We Don't Need More AI Tools. We Need Better Ways to Find the Right Ones.

Guest post by the team behind AIHuntList, an AI tool directory organised around use cases rather than one giant list.

Every week, I discover another AI tool that looks useful.

I open the website. I read the landing page. Sometimes I bookmark it. Sometimes I save the post where I found it. Occasionally, I even sign up.

Then a few weeks later, I need an AI tool for a specific task and realize I can barely remember any of them.

This is a strange problem to have.

A few years ago, the question was: What can AI actually do?

Today, the question is increasingly: Which of these hundreds of tools should I use?

We don't have an AI tool shortage anymore.

We have a discovery problem.

Key facts

• The bottleneck moved from capability to selection. Building is cheap, choosing is expensive.
• Search is excellent when you know the category name and weak when you do not yet know what kind of solution exists.
• Social feeds are the opposite: discovery everywhere, organisation almost nowhere.
• Launch day and long-tail discovery are two separate problems, and most products only plan for the first.
• A directory of 5,000 unorganised tools is not more useful than one of 500 sorted by use case. Structure beats volume.

More Tools Isn't Necessarily Better

The barriers to building software have fallen dramatically.

AI-assisted coding makes it possible for small teams, and sometimes individual developers, to build products that would previously have required much larger teams.

That's great for builders.

It also means the number of products competing for attention keeps increasing.

Open X on almost any day and you'll see another AI writing assistant, image generator, coding tool, research assistant, meeting recorder, video creator, or automation product being launched.

Some are genuinely different.

Many look almost identical at first glance.

But the interesting part is that even tools that appear similar can be designed for very different users.

An "AI video tool" could mean a text-to-video generator, an avatar platform, a screen recorder, an automatic editor, a translation tool, or software for turning podcasts into short clips.

Putting them all into one bucket doesn't help someone who simply wants to solve a particular problem.

And that's where discovery starts to break down.

Search Works Best When You Know What You're Looking For

Google is incredibly good when you know what to search for.

If I want an alternative to a specific product, I can search for it.

If I know I need an "AI meeting transcription tool," I can search for that too.

The harder situation is when I don't yet know what type of solution exists.

Maybe I want to reduce the time I spend making product demo videos.

Do I need an AI video editor? A screen recorder? An AI voice generator? An automated presentation tool?

Before finding a product, I first need to understand the category.

Traditional search isn't always designed for this kind of exploration.

And social media has the opposite problem: discovery is everywhere, but organization is almost nonexistent.

You see a useful tool, save it somewhere, and hope you'll remember it later.

Usually, you don't.

Launching and Discovering Are Different Problems

Builders face a similar issue from the other side.

You can build something useful and still have almost nobody discover it.

That's why launch communities and discovery platforms have become such an important part of the software ecosystem.

A platform like NickLaunches is useful because it gives new products a place to launch in front of people who are actively interested in discovering what builders are creating.

That solves an important moment in a product's life:

How do I get people to notice this exists?

But there's another question that happens long after launch day:

How does someone find this product six months later when they actually need it?

Those are two different discovery problems.

One is about the launch.

The other is about matching an existing need with an existing product.

We need better systems for both.

The Best Tool Is Often Somewhere You've Never Looked

One thing I've noticed while exploring AI products is that popularity and usefulness aren't always the same thing.

The biggest tools naturally dominate search results, social media conversations, and comparison articles.

That's understandable.

But the best tool for a very specific workflow may be something built by a team you've never heard of.

A developer building a niche code-review product doesn't need millions of users to create something excellent.

A small team focused entirely on translating videos may build a better workflow for that task than a huge general-purpose AI platform.

This is one of the most interesting things about the current AI ecosystem.

Specialization is happening incredibly quickly.

But our methods for discovering software haven't evolved at the same speed.

Categories Are More Useful Than Giant Lists

I used to think the solution was simply collecting more tools.

Eventually I realized that a directory with 5,000 unorganized products isn't necessarily more useful than one with 500.

The important part isn't the number.

It's the structure.

When I'm looking for software, I don't want to browse an endless list of AI companies.

I want to start from the problem I'm trying to solve.

Writing → SEO content.

Video → screen recording.

Marketing → ad generation.

Developer tools → code review.

The more specific the intent becomes, the easier it is to compare products that actually compete with each other.

That's also why I think AI discovery will increasingly move toward narrower AI categories and use cases rather than generic lists of "the best AI tools."

This Eventually Became AIHuntList

I started thinking about these problems while spending more and more time discovering AI products myself.

What began as a simple need, keeping track of interesting tools, gradually turned into a much larger categorization problem.

That eventually became AIHuntList.

The idea isn't particularly complicated.

Instead of treating AI products as one giant category, organize them around what people actually want to accomplish.

Today that means thousands of products spread across categories covering writing, development, marketing, images, video, productivity, business, voice, education, and many more specialized use cases.

But the number of tools isn't the part I find most interesting.

It's watching the categories become more specific.

A new category is often a signal that a new workflow is emerging.

And as AI becomes embedded in more professions, I think we'll see many more of them.

Discovery Is Becoming Part of the Product Ecosystem

For builders, creating software is getting easier.

Getting attention isn't.

For users, accessing powerful AI is getting easier.

Choosing what to use isn't.

Those two trends are happening at the same time.

That's why I think discovery platforms, launch communities, directories, recommendation sites, niche search engines, and other approaches we probably haven't invented yet, are going to become increasingly important.

We don't need another endless feed telling us that a new AI tool launched today.

We need better ways to answer a much simpler question:

I have this problem. What should I use?

The AI ecosystem already has more tools than any one person could realistically try.

The next challenge isn't creating more options.

It's helping people find the right ones.

FAQ

Why is it so hard to find the right AI tool?

Because the bottleneck moved. A few years ago the question was what AI could do at all. Today hundreds of products can do the same broad thing, and most discovery surfaces group them by technology rather than by the job you are trying to finish. Search works when you already know the category name. It works badly when you do not yet know what kind of solution exists.

Are AI tool directories still useful?

Useful directories are the ones with structure. A directory of 5,000 unorganised products is not more helpful than one with 500 sorted by use case. What matters is whether you can start from your problem, writing to SEO content, video to screen recording, developer tools to code review, and land on a short list of products that genuinely compete with each other.

What is the difference between launching a product and being discovered?

They solve two different moments. A launch answers how someone notices a product exists this week, which is what launch platforms like NickLaunches are built for. Discovery answers how someone finds that same product six months later when they finally have the problem it solves. A product needs both, and most builders only plan for the first.

Is the most popular AI tool usually the best one?

Not for specific workflows. Popularity dominates search results, social conversation and comparison articles, so large general-purpose platforms are always the easiest to find. A small team focused entirely on one task, video translation or code review for example, often builds a better workflow for that task than a much larger product covering everything.

Browse AI tools by category on AIHuntList