I just got back from STEP 2026 in Dubai. Whilst there were some genuinely amazing businesses there, I also saw a lot of companies that won’t make their first year.
Most startups now splash AI on to all their marketing. AI is not your product. AI itself does not deliver business value. Unless you are a frontier lab, AI is nothing more than a tool in your stack. Nobody is there 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 it does, what problem it solves and whether there’s space for it in the market.
And if you want to sell to real businesses? I've sat across the table from $5bn consultancies evaluating AI tools. They ask about your architecture, your data residency, how to deploy it on-prem and what you actually own. If the answer is 'we call the OpenAI API' – the meeting is over.
Wrappers… Everywhere
There are tens of thousands of AI startups right now whose core premise is:
- Vague idea about product
- Put a bit of a wrapper around an AI model
- Display it to the user
- Charge $29/month
This is not a business. Your users could most likely just use ChatGPT – why would they want another subscription?
It’s not defensible. There’s no IP there. There’s nothing unique. On the contrary your whole business is at risk of changes to a model.
Remember when everyone built apps on top of Twitter and then they changed API rules overnight? That can happen to you if you’re just wrapping a model. It’s even worse here as the frontier models have incentive to compete against you when you come up with a good, simple idea.
Let’s not even get into the fact that you’re open to a huge cost base where you aren’t in control of input or output tokens and just rack up an AI bill behind the scenes.
The playbook right now seems to be:
- Wrapper launches and gets traction
- Model provider notices traction
- Model provider adds features to handle some of this in house
- Business case evaporates
You’re doing market research for OpenAI – and they can execute better than you can.
Stop doing this.
Vibe Coding Is Making This Worse
My most successful summary of Brunellyat STEP 2026 was ‘You know what vibe coding is right? We’re the opposite of that. We actually create real-world enterprise quality software’.
That has to be the opener because vibe coding has got such a bad reputation in the real-world. Security gaps, bugs, scalability, deployments, infrastructure management, compliance – all non-existent.
And vibe coded AI products take the worst of all worlds. The simplest AI wrapper around some basic CRUD operations but lacking any scalability.
Please stop.
There’s A Better Way To Do AI
I’ve spent the last year building Maitento – our AI native operating system. Think of it as a cross between Unix and AWS but AI native. Models are drivers. There are different process types (Linux containers, AI’s interacting with each other, apps developed in our own programming language, code generation orchestration). Every agent can connect to any OpenAPI or MCP server out there. Applications are defined declaratively. Shell. RAG. Memory system. Context management. Multi-modal. There’s a lot.
This is the iceberg we needed to create a real enterprise-ready AI-enabled application.
Why did we need it? Extensibility. Quality. Scalability. Performance. Speed of development. Duct-taping a bunch of Python scripts together didn’t cut it.
I’m not saying you need the level of orchestration that we have – but wanted to emphasise that the moving pieces in enterprise grade AI orchestration are far more complex.
Do you think ChatGPT is just a wrapper around their own API with some system prompts? There’s file management, prompt injection detection, context analysis, memory management, rolling context windows, deployments, scalability, backend queueing, real-time streaming across millions of users, multi-modal input, distributed Python execution environments. ChatGPT itself has a ‘call the model’ step but it’s the tiniest part of the overall infrastructure.
The Uncomfortable Truth
It’s easy to call an API. It’s far harder to build real infrastructure than many founders realise.
Founders want to ship so rush to deliver. But that doesn’t mean you’re actually building a business – you’re building a tech demo.
A demo is not a product. It’s a controlled environment that doesn’t replicate reality.
The gap between impressive demo and production-grade product in AI is wider than in any other category of software. Because AI systems fail in ways that traditional software doesn't. They hallucinate, they lose context, they confidently produce wrong outputs.
Managing that failure mode requires infrastructure. Real infrastructure. Not a try/catch block around an API call.
Build Something That Matters
The AI gold rush is producing a lot of shovels.
Most of those shovels are made of cardboard.
The companies that will still exist in five years are the ones building real infrastructure today. Not just calling APIs. Not chaining prompts. Not 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. It always has been.
If you were able to build it in a few days, so can anyone else.
If it’s difficult for you then it is for your competitors.
And then you may actually have a genuinely novel business.
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