Vibe Coding: Great for Prototypes, Terrible for Production
I've been watching the AI coding hype cycle for a while now, and I keep seeing the same pattern: people get incredibly excited about building apps with AI, create some impressive demos, then hit a wall when trying to build something real.
Don't get me wrong - AI coding tools are genuinely useful. I've used them to prototype ideas faster than ever before. But there's a huge gap between "wow, this works" and "this is ready for paying customers."
Where AI Actually Shines
AI coding tools are fantastic for certain things:
Getting started quickly. Need a basic UI layout? Want to test an idea? AI can scaffold something functional in minutes instead of hours. This is genuinely game-changing for early validation.
Handling boilerplate. Those repetitive chunks of code that every developer dreads writing? AI cranks them out without complaining.
Rapid iteration. When you're in the early stages and just want to see how something might look or work, AI's speed is unmatched.
The problem is that this initial success is deceiving. You end up with something that looks professional and works well enough to demo, which makes you think you're almost done. You're not.
The Production Reality Check
Here's what I've learned from watching teams (including my own) hit this wall: AI-generated code becomes a nightmare when you need to build the less glamorous but absolutely essential parts of a real product.
User management isn't just a login form. You need secure authentication, password resets, account verification, role-based permissions, and all the edge cases that come with managing real users at scale.
Billing is complex. Integrating payment systems, handling subscription changes, managing failed payments, calculating prorated charges, generating invoices - none of this is straightforward, and AI tends to create oversimplified solutions that break in production.
Everything needs to work together. This is probably the biggest issue. AI generates isolated pieces of code that often don't integrate well with each other. You spend more time fixing connections and refactoring than you would have spent just building it properly from the start.
Performance and scalability matter. AI doesn't think about what happens when 1,000 users hit your app simultaneously. It doesn't consider database optimization, caching strategies, or graceful error handling.
The Maintenance Trap
Even if you get past the initial integration challenges, there's a deeper problem: AI-generated code is often hard to maintain. When you need to add a new feature or fix a bug, you're working with code that doesn't follow consistent patterns or architectural principles.
I've seen teams get stuck in what I call the "AI debt spiral" - every new feature requires untangling and rewriting existing AI-generated code, which makes progress increasingly slow and frustrating.
How to Actually Use AI for Development
Despite these limitations, AI can be incredibly valuable if you use it correctly:
Start with clear requirements. Before generating any code, write detailed specifications for what you're building. Use AI to help refine these requirements, but don't skip this step.
Think of AI as a junior developer. It can handle specific tasks well, but it needs guidance and review. Never assume AI-generated code is ready for production without careful examination.
Focus on the right problems. Use AI for prototyping, generating boilerplate, and handling well-defined, isolated tasks. Don't expect it to architect your entire system.
Learn to debug. AI will produce incorrect code. If you can't identify and fix these issues, you'll be stuck. There's no substitute for understanding how the code actually works.
The Skills That Still Matter
The most successful AI-assisted development I've seen comes from people who already understand software architecture, database design, security principles, and system integration. AI amplifies their existing skills rather than replacing them.
If you're new to development, don't assume AI can substitute for learning the fundamentals. You still need to understand:
How to design scalable systems
Security best practices
Database optimization
Testing strategies
Performance considerations
The Bottom Line
AI coding tools are genuinely useful, but they're not magic. They excel at rapid prototyping and handling routine tasks, but they struggle with the complex, interconnected challenges of building production software.
The real opportunity isn't in replacing developers with AI - it's in helping skilled developers work more efficiently. Use AI to handle the tedious parts so you can focus on the strategic decisions that actually matter.
If you're building something serious, invest in proper technical expertise. Use AI as a tool to accelerate development, not as a replacement for understanding what you're building.
The hype around AI coding is understandable, but the reality is more nuanced. Embrace the tools for what they do well, but don't expect them to solve problems they're not designed to handle.