Thanks to the rise of AI coding tools, vibe coding has become one of the most talked-about ideas in software development. The basic idea is simple: describe what you want in natural language, let an AI generate the code, and iterate based on the result.
That can feel almost magical. You can build a website, a prototype, or even a small application without writing every line of code yourself.
But there is an important distinction: using AI to write code is not automatically vibe coding.
The term was introduced by Andrej Karpathy in February 2025 to describe a much more hands-off approach, where the developer relies heavily on AI-generated code, often without reading or fully understanding what was produced.
That distinction matters because AI-assisted development can be extremely useful. The real question is what happens when you rely on the same approach for a product that has real users, real money, and real consequences.
Where Vibe Coding Actually Works
Vibe coding isn't inherently bad. In the right context, it can be extremely effective.
It works particularly well for:
- Building a quick prototype to test an idea
- Creating a proof of concept before committing serious resources
- Small personal tools with no sensitive data and few users
- Experimenting with unfamiliar technologies
- Learning basic programming concepts and software development workflows
For these use cases, speed is often more important than long-term maintainability.
If a weekend project breaks six months later, that may not matter.
The problem begins when the same approach is used to build the actual product a business depends on.
Why It Becomes Risky for Startups and Business Sites
A production application has requirements that go far beyond "does the page work?"
It needs proper authentication and authorization, secure handling of secrets, database access controls, input validation, error handling, logging, testing, backups, monitoring, and a codebase that someone can maintain six months from now.
AI coding tools can generate functional implementations very quickly, but functional code is not the same thing as secure or maintainable software.
An AI-generated application may work perfectly during a demo while still containing problems that only appear with real users: authorization mistakes, exposed credentials, unsafe assumptions, missing edge cases, weak database rules, or fragile integrations.
The bigger issue is often not the AI itself. It's the lack of technical understanding behind the person deploying the result.
When the person building the product doesn't understand the architecture, debugging becomes difficult. Fixing one problem can also introduce another because nobody has a clear mental model of how the system actually works.
That creates a dangerous maintenance cycle: the application grows, the code becomes harder to understand, and every new change becomes more dependent on the AI that created it.
Two Real-World Examples
1. Enrichlead
One widely discussed example involved Enrichlead, a SaaS product built by its founder using Cursor with no handwritten code.
Shortly after launch, the founder reported that the application was being abused: API usage was being consumed, users were bypassing the subscription system, and unwanted data was being created in the database. He also explained that he wasn't technical, which made diagnosing the problems much harder.
The important lesson isn't that AI "failed."
The system did what it was asked to do: it produced something that worked.
The missing part was the engineering judgment required to answer questions such as:
- Is authentication enforced on the server?
- Can a user modify client-side values to unlock paid functionality?
- Where are API keys stored?
- What happens when someone deliberately abuses the application?
- What protections exist around database writes?
These are software engineering and security questions, not just coding questions.
2. A CMS That Was Never Actually Connected
A quieter example comes from developer Dominik Gronkiewicz, who described taking over a project built by a previous team.
The website looked finished: clean design, smooth animations, and all the expected pages.
But under the hood, a Sanity CMS subscription had been paid for months without actually being connected to the website. Headlines, team biographies, and other content were hardcoded directly inside JSX components, leaving the client unable to edit even a single sentence without a developer.
This is a different kind of failure.
Nothing was necessarily "broken" from a visual perspective. The site worked.
But the architecture did not match the business requirement.
A CMS exists so non-developers can manage content. Paying for a CMS and then hardcoding all the content defeats its purpose.
This is exactly the kind of problem that can be missed when the goal is simply to make the application look finished.
The Maintenance Problem Is Often Bigger Than the Initial Build
One of the biggest advantages of vibe coding is also one of its biggest weaknesses: you can create a lot of software before you fully understand what you created.
That is fine for experimentation.
It becomes expensive when the application needs continuous development.
Eventually, someone has to answer questions like:
- Why is this request slow?
- Why can this user access that data?
- Why did this deployment break another feature?
- Which package is responsible for this behavior?
- Where is this secret being used?
- How should this database change be migrated safely?
- What happens when traffic increases tenfold?
At that point, the question is no longer "Can AI build this?"
The question becomes "Does someone understand the system well enough to operate and evolve it?"
Why Experienced Developers Still Matter
A professional developer does much more than write syntax.
They make architectural decisions, think about security, understand trade-offs, design for maintainability, test failure scenarios, review dependencies, debug production problems, and plan for the future.
That doesn't mean experienced developers should avoid AI.
Quite the opposite.
AI can be a powerful development tool when it is used as an accelerator rather than as a replacement for engineering judgment.
There is a major difference between:
"AI wrote the code, so I trust it."
and:
"AI wrote most of the code, and I understand, test, review, and take responsibility for what ships."
The second approach is much closer to modern AI-assisted software development than pure vibe coding.
The Bottom Line
Vibe coding is excellent for exploring ideas, building prototypes, and creating small projects quickly.
For production systems, the risks increase significantly when nobody involved fully understands the generated code and architecture.
The real issue isn't whether AI wrote the code.
The real issue is who is responsible for understanding the system, securing it, maintaining it, and fixing it when something goes wrong.
For a startup or business website, AI can absolutely be part of that process. But it shouldn't be the only engineer in the room.
At Webrium, we use modern development tools, including AI, to speed up development while keeping architecture, security, maintainability, and human review at the center of the process.
If you're building something real and want a second pair of experienced eyes on it, feel free to reach out — benjamindev365@gmail.com. (I'm also the developer behind webrium.dev, if you want to see what I build.)
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