Why Are There So Many Different AI Tools?

You have probably seen a lot of new AI apps.

Not 1. Not 10. Probably hundreds.

"This tool creates websites with AI!"
"This one builds custom software in seconds!"
"This AI tool replaces your deeply human desire for love & connection!" (Etc...)

The truth is: yes, AI is a powerful. And because of that, it creates something I like to call "Entrepreneurial Fomo."

All around the world there are millions of entrepreneurs and business owners who do not want to "miss out" on the gold rush of AI. And so they connect an API, throw together a basic interface, and within mere hours, they have created the latest "AI tool" that promises to change your life.

That is the exact reason why there are so many supposedly different "AI Tools" that mostly just do the same thing. It's human psychology.

Part of why learning AI feels so confusing is specifically because it requires you to say "No" to 90% of what you see online. There are simply too many AI tools, tricks, techniques, and hacks for one person (you) to use. You would literally die before you could test all of them.

The AI Bubble Is Real — Kind Of.

Let's be honest about something most people in the AI space will not say out loud: the bubble is real.

Not in the way doomers frame it. The underlying technology — the models, the research, the compute infrastructure — is genuinely powerful and advancing at a pace that has no historical parallel. OpenAI, Anthropic, Google DeepMind, and a small handful of others are doing work that is legitimately transformative. That part is not hype.

But the ecosystem surrounding those core models? That is a different story.

A massive portion of what you see marketed as a new "AI tool" is, in practice, a thin layer built on top of an existing model — usually GPT-4 or Claude — with a custom prompt, a branded interface, and a $29/month price tag. The company's core product is not the AI. It is the packaging. And when you are selling packaging on top of someone else's technology, you are in an extremely fragile position.

The Stanford HAI AI Index Report 2025 documented that global private AI investment exceeded $88 billion in 2024 — a number that reflects enormous confidence in the space. But that investment is not evenly distributed across genuine innovation. A significant portion flows into companies whose competitive advantage is, essentially: access to an API.

When the underlying models improve — and they always do — those products often get worse by comparison, not better. Because the model itself now does in one step what the wrapper was charging you to do in three. The bubble, when it deflates, will not take the technology with it. It will take the wrappers.

The "80:20" Rule Is More Like "90:10" When It Comes to AI Tools.

You probably know the Pareto Principle — the 80/20 rule. 80% of your results come from 20% of your inputs. It applies across most things: productivity, sales, skills, effort.

When it comes to AI tools, the ratio is even more extreme. More like 90:10.

A basic Claude or ChatGPT subscription — which costs $20 a month at most — gives you access to the same core model that powers hundreds of specialized AI tools charging far more. The writing assistant at $49/month? It is using GPT-4. The research tool at $79/month? Claude underneath. The summarization tool, the email optimizer, the "AI copywriter" — almost all of them are accessing the same three or four foundational models and marking up the access price.

Here is what that means in practice: before you pay for any specialized AI tool, ask yourself what that tool does that you could not replicate with a well-crafted prompt in Claude or ChatGPT. In the vast majority of cases — probably nine out of ten — the honest answer is "not much."

The exceptions exist. Coding environments, complex workflow automation tools, or tools that integrate deeply with your existing software stack can genuinely add value that you cannot easily replicate manually. But those are specific, verifiable use cases — not vague claims like "AI-powered content creation" or "intelligent email drafting."

The 10% of AI tools that actually earn their subscription charge do one of three things: deep software integration, access to proprietary data, or a UI that makes a complex recurring workflow dramatically faster. Everything else is packaging.

Most AI Tools Are Just API Wrappers.

This is the part the marketing does not tell you. When a company says they have built an "AI tool," what that almost always means technically is: they have taken a model built by OpenAI, Anthropic, Google, or Meta — and connected it to a user interface.

That connection is called an API call. The model lives on someone else's servers. The company making the "AI tool" simply sends your input to that model and returns the output back to you. The "AI" in their product is entirely someone else's.

This is not inherently dishonest. Building a useful product on top of existing technology is completely legitimate. But it does mean that what you are paying for when you buy most AI tools is: the interface, the prompt engineering baked in behind the scenes, and sometimes a workflow. Not the AI itself.

The implication for you as a user is significant. If you learn to use the base models well, you can replicate most of what these tools offer for a fraction of the cost. The moat is not the AI. It is the friction. These companies are, in many cases, charging you to not have to think too hard about how to use a tool you could access directly.

That is a fair trade if your time is genuinely worth more than the cost of the shortcut. But you should know exactly what you are buying.

What You Should Actually Use Right Now.

Given everything above, here is the most practical answer: one foundation model, used well.

Claude (Anthropic) or ChatGPT (OpenAI) at the $20/month tier give you access to a model powerful enough to handle the vast majority of what anyone needs from AI today — writing, research, analysis, coding assistance, summarization, brainstorming, editing. That is not a limitation. It is the point. The models are genuinely capable. The problem most people have is not insufficient tools. It is insufficient skill with the tools they already have.

From there, the only reason to add a specialized tool is if you can articulate — specifically — what it does that the base model cannot. Not "it feels easier." Not "the marketing was compelling." A concrete, demonstrable workflow gap.

A few categories where specialized tools do legitimately earn their cost: coding environments like Cursor that wire AI directly into your development workflow; automation platforms like Zapier or Make that connect AI to other apps and run tasks without your involvement; and tools that pull from your proprietary data — your CRM, internal documents, calendar — in ways that a general-purpose chat interface simply does not support.

Everything else: try it first with the base model. You will be surprised how rarely you actually hit a ceiling.

The Compounding Advantage of Going Deep, Not Wide.

There is a temptation — especially for people who are genuinely curious — to try everything. Twenty AI tools. Every new release. Early adopter across the board. It feels productive. It looks like staying current.

It is almost always the wrong strategy.

Skill with AI tools compounds. Every hour you spend getting good at one tool — learning how to prompt it well, understanding its limitations, building intuition for when it helps and when it does not — makes the next hour more valuable. That compounding does not happen if you are constantly switching. It resets every time you start over with something new.

The people getting real leverage from AI are not the ones who have tried the most tools. They are the ones who went deep on one or two and built durable habits around using them. They know what to ask. They know how to structure a request to get a useful response on the first try. They are not wowed by every new product launch because they already have a system that works.

The noise online — the "10 AI tools that will blow your mind" content, the constant rotation of new apps — is optimized to keep you scrolling, not to make you more productive. Saying no to it is not falling behind. It is protecting the compounding advantage you have already started building.

Pick one. Go deep. Add the next one only when you have a specific reason.