Vibe Coding Is Real—But So Is the Tech Debt It Creates

The Rise of the Vibe Coder

The term "vibe coding" was coined half-jokingly to describe a workflow that's become entirely serious: you describe what you want, the AI writes it, you accept or tweak, and iterate. No reading documentation. No memorising APIs. Just intent, output, and judgment.

This workflow is genuinely transformative for a specific category of work—prototypes, internal tools, throwaway scripts, and MVPs where speed matters far more than elegance. A solo founder building a demo for investors doesn't need a perfectly architected codebase. They need something that works tomorrow.

The Debt Profile Is Different

Traditional technical debt accumulates from known shortcuts: skipping tests, hardcoding values, deferring refactors. Vibe coding creates a different debt profile that's harder to see and faster to compound:

• Invisible dependencies — AI-generated code pulls in libraries the developer never consciously chose, often outdated or unmaintained
• No mental model — developers who didn't write the code often can't debug it when it breaks, because they never built intuition for how it works
• Inconsistency at scale — each AI generation is locally coherent but globally inconsistent. Three AI-written modules may solve the same problem three different ways
• Test deserts — unless explicitly prompted, AI generation frequently skips edge case handling and test coverage

What Responsible AI-Assisted Development Looks Like

The engineers getting the best long-term results aren't the fastest prompters—they're the ones who apply judgment about when to vibe code and when to slow down:

1. Prototype freely, refactor before shipping — vibe code the first version, then read and understand it before it goes near production
2. Own the architecture — let AI write the implementation, but define the file structure, data models, and interfaces yourself
3. Require tests as part of generation — always prompt for tests alongside implementation; it forces the AI to think through edge cases
4. Review every dependency — AI will reach for familiar packages. Check that each one is current, maintained, and actually the right tool

The Honest Assessment

Vibe coding is here to stay. The velocity gains are too real to ignore, and the best engineers are using these tools every day. But the teams treating AI generation as a magic black box are accumulating brittle codebases that become progressively harder to extend and debug.

The engineers who will still be productive in three years aren't the ones who delegate thinking to the AI—they're the ones who use AI to execute faster while keeping their own judgment firmly in the loop. That combination is what we hire for at Ortuni, and it's what we look for when evaluating technical partners.