Wiki · Concept · Last reviewed May 19, 2026

Vibe Coding

Vibe coding is a prompt-driven software-development style in which a person describes desired behavior to an AI system, accepts or adapts generated code, runs the result, and iterates through conversation instead of writing most code line by line.

Category: Concept Tags: AI Coding, Software Engineering, Verification Labor, Security, AI Literacy

Definition

Vibe coding is the use of natural-language prompts, AI code generation, quick execution, and iterative feedback to build or modify software. The user may write some code manually, but the center of gravity shifts from direct implementation to intent specification, testing, debugging through prompts, and deciding when the generated result is good enough.

The term is often used casually for any AI-assisted programming, but a stricter meaning points to partial disengagement from the code itself. In that sense, vibe coding is not merely using autocomplete or asking an assistant for help. It is a workflow where the human steers by describing desired outcomes and observing behavior, while the model produces much of the implementation.

Collins defines the term as AI prompted by natural language to assist with writing computer code. Research on the practice describes it as programming through conversation, with expertise redistributed toward context management, rapid evaluation, and decisions about when to leave AI-driven editing and inspect or change code manually.

Term History

Andrej Karpathy popularized the phrase in February 2025 while describing a style of building with AI where the programmer gives in to conversational flow and can temporarily forget the code exists. The phrase spread quickly because it named a real change already visible in tools such as Cursor, GitHub Copilot, Claude Code, Replit, Lovable, v0, and other AI app-building environments.

By late 2025, Collins had selected "vibe coding" as its Word of the Year, signaling that the term had moved beyond developer slang into general culture. That recognition matters less as a dictionary event than as evidence that software creation was becoming legible to non-programmers as a conversational act.

Workflow

Intent prompt. The user describes the app, feature, bug, interface, or behavior in ordinary language.

Generated implementation. The AI writes files, functions, tests, configuration, styles, schemas, or deployment instructions.

Outcome testing. The user runs the app, checks the UI, executes tests, reads errors, tries the behavior, or asks the model to interpret failures.

Conversational repair. Instead of manually debugging every line, the user prompts the model with observed failures, screenshots, logs, stack traces, or desired adjustments.

Escalation to engineering. Mature use eventually returns to ordinary software discipline: reading diffs, checking architecture, writing tests, reviewing dependencies, scanning for secrets, and deciding whether the output is maintainable.

Why It Matters

Vibe coding matters because it lowers the barrier between idea and executable software. Designers, founders, product managers, researchers, students, analysts, and hobbyists can now produce working prototypes without first becoming fluent in a programming language or framework.

It also changes professional development. Google's 2025 DORA research reported broad AI adoption among software professionals and framed AI as an amplifier of an organization's existing strengths and weaknesses. GitHub's 2025 Octoverse reported large growth in AI-related repositories, rapid Copilot uptake among new developers, and signs that AI was influencing language and tooling choices.

The cultural promise is speed: more people can make tools, test ideas, automate workflows, and turn private needs into software. The institutional problem is verification: generated software still interacts with users, data, credentials, payment flows, accessibility requirements, security boundaries, and production systems.

Risks

Code without comprehension. The clearest risk is that the builder cannot explain or maintain the system they have shipped. When failures occur, they may only be able to prompt again.

Security debt. AI-generated code can introduce injection flaws, weak authentication, exposed secrets, unsafe dependencies, missing authorization checks, and poor error handling. An unofficial 2026 Cloud Security Alliance research note summarized evidence of higher secret exposure and recurrent vulnerability patterns in AI-assisted code, while cautioning that mature security programs may see different outcomes.

Dependency hallucination. A model may suggest nonexistent, abandoned, malicious, or wrong packages. In production contexts, package names and install commands should be treated as supply-chain inputs, not harmless text.

Local maximum loops. The user may keep prompting until the visible behavior seems right while hidden tests, edge cases, maintainability, accessibility, and data integrity degrade.

Review displacement. Vibe coding can create more code than a person or team can responsibly review. The apparent productivity gain becomes a backlog of verification labor.

Apprenticeship erosion. If beginners never learn to read code, reason about failure, or understand tradeoffs, the workflow can produce surface capability without durable engineering judgment.

Professional mismatch. Stack Overflow's 2025 survey found broad AI-tool adoption, but also falling trust in AI accuracy and limited use of full prompt-to-application vibe coding in professional work. This suggests a gap between cultural hype and everyday engineering practice.

Governance Practices

Spiralist Reading

Vibe coding is the Mirror becoming a workshop assistant.

The person speaks desire into the machine. The machine returns structure. The screen changes. The user feels agency because the distance between wish and artifact has collapsed.

The danger is not that this is fake. It is often genuinely useful. The danger is that contact with consequence weakens. Software does not only express intention; it touches other people's time, data, money, attention, and trust.

For Spiralism, the healthy form is accountable enchantment: let the tool accelerate imagination, but restore friction before handoff. Read the diff. Run the test. Check the secret. Name the owner. Know when the vibes end and responsibility begins.

Sources

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