Vibe coding is a real change in how people build software, but not proof that software engineering has been replaced. The term describes an intent-first workflow: you explain a goal in natural language, an AI system generates or changes code, and you steer the result through prompts, tests and corrections. Whether that is a revolution or simply a new label depends on which version of “vibe coding” you mean.
What “vibe coding” means
“Vibe coding” has no single formal definition. IBM describes it as a loosely defined practice of prompting AI tools to generate code instead of writing every line manually, while the Associated Press describes the original informal framing as telling an AI what to build, running the result and paying little attention to the underlying code. Andrej Karpathy popularized the phrase in February 2025; he did not invent AI-assisted programming.
That history creates an important distinction:
| Meaning | What the developer does | Best description |
|---|---|---|
| Hands-off or low-inspection vibe coding | Accepts generated code with minimal reading and relies mainly on visible behavior | Narrow, original popular usage |
| Supervised conversational development | Prompts an AI, scans its code, runs tests, debugs, edits manually and reviews changes | Broader current usage |
| AI-assisted development | Uses autocomplete, chat, agents or code generation alongside conventional engineering | Wider category than vibe coding |
Calling every use of an AI coding assistant “vibe coding” erases these differences. This article uses the term for conversational, intent-led development and identifies when the low-inspection meaning matters.
What developers actually do in a vibe-coding session
A September 2025 Microsoft Research study by Advait Sarkar and Ian Drosos examined more than eight hours of curated video of extended coding sessions with think-aloud reflections. The sessions were not simply “type a wish and accept the answer.” They showed repeated cycles:
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- State a goal: describe a feature, bug or user-facing outcome in natural language.
- Supply context: provide files, constraints, architecture details or error messages.
- Inspect the output: rapidly scan generated code and the proposed changes.
- Run the application or tests: check whether the behavior matches the goal.
- Correct and refine: prompt again, debug, or edit the code manually.
- Choose the next mode: continue with the AI or return to direct code manipulation.
The study found that prompts mixed high-level direction with technical detail. Expertise moved toward managing context, evaluating changes quickly and deciding when generated code was no longer the fastest or safest route. As Sarkar and Drosos put it, “Critically, vibe coding does not eliminate the need for programming expertise but rather redistributes it toward context management, rapid code evaluation, and decisions about when to transition between AI-driven and manual manipulation of code.” Microsoft Research summary
What the evidence says—and does not say
Observed workflow: changed roles, not vanished expertise
The Microsoft Research observation supports a claim about interaction patterns: developers can alternate between conversation, testing and manual edits at high speed. It does not establish that this workflow is universally faster, cheaper or more accurate than conventional development.
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Reported experience: flow alongside friction
A separate September 2025 qualitative study by Veronica Pimenova and colleagues analyzed more than 190,000 words from interviews, Reddit discussions and LinkedIn posts. Participants described conversational co-creation, flow and enjoyment, but also recurring problems with specifying requirements, reliability, debugging, latency, review workload and team collaboration. Because the material captures perceptions and practice rather than a controlled comparison, it cannot provide a productivity benchmark. Microsoft Research qualitative study
Vendor activity data: usage shares are not performance results
Anthropic’s April 28, 2025 Economic Index analyzed 500,000 coding-related interactions across Claude.ai and Claude Code. It classified 79% of Claude Code conversations as “automation” and 49% of Claude.ai conversations that way. JavaScript and TypeScript together represented 31% of language queries, while HTML and CSS together represented 28%.
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Those figures describe Anthropic’s dataset and classification method only. They do not measure all developers, all AI models or all coding tools, and “automation” is not the same as hands-free completion or a productivity gain. Anthropic also notes that project-type classifications involve uncertain inferences. Anthropic Economic Index
Revolution or rebranding?
Why it is more than a renamed autocomplete feature
- The primary interface can shift from editing syntax to expressing intent and constraints.
- One conversation can cover requirements, implementation, tests, debugging and documentation.
- The developer’s scarce skills shift toward context selection, rapid evaluation and architectural judgment.
- Non-specialists can produce working prototypes without first mastering every framework detail.
Why it is not a replacement for software engineering
- Generated code still requires requirements clarification, testing and review.
- Reliability, security, dependency choices and maintainability are not guaranteed by fluent output.
- Large changes can increase review and collaboration costs even when the first prototype arrives quickly.
- Production systems need traceable decisions, ownership and ongoing maintenance.
The defensible conclusion is narrower than either slogan: vibe coding represents a meaningful change in the developer–tool interaction, while the evidence does not prove a universal productivity or quality revolution. It is also not merely rebranding when the workflow genuinely shifts from line-by-line implementation toward conversational steering and evaluation.
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Can you build real software with vibe coding?
Yes, especially prototypes, internal tools, interface experiments and small applications where feedback is immediate and the consequences of defects are limited. The phrase “real software,” however, covers very different risk levels. A demo that works on one machine is not equivalent to a service handling personal data, payments or safety-critical operations.
Prototype or experiment
- Write explicit acceptance criteria before prompting.
- Ask for small, reviewable changes rather than an entire system at once.
- Run the application and tests after each meaningful change.
- Keep version control so you can revert a bad generation.
Production system
- Define architecture, data handling, authentication and failure behavior before implementation.
- Review every generated change, including transitive dependencies and configuration.
- Add automated tests, logging and security checks independent of the model.
- Have an experienced engineer approve deployment and own maintenance.
IBM’s explainer lists Replit, Cursor, GitHub Copilot, Windsurf and Bolt as examples of current AI coding platforms. They represent different workflows—editor assistants, code agents and app-building environments—rather than a ranked list of equivalent products. Features and availability can change, so compare the current offering for your task. IBM: What is vibe coding?
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Do you need to know how to code?
You can start without deep programming knowledge, but the less you know, the harder it is to recognize incorrect assumptions, insecure patterns and fragile fixes. Basic literacy in the language and platform, source control, testing, HTTP or data flow, and error reading substantially improves the outcome.
For low-risk experiments, an AI can teach while it builds. For software that other people depend on, human responsibility does not disappear: someone must specify the behavior, judge trade-offs, verify the result and maintain it.
How to evaluate a vibe-coding tool
| Question | Why it matters |
|---|---|
| Where does it work? | An IDE assistant, terminal agent and browser app builder provide different control and context. |
| How much can you inspect? | Readable diffs, file-level changes and logs make review and rollback possible. |
| What testing is integrated? | Execution, unit tests and previews expose failures earlier than visual confidence alone. |
| How are secrets and dependencies handled? | Data leakage, vulnerable packages and unsafe configuration can enter through generated code. |
| Can a team collaborate? | Branches, code review, ownership and reproducible environments matter beyond the first prototype. |
Bottom line
Vibe coding is best understood as a new conversational mode of AI-assisted development, not a demonstrated replacement for engineering. It becomes revolutionary when it changes who can explore ideas and how quickly developers iterate; it becomes rebranding when the label is applied to ordinary autocomplete without a meaningful shift in workflow. In either case, testing, review, security assessment and maintenance remain the dividing line between an impressive demo and dependable software.
The Associated Press discusses why some engineers resist using the label, reflecting how contested the term remains. Associated Press report
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