TL;DR: Vibe coding gets a bad rap from people who think the only valid code is code you typed character by character. They are missing the point entirely. The ability to go from idea to working, good-looking proof of concept in hours is genuinely transformative, and the teams using it to iterate fast, fail fast, and learn fast are lapping the ones waiting for a full development cycle to find out their idea did not land. And if full vibe coding feels like too much of a leap, there is a very compelling middle ground worth knowing about.
There is a certain kind of developer who, upon hearing the term “vibe coding”, makes a face like they have just been handed a plate of something unpleasant. You know the face. It says: that is not real development. That is cheating. Anyone can do that.
And here is the thing: they are right that anyone can do it. That is exactly the point.
Vibe coding, for anyone who has somehow missed it, is the practice of building software by describing what you want in natural language and letting AI tools do the heavy lifting. Tools like Cursor or Bolt let you go from a rough idea to something that actually runs, looks reasonable, and does what you described, in a fraction of the time it would take to build it conventionally. No agonising over boilerplate. No three hours configuring a build system before you have written a single line of actual logic. You describe, it builds, you refine.
The critics will tell you the output is messy, that it does not scale, that a real engineer would never ship it. And they are correct, about production code at least. But they are arguing against a problem that vibe coding was never trying to solve, and in doing so they are missing something genuinely important about how good ideas get validated, killed, or turned into real products.
The Proof of Concept Problem
Here is a situation that will be familiar to anyone who has worked in tech for more than five minutes. Someone has an idea. It might be brilliant. It might be terrible. The only way to find out is to put it in front of people and see what happens.
The traditional route to doing that involves writing a brief, scoping a build, getting resource allocated, going through a development sprint or three, and eventually producing something six to twelve weeks later that you can show to stakeholders. At which point you discover that what they actually wanted was slightly different to what they described, and the process begins again.
This is an extraordinarily expensive way to learn things you could have learned much earlier.
Vibe coding collapses that timeline. A proof of concept that would previously have taken weeks can now be assembled in hours. Not a rough wireframe with placeholder text and a note saying “imagine this looks good.” An actual working thing, with a real interface, real interactions, and enough fidelity that someone can sit in front of it and give you genuine feedback rather than trying to extrapolate from a sketch.
That is not a small improvement. That is a fundamental change in how quickly you can learn whether an idea is worth pursuing.
Fail Fast is Not a Cliche, It is a Competitive Advantage
The tech industry talks about failing fast so often that it has started to sound like wallpaper. But the underlying principle is sound, and vibe coding is one of the most effective implementations of it I have come across.
When the cost of building a proof of concept is low, the cost of being wrong is also low. You can afford to test three different approaches to the same problem and see which one resonates, rather than picking one and committing to it for months before you find out it did not work. You can show a rough version to a stakeholder on Tuesday, incorporate their feedback on Wednesday, and show them something meaningfully different by Friday. You can kill ideas quickly and cleanly, before they have accumulated six weeks of development time and the political weight that comes with it.
Iteration speed is not just a nice-to-have. In a market where the pace of change is what it currently is, the teams that can move from idea to validated learning fastest are the ones that win. Vibe coding is a significant accelerant for that cycle, and dismissing it because the output would not pass a code review is like dismissing a whiteboard sketch because it would not survive a design critique.
The whiteboard sketch was never meant to ship. It was meant to communicate an idea. Vibe coded prototypes work the same way, just with considerably more fidelity and considerably more impact in a room full of stakeholders who struggle to engage with a whiteboard sketch.
Looking Good Matters More Than People Admit
Here is something that technical people sometimes struggle to accept: how something looks affects whether people take it seriously.
A proof of concept that is functionally identical but visually polished will consistently get better feedback than one that looks like it was built in 2003. Not because stakeholders are shallow, but because a polished interface signals care and intent, and it makes it much easier for non-technical people to engage with the idea rather than the presentation.
This is one of vibe coding’s genuine superpowers. The output is not just functional, it looks like something. Modern AI coding tools are remarkably good at producing clean, contemporary interfaces without you having to hand-craft every CSS rule. You get something that you can put in front of a client or a senior stakeholder without having to spend the first five minutes of the meeting explaining that “this is just a prototype, the real version will look different.”
When the prototype already looks credible, the conversation moves faster and goes deeper. That is worth a lot.
Addressing the Elephant in the Room
Right. Let us talk about the criticism directly, because it is not entirely without merit and pretending otherwise does not help anyone.
Vibe coded output is often not production-ready. The code can be repetitive, inconsistently structured, or built on assumptions that would need to be unpicked before you could scale it. There are security considerations that an AI tool will not always surface unprompted. If you take a vibe coded prototype and try to build a production system directly on top of it without proper engineering input, you may find yourself in a difficult position later.
All of that is true. None of it is an argument against vibe coding.
