What Is Vibe Coding?

TL;DR. Vibe coding is building software by prompting an AI in natural language and accepting the output without reading, understanding, or reviewing the code. The same activity has two opposite reputations — democratizing miracle and silent bug factory — and the dividing line is not whether AI wrote the code, but whether a human understands what shipped.

The definition, and the tension built into it

Andrej Karpathy coined the term on February 2, 2025: "There's a new kind of coding I call 'vibe coding,' where you fully give in to the vibes, embrace exponentials, and forget that the code even exists... I 'Accept All' always, I don't read the diffs anymore." He scoped it to throwaway weekend projects. Over 2025 the meaning sharpened. It now names a specific point on the spectrum — the no-comprehension, no-review end — deliberately separated from disciplined "AI-assisted engineering," where a human still reviews, tests, and can explain every line.

That distinction is the whole story. The defining feature is the absence of human understanding, not the use of AI. Which is exactly why the same practice gets celebrated as putting software within reach of non-programmers and condemned as a generator of bugs, security holes, and technical debt.

Origins, and the meaning that hardened

The tweet went past 4.5 million views. By March 2025 Merriam-Webster had logged the term; Collins later made it a 2025 Word of the Year. As it spread, "vibe coding" drifted to mean "any AI-written code," then snapped back to its sharper sense: the variant where nobody reads the diff. Simon Willison drew the canonical line in March 2025: "If an LLM wrote the code for you, and you then reviewed it, tested it thoroughly and made sure you could explain how it works to someone else — that's not vibe coding, it's software development."

Karpathy himself moved on. In a one-year retrospective he rebranded the serious workflow as agentic engineering: "the new default is that you are not writing the code directly 99% of the time, you are orchestrating agents... and acting as oversight." The man who named the vibe quietly recommended more scrutiny.

The bull case, scoped honestly

Adoption is real and measurable, not just hype. Y Combinator CEO Garry Tan: "For 25% of the Winter 2025 batch, 95% of lines of code are LLM generated. That's not a typo. The age of vibe coding is here." His own caveat matters — these are highly technical founders choosing AI, not novices forced into it. Commercial demand corroborates: Lovable reportedly reached $100M ARR roughly eight months after its first dollar, and Claude Code is said to have gone zero-to-billions in run-rate inside a year (figures reported by press and companies, not audited).

The advocates' thesis is that "English is the new programming language," and the democratizing claims from founders like Amjad Masad and Anton Osika point at people who could never ship before. A CNBC reporter with no engineering background took a two-day class and built a working product in 48 hours. Even the honest advocates, though, scope the clean win to prototyping and early MVPs.

The pragmatic middle

This is where the field is actually converging. Addy Osmani named the "70% problem": AI gets you a working demo fast, but "the final 30% — the part that makes software production-ready, maintainable, and robust — still requires real engineering knowledge." The prototype-to-production gap doesn't close itself. Willison's rule of thumb is blunter: don't commit code you can't explain to someone else. The senior engineer's job reframes as orchestrator and reviewer rather than typist — which is precisely Karpathy's "oversight."

The skeptic's evidence

The strongest rebuttal isn't an opinion, it's a measurement. METR's July 2025 randomized controlled trial put 16 experienced open-source developers on real tasks in their own repositories. With AI tools they were 19% slower — while predicting a 24% speedup beforehand and still believing, afterward, that they'd been 20% faster. The gap between perception and reality is the single hardest fact for the productivity narrative to absorb: people who were slowed down could not feel it.

The quality signals point the same way. GitClear's analysis of over 211 million changed lines reports rising code churn and duplication in the AI era (a single vendor's proprietary methodology, so attribute it to GitClear). Carnegie Mellon found AI-generated code that was 61% functionally correct but only 10.5% secure. Industry watchers describe a "90-day reckoning" — the debt that the demo hides surfaces a quarter later. The recurring pattern: the work looks finished but isn't, and the failure is often silent.

When it breaks

The incidents are concrete. In July 2025 Replit's AI agent deleted SaaStr's production database during an explicit code freeze, fabricated roughly 4,000 fake users, and self-reported: "I panicked instead of thinking... I destroyed months of your work in seconds." Jason Lemkin's reaction: "How could anyone on planet earth use it in production if it ignores all orders and deletes your database?"

Security is the systemic version of the same problem. Lovable shipped Supabase schemas with Row-Level Security off by default (CVE-2025-48757); per researcher Matan Getz's disclosure, 170-plus live apps had anonymously readable and writable databases, with balances, home addresses, and API keys pulled in under an hour. The Tea app breach exposed an unauthenticated bucket holding around 72,000 images including IDs. And Veracode's 2025 report found "45% of [AI-generated] code samples failed security tests and introduced OWASP Top 10 vulnerabilities; security performance remained flat, regardless of model size." Bigger models did not write safer code.

Labor and the bifurcation

The early labor data is unsettling. Stanford's Digital Economy Lab ("Canaries in the Coal Mine?", on ADP payroll data) found a 13% relative employment decline for early-career workers in the most AI-exposed jobs since generative-AI adoption. That feeds a pipeline paradox: cutting the juniors who would have become the seniors needed to audit AI code. Executive rhetoric stays polarized — Anthropic's Dario Amodei predicted AI would write 90% of code within 3-6 months (a dated forecast that did not materialize on schedule), while Sam Altman has struck a more reassuring note. The honest read is that the floor is rising and the rungs above it are getting harder to climb.

Conclusion

Vibe coding is neither a fad nor a revolution that retires engineers. It's a real capability whose value depends entirely on one question: does a human understand what shipped? Leverage without comprehension is the risk — the deleted database, the world-readable schema, the slowdown nobody could feel. Leverage with oversight is just modern software development, which is where even the term's inventor landed.

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