What Is Vibe Coding? A Generation of Non-Engineers

Vibe coding lets you ship apps without understanding code. But what happens when it breaks? Understand what vibe coding is — and why it matters that you move beyond it.

The Term That Named a Generation of Coders

In early 2025, Andrej Karpathy tweeted about a new coding style he called "vibe coding" — writing software by feeling your way through it: prompt an LLM, accept the output, run it, prompt again. No deep understanding required. The term went viral because it named something millions of people were already doing. If you've built an app with Cursor or ChatGPT without really knowing what the generated code was doing, you've vibe coded. There's no shame in it. The question is whether you want to stay there.

What Vibe Coding Actually Looks Like

A typical vibe coding session: you open Cursor, describe what you want in plain English — "build me a todo app with React and a PostgreSQL backend" — and watch code materialize. You copy it into your project, run it, see errors, paste the errors back into the AI, get fixes. Repeat until it works. You might ship something in an afternoon that would have taken a junior developer a week. The productivity feels real. But look closely at what's happening: you're operating the AI like a vending machine, not writing software.

// What vibe coding produces
// You don't know why this works. You just know it does.
const fetchUsers = async () => {
  const res = await fetch('/api/users')
  const data = await res.json()
  setUsers(data) // Where did setUsers come from? Who knows.
  setLoading(false) // Race condition? Maybe. You won't know until prod.
}

// What engineering looks like
const fetchUsers = async (): Promise => {
  setLoading(true)
  try {
    const res = await fetch('/api/users')
    if (!res.ok) throw new Error(`HTTP error: ${res.status}`)
    const data: User[] = await res.json()
    setUsers(data)
  } catch (err) {
    setError(err instanceof Error ? err.message : 'Unknown error')
  } finally {
    setLoading(false)
  }
}

Why Vibe Coding Feels So Good (At First)

The feedback loop is intoxicating. Describe something, get code, see it work. The dopamine hit of shipping is real. For people who previously couldn't code at all, vibe coding is genuinely magical — it collapses the gap between idea and implementation. For people who can code a little, it feels like a superpower multiplier. This is not an illusion. AI tools genuinely accelerate certain kinds of work. The problem isn't the acceleration. The problem is what you're not learning while you're accelerating.

The Invisible Wall Vibe Coders Hit

Every vibe coder eventually hits the wall. It usually happens around the same inflection point: the project gets complex enough that the AI can no longer hold the whole context, or a bug appears that requires understanding what the code is actually doing, or you need to integrate with a system that has real constraints. Suddenly the magic stops. The AI gives you code that conflicts with itself. You can't debug because you don't know what "debugging" even means in this codebase. You've built a house on sand and now the tide is in. This is the moment that separates people who become engineers from people who stay vibe coders forever.

Vibe Coding vs. AI-Assisted Engineering

The distinction that matters is not whether you use AI — every good engineer should. The distinction is whether you understand what the AI produces. An AI-assisted engineer uses Copilot to write boilerplate, then reads every line before accepting it. They prompt Claude to suggest an architecture, then evaluate that architecture against their knowledge of the system. They let AI accelerate the typing while they do the thinking. Vibe coders let AI do both. That's the difference. Beyond Vibe Code is built around exactly this transition — from accepting AI output to directing it.

Is Vibe Coding Ever OK?

Yes, in the right contexts. Building a throwaway prototype to validate an idea? Vibe coding is fine. Writing a personal script you'll use once? Fine. The problem is when vibe coding becomes the default approach for production software, or when it becomes a ceiling that prevents you from ever developing real skills. The engineers who will thrive in the AI era aren't the ones who can prompt the best — they're the ones who can evaluate what the AI produces and know when it's wrong. You can't do that without fundamentals.