AI-Generated MVPs: 7 Critical Mistakes That Undermine 90% of Them

Startup founders are using AI coding tools to build MVPs in just days, not months. These tools offer faster development and lower costs. But our technical audits reveal that most AI-generated apps have recurring issues that put scalability, security, and long-term success at risk. 

Over the last 18 months, our team has reviewed more than 50 MVPs created with AI coding assistants such as Claude, Cursor, ChatGPT, and v0. While these tools let some founders launch working products in under two weeks, the code quality often falls short of production standards. 

The problem isn’t with AI tools themselves. Everything has its flaws. The issue is that these tools focus more on immediate functionality than on sustainable design. In this article, we will explore seven common mistakes that compromise 90% of AI-generated MVPs and offer practical advice for creating production-ready applications that reach profitability.  

Why Code Quality Is Important Not Just for Demo

AI-generated MVPs are great for testing ideas and seeing if there’s real demand. Founders use them to try out concepts, attract early users, and get their first clients. But moving from a proof-of-concept to a scalable product often reveals major technical limits. 

Our audits often find serious gaps that don’t show up in early development: 

• Security flaws that make user data vulnerable 

• Bottlenecks in performance that appear under load   

• Architectural limitations that hinder new feature development 

• Code structure that’s preventing developers onboarding into the project  

These hidden problems can quickly become major crises if an MVP suddenly takes off. The technical base becomes a weakness, production issues rise, and the user experience suffers. 

The financial impact is bigger than just quick fixes. Investors might notice these problems, which can slow or stop funding. Dev teams will struggle and leave if they keep dealing with messy code. What looked like a fast start can quickly become a pile of tech debt, lost team morale, and shrinking costs. 

Understanding AI Code Generation Limitations

AI coding tools tend to follow set patterns. They’re designed to get the job done, usually by writing code that works in a development environment. This is fine for building a prototype, but when you use that code in real situations, problems do show up. 

The main problem is that AI doesn’t plan ahead. It can’t see the bigger picture or predict what might go wrong months later. When it generates database queries, authentication, or app structure, it focuses on making the ideal scenario work. It gives little attention to edge cases, security, or scalability. 

We’ve spent a lot of time looking at what these tools create, and the same problems keep coming up. These aren’t just random mistakes or rare bugs. They’re built into how large language models write code. Once you know these patterns, it’s much easier to use AI coding tools without repeating the same mistakes.

Seven Big Mistakes AI Code Makes

1. Monolithic Component Architecture

What we see:  

AI tools churn out giant components – especially in React. You’ll find files that are 2,000 lines long, stuffed with data fetching, business logic, UI, form validation, error handling, etc. It’s the opposite of clean separation.    

Technical impact:  

If you change one part, you might break something else. You can’t reuse the code, so you must manually copy it in many places. The app also slows down because the whole component updates whenever anything changes. 

How to fix it:  

Break things up. Pull out data fetching into custom hooks, separate business logic into utility modules, and create presentation components for UI rendering.  

Approximately 84% of the apps had at least one component longer than 1,000 lines. 

2. Zero Database Optimization

What we see:  

AI-generated schemas usually skip performance improvements. There are no custom indexes or optimized queries, just the basics to make things work. This is fine with a small dataset, but it fails when your app gets real users. 

Technical impact:  

Database performance gets worse as data grows. Apps with 1,000 records respond quickly, but the same queries with 100,000 records can time out. This problem often appears when startups reach 10,000 to 50,000 users, just as they start to grow. 

Query performance data:

How to fix it:

Add indexes to all your foreign keys and any columns you query often. For common filters, use composite indexes. Regularly analyze your queries, so you spot slowdowns before they become an issue.   

Approximately 92% of apps had no custom indexes, just the default primary keys. 

3. Security Credential Exposure

What we observed:  

AI-generated code routinely exposes sensitive credentials in client-side JavaScript. API keys, database URLs, and third-party service tokens appear directly in frontend code, visible to anyone who views the application source.  

Technical impact:  

This isn’t a small mistake. We’ve seen:   

• OpenAI API keys run up $12,000+ in unauthorized charges in two days   

• Stripe secret keys used for fake payments   

• Database credentials leaked, leading to total data loss   

• Third-party APIs getting abused until accounts receive a ban 

The financial and reputational consequences are severe. One startup lost its entire Postgres database because a bot found the credentials in its front-end code. It’s that easy.  

Resolution approach:  

Design a proper client-server architecture. Keep sensitive operations on the server, locked up in environment variables. If you need to access something from the client, go through a secure API route or serverless function.  

At least 68% of apps leaked at least one critical credential in their frontend code. 

4. Inadequate Error Management

What we see:  

AI-generated code almost always assumes everything goes right. If there’s error handling at all, it’s usually just a try-catch block with no useful logging, no real user feedback, and no recovery plan. Most apps skip error boundaries and don’t handle broken network calls or bad API responses.   

Technical impact:  

Even minor issues can crash entire pages. A slow network or a single bad response can wipe out the user experience. And since there’s no logging or diagnostics, tracking down what went wrong is a nightmare.  

When problems happen in production, debugging becomes a guessing game. If you don’t track errors, your team won’t know what broke, when it happened, or who it affected. You’re left in the dark and can’t see how often issues occur or how serious they are, so you can’t prioritize fixes. 

