Code generation - whether through traditional templates or cutting-edge AI tools - has become a staple in modern software development. It promises rapid prototyping, productivity gains, and automation for repetitive tasks. But as with any powerful tool, there are significant downsides that can undermine these gains if you are not careful.
In this post, we’ll break down the main AI code generation risks that can negatively impact software development. We’ll categorize the risks, explain each in plain language, and provide links to authoritative sources for deeper exploration.
AI Code Quality and Maintainability Issues
The Problem
Generated code often looks like a shortcut, but it can create headaches down the road. AI-generated code, in particular, presents some of the most overlooked risks, such as high “code churn” - that is, code that’s quickly rewritten or discarded, leading to more mistakes and technical debt. The code may be hard to read, filled with unnecessary abstractions, or riddled with special-case logic that’s difficult to maintain.
Why It Matters
When code is opaque or messy, onboarding new developers takes longer, bugs are harder to fix, and your codebase becomes a minefield.
Code Quality Resources
- AI code churn and tech debt (GeekWire)
- Maintenance nightmares (Stack Overflow)
- Debugging and maintainability pitfalls (Lucenia)
Security Vulnerabilities in AI-Generated Code
The Problem
AI and automated tools frequently generate code with security flaws. Studies show that nearly half of AI-generated code suggestions contain vulnerabilities like SQL injection or improper authorization. These tools may also use outdated libraries or insecure patterns, especially if their training data is old.
Why It Matters
Security bugs can be expensive to fix and disastrous if exploited. One of the most serious AI code generation risks is assuming generated code is safe by default.
Code Quality Resources
- Security risks of AI-generated code (ITPro)
- 48% of AI code suggestions vulnerable (TechTarget)
- Cybersecurity risks of AI-generated code (CSET)
- Risks of automated code generation (Veracode)
Loss of Context and Design Integrity
The Problem
Code generators lack a holistic understanding of your project’s goals, constraints, and long-term vision. They can’t account for non-functional requirements like performance, scalability, or compliance, and may produce code that doesn’t fit your architecture or business logic.
Why It Matters
You risk ending up with a patchwork of code that works in isolation but doesn’t play well with the rest of your system.
Code Quality Resources
Performance and Efficiency Issues
The Problem
Generated code often includes unnecessary logic or handles data inefficiently. For example, it might add extra fields to methods like `equals()` and `hashCode()`, which can degrade performance at scale.
Why It Matters
Performance bottlenecks can creep in unnoticed, only to become major issues when your system is under load.
Code Quality Resources
Developer Skill Degradation and Over-Reliance
The Problem
If developers lean too heavily on code generators, they risk losing their edge. Over time, they may become less skilled at writing, debugging, and understanding code. There’s also a tendency to blindly trust generated code, skipping critical reviews and validations.
Why It Matters
A less skilled, less engaged team is less capable of innovating or solving complex problems. This over-reliance is another one of the under-appreciated AI code generation risks that can weaken engineering teams over time.
Code Quality Resources
- Over-reliance and skill loss (Revelo)
- Complacency and disengagement (ITPro)
- Overconfidence in AI code (TechTarget)
Bias, Legal, and Compliance Risks
The Problem
Code generation models can encode and propagate harmful biases from their training data. They might also inadvertently include proprietary or copyrighted code, leading to legal trouble. And since AI doesn’t understand regulations, it can generate code that puts you out of compliance.
Why It Matters
Biases can harm users, and legal or compliance issues can quickly escalate into costly problems.
Code Quality Resources
Systemic AI Code Quality Risks
The Problem
As more AI-generated code enters public repositories, future models may be trained on lower-quality or insecure code, compounding the problem. Widespread adoption of code generation tools may also introduce new classes of vulnerabilities.
Why It Matters
These systemic risks can shift the entire software development landscape, making everyone’s code less secure and reliable. Understanding these long-term AI code generation risks is essential for industry-wide resilience.
Code Quality Resources
Key Takeaways: Use Code Generation Wisely
AI code generation risks are real and growing. Code generation is a powerful ally—but only when used thoughtfully and with full awareness of its limitations. From security vulnerabilities to tech debt, knowing the limits of AI-generated code is critical for long-term code health. To avoid the pitfalls:
- Always review and test generated code.
- Stay vigilant about security and compliance.
- Invest in developer training and codebase literacy.
- Use code generation as a tool, not a crutch.
Want to better manage the risks of AI-assisted development?
Learn how Flux’s AI code evaluation platform surfaces issues in AI-generated code before they hit production.
Ted Julian is the CEO and Co-Founder of Flux, as well as a well-known industry trailblazer, product leader, and investor with over two decades of experience. A market-maker, Ted launched his four previous startups to leadership in categories he defined, resulting in game-changing products that greatly improved technical users' day-to-day processes.
