Vibe Coding Has a Stop Sign.
Most Enterprises Drive Right Past It.
The demo was impressive. Forty-eight hours from idea to working prototype. No sprint planning, no ticket backlog, no engineering bottleneck. Just a product manager, a clear brief, and an AI that could build.
The CTO saw it on a Friday. By Monday, someone had put it in front of a customer.
That’s where it went wrong.
Not because vibe coding failed. Because nobody had decided where vibe coding was supposed to stop.
What it actually is
Vibe coding — describing what you want in plain English and letting AI generate the working code — is a genuine capability shift for enterprise teams. The speed is real. The accessibility is real. A non-engineer can go from concept to demonstrable prototype in hours, not weeks.
I’ve seen this first hand. A board member of a UK software company — someone who hasn’t written a line of code in twenty-five years — was prototyping a working interface inside an afternoon. The barrier isn’t technical skill anymore. If you have a clear outcome in mind and understand what data and systems you’re working with, you can move from idea to something demonstrable without an engineer in the room.
That matters. In large organisations, the distance between an idea and a testable version of that idea is usually measured in months. Vibe coding collapses it to days. Used correctly, that’s a competitive advantage.
The problem isn’t the tool. It’s the assumption that fast to build means ready to ship.
The pattern I’ve seen work
The teams that get this right share one characteristic: they treat vibe-coded output as a question, not an answer.
The prototype exists to answer a specific business question. Does this workflow make sense to the user? Will the customer actually engage with this feature? Is the underlying logic sound before we invest in building it properly?
When the prototype answers those questions — the work is done. Full stop.
What follows is a decision gate, not a deployment checklist. If the answer is yes, we know enough to proceed — the output gets handed to engineering with a brief. It gets rebuilt, properly, with security review, data governance, integration testing, and a human being accountable for what goes into production.
The vibe-coded version gets retired. It served its purpose.
The pattern I’ve seen fail
There’s a gap that most organisations aren’t naming clearly enough. Vibe coding gets you to a prototype. A prototype is not an MVP. An MVP can be operated, monitored, governed, and owned. Someone is accountable for it when it breaks. It has been through security review. It sits on data it’s authorised to touch. A vibe-coded prototype has none of that. The distance between the two isn’t a sprint. It’s a different kind of work entirely.
The failing version looks almost identical in the early stages. Same tool. Same speed. Same impressed stakeholder in the room.
The difference is the absence of a defined stopping point.
The prototype works well enough in the demo that someone decides it’s good enough to ship. Engineering is busy. The backlog is long. The business wants to move. So the vibe-coded version gets promoted — sometimes quietly, sometimes with full leadership visibility — into something customer-facing or operationally critical.
Nobody has reviewed the underlying code. Nobody knows what data it’s handling or how. Nobody owns it when it breaks.
Gartner has consistently found that a significant proportion of generative AI projects are abandoned after proof of concept — due to poor data quality, inadequate risk controls, escalating costs, or unclear business value. The precise figure varies by report, but the direction of travel is unambiguous and has been worsening each year they’ve measured it. Vibe coding accelerates you to the proof of concept faster than ever before. It does nothing to fix any of those four failure modes.
McKinsey’s 2025 State of AI puts a sharper point on the commercial side. Only 6% of organisations are seeing meaningful EBIT impact from AI. What separates them from the other 94% is not the tools they use. It is that they set outcome-based KPIs before deployment — not after the pilot impressed someone in a boardroom. The demo is not the decision. The decision is what you measure, and when you start measuring it.
The stop sign
The teams I’ve seen use this well have something explicit in place before the prototype is built. Not a lengthy governance framework — a single question, agreed in advance:
What does this prototype need to prove, and what happens to it when it’s proved it?
If the answer to the second part isn’t “it gets rebuilt properly,” the work hasn’t started yet.
That sounds obvious. It isn’t. In practice, the pressure to ship something visible — especially when leadership has seen a demo they liked — is significant.
The stop sign has to be erected before the demo happens, not after.
The AI Readyist publishes every other Tuesday. If this was useful, subscribe — and share it with the CTO who just saw a demo they liked.
cspanswick.substack.com