You’re Not Building AI. You’re Building a Form. And the Form Happens to Talk. You’re Not Building AI. You’re Building a Form. And the Form Happens to Talk. Let’s be honest. Scroll through Product Hunt or Y Combinator’s latest batch and you’ll see a pattern. Another AI-powered solution, another slick UI, another bold claim about disrupting an industry. But when you actually use the product, what you get is usually this: a glorified form that sends your input to GPT-4, then displays the response and maybe saves it to a database. It feels like a magic trick until you realize everyone is using the same rabbit and the same hat. The new wave of AI startups, for all their buzz, often relies on a very basic loop. You input text. The app sends it to an API. You get a response. Maybe it logs the result. Maybe it summarizes it. Maybe it suggests the next thing to type. But structurally, it’s barely different from what we were building with Rails scaffolding in 2010. Except now, instead of handling CRUD with a database, the "business logic" is prompt engineering. And we’re calling that innovation. The Pattern: Rinse, Repeat, Raise Here’s how the formula usually plays out: User types something vaguely specific.
Prompt goes to GPT or Claude.
The response is prettified and shown back to the user.
Optionally, it gets stored, tagged, or emailed. User types something vaguely specific. Prompt goes to GPT or Claude. The response is prettified and shown back to the user. Optionally, it gets stored, tagged, or emailed. You slap a clean interface on it, call it a co-pilot, and claim it's built with cutting-edge artificial intelligence. Sprinkle in some buzzwords: semantic search, retrieval-augmented generation, intelligent agents. But under the surface, it's just OpenAI doing all the hard work. Investors don’t seem to mind. The market is frothy. There’s a feeding frenzy for anything with "AI" in the pitch deck, even if the underlying product is little more than prompt chaining with a front-end. And to be fair, some of these wrappers do offer real value — if only because they save people time. But we have to ask: Is this innovation, or is it a game of who can reskin ChatGPT the fastest? Why This Model Feels Hollow For one, these startups are dangerously dependent on someone else’s infrastructure. If OpenAI, Anthropic, or Google decides to change their pricing or API terms, the entire business model crumbles. There’s no moat, no IP, and very little defensibility. Everyone's feeding from the same API, though. Then there’s the lack of technical depth. Many of these teams aren’t touching the model itself. They aren’t doing fine-tuning, they’re not building novel architectures, and they’re certainly not pushing the boundaries of multi-agent systems or autonomous reasoning. They’re building UX around a black box and hoping the box keeps doing most of the work. It’s like the early mobile app days all over again. Remember flashlight apps? Same codebase, different icon. Now it's prompt templates instead of light bulbs. Why It Still Works (For Now) Despite all this, some of these startups do take off. Why? Because distribution still matters more than architecture. Most users don’t care if your model is fine-tuned or if you just piped GPT-4 into a chat bubble. They care if it helps them get something done faster. Good UX, good onboarding, and clever niching can go a long way. An AI writing assistant tailored specifically for insurance adjusters might not be groundbreaking tech-wise, but if it nails the user experience and speaks their language, it wins. For a while. The danger is in mistaking a distribution hack for a long-term moat. If you’re not building differentiated technology, someone else can replicate what you’ve built in a weekend. And with open-source models improving rapidly, they might even do it cheaper. What Real AI Innovation Could Look Like If you actually want to build something defensible and genuinely innovative in the AI space, the bar is higher. Here are a few directions that move beyond the GPT-wrapper playbook: Fine-tuned models for specialized tasks, trained on proprietary data
Multi-agent systems that coordinate complex workflows without constant human prompting
Long-term memory architectures that allow true context retention
Modalities beyond text: multimodal models, voice interaction, real-time vision
Infrastructure tools that help others build, monitor, and scale LLM applications Fine-tuned models for specialized tasks, trained on proprietary data Multi-agent systems that coordinate complex workflows without constant human prompting Long-term memory architectures that allow true context retention Modalities beyond text: multimodal models, voice interaction, real-time vision Infrastructure tools that help others build, monitor, and scale LLM applications This is harder. It takes technical depth. It takes a clear view of where language models break down and where other paradigms are needed. But it’s where the real breakthroughs will come from. Conclusion: Are You Building a Product, or Just a Prompt? This isn’t a call to gatekeep. Not everyone needs to build the next GPT. There’s value in good UI, in user-centric design, and in making powerful models accessible to more people. But if you’re serious about building in AI, you should be asking tougher questions. Are you creating new capabilities, or just repackaging existing ones? Are you innovating, or just rewrapping? And if OpenAI pulled the plug tomorrow, would your product still do anything at all? Because if the answer is no, then maybe you’re not building an AI startup. Maybe you’re just building a fancy form.

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

This piece was written for humor or satire and may include nonfactual statements, stories, or anecdotes.

Why Most AI Startups Are Just Fancy CRUD Apps With a GPT Wrapper

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