Use AI to Validate MVPs Fast, But Don't Rush Into Production
"I built an MVP with AI—do you think I can productize it and make money?"
I've been getting this question a lot lately. Every time I hear it, I think of the classic analogy: building a house with LEGO blocks.
AI has indeed made building with LEGOs faster than ever. Ideas that used to take months to validate can now run in weeks or even days. But the problem is, many people think "it works" means "it can make money."
AI's Value: Making Validation Faster Than Ever
I'm not denying AI's value. As a contributor to the Sealos open-source project (15.6k stars), I know how important the speed of technical iteration is.
AI's biggest value is in rapidly validating whether an idea has market potential.
In the past, an indie developer wanting to validate a SaaS idea needed to:
- Learn frontend frameworks
- Build backend APIs
- Design databases
- Handle user authentication
- Integrate payment systems
- Deploy and go live
This process took at least 3-6 months. Now with AI, the same functionality might run in 1-2 weeks.
This is the biggest gift AI gives indie developers: minimizing validation costs.
MVP to Production: The Invisible Gap
But there's a trap here.
An MVP built with AI is like a house assembled from various shaped LEGO blocks. It can indeed stand up and looks legitimate, but try adding a balcony?
The whole thing might collapse.
Real Pain Point: Want to Add a Feature, System Crashes
I've seen too many cases like this:
- User says: "Can you add an export feature?" Turns out the data table structure doesn't support it
- Boss says: "We need to support multi-tenancy." Turns out the entire permission system needs rewriting
- Product says: "This page needs caching." Turns out there's no caching layer, performance just crashes
This is the typical problem with AI-generated code: it works, but doesn't mean it scales.
Reading Code Is Harder Than Writing Code
This reminds me of an article I read recently: "Writing Code Is Easy, Reading Is Hard."
AI-generated code has extremely high costs for understanding and maintenance. What you see might be a simple function, but to truly understand it, you need to:
- Trace where this function is defined
- Understand what data structure it returns
- Know how it interacts with the database
- Find out if it has side effects
- Figure out who else is calling it
To understand one function, you often need to look at five other files. This is why many people would rather rewrite than maintain AI-generated code.
Engineering Quality: The Key from MVP to Production
So what determines whether your product can go from "working" to "profitable"?
Engineering quality.
Specifically, three dimensions:
1. Readability
Code should be understandable to humans, including your future self 6 months from now.
2. Extensibility
New features shouldn't require rewriting the entire system.
3. Maintainability
When problems occur, you should be able to locate and fix them quickly.
My Practical Recommendations
Based on my work experience with Sealos and years of development practice, my advice is:
MVP Phase: Speed First
- Build quickly with AI, don't obsess over code quality
- Focus on user feedback, validate core assumptions
- Log technical debt, but don't address it immediately
After Validation: Quality First
- Assess existing code's engineering quality
- Identify core business logic
- Refactor or rewrite critical modules
- Establish testing and monitoring systems
The Tipping Point: When Should You Care About Engineering Quality?
In my experience: when you get your first paying customer.
At this point, you have proof of revenue and user feedback. It's time to turn "it works" into "it works reliably."
Final Thoughts
AI has changed the rules of the game, but hasn't changed the game itself.
Rapid validation + solid engineering = sustainable product.
AI lets us fail faster, but what ultimately determines product success is still that old question: Does your product really solve users' pain points? Can your technical architecture support business growth?
Use AI to validate ideas quickly, use engineering quality to support productization. That's the right approach for indie developers in the AI era.
If you're also building products with AI, feel free to share your experiences and challenges. Let's discuss how to find the balance between rapid validation and engineering quality.