Let me say something that might surprise you coming from a developer: vibe coding actually works, and I mean that genuinely.
You open Cursor or Bolt or whatever tool you prefer, describe what you want, and something that looks like a real product appears on your screen. For prototypes, for validating an idea, for showing an investor something tangible before you've committed to a full build - it's genuinely useful, and the speed is hard to argue with. The barrier to building something that looks like a product has never been lower, and that's a good thing.
But there's a gap that doesn't get talked about enough in all the excitement, and it's the gap between something that looks like a product and something that actually is one.
What AI doesn't know about your business
When you vibe code something, the AI is making hundreds of small decisions on your behalf - how data is structured, how authentication works, how different parts of the system communicate, how errors are handled when something breaks at 2am with real users on the platform. It makes those decisions based on patterns from across the internet, not based on your specific situation. Not based on what happens when your user base grows from 50 to 5,000. Not based on the compliance requirements of your industry, or what your next feature will need from the architecture you're building today. The result is code that works in the way a sketch works - it captures the idea, it's fast to produce, and it's enormously useful as a starting point. What it isn't is a foundation that someone thought through carefully with your specific product in mind.
Where things tend to break down
The problems with vibe-coded projects almost never show up while you're building. They show up later, and they tend to cluster in the same places.
Security is the one that worries me most, because it's invisible until it isn't. AI-generated code is often technically functional but leaves gaps that aren't obvious from the outside, authentication logic that handles the happy path but fails under edge cases, endpoints that are accessible when they shouldn't be, input handling that works fine until someone tests it deliberately. You won't catch these by clicking around the interface.
Architecture is where the second version of anything gets expensive. The first version is always easy to build, the hard question is whether what you've built can grow. When you need to add a new module six months from now, or support a new user role, or integrate with a system you didn't anticipate, that's when you find out whether the foundation was designed to expand or just designed to work right now.
Performance is the one founders tend to underestimate because a prototype with ten users feels fast. A production system with ten thousand is a different problem entirely, and database queries that run fine in development have a way of becoming serious bottlenecks once there's real load on the system.
And then there's maintainability, which is the quiet one that shows up last but costs the most. Code that nobody fully understands - not you, not the next developer who joins, sometimes not even the AI that wrote it - becomes a liability the moment anything needs to change. And something always needs to change.
What you actually need
I want to be clear that none of this is an argument against using AI tools, we use them ourselves, and they save genuine time on genuine tasks. The argument is about understanding what they're good at and what they're not.
AI is good at generating code that fits a pattern. What it can't do is understand tradeoffs, weigh your business requirements against your technical constraints, or tell you when a simpler approach would serve you better in the long run. It has no way of knowing whether the decision it just made will cost you in six months. It can't look at what's been built and reason about what it will take to maintain, extend, or hand off to someone else.
That judgment is what a developer brings to a project, not just the ability to write code, but the ability to make decisions about code. To look at what exists and understand what it will take to make it real. To ask the questions that don't come up when you're moving fast and everything seems to be working.
Vibe coding lowers the cost of starting something. It doesn't lower the cost of finishing it, and it doesn't lower the cost of fixing something that was built without that kind of thinking in the loop.
The projects that come to us
We increasingly hear from founders who built something with AI, got it to a point where it mostly works, and now need to take it somewhere real - a proper launch, a product that actual users will depend on, something that needs to hold up when it matters. Sometimes the foundation is solid and we can build on it directly. Sometimes we have to have a harder conversation about what it would take to get there.
The ones who built with AI and had a developer involved from the start, even just to review key decisions, to ask the questions the AI won't think to ask, almost always end up in a better position when that moment comes. Not because the AI did less, but because someone was there to understand what it was doing.
That's not an argument against vibe coding. It's an argument for knowing what it is: a powerful tool that works best when someone who actually understands software is still in the room.
If you're at the point where your vibe-coded project needs to become a real product or if you want to build something properly from the start - that's the conversation we have every day at CodeIT Dynamics.