The Hidden Risks of AI-Generated Code
Whether you are a founder or CTO working with AI-generated code, this article is for you. In Appricotsoft, we have reviewed hundreds of AI-assisted MVPs and full-scale applications, and though AI coding tools like Copilot and ChatGPT speed up things, they consistently generate serious, and often subtle, problems that will hurt later in life.
From security risks to maintainability nightmares, here are the 20 most common problems we keep finding in AI-generated code reviews – and what to do about them.
Introduction: Why AI Code Needs Human Review
AI-powered code tools are powerful accelerators. They can generate boilerplate, scaffold components, and even suggest full implementations. But they don’t understand your business logic, your product context, or future scaling plans. And without proper human oversight, that’s a recipe for trouble.
At Appricotsoft, we believe that building software you can be proud of means delivering code that’s clean, maintainable, and actually solves real problems. That is why our Code Audit Service for AI-generated products is focused on “can it scale, evolve, and stay secure?” rather than just “does it run?”
But what does this AI code audit really include? Is it merely a bug check, or is it more in-depth?
Let’s unpack what a real, professional audit of AI-generated code involves-and why it might be the most important thing you do before launch.
20 Common Issues We Catch During Code Reviews for AI
1. Lack of Understanding of Business Logic
AI is not familiar with your business model. We tend to discover generic code without considering rules for your domain. There is tight coupling. Features will be brittle. Then it’ll require additional work down the line.
2. Over-engineering for Simple Tasks
Machine learning tools tend to provide overly complex code. A function which would have taken 5 lines now swells with unnecessary abstractions.
3. Insecure Authentication and Authorization
“Hardcoded secrets. Incorrectly configured roles. Insecure endpoints. We’ve seen it all – and especially in MVPs that bypassed full security reviews.”
4. Poor Error Handling
The code generated by AI has a natural tendency to avoid bugs or just fail silently.
5. Unscalable Data Structures
From suboptimally indexed queries to in-memory storage used within persistent logic, the path not taken by AI is often what’s easy, not what scales.
An index is a data
6. Missing Logging & Monitoring
Lack of Observability: This is a significant risk associated with AI, as it does not automatically log data unless prompted to do so, and even then, the logging tends to be sporadic.
7. Lack of Comments or Documentation on the Code
You are getting a wall of logic with zero context. Any future developers will have a hard time trying to understand it unless it’s clearly documented.
8. Copy-Paste Reuse Instead of Abstraction
We often find that instead of taking out reusable functions or modules, we come across blocks of code repeated throughout, hence yielding bloated, unmanageable codebases.
9. Unvalidated User Inputs
Basic validation is often missing. This creates serious security holes for web applications or APIs that are exposed to user data.
10. Unit and Integration Tests are Lacking
Tests are either missing or superficial. Very seldom does an AI tool create a testing strategy unless being explicitly asked to do so, but even then, it usually misses important edge cases.
11. Poor Folder and File Structure
Your repo might “work,” but good luck navigating it. We often refactor AI-generated projects just to make them readable.
12. Async/Promises Abuse
AI is prone to mishandling async logic, causing race conditions, unhandled rejections, or blocked event loops.
13. Hardcoded Data for Production Code
Mock data gets left in production. We’ve seen AI-written services send test emails – or worse, store user passwords in plaintext.
14. Unneeded Dependencies
Packages pulled in “just because.” AI models tend to over-install libraries rather than solve things with built-ins.
15. Poor Frontend UX Decisions
from inaccessible forms to unreadable contrast choices, AI just doesn’t think as a designer would. We regularly flag UI/UX issues in React and Vue apps.
16. Tight coupling between modules
Instead of writing loosely coupled services, AI code often binds logic across components, making changes fragile and risky.
17. No Configuration Management
AI forgets environment handling, so code relies on local configs or breaks across dev/staging/prod.
18. No consideration given to edge cases.
The happy path works, but error states, retries, and unusual user behavior? Not handled at all.
19. Tech Stack Mismatch
We find AI-generated components frequently in libraries that are not representative of the rest of the stack, for example, using Tailwind in a Material UI project.
20. No Version Control Hygiene
AI doesn’t write commit messages, structure branches, and respect the PR workflows. That’s all on you.
Why These Mistakes Matter
As for your MVP and scale business, it might seem okay to think, “It works, let’s move on”. But here are the reasons why these issues create compounding problems down the line:
• Small bugs become tech debt
• Vulnerabilities transform into Data Breaches
• Unscalable Architecture Slows Down Your Launch
• Morale suffers when code is difficult to maintain.
All founders who are non-technical or work as the Startup CTO need to have an in-depth knowledge of these issues to productively deliver.
What Founders Should Do About It
Here is our completely honest take as a result of having performed some six hundred reviews of early and growth-phase businesses using AI:
• Invest in a Technical Audit for Startups
Get a professional code audit from expert individuals who have insight into how AI code fails in the real world. For more information on this, refer to the article on “What an AI Code Audit Really Includes.“
• Fix AI-Generated Code Before It Breaks
If you are already using AI to create your MVP, don’t panic, but also start planning a cleanup. Our code refactor service facilitates startups in stabilizing their codebase of AI-generated code quickly.
• Prioritize Security and Testing
Emphasize the Importance of It’s much cheaper to deal with these problems when you’re just starting out than it will be if you do get hacked or if you go down for user traffic.
• Work with a Partner Who’s Done This Before
If possible, it’s always We have experience at Appricotsoft in helping start-ups in Europe, the US, and worldwide convert AI-based prototypes into actual products. We understand what to search for and how to clean it up fast and safely.
Why Founders Love Our AI Code Audits
Our audit process is designed for the non-technical founders and CTOs who are too busy. We don’t just throw jargon at you. Instead, we give clear, actionable insight. We prioritize:
• Real business impact, not just technical nitpicking.
• Fast turnaround times – so you’re not mired in analysis mode.
• Transparency – we tell you what’s critical, what’s nice-to-have, and what can wait.
We have done this for AI tools in finance, health, logistics, e-commerce, and SaaS – be it fixing ChatGPT code problems or scaling a back-end created by Copilot.
Final Musings: AI Code Requires True Engineers
The future of AI will only see it improve – and it won’t be replacing qualified programmers any time soon. Increased use of AI-generated code means the dangers of using such code will only increase too. Founders who focus heavily on code quality will have a huge edge.
You started something with AI coding tools. Want the assurance of pride of workmanship prior to launching to the world at large? Get in touch. We’ll take a look at it as if it belonged to us, because at Appricotsoft, we pride ourselves on one thing: developing software we’re proud of. Let’s talk.