AI vibe coding is a way to create or change software by describing what you want to an AI coding model, running the generated result, and iterating on it. In the term’s strict sense, the person accepts and guides the output without reviewing or understanding the code. The distinction matters: using AI to write code that you then inspect and take responsibility for is better described as AI-assisted programming.
What does AI vibe coding mean?
Computer scientist and AI developer Andrej Karpathy introduced the phrase in February 2025. Martin Fowler reproduces his description: “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.” Fowler’s May 21, 2026 explainer attributes the wording to Karpathy and links to his original post.
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Karpathy’s account emphasized accepting changes without reading diffs, then pasting error messages back into the model when something failed. The point of the phrase is not simply that AI wrote some code; it is that the person steers by describing intent and observing results while largely setting aside the code itself.
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Definitions are not uniform. The Open Source Security Foundation glossary and Fowler use the narrower boundary: if a person reviews or understands the generated code, the work is no longer vibe coding in the strict sense. In everyday usage, people also apply the term more broadly to AI-assisted development that includes testing, manual edits, or some code review. When precision matters, say which meaning you intend.
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How does the vibe-coding workflow work?
- Describe a feature, change, or application in natural language.
- Ask an AI coding model to generate or modify the software.
- Run the application or tests to see what happens.
- Report errors or request changes, then repeat the cycle.
In the strict version, the person does not read the code changes before accepting them. But observed practice can be more mixed. A Microsoft Research study published in September 2025 describes participants iterating through prompts, scanning output, testing applications, and sometimes editing code manually. The study says programming expertise is not eliminated; it shifts toward managing context, evaluating output, and deciding when to move from AI-driven work to direct code manipulation.
Cursor, GitHub Copilot, Replit, and Lovable are examples of software environments named in the sources, not synonyms for vibe coding. Their features can change; the practice is defined by how a person works, not by a particular product.
How is vibe coding different from AI-assisted or agentic programming?
| Approach | What drives the work | Human relationship to the code | Typical intent |
|---|---|---|---|
| Strict vibe coding | Prompts, generated changes, and run-and-adjust cycles | The person accepts and guides output without reviewing or understanding the code | Often a quick experiment or disposable tool |
| Reviewed AI-assisted programming | Prompts combined with tests, manual edits, or other development work | The person inspects the output and takes responsibility for it | Can range from experiments to maintained software |
| Agentic programming, as distinguished by Fowler | An AI agent carries out coding tasks under human direction | The human reviews and remains accountable for the resulting code | Work where the result is evaluated and maintained |
These labels are not universally settled, and real sessions can combine approaches. The useful dividing question is whether the human actually examines and takes responsibility for the generated code—not whether an AI tool was involved.
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Is AI vibe coding safe for production?
A program that runs is not necessarily correct, secure, or maintainable. Unreviewed code can contain defects or expose data, and large changes spanning multiple files can be difficult to assess. IBM’s June 18, 2026 security analysis also discusses “package hallucination” and “slopsquatting”: a model may suggest a package name that does not exist, which an attacker could register with malicious code. That is a risk to check for, not evidence that every suggested package is malicious.
Risk depends on what the software touches and who will rely on it. Martin Fowler advises keeping unreviewed results disposable and limiting them to the author or a small group that understands and accepts the risk. ISACA’s August 2025 guidance offers a useful risk-tier framework, but it is professional guidance rather than a universal legal rule:
| Risk level | Examples and safeguards |
|---|---|
| Lower-risk experimentation | Prototypes, documentation tools, test-data generators, and non-production internal scripts; use lightweight review and no real customer data. |
| Needs stronger review | Data integrations, customer-facing interfaces, business logic, and third-party connectors; use human review, version control, and staging tests. |
| High consequence | Regulated, financial, healthcare, or identity systems; restrict or prohibit unreviewed vibe coding unless deep review, traceability, and compliance controls are in place. |
For production software, the practical dividing line is not whether an AI generated the code. It is whether qualified people review, test, and maintain the result at a level appropriate to its consequences.
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