TL;DR 27 sites later, I've learned that AI-generated apps are fantastic for validation—but terrible for scaling. Here's what happens when your MVP gets traction.
The enquiries started trickling in over the last few months: “Can you help fix my Lovable.dev app?” or “I need to migrate my bolt.new prototype to something more robust.” What began as occasional requests has become a steady stream—I’ve now helped fix or migrate over 27 sites created with AI coding platforms.
The pattern is always the same: Someone has a great idea, uses Lovable.dev, v0, bolt.new, or Cursor to build a working prototype in hours instead of weeks, gets some initial traction, and then reality hits.
The Lovable Promise (And Why It’s Seductive)
I’ll admit it: watching Lovable.dev generate a full-stack application from a simple prompt is genuinely impressive. You describe what you want, and within minutes you have a React app with Tailwind CSS, a Supabase backend, authentication, and even deployment ready to go.
For non-technical founders, this is revolutionary. No more waiting weeks for developers or spending thousands on an MVP that might not work. Tools like Lovable and bolt.new have democratized software creation in a way that seemed impossible just two years ago.
The appeal is obvious:
- Speed: Working prototype in days, not months
- Cost: No upfront developer fees
- Iteration: Easy to modify and test new features
- Ownership: You control the generated code
For idea validation and early-stage testing, these platforms are fantastic. I’ve seen brilliant concepts brought to life that might never have existed otherwise.
When Vibe Coding Meets Reality
But here’s what happens when your AI-generated MVP starts gaining real users:
Security Becomes Critical
Recent audits found up to 19% of code suggestions by AI tools contained vulnerabilities.
That innocent-looking user input field? It’s probably vulnerable to injection attacks. The authentication system that “just works”? It might be exposing user data. Studies show that AI-generated code contains security vulnerabilities in up to 36% of cases.
I’ve personally found:
- Hardcoded API keys in client-side code
- Missing input validation on critical forms
- Broken authentication flows that can be bypassed
- Database queries that leak sensitive information
Performance Hits the Wall
AI tools optimize for “working” not “working well.” That beautiful dashboard starts crawling when you have more than 50 users. The database queries become exponentially slower. The frontend bundle is 10MB because the AI included every library it thought might be useful.
Technical Debt Compounds
AI doesn’t refactor—it adds. Every new feature request creates more inconsistencies. Error handling is copy-pasted across components. Business logic is scattered between frontend and backend with no clear separation of concerns.
One client came to me after their Lovable.dev app had grown to handle payments. The AI had created separate Stripe integrations for subscriptions, one-time payments, and refunds—none of which talked to each other. The result was an accounting nightmare that took days to untangle.
The Professional Difference
After fixing dozens of these applications, I’ve identified the key areas where human expertise makes the difference:
Architecture That Scales
AI builds features; humans design systems. Understanding how components interact, where to place business logic, and how to structure code for maintainability requires experience that current AI tools lack.
Security First
Professional developers think in terms of attack vectors and defense layers. We validate inputs, sanitize outputs, implement proper authentication flows, and follow security best practices that aren’t obvious from AI training data.
Performance Optimization
Real-world performance requires understanding how databases work under load, when to implement caching, how to optimize bundle sizes, and when to split monolithic applications into services.
Maintainable Code
The difference between working code and maintainable code is documentation, consistent patterns, proper error handling, and clear separation of concerns—skills that come from years of maintaining production systems.
The Sweet Spot Strategy
I’m not here to bash vibe coding. Many of the applications I’ve helped migrate started as genuinely good ideas that needed rapid validation. The issue isn’t the initial AI generation—it’s knowing when to transition to professional development.
Here’s the pattern I recommend:
- Validation Phase: Use Lovable.dev, bolt.new, or similar tools to build and test your MVP quickly
- Traction Signals: Once you have paying users or consistent growth, it’s time to professionalize
- Migration Strategy: Keep what works, rebuild what doesn’t, and implement proper architecture from day one
The entrepreneurs who succeed with this approach treat AI-generated code as sophisticated prototyping tools, not production platforms.
Real Success Stories
Of the 27+ sites I’ve worked on, the most successful transformations came from founders who:
- Started with AI tools to validate their concept quickly
- Recognized when their prototype was becoming a liability
- Invested in proper architecture before technical debt became overwhelming
One standout example: A SaaS tool built with Lovable.dev that handles document processing. The AI-generated version worked beautifully for the first 100 users but crumbled under load. After rebuilding the backend with proper queuing, caching, and error handling, it now processes thousands of documents daily with 99.9% uptime.
Another: An e-commerce platform that started as a v0 prototype. The founder kept the frontend design (it was genuinely excellent) but rebuilt the backend to handle inventory management, payment processing, and order fulfillment at scale.
When You Need Help
If you’re reading this and thinking “this sounds like my application,” you’re probably at the inflection point where professional development becomes essential.
The warning signs I see most often:
- Users reporting bugs or security concerns
- Performance degrading as usage grows
- Difficulty adding new features without breaking existing ones
- Spending more time on maintenance than new development
Tomorrow’s Tools, Today’s Foundation
AI coding tools will continue improving. Lovable v2 already includes better collaboration features and enhanced debugging. Future versions will likely address many current limitations.
But regardless of how sophisticated AI becomes, the fundamental principles of building maintainable, secure, scalable applications remain human skills. AI accelerates development; experience ensures sustainability.
The most successful startups I work with treat AI as a powerful prototyping tool that gets them to market faster, then invest in proper engineering when they’ve validated their concept and need to scale.
If you’ve built something with Lovable.dev, bolt.new, or similar platforms and are hitting the limits of vibe coding, I can help. From security audits to complete rebuilds, I specialize in transforming AI-generated prototypes into production-ready systems.
Get in touch for a consultation and let’s turn your MVP into something that can handle real success.