Why AI-Generated Code is a Risk-especially for Startups
AI tools don’t understand your business logic. They predict code based on patterns, not product goals. That’s great for boilerplate code – but dangerous for anything nuanced.
Most AI-generated bugs don’t show up right away. They pass basic tests and look fine on the surface. But they often introduce subtle issues that explode under real user pressure.
As a custom software development company, we’ve seen the same story play out many times: a founder uses AI to build an MVP, launches quickly, gains traction… and then hits a wall. The code can’t scale. Features break. Refactoring takes longer than building it right in the first place.
That’s where an expert-led AI code audit, or a startup code audit, becomes indispensable.
The Most Common Bugs AI Creates (and How to Spot Them)
AI coding tools are changing the way we build software – fast. Whether you’re using GitHub Copilot, ChatGPT, or other AI-powered assistants, it’s never been easier to go from idea to prototype. But here’s the catch: with speed comes risk.
At Appricotsoft, we’ve worked with founders across Europe and the U.S. who turned to AI for early-stage development – and ended up needing serious code repair before scaling. Why? Because AI-generated code brings its own unique set of problems. In this post, we’ll break down the most common bugs AI tools create, why they happen, and how to catch them before they cause damage.
Whether you’re a startup founder, product owner, or non-technical decision-maker, this guide will help you better understand what’s really happening under the hood of your AI-built MVP – and how to make sure it’s ready for prime time.
The 6 Most Common Bugs in AI-Generated Code
1. Silent Logic Failures
AI code often “looks right” but does the wrong thing. For example, it might calculate totals incorrectly, loop over data with off-by-one errors, or return results that appear plausible but are fundamentally wrong. These are hard to spot without deep code reviews and real-world usage.
How to catch it: Run strict unit tests with edge cases. Have a robust QA process in place. And, finally, have a human developer double-check business logic.
2. Insecure Code Patterns
AI doesn’t always follow security best practices. It might store sensitive data in plain text, skip input validation, or write vulnerable SQL queries.
How to catch it: Run a secure ai code scan using tools like SonarQube or Snyk. Have your code reviewed by a senior developer or request a code audit service from a trusted team.
3. Missing Error Handling
AI-generated code often lacks proper try-catch blocks or fallback mechanisms. It assumes things will work – until they don’t.
How to catch it: Search for unhandled exceptions in logs. Use monitoring tools like Sentry or Rollbar to surface runtime errors early.
4. Redundant or Dead Code Overuse
Because AI tries to predict what you might want, it often includes unused functions, repeated logic, or unnecessary complexity. This bloats the codebase and increases technical debt.
How to catch it: Use static code analysis tools. Run a code quality audit or AI-generated code review and clean up redundancies.
5. Poor Scalability Architecture
AI can write code, but it doesn’t design systems. That means database queries that don’t scale, data structures that collapse under load, and architecture that isn’t modular.
How to catch it: Stress-test the app. Evaluate performance under load. And bring in a team that knows how to build scalable backend architecture – like we do at Appricotsoft.
6. Abuse of Third-Party Libraries
AI sometimes pulls in dependencies you don’t need – or uses them incorrectly. This leads to bloated apps, license conflicts, and potential security risks.
How to catch it: Review your package.json or requirements.txt carefully. Remove unnecessary dependencies and validate usage against documentation.
Real-World Example: When an AI MVP Breaks
We recently worked with a founder who used AI tools to build an MVP for a logistics platform. The code launched fast and functioned well in demos – but users started reporting errors when syncing shipments. After a deep dive, we discovered:
• Hardcoded logic that assumed certain date/time formats broke in different time zones.
• API retries were missing error thresholds, leading to cascading failures.
• A third-party module that hadn’t been updated for two years.
What looked like “minor bugs” turned out to be deep structural flaws. We had to refactor the app before they could onboard more clients.
This kind of situation is exactly why we offer a technical audit for startups and ai mvp development advisory. Founders need more than working code – they need confidence in how that code behaves in the real world.
How to Spot AI-Created Bugs-Even If You're Not Technical
You don’t have to be a developer to ask the right questions. Here is a checklist to use when evaluating your AI-generated code or outsourced MVP:
✔ Does the app behave consistently with real user data?
✔ Automated tests are available, with high test coverage?
✔ Is error handling visible and logged?
✔ Are dependencies up to date?
✔ Does the code undergo a review by a professional?
If you answered “no” to any of these, it’s time to consider a professional code review service.
What Founders Should Do Differently
If you’re building an MVP using AI tools or developers who use them-here’s how to protect your investment:
Don’t skip the audit – get a software audit service before launch/investor demo.
Think beyond the demo – Ask “what happens at 10x users?” or “what if the API fails?”
Work with a team that’s seen this before: At Appricotsoft, we specialize in startup code audits and scalable architecture. We’ve helped companies avoid AI-driven pitfalls – and turn quick builds into solid products.
Building Safe AI-Powered Software
AI tools are powerful-but they’re not replacements for senior engineers, product thinking, or quality assurance. As a founder, your job isn’t just to build fast. It’s to build right.
If you are unsure about your AI-generated MVP being safe for scaling, let’s talk. At Appricotsoft, we make AI development safe and sustainable by pairing automation with expert oversight. That’s how we help founders go from risky code to reliable platforms.
Ready for an AI code review? Learn more about how we help here: appricotsoft.com/blog
Related Reading
Conclusion
AI can help you build fast, but if left unsupervised, it will also bury bugs deep inside your product’s DNA. Silent logic errors, security gaps, and scalability traps are common in AI-generated MVPs. Don’t wait until customers are frustrated or funding is at risk. A simple AI code audit now saves months of expensive rework later on. Here at Appricotsoft, we help founders turn AI experiments into launch-ready products-with speed, safety, and scalability in mind. Let’s talk