.
If an outdated, insecure method was popular in the past, AI might still recommend it, completely unaware that it’s obsolete.
Example? Copilot suggesting strcpy in C, which is vulnerable to buffer overflow, just because it was trained on tons of old, unsafe code.
More Code Than Necessary
AI can take a simple task and turn it into a bloated mess. Instead of suggesting a clean .filter(), it might generate a manual loop that iterates over each item one by one.
This unnecessary boilerplate might not seem like a big deal, but over time, it makes the codebase harder to read, modify, and maintain.
AI Isn’t Trying to Write Bad Code—It’s Just Optimizing for the Obvious Path, Not the Best One
That means if you don’t review it critically, you could be stacking up technical debt at lightspeed.
Engineering leaders need to reinforce strict code reviews and treat AI-generated code as a first draft, not a final version.
The temptation to accept quick AI suggestions might save time today, but could cost you days—or even weeks—of painful rework later.
How to Prevent AI-Generated Code from Wrecking Your Technical Debt
AI-powered coding is here to stay, but without proper controls, it can skyrocket technical debt. The good news? There are ways to keep the speed while maintaining quality. Here’s what engineering leaders can do to mitigate the risks:
Stricter Code Reviews
If code reviews were critical before, they’re non-negotiable now. AI-generated code might work, but it can duplicate existing logic, ignore validations, or break project standards.
To catch these issues, reviewers need to level up and use specific checklists, including:
✅ Checking for duplicate logic in the codebase
✅ Ensuring code follows internal standards
✅ Verifying that all edge cases (including failure scenarios) are covered
On top of that, challenging the author on unclear sections forces them to truly understand the code before merging.
Or better yet, automate code reviews with AI. Tools like Kodus can catch issues before they hit production, ensuring no low-quality code slips through.
Building a Culture of Responsible Engineering
AI doesn’t replace critical thinking. Developers need to evaluate AI-generated suggestions, manually test the code, and validate its effectiveness.
A great practice? When submitting a PR, require devs to explain what the code does and why it’s correct. If they can’t articulate it clearly, it probably shouldn’t be merged yet.
Clear AI Usage Guidelines & Team Training
Set explicit policies on when and how to use AI. For example:
✅ Use AI for boilerplate code, tests, and non-critical implementations
🚫 Avoid AI for security-sensitive code, compliance logic, or financial transactions
Also, train the team on AI code review techniques and prompt engineering best practices to improve the quality of AI-generated suggestions from the start.
Continuous Refactoring & Maintenance
Leaving AI-generated code untouched? Bad idea. Small inefficiencies quickly snowball into massive liabilities.
✅ Dedicate a percentage of each sprint to reviewing AI-generated code, removing redundancies, and improving suboptimal sections.
✅ Celebrate devs who improve existing code, reinforcing long-term quality over short-term speed.
Rethink Performance Metrics
If your company measures productivity by lines of code written, you’re incentivizing technical debt.
Balance output with quality metrics, like:
- Post-release bug count
- Code complexity
- Review feedback score
This prevents AI from becoming a mindless code churn machine.
Architectural Decisions Should Be Human
AI can suggest useful code, but it shouldn’t dictate software architecture.
Regular architecture reviews help prevent parallel, inconsistent solutions from creeping in and keep the system coherent.
Create a Feedback Loop with AI
Teams can train AI to align with their standards by:
- Refining prompts
- Using AI tools that allow model customization on internal codebases
- Documenting AI-generated mistakes and sharing them across the team
The goal? Make AI work for you, not against you.
Bottom Line
Don’t accept AI-generated code blindly. Keep humans in control, reinforce engineering best practices, and use AI as a tool—not a shortcut.
Conclusion
AI-powered coding assistants are rewriting the rules of software development. In just a short time, we’ve gone from writing every line manually to having smart suggestions and even entire code blocks generated on demand. The productivity boost is undeniable, but with that speed come real challenges around technical debt and code sustainability.
For engineering leaders, the challenge isn’t whether to adopt AI or not—this technology isn’t going anywhere. The real job is figuring out how to get the best out of AI without sacrificing software quality.
That means treating AI like an ultra-fast intern—it can crank out code quickly, but it needs guidance, review, and constant adjustments.
Used responsibly, AI can supercharge productivity without wrecking best practices. But without control, it can turn your codebase into a maze of unnecessary complexity and never-ending rework.
