Technical Debt: Is AI-generated code increasing maintenance burden?

Introduction

Large language models (LLMs) are transforming the software development process. Microsoft reports that up to 30% of their code is AI-generated, and Google claims over 25% of its new code comes from AI. Some commentators have wondered how much of this code reaches production where it must be reliable and maintainable? This question is critical, as it ties directly to the quality of AI-generated code and its potential to create technical debt – the future cost of fixing poorly written code.

In this article I explore ways to ensure that all software written by AI coding assistants is good enough to be released to production along with a framework you might consider adopting.

What is maintainable code?

I've always told my developers they're not paid to write code; they're paid to ship software. Code has no value unless it's used so generating vast amounts of code with LLMs may sound impressive, but does it lead to usable, high-quality software? The answer depends on whether AI-generated code is maintainable over time.

Let's first understand at a high level what we mean by maintainable code because there's several aspects to this. We can say that maintainable code is code that is easy to read and understand. This requires meaningful variable and function/method names, consistent formatting, and minimal complexity. It should contain useful comments to explain blocks of code, be modular, and testable. However, the reason for this is to ensure changes can be made without breaking the existing code or requiring major changes.

In addition, we need to ensure that the code adheres to best security practices and doesn't contain vulnerabilities. It should also execute efficiently and handle errors gracefully. Writing reusable code is important to ensure consistency of future code because the more code that's reused the less code that needs to be written. This is not an exhaustive list and there are many good books and articles that focus on this subject.

As any good software developer should know there's a difference between code that works and code that's maintainable. Ideally, code is both functional and easy to update but in practice even human-written code often falls short. I've seen it far too often that quality is often the first casualty when there's continuous pressure in organisations to ship software. If human developers struggle to write maintainable code then why should we expect LLMs to do better? At least AI wouldn't be making things worse in this scenario but I think AI is an opportunity to make things much better.

Code Review as Bottlenecks

The traditional antidote to creating unmaintainable code is the code review, often conducted via pull requests before the feature branch is merged to the main code base. Only at this point is the entirety of the solution being developed available and can be judged in the context of the existing application. Whether the project is following agile or waterfall, code will still need to be merged and so this is the optimum point to ensure the code doesn't contribute to technical debt.

But reviewing AI-generated code poses a challenge for the senior developer: LLMs produce code so quickly that they may struggle to keep up. Thorough reviews require time and expertise, which can erode the productivity gains of using AI coding assistants. Some developers even report lower productivity when using AI tools, likely due to the time spent debugging or reviewing AI-generated code.

So how, as developers, do we ensure AI-generated code is maintainable without reading every line? There are many commercial code quality and security tools available to help us in this endeavor like SonarQube, CodeClimate, Snyk and others. However these tools flag potential problems but someone still needs to check the issues. Furthermore more code generated means potentially more false positives all of which need verifying.

The LLM Reviewer

One solution is to fight fire with fire and use LLMs to review AI-generated code. A reviewing LLM could assess code for readability, modularity, and adherence to coding standards, flagging issues for senior developers to verify. We could rely on the model's good judgement here along with a carefully worded prompt but documenting this in markdown documents would be preferable. These documents could easily be consumed by the LLM while also easily read and maintained by human developers. The new code being reviewed would also need to align with architectural or design considerations and again this should be provided in documentation or perhaps a deepwiki (e.g. a project-specific knowledge base).

However, is this any better than using the code quality and security tools mentioned above where someone still needs to check any issues that get flagged because it's vital to keep humans in the loop? Yes for two reasons. Firstly, we should expect many fewer false positives because the AI reviewer is "smarter" and should only flag real problems that need resolving. Secondly, if the AI reviewer does discover an issue then it can suggest the solution. Both of these should vastly reduce the time required by the human developer in the review process.

This LLM review stage could be built into the DevOps workflow providing the senior developer the information needed before they approve a pull request and merge the code. Note that this approach has limitations because LLMs may misjudge project-specific context or long-term maintainability as they lack guardrails that prevent hallucinations. However these can be overcome by using a fine-tuned LLM trained on your code base, your coding guidelines and specific documentation rather than using the general purpose LLMs used by the AI coding assistants.

As AI coding assistants become ubiquitous, the industry must develop robust tools and practices to balance productivity with quality. Developers and organizations should experiment with LLM-based reviews and share best practices to ensure AI-generated code doesn't pile up technical debt, paving the way for sustainable software development.

Avoiding AI-Generated Technical Debt

Below is a framework summarising the points above you can use in your development organisation.

