AI for Developers: The Hidden Technical Debt of “Vibe Coding”
About the Author – Carlos Ríos
Founder & CEO of Dappsco (formerly Daniel Apps), Carlos is a self-made entrepreneur and electronic engineer who has led near-shore software teams since 2012.
Under his guidance, Dappsco blends Latin-American talent with North-American standards to deliver mobile apps, AI-powered platforms, and enterprise solutions for clients across fintech, health, logistics, and beyond.
The peed that tools like Cursor or Claude Code offer is undeniable. What used to take days can now be done in minutes thanks to AI assistance and automation. But with that efficiency comes a phenomenon known as “vibe coding.”
What is vibe coding?
Vibe coding happens when you generate code that works and “feels right,” but you don’t actually understand why it works. It’s that magical moment when the app runs, features are delivered, but the knowledge behind the code is unclear—or even nonexistent.
I’ve seen developers delivering features at lightning speed, but when something breaks, they don’t know where to start. It’s like having a black box in production: the code works, but you don’t know how—or why.
The new technical debt
Technical debt isn’t just poorly written code or weak architectures anymore. There’s a new, invisible kind: code that works but that we rely on without really understanding it. This debt accrues interest, and it usually shows up at the worst possible moment.
For example, we built a feature with AI in 10 minutes and everything seemed perfect. But when we changed the business logic, we realized nobody knew how to adjust it without breaking other parts of the system.
How to handle AI-generated code
In our team, we’ve implemented some practices to prevent vibe coding from becoming a problem:
Understand before merging:
If you can’t explain in 2 minutes what the code does, study it before integrating.
Document AI decisions:
Not just what the code does, but why the AI chose that solution and why we validated it.
Critical testing:
If we don’t understand how it works internally, we at least need clear tests for what the code should do.
Continuous refactoring:
AI gives us a first draft; we decide if it’s good to go, and it almost always needs improvements.
Reinvented pair programming:
One person drives the AI, the other questions and validates decisions, keeping knowledge in the team.
Speed with judgment
These tools are amazing for productivity, but speed without judgment is just technical debt in disguise. In our team, we use AI every day—but always with human checkpoints. We don’t distrust the technology; we know we’ll be the ones maintaining this code in the future.
Conclusion
Vibe coding isn’t an inevitable consequence of AI, but recognizing it and managing it is key to avoiding problems down the line. Speed is only useful when paired with understanding and good practices.
What’s your experience with AI-generated code? Has speed ever caused more problems than it solved? I’d love to hear your stories and lessons in the comments!
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