Your AI Product Is Not A Real Business

Source: Dev.to

Observations from STEP 2026

I just got back from STEP 2026 in Dubai. While there were some genuinely amazing businesses, I also saw many companies that won’t make it past their first year.

AI Is Not Your Product

Most startups now splash “AI” onto all their marketing. AI itself does not deliver business value. Unless you are a frontier lab, AI is just a tool in your stack. Nobody is shouting “MongoDB‑enabled trading platform”.

• Users don’t care if it’s AI.
• Investors don’t care if it’s AI.

They care about what the product does, the problem it solves, and whether there’s market space for it.

What Real Enterprises Expect

When selling to real businesses, I’ve sat across the table from $5 bn consultancies evaluating AI tools. They ask about:

• Architecture
• Data residency
• On‑prem deployment
• Ownership of the solution

If the answer is “we call the OpenAI API,” the meeting is over.

The Common AI Startup Playbook

Many AI startups follow this pattern:

1. Vague product idea
2. Wrap an AI model
3. Display it to the user
4. Charge $29/month

This is not a business. Users could just use ChatGPT—why pay for another subscription? The model is not defensible, has no IP, and is vulnerable to changes in the underlying AI service.

Remember when everyone built apps on top of Twitter and the API rules changed overnight? The same risk applies when you merely wrap a model. Frontier model providers have an incentive to compete directly with you if you come up with a good, simple idea.

Cost and Dependency Risks

Relying on external APIs means you’re exposed to a huge cost base you don’t control—input and output tokens can rack up an AI bill behind the scenes.

Typical playbook:

1. Wrapper launches and gains traction
2. Model provider notices traction
3. Provider adds features to handle the use‑case in‑house
4. Business case evaporates

You end up doing market research for the model provider, who can execute better than you.

The “Vibe Coding” Trap

My most successful summary of Brunelly at STEP 2026 was: “You know what vibe coding is, right? We’re the opposite of that. We actually create real‑world enterprise‑quality software.”

Vibe coding has a bad reputation: security gaps, bugs, scalability issues, deployment headaches, and compliance problems. Vibe‑coded AI products combine the worst of both worlds—simple AI wrappers lacking any scalability.

Building Real Enterprise‑Ready AI Infrastructure

I’ve spent the last year building Maitento, an AI‑native operating system—think of it as a cross between Unix and AWS, but AI native. Key concepts:

• Models as drivers
• Different process types (Linux containers, AI agents interacting, apps in our own language, code‑generation orchestration)
• Agents can connect to any OpenAPI or MCP server
• Declarative application definitions
• Shell, RAG, memory system, context management, multi‑modal support

This is the iceberg needed to create a real enterprise‑ready AI‑enabled application. We needed extensibility, quality, scalability, performance, and speed of development—duct‑taping Python scripts together didn’t cut it.

You don’t need the exact level of orchestration we have, but the moving pieces in enterprise‑grade AI orchestration are far more complex than most founders realize.

Why ChatGPT Is More Than a Simple Wrapper

ChatGPT isn’t just a wrapper around its own API with some system prompts. It includes:

• File management
• Prompt‑injection detection
• Context analysis and memory management
• Rolling context windows
• Deployments and scalability
• Backend queueing and real‑time streaming for millions of users
• Multi‑modal input
• Distributed Python execution environments

Calling an API is easy; building the surrounding infrastructure is hard.

Demo vs. Product

Founders often rush to ship a demo, but a demo is not a product. It’s a controlled environment that doesn’t replicate reality. The gap between an impressive demo and a production‑grade AI product is wider than in any other software category because AI systems can hallucinate, lose context, and confidently produce wrong outputs. Managing these failure modes requires real infrastructure—not just a try/catch around an API call.

The Real Shovels in the AI Gold Rush

Most “shovels” being produced are made of cardboard. The companies that will still exist in five years are the ones building real infrastructure today— not just calling APIs, chaining prompts, or wrapping someone else’s intelligence in a pretty interface and calling it innovation.

Build the thing that’s hard to build. That’s the only strategy that works. If you can build it in a few days, anyone else can. If it’s difficult for you, it will be difficult for your competitors, and you may actually have a genuinely novel business.