There’s a new phrase making the rounds in developer circles, startup communities, and even TikTok feeds: vibe coding.
If you’ve seen someone casually claim they “built an app in two hours” using nothing but natural language prompts, you’ve already encountered it in the wild.
But what is it really? Is it a genuine shift in how software gets made, or just another overhyped buzzword? Let’s break it down.
The Short Version
Vibe coding is a way of building software where you describe what you want in plain language, let an AI system generate most of the code, and then steer the output through prompts, testing, and feedback rather than writing every line yourself.
The term was coined by AI researcher Andrej Karpathy in early 2025.
His description was memorable: you “fully give in to the vibes” and “forget that the code even exists.” Since then, it’s been picked up by major dictionaries and tech institutions alike, with IBM, Google Cloud, and Merriam-Webster all publishing their own definitions.
How It Works in Practice
The typical vibe coding workflow follows a consistent loop:
Start with a description, not a spec. You might tell an AI something like: “Build a small web app where users can log in, upload a CSV, and see a line chart of sales by month.” No wireframes, no technical architecture doc — just intent expressed in human language.
The AI generates the code — and often the project structure. It picks stack defaults, scaffolds files, writes handlers, and may even set up tests or deployment configurations depending on the tool you’re using.
You run it, break it, and describe what’s wrong. When something doesn’t work, you don’t open the codebase and start debugging line by line. Instead, you paste the error, describe the unexpected behavior, or share a stack trace and ask the AI to fix it.
When you want changes, you re-prompt instead of refactoring. Want simpler state management? Ask for it. Need to extract a reusable component? Describe the desired outcome. Switching to TypeScript? Just say so.
Some platforms are pushing this even further, covering the full lifecycle from idea to deployed app — with AI assisting on requirements, architecture, environment configuration, and deployment, while the human stays in the driver’s seat for direction and validation.
Why It Caught On So Fast
Three forces converged to make vibe coding go from niche practice to mainstream conversation.
First, Karpathy’s framing gave it a name. Developers had already been leaning heavily on LLMs for code generation, but they didn’t have a shared label for the pattern. “Vibe coding” was sticky enough to become the term of record.
Second, TikTok and short-form video turned it into spectacle. Creators started publishing “I built an app in two hours” videos showing prompts turning into working products in a single recording session. These demos made the workflow feel almost magically accessible.
Third, a mini-ecosystem of tools, templates, and tutorials emerged around the trend, lowering the barrier further and reinforcing the perception that anyone could ship software this way.
The Real Benefits
Strip away the hype and there are genuine reasons vibe coding resonates with both developers and non-technical builders.
Speed. Prototypes, internal tools, and proof-of-concepts that used to take days or weeks of boilerplate work can be collapsed into a few prompt iterations. For teams with more ideas than engineering bandwidth, that’s a real unlock.
Accessibility. Non-engineers — product managers, designers, domain experts — can turn business ideas into working tools without needing full-stack expertise. The gap between “I know what I need” and “I have a working version” gets dramatically smaller.
Higher-level focus. Developers who do know how to code can offload repetitive, mechanical work to AI and spend more time on product strategy, user experience, and domain logic — the parts that arguably matter most.
Cheap experimentation. Trying three different UI layouts, swapping architectures, or testing an alternative algorithm costs almost nothing when the AI can regenerate alternatives from a new prompt. That makes exploratory work far more practical.
The Risks Nobody Should Ignore
For every enthusiastic demo, there’s a seasoned engineer raising a valid concern. The criticism isn’t unfounded.
You might not understand what you shipped. Vibe coding explicitly encourages accepting code you haven’t deeply read. That works fine for a throwaway prototype, but it can hide edge-case bugs, performance problems, and maintainability nightmares that only surface later.
Security doesn’t vibe-code itself. AI-generated code can import vulnerable patterns, misuse libraries, or mishandle authentication. Organizations still bear full responsibility for securing code they didn’t manually review — and that’s a serious gap when nobody on the team fully understands the internals.
Prototypes have a way of becoming production systems. Quick builds that were never meant to last can calcify into real products without proper testing, documentation, or observability, creating a backlog of technical debt that’s expensive to unwind.
Skill erosion is a real concern. Some engineering leaders worry that heavy reliance on prompts creates developers who can ship impressive demos but can’t debug or architect systems when the AI outputs fail. The “new worst coder” archetype — someone who can prompt but can’t problem-solve — is a recurring theme in these discussions.
The social media happy path is misleading. Those two-hour build videos tend to show the happy path. The scoping, refactoring, testing, and security review work that normally follows? Not so viral.
Vibe Coding vs. Traditional Development vs. AI-Assisted Coding
It helps to think of vibe coding as one point on a spectrum rather than a binary replacement for traditional development.
In traditional coding, the developer designs and writes code line by line, treats the codebase as a primary artifact to be understood and maintained, and brings deep language and framework knowledge to the table.
In classic AI-assisted coding (think autocomplete tools like Copilot), the developer is still writing and structuring all the code. AI fills in gaps at the cursor level, but the human retains full visibility and control.
In vibe coding, the primary activity shifts to describing intent, prompting, and reviewing behavior. Code becomes more of an implementation detail — sometimes one the builder never deeply reads.
Each approach has its sweet spot. Vibe coding excels at prototypes, internal tools, and exploratory projects.
Traditional coding remains essential for performance-critical, long-lived, and safety-sensitive systems.
AI-assisted coding sits in between, boosting productivity in everyday development without fundamentally changing who owns the code.
In practice, many teams blend all three: vibe-code an early prototype, then gradually replace or harden the critical pieces with conventional engineering and more granular AI assistance.
Where This Is Heading
Vibe coding isn’t going away, but it’s also not going to replace software engineering as a discipline.
The most likely trajectory is that it becomes a standard part of the development toolkit — one approach among several, chosen based on context.
The teams that benefit most will be those that treat vibe coding as a starting accelerator rather than a finishing strategy: use it to explore ideas fast, then apply engineering rigor to the pieces that need to last.
The real skill, increasingly, isn’t just knowing how to code or how to prompt. It’s knowing when each approach is the right one — and having the judgment to recognize when a quick vibe-coded prototype needs to be rebuilt properly before it becomes someone’s production dependency.
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