Here’s the kind of mess you run into with this pattern:  

• Network timeout – app crashes completely  

• 404 response – JSON parsing fails  

• Data.profile is null – runtime exception  

• Settings undefined – application freeze  

How to fix it:  

Implement layered error handling at the network, component, and application levels. Add error boundaries to contain failures. Plug in production monitoring tools like Sentry or LogRocket, so you actually know what’s going wrong. And when you show users an error, give them a way forward – don’t just leave them stranded.  

A little over 76% of apps didn’t have solid error handling in place. 

5. Systematic Code Duplication

What we observed:  

AI tools spit out the same logic in response to different prompts, instead of using what’s already there. We see the same function copied and pasted into five or ten files, with tiny tweaks that end up causing different behavior. It’s a maintenance nightmare. Every time you find a bug, you have to fix it in a bunch of spots.   

Technical impact:  

When you add a new feature, you end up updating several versions of the same code. Some copies get fixed, others don’t, and the app starts to behave differently in different places. The codebase grows too large, which hurts performance and increases bundle size. 

The codebase gets bigger than it needs to be, which affects bundle size and app performance. It’s also harder for new developers to figure out which version of the duplicated code is the right one. 

How to fix it:  

Pull out shared logic into central utility modules. Set up clear rules for organizing code, so people don’t duplicate things. And don’t skip code reviews – catch redundant code before it makes it into the main branch.  

Slightly less than 88% of apps had significant code duplication (the same logic in at least three places). 

6. Missing Input Validation

What we observed:  

AI-generated code often assumes all user input is correct and safe. When there is validation, it usually only checks for types and ignores security, edge cases, or data integrity. 

Technical impact:  

Unvalidated inputs create multiple vulnerability classes:  

• SQL injection vulnerabilities enabling database manipulation  

• Cross-site scripting (XSS) attacks compromising user sessions  

• Business logic bypasses through unexpected input values  

• Data corruption from malformed entries  

• Resource exhaustion from oversized inputs  

How to fix it:  

You need to validate inputs everywhere – on the frontend for user experience, on the backend for security. Use schema validation libraries like Zod or Yup to set clear rules for input. Always use parameterized queries or reliable ORMs to block SQL injection.  

Approximately 94% of apps didn’t validate inputs properly on critical endpoints. 

7. Absence of Observability

What we observed:  

AI-generated applications lack monitoring, logging, and diagnostic instrumentation. If a problem shows up in production, the team only hears about it when a user complains (if they even bother to complain). A lot of issues just slip through the cracks, quietly ruining the user experience.  

Technical impact:  

Without observability, teams can’t:   

• Spot which features are most error-prone  

• See how the app performs for real users   

• Catch security incidents or abuse   

• Prioritize what to fix based on real impact   

• Confirm that a bug fix actually worked out   

For example, we found a case where a bug affected 30% of users for three weeks. The team only learned about it when someone tweeted about the problem. 

Resolution approach:  

Implement comprehensive observability from day one.  

Recommended observability stack for MVPs:  

• Error tracking: Sentry (free tier: 5,000 errors/month)  

• Analytics: PostHog or Plausible (generous free tiers)  

• Uptime monitoring: UptimeRobot (free for 50 monitors)  

• Application logs: CloudWatch or similar platform logging  

At least 82% of applications had no error tracking or monitoring infrastructure.  

Common Pattern: Demo-Ready vs. Production-Ready Code

We’ve seen a clear pattern in every audit: AI coding tools are made to look good in demos, but they don’t hold up in real-world use. These tools are good at producing code that compiles easily, shows off main features, deploys to test environments, works with expected inputs, and handles small datasets (a few hundred or maybe a thousand records). 

In a demo, the code looks great. But when you move it to production, things fall apart. The code doesn’t scale. If you add 100,000 users or millions of records, it fails. It can’t handle edge cases or unexpected errors. Security problems appear. Other developers have trouble maintaining or improving the project. And when something goes wrong in production, you have no way to see what’s happening.   

This isn’t just a temporary glitch. It’s baked into how large language models approach coding. If you get this, you can actually use AI tools better – just don’t expect them to do all the heavy lifting for you. 

Exoft’s Approach to AI-Accelerated Development

We use AI coding tools to speed up development and keep our work at a high quality. Our process blends the efficiency of AI with the skills of our team. 

Technical Review Framework  

We carefully review every component created with AI. 

1. Architecture verification: Ensure proper separation of concerns and scalability;  
2. Security assessment: Identify and remediate vulnerabilities;  
3. Performance analysis: Test under realistic load conditions;  
4. Code quality audit: Verify maintainability and extensibility.  

Development Process Integration  

We include AI tools as part of our full development process. 

• Requirements definition with quality criteria  

• AI-accelerated initial implementation  

• Automated testing and quality checks  

• Professional code review and refactoring  

• Comprehensive documentation  

• Production monitoring and instrumentation  

With this approach, you get the speed of AI development and results that are ready for production. 

Conclusion  

AI coding tools have made it much faster to build and launch MVPs. Founders can turn ideas into working products more quickly than before. However, moving fast does not always mean a product is ready to grow. 

After reviewing over 50 AI-generated MVPs, we noticed a common trend. Most apps worked well in demos and early tests, but had structural problems that made them unfit for production. We found seven issues in almost 90% of the systems: monolithic components, missing database optimization, exposed credentials, weak error handling, duplicated logic, lack of input validation, and little or no observability. 

The answer is not to stop using these tools, but to use them carefully. Setting clear requirements, doing structured code reviews, adding basic quality checks, and planning for technical debt early all make a real difference. 

If you are building an MVP with AI tools or want to check the quality of an AI-generated app, reach out to Exoft. We offer full technical audits, find key risks, and make improvements so your product is ready to grow.