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Vibe coding works. No question. Tools like Claude, Cursor and GitHub Copilot can get a working app off the ground faster than anything we’ve seen in nearly 20 years of building software. And for the right problem, that speed is genuinely useful. The real debate is what happens next. Vibe coding limitations don’t show up at launch. They appear, often inconveniently, six months later, when someone wants to add a feature, hand it to a new developer, or scale it past the handful of users who were using it on day one. That’s where the conversation gets more complicated, and it’s one we’re having with clients more often.
Speed over longevity is the typical trade-off of vibe coding. If you need a low-stakes internal tool, a prototype to test an idea, or a quick dashboard that only a few people will use, a fast AI-assisted build can be the right call. The code ships fast, the problem gets solved, and you didn’t spend three months and a significant budget to get there. For simple applications especially, that trade-off often makes sense. Simple tools are re-creatable and low cost to rebuild. The cost of overengineering something that didn’t need it is real, too.
How do you know when vibe coding will suffice? The test we use goes like this: if this broke tomorrow, would you be comfortable rewriting it from scratch? If the answer is yes, build it fast and move on. If the answer is no, it needs more engineering behind it.
The ceiling isn’t in what the tool can generate but in what gets left out. For example, vibe-coded applications tend to solve for the immediate problem in front of them. They’re written to make the current use case work, not to solve for the next 10 use cases. That means limited reusability, limited documentation, and architecture that can be difficult to reason through when something goes wrong.
Our engineers have seen this pattern repeatedly when clients come to us with existing AI-generated code. The code works, but it does so in ways that are hard to follow. The cost of that opacity shows up like a deer on a darkened road when you need to extend the application, fix it under pressure, or hand it to someone who didn’t build it in the first place.
Language choices matter here, too. JavaScript is a common output from AI coding tools, and that’s fine for a lot of things. But it’s not always the right choice for performance, scalability, or long-term maintainability, especially when you’re building something that needs to hold up under real load or integrate with enterprise systems. The language the tool uses by default isn’t always right one for the job.
Debugging an AI-generated codebase takes longer than debugging code a senior engineer wrote with intention for a few reasons. The logic paths are less predictable, which means the decisions are harder to trace. When something breaks at an inconvenient time, the time it takes to find the problem and fix it is real cost, even if nobody budgeted for it.
There’s also the issue of ongoing support. Any software you build, you own. That means you get up in the middle of the night to debug the thing that broke. Updates, infrastructure, and the eventual moment when the person who knows the system best moves on – that all becomes your problem. Vibe-coded applications are no different. They just tend to come with less of the documentation and architecture that make support manageable over time. And let’s be real: ongoing support is not always top of mind when you’re making magic happen with the latest AI model.
This is part of why we treat quick builds as one specific option in a broader set of choices and not a default approach. The simpler the application, the more that trade-off makes sense. The more complex it gets, and the longer you plan to depend on it, the more the equation shifts.
One of the more common questions we hear when a client brings us an existing codebase is some version of “the app is already built, so why does this cost what it costs?”. It’s a fair question that deserves a straight answer. Reading code you didn’t write takes time. We know this because we frequently take over existing systems for continued augmentation, maintenance, and yes, sometimes a rebuild.
Reading AI-generated code takes even more time. Before a senior engineer can add to something, fix something, or make a judgment call about what to keep and what to replace, they must understand what they’re working with. That means tracing logic that wasn’t written with the next developer in mind and identifying decisions that weren’t documented. The app being built already doesn’t reduce that work. In fact, in some cases, the work increases. What you’re paying for is the time it takes to understand your codebase well enough to make it better without breaking what’s working.
If any of these situations sound familiar, it’s worth having a conversation:
In some cases, the right answer is a targeted modernization. In others, a clean rebuild is faster and cheaper over 12 months than continuing to work around the limitations. We’ll tell you which one we think applies before committing to an approach.
Vibe coding is one expression of a broader question that runs through this whole series: when does AI help, and when does it create more work you didn’t plan for?
The answer is the same whether you’re talking about a quick AI-assisted build or an AI feature inside a larger product. AI is a tool, a truly useful one in the right situation. But it doesn’t replace the judgment that goes into building software that’s meant to last, scale, and be maintained by someone other than the person who first had the idea.
That judgment is what a good engineering team brings. And it’s usually cheaper and easier to bring it in at the start than to retrofit it later.
Vibe coding is a real capability change. We use it at Volano, and we’re not here to tell you it’s a bad idea. What we are here to remind you of is that the speed of the initial build isn’t the whole story. The full cost of any software includes what you pay to develop it, maintain it, extend it, fix it, and eventually replace it. Vibe coding can make the first part faster, but that approach doesn’t change the rest.
Need help figuring out what you’ve got and where it goes from here? We’re happy to take a look.
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