Andrej Karpathy coined the term "vibe coding" in February 2025 to describe a new way of building software: describe what you want in plain English, let the AI generate the code, and just... go with the vibe. Don't read the code too carefully. Don't try to understand every line. If it works, ship it.
A year later, vibe coding isn't just a meme. It's an entire ecosystem. Cursor, Vercel v0, Lovable, and a dozen other tools have turned it into a real workflow. Non-technical founders are building and shipping apps. Product managers are prototyping features in hours instead of weeks. The barrier to creating software has never been lower.
Which is exactly why we need to talk about what happens next.
The Numbers That Should Worry You
A CodeRabbit analysis and the Veracode 2025 State of Software Security report found that 45% of applications built primarily through AI code generation contain critical security vulnerabilities. Not minor warnings. Not style issues. Critical vulnerabilities, the kind that let attackers steal data, inject malicious code, or take over systems.
Separately, studies show that AI-generated code has 1.7x more major issues than human-written code. And this isn't because AI writes worse code than bad developers. It's because AI writes code without understanding context, without thinking about edge cases, and without the defensive paranoia that experienced engineers develop through years of dealing with things breaking in production.
Red Hat published an article on February 17, 2026 titled "The Uncomfortable Truth About Vibe Coding" that laid it out plainly: AI can generate code fast, but speed without understanding creates a particular kind of technical debt. The kind where nobody on the team knows how the system actually works.
The Technical Debt Nobody Sees Coming
Traditional technical debt is painful but manageable. You know the code is messy, you know where the shortcuts are, and you can plan to fix them. Someone on the team wrote it and understands it, even if it's not pretty.
Vibe-coded technical debt is different. Nobody understands the code because nobody wrote it. When something breaks (and in production, something always breaks) there's no institutional knowledge to fall back on. You're debugging code that was generated by a model that can't explain its own reasoning, modified by prompts that weren't documented, and tested by someone who accepted "it seems to work" as sufficient validation.
This is the new technical debt, and it compounds faster than the traditional kind because the speed of generation outpaces any team's ability to review what's being built.
It's Not All Bad, But It Requires Understanding
I want to be clear: AI-assisted coding is genuinely useful. It accelerates prototyping, reduces boilerplate, and helps experienced developers move faster. The tools are getting better. Google Cloud published a comprehensive guide to vibe coding. Wikipedia has an article on it. It's a real thing.
The problem isn't using AI to write code. The problem is using AI to write code you can't evaluate. If you can read the output, spot the security holes, understand the architecture choices, and fix the bugs, then AI coding tools make you faster. If you can't, you're building on sand.
This distinction matters enormously for enterprises. A hackathon prototype built with vibe coding? Fine. A customer-facing application handling payment data built by someone who can't read the code the AI generated? That's a lawsuit waiting to happen.
What This Means for Your Career
Here's the irony: vibe coding doesn't eliminate the need for understanding software. It makes that understanding more valuable. As more code gets generated by AI, the people who can evaluate, debug, and secure that code become the critical bottleneck.
This is true even if you're not a developer. If you're a product manager reviewing an AI-built prototype, you need to know what questions to ask. If you're an executive approving an AI-generated application for production, you need to understand the risks. If you're a founder building your MVP with Cursor, you need to know when the vibe is leading you off a cliff.
The tools will keep getting better. The quality will improve. But "understanding what AI produces" isn't a temporary skill. It's the foundation of working with AI effectively, in coding and everything else.