💻The Flat World of AI Apps

Two years into the greatest hype wave in tech history, and what do we have to show for it?

Thousands of “AI products” that are barely more than wrappers around the same language models. A sea of startups claiming to disrupt industries by… calling the same OpenAI API. People chaining prompts together and calling it architecture. Copilots, assistants, bots, all powered by the same rented brain.

Welcome to the flat world of AI.

It’s a world where technical sameness is dressed up as innovation. Where startups fight each other with identical weapons. Where the difference between companies often boils down to which font they chose for their landing page. And the deeper problem? Most founders don’t even realize they’re standing on a featureless plane. They’ve mistaken rented intelligence for invention.

Let me be blunt: if you don’t own the core of your AI product, the actual intelligence, then your business has no moat. Anyone can copy you, and someone will. Your prompt can be reverse engineered. Your chain can be replicated. Your API calls can be rerouted. You have no edge.

And worse, you're replaceable by a better wrapper.

The Same Brain, Everywhere

Using OpenAI’s API is not like using AWS or Google Cloud. Cloud infrastructure is modular, invisible, and interchangeable. You can lift-and-shift workloads. You can switch providers with effort, but it’s doable. When you use AWS, you still write your code. You still build your logic.

But when you use OpenAI to power your product’s “intelligence,” you’re outsourcing the very thing your business claims to offer. You're borrowing a brain that’s also being rented by thousands of others. Your startup isn’t “AI-powered” — it’s API-dependent.

This creates a dangerous illusion of progress. It feels like you’re building. You’re deploying. You have a user flow, a chatbot, a dashboard. But all you’re really doing is sending text to someone else’s model and displaying the result. You are a UI on top of someone else’s cognition.

That’s not innovation. That’s latency.

The Wrapper Era

Let's name the elephant in the room: 90% of AI startups today are wrappers.

• A Chrome extension that calls GPT-4 to summarize your Gmail.
• A Slackbot that calls Claude to answer FAQs.
• A Notion plugin that calls Mistral to write meeting notes.
• A customer support tool that chains prompts into a flow.

This isn’t a tech stack — it’s a prefab.

When everyone has access to the same brain, the only thing that changes is the costume. UI polish. Prompt tweaks. API gymnastics. And yes, there's value in packaging, but it’s a thin layer. The barrier to entry is low. The switch cost is lower. The defensibility is non-existent.

Your clever prompt is not IP.

Your chaining tool is not infrastructure.

Your “copilot” is not a moat.

The Illusion of Competence

The scariest part is how easy it is to fool yourself. When GPT-4 gives you a beautiful answer, you feel smart. When your app strings together three model calls and returns a result, it feels like a system. And when a user says “wow, this is cool,” it feels like validation.

But here’s the problem: none of that belongs to you.

You didn’t train the model. You didn’t build the architecture. You don’t even control the updates. One API change from OpenAI and your product could break or degrade overnight. If they change token pricing, your margins evaporate. If they release an internal feature that overlaps with your app, you’re dead.

What kind of startup lives like that?

It’s not product-market fit. It’s prompt-market noise.

Back When Builders Built Models

Let’s rewind five years. Before the era of universal LLM APIs. Before GPT-3 dropped like a god in a bottle.

If you wanted intelligence in your product, you had to earn it. You had to collect data, clean it, annotate it, train a model, tune it, debug it. You had to understand loss functions, architectures, optimizers. You had to suffer through GPU errors, vanishing gradients, convergence nightmares.

It was painful. It was slow. But what you got at the end was yours. A model no one else had. A capability only your team could deliver. It wasn’t “prompt engineering.” It was engineering, period.

And that pain was your moat.