Vibecoding: The New Era of Software Development

What Is Vibecoding? Definition and Origin

Vibecoding describes building software by iteratively prompting AI models and accepting generated code without detailed inspection. The term emerged in early 2025 when Andrej Karpathy, former Director of AI at Tesla and founding member of OpenAI, documented his workflow: “It’s not really coding—I just see stuff, say ‘accept accept accept,’ and the app grows.”

The practice leverages large language models integrated into development environments. Unlike traditional programming where developers control every character, vibecoding treats AI as an autonomous implementation partner. Simon Willison later proposed the more disciplined term “vibe engineering” to distinguish professional applications from casual experimentation.

Who Coined the Term “Vibe Coding”?

Andrej Karpathy introduced “vibe coding” to public discourse through social media posts in February 2025, describing his personal development workflow with AI assistants. Karpathy, who holds Stanford PhD in computational neuroscience and led AI infrastructure at both Tesla and OpenAI, documented creating functional applications through conversation alone.

The term resonated immediately because it captured an emerging behavior pattern. Prior to Karpathy’s coinage, developers lacked precise vocabulary for describing this human-AI collaboration model. Within weeks, “vibecoding” appeared in technical discussions, job descriptions, and eventually ICSE 2026 conference materials.

Vibecoding vs Agentic Engineering: Key Differences

Vibecoding prioritizes speed through accepting AI suggestions; agentic engineering imposes structured validation. Addy Osmani, engineering leader at Google, documented this distinction in his analysis of production AI workflows.

| Dimension | Vibecoding | Agentic Engineering | |—||| | Review intensity | Minimal, often none | Mandatory code review | | Testing approach | Post-hoc if at all | Test-driven from start | | Error handling | Developer notices runtime issues | AI generates tests validating edge cases | | Documentation | Implicit in prompts | Explicit in AGENTS.md rule files | | Use case fit | Prototypes, MVPs, experiments | Production systems, enterprise software |

Simon Willison’s “vibe engineering” sits between these poles—maintaining conversational development while enforcing quality gates through automated testing and review checkpoints.

How Do Vibecoding Platforms Actually Work?

Modern vibecoding platforms combine large language models with execution environments that translate natural language into functioning applications. When a user describes “a todo app with local storage,” the platform:

1. Parses intent through multi-stage prompts
2. Generates appropriate framework code (React, SwiftUI, etc.)
3. Creates database schemas and API endpoints
4. Renders preview instantly for iteration

Emergent, the highest-funded vibecoding startup, raised $70M Series B from SoftBank Vision Fund 2 and Khosla Ventures in January 2026. The platform processes natural language into full-stack applications across web and mobile, claiming 5 million users across 190+ countries. Competitors include Lovable, Cursor, and Replit, each optimizing different aspects of the workflow.

Emergent’s $300M Valuation: Market Signal Analysis

Emergent achieved $300M post-money valuation in January 2026 after raising $70M in Series B funding. The SoftBank Vision Fund 2 and Khosla Ventures co-led the round, with Y Combinator participating. Vinod Khosla commented publicly on the speed of adoption, noting the platform reached significant scale faster than traditional SaaS benchmarks.

Key metrics disclosed at funding:

• $50M ARR (Annual Recurring Revenue)
• 5 million users across platform
• 190+ countries with active usage
• 7 months from launch to Series B [inferred from press materials]

The valuation reflects investor confidence that vibecoding represents a platform shift rather than temporary tooling improvement.

What Are the Hidden Risks of Vibecoding?

Security vulnerabilities emerge from unvetted AI-generated code. Models trained on public repositories reproduce known vulnerabilities—developers accepting without review inherit these flaws. OWASP Top 10 issues appear in vibecoded applications at rates proportional to training data contamination.

Maintenance debt accumulates when vibecoded prototypes enter production. Code lacks consistent patterns, comments, or architectural coherence. Teams report spending 3-5x refactoring time compared to conventionally built features [inferred from engineering discussions].

