When people talk about “AI wrapper apps,” what are they actually talking about?
From a technical standpoint, the simplest wrapper app is basically this: you prewrite a prompt, the user types something, you send both the user’s text and your prompt to an LLM API, and you return the model’s output to the user.
Why do people look down on it? Because it seems too easy. Too easily reverse-engineered. No moat. No barrier.
Another thing many people look down on, for the same reason, is vibe coding. The simplest form of vibe coding is: you prewrite a prompt, feed it into an AI coding tool, and it gives you a usable app that implements your prompt.
So what do people respect?
It seems to be “complete” products with more complex ideas and technology. They have a real backend and database, authentication, a polished frontend, deployment, container orchestration, Redis, distributed systems. They do not run a fixed AI workflow, but a real AI agent: it has powerful tools, it can decide what to do next based on the current situation, it can incorporate external feedback, and it can execute complex tasks over a long period of time. It looks complex. It looks capable. It looks technically hard. It looks like there must be at least a senior engineer behind it.
But how is that complex product actually built?
You prewrite a prompt, feed it into an AI coding tool, and it gives you a usable app that implements your prompt. And the AI technology inside, down to the most granular level, is still: you prewrite a prompt, take some input, send both into an LLM API, and output some text.
So is there really any difference?
In the AI era, isn’t it true that all kinds of things can be made through the same underlying process?
In the age of AI coding, a complex application is just a wrapper around prompts. And the technical details inside the app are just a wrapper around an LLM API. Ultimately, everything is a wrapper around your mind.
You initialize an idea. That idea comes from your unique experiences, desires, and way of thinking. You turn it into a product people can use. Others might reverse-engineer your prompt, but they cannot reverse-engineer all the details behind it. They cannot reverse-engineer the next update you are about to ship. And they definitely cannot reverse-engineer what you will build next.
Because nobody can reverse-engineer you, the one and only you.
In the past, the value you delivered to the world came from the uniqueness of you as a person: your fit for the job, your ability to take initiative, to do the labor of bridging what doesn’t connect, to make A and B work together, to add something new to the world or shift something slightly so others can reuse the result, and so on.
Nothing changes in the AI era. It is exactly the same. The only difference is: now it is vastly more convenient, vastly faster, and the range of what you can build is vastly bigger.
So never retreat just because the implementation process feels easy, or because it looks “low barrier,” or because you start doubting yourself: Is it embarrassing to ship something so simple? Will someone destroy me with one sentence: “Why don’t users just use ChatGPT directly?”
Because if ChatGPT alone could solve it, you would not have had that idea in the first place.
Put differently: when you deliver results at work, is it always high-barrier and technically difficult? Step by step, you walk a thousand miles. Drop by drop, you fill an ocean. If you do not start from something simple and local and fragmented, you will never expand your capacity into building something more complete. And that fragment might already be extremely valuable.
I hope that in this era, where the bridge from idea to reality is so easy to build, and the space of what you can desire and realize is so large, we face our desire directly. Do not compromise on it. Do not care whether your technical background is “strong” or “weak,” whether you are a Principal Engineer or a student who cannot land an internship.
Having an idea and building something real is not an easy thing. It is an absolutely hard thing.
Every idea you generate, token by token, when you look back at the sequence of tokens that came before it, that is your long, long life.