The phrase vibe coding started as a half-joke. It described a way of making software where you stop reading the code, tell an AI what you want, accept what comes back and keep nudging until it feels right. The joke stuck because it described something many people were already doing, and companies built products around it.
If you write code for a living, your first reaction may be a wince. If you do not, the idea may sound like freedom. The truth sits somewhere between, and the way to see it is to look at what the method is good at.
The vibe coding workflow in plain terms
You open a tool and describe the thing: a page where customers upload receipts and see a monthly total. The system produces an application with a form, a database and a chart. You try it, notice the chart is confusing, ask for a bar chart instead, and so on. At no point do you need to understand the underlying framework.
This is not magic. Language models have read enormous quantities of public code and learned the common patterns. Requests that resemble well-trodden patterns tend to come out right. Requests that are unusual tend to come out strange.
Where vibe coding shines
- Throwaway scripts and one-off tools where correctness can be checked by eye.
- Prototypes built to test an idea quickly.
- Small business utilities with a handful of users.
- Learning, since you can ask the model to explain what it wrote.
For experienced developers, the gain is also real. They offload boilerplate and review what comes back, which is faster than writing it by hand. The difference is that they still read it.
A concrete example helps. A small shop owner asks for a page that applies a ten percent discount for returning customers. The generated page does it, and the demo looks perfect. What the owner may not realize is that the discount is calculated in the visitor’s browser, so a curious customer can change the price before paying. An engineer would spot that in a minute. A person who never reads the code never will.
That is the real argument for keeping a human in the loop. It is not that the tools are bad. It is that they are very good at producing plausible output, and plausible is not the same as correct.
Where vibe coding fails quietly
The danger of vibe coding is that failure is silent. The app seems to work, yet it stores passwords carelessly, exposes one user’s data to another, mishandles money by rounding errors, or breaks when two people use it at once. Those flaws do not show up during a friendly demo.
Maintenance is the other trap. Code nobody understands is expensive to change. When the model-generated project grows large, adding a feature may require untangling choices that no human ever made on purpose.
Security teams have also noted that generated code sometimes reaches for outdated libraries, or suggests packages that do not exist, which attackers can then register and fill with malware. Anything that touches sensitive data deserves review by someone who knows what to look for.
The same pattern of AI doing the first draft is showing up elsewhere. Browsers now ship assistants that summarize pages and fill in forms, as covered in what changes when a browser has an AI assistant built in, and synthetic voices follow a similar path of impressive demos with details that need care, as how synthetic voices are made explains.
Learning to code in this era looks different too. Beginners can get working results on day one, which is motivating, but they still benefit from reading what the tool wrote, one small piece at a time. Asking the model to explain a function in plain language, then changing a value to see what happens, builds understanding that a finished app never will.
How to read the business claims
Products built around vibe coding draw strong attention and large funding because adoption can grow quickly. When you see a headline about a company in this space announcing impressive revenue, a few questions help.
What does annualized mean? Often it takes a recent month or week and multiplies it up. That number can look dramatic while hiding the fact that subscribers may cancel soon after a trial.
How many users stay? Experimenting is cheap, and many people build a single project and move on. Retention after three or six months says more than sign-up counts.
What are the costs? Every prompt uses paid computing power from an AI model provider. A company charging a flat subscription while paying per use can see margins squeezed as customers build more.
Who owns the moat? If the underlying model comes from another firm, the product’s lasting advantage has to come from design, templates, deployment and integration, not from the intelligence itself.
A sensible way to use vibe coding
Treat generated software as a first draft written by a quick but overconfident colleague. Use it freely for things that are cheap to be wrong about. Slow down when real money, health or personal data is involved. Keep the project in version control, so you can undo changes. Ask the tool to write tests, and run them.
The best outcome of vibe coding is that more people can make useful things. The worst is a pile of apps that no one can fix. Reading just enough of the code to know which one you have is a small price.
Image: Crew crew / Wikimedia Commons, CC0.
