The term vibe coding entered the mainstream in early 2025 and has not left. The idea is straightforward: you describe what you want in plain language, an AI coding assistant generates the code, you test whether it works, and you iterate. No deep programming knowledge required. The "vibe" in the name captures the looser relationship with the underlying code — you are not reading it line by line, you are accepting it, running it, and prompting your way through problems as they appear.
By mid-2026, the tools supporting this approach have become genuinely capable. Non-technical users are building dashboards, automation scripts, internal tools, and basic web applications in hours rather than weeks. The barrier to getting software built has dropped significantly.
What has not changed is what makes software reliable in a real business environment. This post is about the gap between those two things: what vibe coding can produce, where it consistently falls short, and how to use it without creating problems that cost more to fix than the original tool was worth.
What vibe coding actually is
Vibe coding is a development approach where you describe what you want in natural language and an AI model generates the code, often without the user reading or deeply understanding what is produced.
A non-technical person describes their problem — "I need something that pulls our weekly sales figures from our CRM, organises them by region, and emails me a summary every Monday morning" — and an AI coding assistant generates a working script to do it. The person tests it, reports back what is wrong, and the AI adjusts. The loop continues until the output behaves as expected.
This is distinct from AI-assisted development, where a developer uses AI tools to write code faster while still reading, reviewing, and understanding every line. In vibe coding, the understanding step is frequently skipped. The focus is on whether the output does what you wanted, not on how it does it.
That distinction matters more than it first appears, and it is where most of the practical limitations come from.
What it can produce
Vibe coding works well in a specific range of situations, and within that range it is genuinely useful.
Internal tools and reporting scripts. Data summary tools, simple calculators, formatting scripts, internal approval workflows — these are bounded tasks with low security exposure and a limited number of ways they can go wrong. They also tend to be the tools that would never get built at all if they required a developer, because the business case does not justify the cost. Vibe coding makes them feasible.
Prototypes and proof of concepts. If you want to test whether a software idea is worth investing in properly, a vibe-coded prototype can get you to an answer quickly. The prototype is not the product — it is evidence. Getting that evidence cheaply is legitimate, as long as you do not mistake the prototype for the finished thing.
Automation of clearly defined, repetitive tasks. Tasks that follow consistent rules — renaming and organising files, processing data in a standard format, generating reports from a template — translate well into code that AI can generate reliably. The consistency is what makes them tractable.
Simple front-end tools. Basic web forms, calculators, simple configurators. Complexity has a ceiling here, but the ceiling is higher than it was twelve months ago.
What these situations share is that they are relatively self-contained, the stakes of something going wrong are manageable, and the tool's lifespan does not depend on it being maintained and extended by someone other than the person who built it.
Where it consistently falls short
The practical limits of vibe coding are less about what an AI can generate and more about what happens to your business when that generated code encounters real conditions.
Security
AI-generated code has well-documented and consistent security problems. SQL injection vulnerabilities, improper authentication handling, missing input validation, hardcoded credentials, exposed data in error messages — these appear in vibe-coded output regularly. It is not that the AI is unaware of security concerns; it is that vibe coding optimises for "does this do what was asked" rather than "is this safe under adversarial conditions."
For any system that handles customer data, financial records, or anything accessible to external users, security weaknesses in vibe-coded software are a real risk. A data breach that traces back to an AI-generated script is not a theoretical concern — it is a predictable outcome of skipping the review step.
Maintainability
Code you do not understand is code you cannot maintain. When a vibe-coded tool breaks — and it will break, usually when something in the surrounding environment changes — the person responsible faces a poor situation. They have to either understand code they have never read in detail, or prompt their way through fixing a problem they cannot diagnose properly.
This creates technical debt at an accelerated rate. The tool works on day one. Twelve months later, it fails in ways that are slow and expensive to diagnose because nobody ever understood why it worked in the first place.
Complexity ceilings
Vibe coding works on bounded, well-defined problems. As complexity increases — multiple integrations, conditional logic with exceptions, anything that needs to handle edge cases gracefully — the reliability of AI-generated output drops and the human effort required to get it right rises sharply. The point at which vibe coding becomes more work than conventional development arrives faster than most people expect.
What to do before you vibe-code something
Most of the problems with vibe-coded software come from using it in the wrong context. Answering a few questions before you start can save significant cleanup later.
What is the consequence if this breaks? If the answer is "we lose some time fixing it," vibe coding is probably fine for this task. If the answer involves customer data, financial records, a public-facing system, or anything where an outage has direct commercial consequences, it is not the right approach.
Will someone who didn't build it need to modify it later? If the answer is yes, the fact that nobody understands the code will become a problem. Software built to be maintained needs to be built with that in mind.
Does it touch sensitive data or external systems? Any integration that connects to your database, handles user credentials, processes payments, or sends communications on your behalf needs security review — from someone qualified to spot the problems AI tends to generate — before it runs in a production context.
Is this a prototype or the actual product? There is a real and important gap between a working demo and a production system. If you are vibe-coding a prototype to test an idea, that is a legitimate use. If you are vibe-coding something that will run your business, that is a different thing and the same standard does not apply.
How I use it in practice
I use AI-assisted development tools extensively in my own work. They accelerate the parts of development that are mechanical — scaffolding, boilerplate, repetitive transformations, first drafts of standard components. They are genuinely useful for exploring an approach quickly before committing to an architecture.
What they do not replace is the judgment that makes software reliable: how to structure a system so it can be maintained, which security risks to address, how to handle failure cases gracefully, what the next person to work on this code will need to understand it. That judgment comes from engineering experience, and it is what separates software that holds up from software that creates problems six months after it was built.
If you are a business considering vibe coding for something you actually depend on, my honest recommendation is to use it for the prototyping and exploratory work it is genuinely good at, and to involve a developer with production experience before anything consequential goes live.
The businesses that get durable value from custom software are the ones that invest in engineering appropriately for what they are building — not too much for a simple internal tool, not too little for something customer-facing or data-sensitive. Vibe coding has shifted where that line sits, but it has not eliminated it.
If you have an idea for software and are not sure whether to vibe-code it, have it built properly, or buy something off the shelf, that is a conversation worth having before you invest time or money in the wrong direction. Get in touch and we can work out what the right approach is for your situation.