1. Assessment Phase

Before adopting AI-generated code at scale, establish a baseline of risks and expectations.

• Code Scope Analysis

  • Define what types of code can safely be AI-generated (e.g., utility functions, boilerplate vs. core business logic).
  • Identify high-risk areas (security-sensitive, performance-critical, regulatory-compliant).

• Technical Debt Risk Profiling

  • Evaluate likelihood of AI code creating long-term maintenance issues.
  • Score areas on dimensions such as: complexity, business criticality, change frequency.

• Baseline Metrics

  • Capture current defect rates, code review times, and maintainability scores using code quality and security tools.
  • These become benchmarks for evaluating AI impact.

2. AI Code Production Controls

Set clear rules for how AI-generated code enters the codebase.

• Guardrails for Generation

  • Require AI outputs to follow project-specific coding standards (via prompts and config).
  • Limit AI generation for business-critical logic unless explicitly approved.

• Documentation by Design

  • Require AI outputs to include inline comments and rationale (ideally LLM-assisted summaries).
  • Store AI prompts and responses for traceability.

• Ownership Principle

  • A human developer is always accountable for AI code quality.
  • Encourage developers to think of AI as a "junior pair programmer" that needs oversight.

3. Multi-Layer Review Workflow

Balance human oversight with automation to avoid bottlenecks.

• LLM Reviewer

  • Deploy an LLM-based review agent in CI/CD.
  • Configure it to:
    • Check readability, modularity, adherence to standards and security.
    • Cross-reference against architectural docs or design rules.
    • Provide fix suggestions alongside flagged issues.

• Human-in-the-Loop

  • Senior developer reviews flagged issues and AI suggestions.
  • Focus on business logic and architectural fit, not line-by-line syntax.

4. Feedback & Continuous Improvement

Treat AI-assisted coding as an evolving system, not a static process.

• Feedback Loops

  • Collect developer feedback on AI usefulness, false positives, and review burden.
  • Measure review efficiency (time to approve PRs, defect escape rate).

• Prompt & Model Refinement

  • Continuously improve LLM prompts based on observed gaps.
  • Maintain project-specific "deepwiki" of design principles for the LLM reviewer.

• Quality Metrics Dashboard

  • Track maintainability index, defect density, mean time to fix (average time to resolve defects), and rework rate for AI-generated vs. human code.
  • Share with leadership to assess ROI and risk trends.

5. Governance & Risk Management

Ensure AI coding practices are aligned with organisational and industry standards.

• Policy Enforcement

  • Define thresholds for when AI-generated code requires additional scrutiny.
  • Document approval flows for merging AI code into production.

• Knowledge Retention

  • Store AI prompts, review summaries, and decisions in a searchable repository.
  • Reduce "black box" risk (where processes lack transparency) by making decisions auditable.

• Escalation Path

  • Define when senior engineers, security officers, or architects must intervene (e.g., critical system modules).

6. Cultural Alignment

Embed AI use into team culture without undermining engineering discipline.

• Mindset Training

  • Train developers to see AI as an accelerator, not a substitute for design thinking.
  • Reinforce that shipping maintainable software is the goal, not writing more code.

• Reward Quality, Not Quantity

  • Adjust performance metrics: reward maintainability improvements, reduced review burden, and sustainable velocity.
  • Avoid incentivising "lines of AI-generated code."

Summary Playbook

A project manager or tech lead can apply this framework as:

1. Assess → Define risks, baseline metrics.
2. Control → Set rules for AI code generation.
3. Review → Layer static tools, LLM reviewers, and human oversight.
4. Feedback → Continuously improve prompts, practices, and dashboards.
5. Govern → Enforce policies and auditability.
6. Align → Build culture around sustainable, maintainable AI use.

Executive Framework: Managing AI-Generated Technical Debt

Conclusion

The rise of AI-generated code presents both opportunities and challenges for software development teams. While LLMs can dramatically accelerate code production, the risk of accumulating technical debt requires a thoughtful, systematic approach to quality assurance.

By implementing the framework outlined above, organizations can harness the productivity benefits of AI coding assistants while maintaining code quality standards. The key is not to view AI as a replacement for human judgment, but as a powerful tool that requires appropriate guardrails and governance.

As the industry continues to evolve, those who successfully balance AI productivity with code maintainability will gain a significant competitive advantage. The investment in proper AI code governance today will pay dividends in reduced technical debt, faster delivery cycles, and more sustainable software development practices tomorrow.