Vibe Coding Boosts Speed but Demands Human Oversight

When AI feels like babysitting

Carla Rover, a 15-year web developer now building a startup with her son, captured a growing pain many engineers know too well: she spent 30 minutes sobbing after restarting a project produced by an AI co-pilot. What began as a speed hack to ship an impressive demo became a full rewrite when automated checks and later manual reviews uncovered numerous errors.

Vibe coding — the fast sketching of ideas with AI-powered coding assistants — is being embraced for prototyping, UI design, and scaffolding. But experienced engineers describe it like hiring a clever but unreliable helper: it can do some tasks well, invent results, or silently introduce bugs and security holes.

The numbers back the frustration. Fastly surveyed nearly 800 developers and found at least 95% spend extra time fixing AI-generated code, with senior developers carrying most of that verification burden. Issues range from hallucinated package names to deleted data and subtle security vulnerabilities that can be harder to spot than human mistakes.

Why AI code needs a human in the loop

Engineers interviewed by TechCrunch used vivid analogies: a smart six-year-old asked to pour coffee, a stubborn teenager who sometimes breaks things, or a toxic coworker that fabricates explanations. The common theme is predictable: AI can accelerate mundane tasks but struggles with systems thinking, consistency, and admitting mistakes.

That matters because AI tends to optimize for quick, surface-level fixes. Left unchecked, it can duplicate features across a codebase, skip integration patterns, or produce insecure shortcuts — the exact kind of technical debt that sinks startups and complicates scale.

What teams are already doing

• Mandatory peer review and manual scanning before merging AI-generated changes.
• Access controls and approved tool lists to limit which AI agents can modify repos.
• Automated security scanning and integration tests to catch common vulnerabilities early.

These controls are becoming standard in organizations that want the speed of AI without the hidden costs. Some companies even create new roles focused on cleaning up AI-produced code — a signal that the industry is adapting but still finding its footing.

A pragmatic path forward

Vibe coding is already reshaping jobs. Senior engineers will spend less time typing boilerplate and more time specifying, guiding, and validating AI output. Junior engineers can learn faster by reviewing AI suggestions line-by-line — if mentors remain engaged and teams enforce review discipline.

For product and engineering leaders, the practical questions are clear: how do you keep velocity without inviting hidden risk? The answer lies in stitchwork between policy, automation, and human expertise — guardrails that let AI accelerate work without becoming a liability.

Carla Rover’s lesson was costly but instructive: the co-pilot can be a powerful teammate, but it isn’t an employee. Treat AI output as draft material — useful for ideation and scaffolding, never as a final ship-ready artifact without review. That mindset will save tears, time, and reputations.

The new normal will be messy but productive: more prototypes, faster iterations, and an 'innovation tax' of verification work. Teams that formalize review paths, enforce security scanning, and train engineers to guide AI will extract the upside while containing the downside.