Why vibe-coded products stall the moment they hit production

What vibe coding actually is

Vibe coding is building software by describing what you want to an AI model and accepting most of what it writes, without reading it line by line. You steer with intent instead of syntax. The term caught on because it names something real: for the first time, the distance between an idea and a running interface is measured in hours.

It is not a synonym for using AI to code. An engineer who reviews every diff, keeps a mental model of the system, and rejects half of what the model suggests is doing something different. The distinguishing feature of vibe coding is that nobody holds the whole picture. The code exists, it runs, and no human has a complete model of why.

Everyone wants to vibe code now. Almost no one thinks about who maintains what's left.

It became a trend. "I built a SaaS in 48 hours with AI." "I launched a product without writing a single line." "The agent handles architecture, I handle the idea."

Why it feels like it works

Because for the first stretch it genuinely does. The early phase of a product is mostly greenfield: no legacy data, no users depending on yesterday's behaviour, no compliance requirement, no integration with a system somebody built in 2011. Every decision is reversible because nothing is load bearing yet.

In that environment the model is extraordinarily effective. It writes plausible code fast, and plausible is enough when the only judge is whether the screen renders. The feedback loop is immediate and flattering.

The trap is concluding from this that the hard part is solved. What actually happened is that you spent the phase of the project where correctness was cheapest.

Then production happens

Then production happens.

The first paying customer hits a bug that lives somewhere nobody can locate. The second feature forces rewriting half of what already exists. After a few weeks the agent has lost track of its own code. A migration breaks because the domain was never modeled in the first place.

And the founder thinks the next model will fix it. It won't.

The four failures, and what actually causes each one

The bug nobody can locate. Debugging is not reading code, it is having a model of where a given behaviour could originate. When no human ever built that model, every bug becomes an exhaustive search. The code is readable; the system is not legible.

The feature that forces a rewrite. This is the absence of boundaries. Generated code tends to solve the immediate request in the most direct way available, which usually means reaching straight into whatever data it needs. Do that fifty times and everything touches everything, so no change is local anymore.

The agent losing track of its own code. A model works from the context it is given. Past a certain size the codebase no longer fits, so the model sees fragments and confidently writes a fifth version of something that already exists four times. The result is not one system, it is several overlapping ones.

The migration that breaks. When the domain was never modeled, the database schema is an accident of the order in which features were requested. Migrating it means discovering the rules of your own business from the data, which is exactly the work that was skipped at the start, now performed under pressure with customers attached.

The part that got faster, and the part that did not

Speed of writing was never what held people back from building products. What held them back was understanding the problem, modeling the domain, deciding what scales and what turns into debt.

The easy part got faster. The hard part stayed exactly where it was.

This is worth sitting with, because it predicts where the next few years go. If typing was ten percent of the difficulty and typing is now free, you removed ten percent. The other ninety percent, which is judgment about structure, is untouched, and it is now the entire bottleneck.

Why the next model will not fix it

The bet that a stronger model dissolves the problem misreads what the problem is. A better model produces better code per request. It does not produce a coherent system across two thousand requests made over four months by someone who did not track the consequences.

Coherence is not a property of any single output. It comes from decisions held consistently over time: this is our boundary, this is our source of truth, this invariant is never violated. Something has to hold those decisions. Today that something is a person, or a written document that a person maintains.

Models will keep getting better at the local task. The global task is a different kind of work, and it is not obviously on the same curve.

What to do before the first prompt

Before asking for the first prompt, model the domain. Then delegate the execution.

In practice that is one page, not a design document. Name the three to five nouns your business actually has and what each one means. Write down the rules that must never be violated, the ones where a violation is a real incident rather than a bug. Decide the boundaries: what is allowed to know about what.

Then let the model write inside that structure. The difference is not how much AI you use, it is whether the AI is filling in a shape you defined or inventing the shape as it goes. The first compounds. The second accumulates.

When vibe coding is exactly the right call

It would be a bad conclusion to read this as an argument against the technique. Vibe coding is the correct tool whenever the value of the artifact is the information it gives you, not the artifact itself.

Prototypes meant to be thrown away. Testing whether a flow makes sense before anyone commits. Internal tools with five users where the cost of being wrong is a conversation. A demo that has to exist by Thursday. In all of these, structure is overhead, because the thing is not going to live long enough to pay it back.

The mistake is not using it. The mistake is not noticing the moment a prototype quietly became the product, which almost always happens without a decision being made.

The line between a demo and a system

Vibe coding validates an idea over a weekend. It doesn't sustain a product running in production with an SLA. Whoever confuses the two finds out the price in month 4, when the code stops evolving and nobody inside can read what was written.

The practical test is a single question: if this breaks at two in the morning, is there a person who can reason about why. If the answer is no, you do not have a system yet, you have a demo that happens to have users.

That gap between something that demos and something that holds in production is where Studio Labs lives. It's also why we build the thing that runs in production instead of handing over a deck. If you are working out what shape the thing should take, what an AI agent actually is covers the same question from the other side, and it is the reasoning behind how we build production AI, end to end.