Exploring the real AI industry structure and why it feels like everyone is launching an AI tool.

Have you ever searched for a specific AI tool, say for creating videos or writing marketing copy, and felt like you were staring at an endless wall of new companies? You’re not alone. It seems like every day, dozens of new AI startups pop up, all promising to be the perfect solution for your specific need. It gets you thinking: are there really thousands of unique AI technologies out there? The short answer is no. To understand what’s really going on, you need to look at the AI industry structure. It’s a fascinating setup that, once you get it, makes everything clear.

It’s a great question. On one hand, you have the giants—the multi-billion dollar players we all know, like OpenAI (the creators of ChatGPT), Google (with its Gemini model), Meta (with Llama), and Anthropic (with Claude). These are the companies pouring billions into research and development to build what are known as “foundation models.”

Think of a foundation model as a massive, general-purpose brain. It’s been trained on a gigantic portion of the internet and can read, write, and understand language on an incredible level. But it’s not inherently specialized. It’s like a brilliant, freshly graduated student who knows a lot about everything but hasn’t started a specific career yet.

The Real AI Industry Structure: Layers of Innovation

So, what about the thousands of other companies? This is where the true AI industry structure comes into play. The vast majority of those smaller AI companies are not building their own foundation models from scratch. That process is incredibly expensive and complex, requiring resources that only a handful of organizations in the world possess.

Instead, they build on top of the giants.

The big players like OpenAI and Google don’t just keep their powerful models for themselves. They rent out access to them through something called an API (Application Programming Interface). An API is basically a secure and managed doorway that lets one piece of software talk to another.

These smaller companies pay to send requests to the big AI “brains” and get responses back, which they then package into their own unique products. They are, in essence, building a specialized service on rented technology. Think of it like a restaurant. The restaurant doesn’t generate its own electricity; it buys it from the power plant. But it uses that electricity to create a unique dining experience that you can’t get at the power plant itself.

So, Are They Just Wrappers? And What’s the Value?

Calling these companies “wrappers” is common, and while technically true, it can sometimes miss the value they bring to the table. This layered approach is a core feature of the current AI industry structure, and it fosters incredible innovation. Here’s the value these specialized companies add:

• A Focus on a Specific Problem: While a model like ChatGPT is a generalist, a smaller company can fine-tune and prompt it to be an expert in a single area. They might train it on legal documents to create an AI assistant for lawyers or feed it thousands of top-performing ads to build an AI marketing copywriter. You’re not paying for the raw AI; you’re paying for its specialized education.

• User Experience (UX): Let’s be honest, interacting with a raw API isn’t exactly user-friendly for most people. These companies build beautiful, simple web interfaces that solve a specific problem without requiring any technical knowledge. You click a few buttons, and the complex API calls happen in the background. This ease of use is a product in itself.

• Workflow Integration: The real magic happens when AI is integrated directly into the tools you already use. A company might build a plugin that brings AI writing assistance directly into Microsoft Word or Google Docs. They handle the complex work of connecting the AI to your workflow, saving you time and effort. You can learn more about how APIs are the engine of this new economy from sources like the Harvard Business Review.

The companies building these foundational models are creating the core infrastructure, much like how utility companies provide power. Stanford’s Human-Centered AI Institute has a great explanation of what foundation models are and why they’re so important. The thousands of startups are the ones building the actual appliances and tools that use that power to do useful things for you.

So next time you see a new AI tool, you can see it for what it likely is: a clever and useful application built on the shoulders of giants. It’s not a sign of deception, but rather a signal of a healthy, rapidly expanding ecosystem where powerful technology is becoming more accessible and useful to everyone.