The Real Reason AI-Built Products Keep Failing

These days, when I see people talk about building products with AI, one thought comes to mind first.

Now many more people can actually test their own ideas.

It is clearly a good change that AI has made it easier to write code. In the past, even building a small service meant finding a developer, writing a plan, designing it, and waiting for months. Now, with tools like Claude Code, Codex, and Cursor, we can build products much faster.

But there is one thing we need to think about carefully here.

A result that looks convincing on the surface is not the same as something people actually want.

What We Miss More Easily as Building Gets Faster

Vibe coding itself is not necessarily the reason products fail. The issue is that, because we can now build so quickly, it has become easier to miss something important.

Who really needs this?

When an idea comes to mind, we want to build it right away. Once there is a screen, a button works, and data gets saved, it already feels like a product. I also naturally feel some expectation when I see something like that.

But having a UI does not mean there is demand. A feature working does not mean customers will pay for it.

In the Past, the Slow Process Worked as a Filter

In the past, making a service was a difficult process.

Planning, finding a developer, designing, building, testing, and launching all took a long time. That slow process was frustrating, but at the same time, it worked as a kind of filter.

When something costs a lot to make, people naturally ask questions.

• Do I really need to build this?

• Is there anyone who will pay for it?

• Is the problem I experienced also important to other people?

• Is this a problem that must be solved now?

Thinking for a long time does not always lead to a good product. But at least it was less common for an impulsive idea to immediately look like a real product.

Now, much of that friction has disappeared.

When an idea comes to mind, we write a prompt. When we write a prompt, a screen appears. When a screen appears, it feels as if we have made a product.

This speed is a major advantage. But it also requires care. Implementation can easily move ahead of validation.

Once There Is a Result, It Is Easy to Misread It

The biggest feature of vibe coding is that a result appears right in front of you.

Buttons work, pages move, and data gets saved. An idea that was only in your head yesterday can be built today.

This experience can easily give us confidence.

• Maybe it just needs a little more polish?

• If I add a landing page, maybe I can start selling it right away?

But this is where we need to be careful.

Having a UI and having demand are different things. A feature working and a customer paying for it are also different things.

Vibe coding has greatly expanded our ability to build, but it does not automatically tell us what we should build.

The Most Common Failure Is Acting as If You Did Market Research

Many failures happen in a similar order.

First, an idea comes from a personal inconvenience.

I found this uncomfortable, so there must be many people like me.

Up to this point, there is no problem. Many good products have started from personal inconvenience.

The problem comes next.

Instead of meeting real people and asking them, we ask AI to do market research. It gathers search results, organizes competing services, and classifies possible customers. Then, on paper, the market can look quite convincing.

But this alone is not enough.

The market summarized by AI is not the same as real customer behavior. You need to check directly whether people are really spending time, spending money, or still putting up with the problem today.

A Good Response and a Strong Signal Are Different

When you tell people around you about an idea, they usually respond kindly.

• That sounds like a good idea.

• I would try it if it came out.

• It would be convenient if something like that existed.

These are kind responses, and they are worth appreciating. But they are not yet strong signals.

What matters more is behavior.

• Do they take time to explain the problem in detail?

• Do they show you what they currently use instead?

• Do they describe specific inconveniences?

• Are they already spending money or time to solve it?

• Is the problem serious enough for them to change their current way of doing things?

People can speak positively out of courtesy. But actual behavior gives more honest information.

Questions to Ask Before Building More Features

Before adding more features through vibe coding, it is useful to ask these questions first.

• When was the last time you experienced this problem?

• How are you solving it now?

• What is the most inconvenient part of that method?

• Have you ever spent money or time trying to solve this problem?

• Is this problem big enough for you to change how you currently do things?

These questions are not for evaluating the idea. They are for checking whether there is a real problem.

What matters is not whether the other person says, “It sounds good,” but whether they can talk about their actual experience in detail.

The Value of Vibe Coding Is Not Only in Building Faster

The real value of vibe coding is not in quickly turning any idea into something that looks like a product.

Its value is in quickly testing a validated problem, building small, watching customer reactions, and improving from there.

AI greatly increases the speed of building. So now, the more important ability is not simply the ability to build quickly.

It is the ability to slowly confirm what to build.

Being able to build quickly means we have gained a good tool. But a good tool does not find a good problem for us.

The more we build products with AI, the more often we need to stop and ask.

Is this something I want to build, or something people actually need?

The sooner we check this question, the more vibe coding becomes not just a fast production tool, but a better tool for experimentation.