Why So Many AI Services Fail in the Same Way Lately

These days, tools like Claude make it possible to build services very quickly.

If you explain an idea, a screen appears, buttons work, and data gets saved. Work that would have taken weeks in the past can now become something fairly convincing in a single day. This is clearly a major change.

Now anyone can test their own ideas directly. You do not need to find a developer first, and you do not need to write a long planning document before starting. It has become possible to learn by making something first.

But the more I build different things with AI, the more often one thought comes to mind.

“Would anyone really keep using this?”

This is not a problem caused by being unable to build. These days, most things can be built reasonably well. The screens can look good, the features can work, and even the landing page can look polished.

The problem is somewhere else.

As building becomes too fast, it becomes easier to miss the most important question.

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Why Most AI Services Fail

When you build a service with AI, it is easy to misunderstand what you have.

If there is a screen, it feels like you have a service. If the feature works, it feels like people will use it. If you add login and payment, it can feel as if the service is complete.

I know that feeling too.

When something starts moving in front of you, the idea in your head begins to feel real. It is easy to think that if you polish it just a little more, someone will use it.

But users do not move that way.

Users do not care how quickly I built it. They also care less than we think about how smart the service is, at least not for very long.

What people look at is much simpler.

• Does this make my work less annoying?

• Is this easier than the way I do it now?

• Is there a reason to open it again?

If a service cannot answer these questions, it gets abandoned.

At first, people may click around because it feels new. But novelty does not last long. In the end, people keep only what truly helps them.

I think most AI services will fail for this reason.

They will not fail because they could not be built. They will fail because they did not solve a problem people truly wanted solved.

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What Gets Harder as Building Gets Easier

In the past, building a service was slow.

You had to plan, design, develop, test, and release. It was a frustrating process, but that slowness played one important role.

It made you keep asking whether this was really worth building.

Who would use this? Are people still uncomfortable because of this problem? Is it important enough to pay for? Is it clearly better than the way they already do it?

Of course, thinking for a long time does not automatically produce a good service. But at least it made you pause once before building.

Now that pause has mostly disappeared.

When an idea comes to mind, you write a prompt right away. When you write a prompt, a screen appears. Once a screen appears, it already feels like you have done quite a lot.

That is why we need to be more careful.

You may already be polishing a service before you have properly looked at the problem.

There is a feature.

There is a screen.

There is a name.

But the reason people should use it every day is still missing.

People do not want to use one more app. They already use many.

To use a new service, they have to overcome inconvenience. They need to sign up, get used to it, enter data, and change the way they already work.

If the problem is not strong enough to overcome that inconvenience, people will not move.

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“That’s a Good Idea” Is Not Very Useful Feedback

When you talk about an idea, people usually respond kindly.

“That’s a good idea.” “I’d try it if it came out.” “That would be convenient.”

Those are nice things to hear. But I no longer trust these reactions very much.

People often say something is good out of politeness. It is even easier to say when they do not have to pay money, spend time, or change their own habits yet.

What matters is not what people say. It is what they do.

People who truly have the problem respond differently.

They explain the situation they have experienced in detail. They show how inconveniently they are solving it now. In many cases, they are already paying money or spending a lot of time on it.

They also explain quite specifically what would make them switch.

I think “I’m forcing it to work like this right now” matters much more than “I’ll try it when it comes out.”

Opportunities for services do not come from praise.

They come from the discomfort people are already putting up with.

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The Mistake of Leaving Market Research to AI

These days, many people also ask AI to do market research.

AI can quickly organize which markets are growing, what kinds of customers exist, what competing services there are, and what problems people are experiencing.

I use it often too. As a starting point, it is quite useful.

The market that AI summarizes is not the same as real human behavior.

For example, let’s say you are building a tax management tool for solo founders. If you ask AI, it will give you a plausible answer.

• The number of solo founders is increasing.

• Tax filing is difficult.

• Existing services are expensive.

• There is demand for automation.

All of these may be true.

But it is risky to build based only on this.

What you really need to see is a much more specific scene.

When was the last time that person got frustrated because of taxes? Are they currently using an accountant, Excel, or Hometax? What is the most annoying part? Are they already paying for a solution? Is the problem big enough that they would trust a new tool with sensitive information?

These things are hard to see from documents alone.

You need to ask people directly. If possible, you should also see how they actually work.

AI can summarize a market quickly.

But to know whether people will really move, you eventually need to look at people’s behavior.

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Becoming a Habit Matters More Than Having Good Features

Many people start with features when they build a service.

It should have summarization. It should have automatic categorization. It would be better if it also had notifications and sharing.

But having many features does not mean people will keep using the service.

Now, when I look at a service, I look at this question before I look at its features.

“Does this service naturally fit into the user’s daily life?”

• Do they open it naturally in the morning?

• Does it become part of their work flow?

• Does it replace a tool they already use?

• Is there a reason to share it with teammates?

• Does not using it make things inconvenient again?

If a service cannot answer these questions, it is easily forgotten.

This is especially true for AI services.

At first, people may try it because it feels new. But using something because it is new and continuing to use it are completely different things.

People do not keep using a service simply because it is smart.

They keep using a service because it makes their work less annoying.

That is why I think “When will they open it again?” matters more than “What can it do?”

A service that people do not open again will eventually disappear, no matter how well it is built.

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The Faster You Build, the More Slowly You Need to Look

It is true that AI has increased the speed of building services.

Because of that, more people will be able to build things directly and test more ideas. I think this is a very good change in itself.

But just because building has become faster does not mean judgment has become faster too.

It is closer to the opposite.

The faster you can build, the more slowly you need to look at what you should build.

In the past, services failed because implementation was hard.

Now, implementation has become so easy that failure can happen even more easily. Problems that are not very important can be turned into service-like products too quickly.

AI is a very good tool.

But a good tool does not find a good problem for you.

In the end, what matters is this.

• Are people truly uncomfortable?

• Are they already taking action because of that problem?

• Can my service become part of that person’s day?

If a service cannot answer these questions, it will not last long, no matter how convincing it looks.

AI services do not fail because they cannot be built.

Most of them eventually disappear because they do not become part of people’s habits.