It is an argument against using the wrong tool for the wrong job, which is a mistake you can make with any tool. A hammer is not the right instrument for every task, but we do not therefore conclude that hammers are bad. The criticism of vibe coding almost always conflates “this should not be shipped as-is” with “this should not exist,” and those are very different claims.
Used for what it is actually good at, which is rapid ideation, fast prototyping, stakeholder validation, and accelerated learning, vibe coding is not a shortcut. It is leverage. And the professionals dismissing it because it does not produce enterprise-grade code on the first pass are going to find themselves at a meaningful disadvantage against the ones who have figured out where it fits in the process.
The Middle Ground Nobody Talks About Enough
Full vibe coding, where you hand the wheel almost entirely to the AI, is not the only option. There is a middle ground that deserves more attention, and for many developers and technical professionals it is actually where the most day-to-day value lives.
Using GitHub Copilot or Claude directly inside an IDE like Visual Studio Code gives you AI assistance that sits alongside your work rather than replacing it. You are still writing code. You are still making architectural decisions. You are still in control of what gets committed. But you have an extraordinarily capable assistant sitting next to you that can complete what you are typing, suggest the next logical block, explain what a piece of unfamiliar code actually does, and catch things you might have missed.
The experience, once you get used to it, is genuinely hard to give up. It is the difference between having a very fast, very knowledgeable pair programmer available at all times and working alone. For experienced developers it accelerates the parts of the job that are mechanical or repetitive. For less experienced developers it provides a level of guidance and error-catching that would previously have required a senior colleague looking over your shoulder.
What AI in the IDE is Actually Good At
Let us be specific, because “AI makes you faster” is not a particularly useful observation on its own.
It is excellent at boilerplate. The structural code you have to write for the hundredth time, the setup, the configuration, the repetitive patterns that follow conventions you already know: all of that gets dramatically faster. You write the intent, it fills in the scaffolding.
It is excellent at context-aware completion. When you are working within a codebase and you start typing something, a good AI assistant has read enough of the surrounding code to understand what you are probably trying to do and complete it sensibly. This is considerably more useful than autocomplete and considerably less annoying than it sounds.
It is excellent at explaining unfamiliar code. You inherit a codebase, you find a function that does something non-obvious, you ask the AI what it does and why. This used to mean digging through documentation, git history, or finding whoever wrote it and hoping they remembered. Now it takes about ten seconds.
It is excellent at unit test generation. Describe what a function should do, ask for tests, get a reasonable first pass that you can refine. Not perfect, but a very solid starting point that saves meaningful time.
It is excellent at catching errors before you run anything. Not just syntax errors, which your IDE was already doing, but logical errors, missing edge cases, and the kind of subtle mistakes that only show up in testing if you thought to test for them.
What AI in the IDE is Not So Good At
In the spirit of being honest rather than just enthusiastic, there are real limitations worth understanding.
It can be confidently wrong. AI assistants will sometimes suggest code that looks entirely plausible and does not work, or works in most cases and breaks in one important one. You cannot turn your critical thinking off just because the suggestion looks reasonable. Review what it gives you. Always.
It struggles with genuinely novel architecture decisions. If you are designing something that has not been done in a broadly similar way before, the AI is going to be less useful. It is drawing on patterns it has seen. When you need to invent a pattern, you still need to do that thinking yourself.
It can make your code harder to reason about if you let it. If you accept suggestions without understanding them, you can end up with a codebase that works but that nobody, including you, fully understands. This is a problem. The discipline of understanding what you are committing is still your responsibility, and arguably more important than ever when some of it was not written by you.
It has a context window, not a full understanding of your system. It knows what it can see in your open files and your prompt. It does not have a holistic understanding of your entire architecture, your team’s conventions, or the decisions made three years ago that explain why something is the way it is. The more context you give it, the better it performs, but there are limits.
Where This Sits in the Bigger Picture
Vibe coding does not replace engineering. It does not replace architecture, or proper requirements gathering, or the kind of deep technical thinking that goes into building something that needs to handle real load with real users over a long period of time. I wrote about all of that in my last post, and I stand by every word of it.
What the full spectrum of AI coding tools gives you, from full vibe coding at one end to AI-assisted development in a professional IDE at the other, is a set of options for matching the tool to the task. Fast prototype to validate an idea? Vibe code it, get it in front of people, learn something. Production feature in a mature codebase? Work in your IDE with AI assistance, stay in control, review everything, ship with confidence.
The teams that are going to thrive are not the ones who pick one of these approaches and defend it against all others. They are the ones who understand what each tool is for and move fluidly between them depending on what the moment requires.
The fast feedback loop is not a replacement for good delivery. It is what makes good delivery possible. And having the right AI assistance at every stage of that journey, from the first rough idea to the production release, is one of the most significant changes to how good software gets built that I have seen in four years of working in this space.
Vibe first. Engineer second. Ship something people actually want.
