Vibe Coding, AI, and the Illusion of “Instant Software”

What Is Vibe Coding, and Why Does It Matter for Your Business?

If you’ve been hearing more about AI in business conversations lately, you’re not imagining it. Artificial intelligence, the technology behind tools like ChatGPT, Copilot, and similar platforms, has moved from a tech industry curiosity into something businesses across every sector are now actively exploring.

One of the more recent developments coming out of that shift is something called vibe coding. The term was coined in early 2025 by AI researcher Andrej Karpathy, and it describes a way of building software that looks very different from traditional development. Instead of writing structured code line by line, which is how code is traditionally created, a person describes what they want in plain language and an AI tool builds it.

For example, you might say: “I need an internal tool that tracks customer orders and sends a follow-up email after three days.” There are third party tools that already do exactly that but you choose to do it yourself, maybe to save money or because the off the shelf product has too many things you don’t need. So, the AI generates the software. You review it, adjust it, and repeat until it works. No formal programming background required.

The appeal is obvious. A business owner, with no formal experience in development, can move from idea to working tool in hours instead of weeks. Marketing teams can build internal dashboards or automation without waiting on a development queue. Early momentum feels real and exciting, and in many cases it is. Research supports this: AI-assisted development is genuinely accelerating timelines, and some funded startups are now reporting that 95% or more of their codebase is AI-generated.

But speed has a way of hiding what’s underneath.

What Can Go Wrong and Why It Doesn’t Show Up Right Away

The structure problem

AI-generated code is typically built to solve the immediate request. It doesn’t naturally account for how a system will need to grow or change. Over time, this leads to code that works but isn’t easy to follow, features that are layered on top of each other without consistency, and little or no documentation explaining how the pieces connect.

Glide, a software development platform, describes AI-generated codebases as “brittle and poorly organized under the hood”, meaning the code that passes initial tests but accumulates what developers call technical debt quickly. Experienced engineers describe debugging AI-created code at scale as “practically impossible.”

The security problem

This is where things get more serious. AI models learn from vast amounts of publicly available code including outdated, insecure, and flawed code written by developers over decades. When an AI generates new code, it reproduces those patterns, including the vulnerabilities.

The numbers from recent research are hard to ignore. Veracode’s 2025 GenAI Code Security Report, which tested over 100 AI models across 80 different coding tasks, found that AI-generated code introduced security vulnerabilities in 45% of cases and that AI systems chose the less secure approach nearly half the time, even when a more secure option was available. A separate large-scale academic study found that at least 62% of AI-generated programs contained known vulnerabilities.

Research from Apiiro analyzing Fortune 50 enterprises found that AI-assisted code introduced 2.74 times more security vulnerabilities than human-written code, with privilege escalation flaws up 322% and architectural design issues up 153%. By mid-2025, AI-generated code was adding over 10,000 new security findings per month across the repositories they studied, a tenfold increase from just six months earlier.

For a landing page or basic informational site, the risk is relatively low. For any tool that handles customer data, payments, login systems, or internal business information, these are not theoretical concerns. They are documented patterns already appearing in real systems.

The scalability problem

Many vibe-coded tools are built for the moment they’re created. They aren’t designed with growth in mind. As usage increases, businesses start running into performance slowdowns, data handling issues, and systems that weren’t built to support real traffic or real volume. A survey of 18 CTOs conducted in August 2025 found that 16 of them reported production disasters directly caused by AI-generated code, ranging from performance collapses to data corruption to broken subscription systems.

There’s also a compounding dynamic that developers describe consistently: the first few features of a vibe-coded project come together quickly. But each new feature takes longer, because the AI must understand more and more of an increasingly tangled codebase. Eventually, the time spent explaining the existing system to the AI starts to outweigh the time it would have taken to write the code properly in the first place.

The Ownership Problem: What Happens When Nobody Understands How It Works?

There’s another layer to this that doesn’t come up often enough in business conversations.

When a system is built through layered prompts and AI output, there’s often no clear understanding of how it actually works internally. This becomes a real problem the moment something breaks or the moment the person who built it is no longer available (colloquially referred to as HBB, or hit-by-a-bus scenario). In the latter case, if the one person who understands the system is suddenly unavailable, the business is left with something that runs but that nobody can explain, maintain, or safely modify.

At that point, businesses often find themselves:

• Spending significant time trying to reverse-engineer their own tools
• Bringing in developers who inevitably recommend partial or full rebuilds
• Losing the time they originally saved, and then some

The irony is that the appeal of vibe coding is speed and cost savings. But when the hidden costs of fixing, rebuilding, and managing insecure or unmaintainable systems are added up, those early savings often disappear. For many businesses, the outcome is that they spend just as much time and money (sometimes more) than if they had worked with experienced professionals from the start.

When Vibe Coding Actually Works

It’s worth being clear: vibe coding and AI-driven development are not going away, and they aren’t without value. Used in the right context, these tools are genuinely powerful. They work well for:

The consistent thread in the most successful vibe-coded projects is that they are either small enough to stay manageable, or they involve experienced developers who can catch the AI’s architectural mistakes before they compound. The tools work best as an accelerant for human expertise not as a replacement for it.

How We Approach This at Fifth and Missing

But every project we work on is still guided by developers and designers who understand how systems need to function in the real world not just on day one, but six months or two years down the line. The AI helps but the humans manning the keyboards are accountable.

That difference shows up in ways that aren’t always obvious at the start. Clean structure. Maintainability. Scalability. Security built in, not bolted on. These things don’t make for flashy before-and-after screenshots, but they determine whether a tool continues to support a business or becomes a liability. In fact, when done right, a business owner might not even notice the impact of a scalable or secure system. It simply continues to work as expected. Inversely, it becomes obvious in a hurry when there’s a security breach or the estimated time and cost to expand part of the system seems inordinately high.

When a business works with us, they’re not working with an AI prompt. They’re working with people who understand their objectives, who ask the questions the AI won’t ask, and who build things meant to last.

What's the future of vibe coding?

Vibe coding is opening doors. It is making software more accessible and reducing the friction between idea and execution. For businesses that use it thoughtfully, to prototype, to experiment, to support experienced teams, it’s unquestionably a meaningful shift.

What’s less clear is how it reshapes expectations over time, especially as more companies begin to feel the long-term effects of what was built quickly. The research on security, maintainability, and scalability is still catching up to how fast the adoption is moving. The businesses making the most of this moment are the ones treating AI as a powerful tool in the hands of skilled people, not as a bypass around the need for skill at all.

It remains to be seen how vibe coding will change the landscape for businesses. But the companies that come out ahead will almost certainly be the ones who understood the difference between building something fast and building something that works.