The Endgame of Vibe Coding Is Not MVP. It Is Production.

Today, a product manager, a designer, or even someone who does not know how to code at all can use an Agent to build an MVP, and some of them have even started teaching others vibe coding.

But here is the question:

Can an MVP built purely by an Agent really be deployed to production and used by real users?

When an Agent is changing code for you inside a black box, if you do not understand the system, you actually feel insecure.

You get this feeling:

“It looks like it is running, but I do not know why it works, and I do not know under what edge cases it will break.”

It is a bit like investing in stocks.

If you do not understand the industry, you might make money in the short term, but you may not be able to hold onto that position.

Because you do not have fundamental judgment.

What is even more troublesome is that this feeling of “not being sure” is not just a psychological problem.

It turns every iteration into blind fixing.

After a few rounds of blind fixes, AI-generated code starts to enter its “half-life.”

The code does not suddenly break.

Instead, every change that no one truly understands pushes it a little further away from being maintainable.

In the first week, you build an MVP and think:

“Wow, this is amazing.”

In the second week, you want to add a new feature. The Agent changes the code, but then one of the original pages stops opening.

You do not know why.

In the third week, a user reports that a certain button no longer works.

You open the Agent and start asking questions, but then you realize you first need to explain the entire project context to it.

And that context has already become so complicated that even you cannot explain it clearly anymore.

This is the real dilemma of AI coding:

It is not that AI cannot write the code.

It is that after AI writes it, you cannot catch it.

Once more users come in, problems start to appear:

Login errors, data loss, permission leaks, slow pages, API timeouts, and increasingly heavy database queries.

What is even more troublesome is that many bugs are not as simple as “one line of code was written wrong.”

They are system design problems.

For example, in the beginning, Claude helps you build an essay grading tool.

The demo works fine.

Ten users are also fine.

But if a class of 30 students submits essays at the same time, and each essay needs to generate:

Scoring

Feedback

Rewriting suggestions

Practice questions

Then the system may start timing out, queuing, and failing.

At that point, the right solution is not:

“Just ask AI to fix the bug again.”

Instead, you need to redesign the architecture.

For example:

Synchronous requests need to become asynchronous task queues.

The database needs to record task status.

Repeated calls need caching to reduce cost.

Failures need a retry mechanism.

And if you add teacher accounts, student accounts, and class management, you also need a permission system.

In other words:

Each student should only be able to see their own results.

Each teacher should only be able to see the data from their own class.

AI can help you write this code.

But from an MVP to a real production-ready system, there are countless decision points in between.

This is not something one AI brain can fully close the loop on by itself.

The human brain is responsible for judging the direction, while AI amplifies execution.

You first need to know where the problem is, and you also need to know which direction to ask it to move toward.

This is what it feels like when you “cannot catch it”:

You have something in your hands that can run, but you do not dare to touch it.

As long as you do not touch it, it stays alive.

The moment you try to change it, it breaks.

So, will AI replace programmers?

I think it will replace some people who only know how to mechanically write code.

But it will not replace programmers who truly understand systems, products, tradeoffs, and maintenance.

🐥 We are Hatch Hatch, an AI-native tech education company for students ages 9–15.