Skill atrophy concerns technical leaders. Junior developers who rely entirely on AI suggestions never practice fundamental debugging, optimization, or security analysis. Addy Osmani documented this as “the hidden cost” in his agentic engineering series.

SE4SM Framework: Making Vibecoding Enterprise-Ready

SE4SM (Software Engineering for Software Makers) provides structured methodology for AI-assisted development. The framework introduces two core pillars:

Intent Engineering replaces vague prompting with requirement refinement through multi-agent dialogue. Systems ask clarifying questions, generate acceptance criteria, and validate understanding before generating code. This transforms “build a login page” into complete specification including auth flows, error states, and security requirements.

Realization Engineering implements an Agent Execution Environment (AEE) where specialized agents handle different development phases. Architecture agents design systems, coding agents implement, testing agents validate, and security agents audit—all coordinated through human-defined constraints.

The framework received technical briefing at ICSE 2026 in Rio de Janeiro, marking academic validation of structured AI-assisted development.

Can Vibecoding Replace Professional Developers?

No empirical evidence supports developer replacement claims. Organizations adopting vibecoding report productivity gains of 30-50% for prototyping but no reduction in senior headcount [inferred from case studies]. The pattern shows:

Task Type	Vibecoding Impact
Greenfield prototypes	3-5x faster
Bug fixing	Neutral to slower
Architecture decisions	Requires human expertise
Security auditing	Critical human oversight needed
Legacy integration	Human guidance required

Andrej Karpathy himself clarified that vibecoding describes his personal workflow, not a replacement for software engineering discipline. The industry consensus maintains vibecoding as augmentation, not substitution.

Best Practices for Production-Grade AI-Assisted Development

Implement mandatory code review gates for all AI-generated code. Treat AI contributions like junior developer submissions—review for correctness, security, and maintainability before merging.

Maintain AGENTS.md rule files documenting project constraints, architecture decisions, and patterns. AI tools read these files to maintain consistency across generations. Addy Osmani documented this practice as essential for scaling vibecoding beyond prototypes.

Enforce test-driven development by requiring tests before or alongside implementation. Simon Willison’s “vibe engineering” approach generates tests first, then implementation, then validation—maintaining conversational flow while ensuring coverage.

Designate AI Architects—engineers responsible for prompt strategy, tool selection, and quality standards. This role emerged specifically to bridge vibecoding velocity with engineering rigor.

Intent-Based Interfaces: The UX Revolution Ahead

Software interfaces will shift from navigation-based to intent-based interaction. Current applications require users to find features through menus and buttons; intent-based interfaces let users describe goals while AI assembles experiences dynamically.

Theory of Mind (ToM) capabilities in modern AI enable this shift. Systems infer user context, goals, and constraints from conversation rather than explicit commands. A single interface adapts to different users performing the same task based on their expertise level and preferences.

Multi-agent teams behind the scenes compose functionality: planning agents decompose intent, execution agents run operations, presentation agents format output appropriately for device and user. Users experience unified conversation while dozens of specialized agents collaborate.

How to Transition from Prototype to Scalable Product?

Refactor AI-generated code before scaling. Vibecoded prototypes optimize for demonstration speed, not production stability. Extract core logic, implement proper error handling, and establish consistent patterns.

Implement comprehensive testing suites covering unit, integration, and acceptance criteria. SE4SM framework recommends test generation during Intent Engineering phase—tests exist before implementation completes.

Establish security review gates using automated scanners and manual auditing. AI-generated code may contain vulnerabilities from training data; production deployment requires explicit validation.

Migrate to scalable architecture replacing prototype shortcuts. Temporary authentication, in-memory storage, and synchronous processing must evolve to production-grade patterns before user load increases.

New Roles in the AI-Driven Development Team

AI Architect positions now appear in job descriptions at organizations adopting vibecoding. Responsibilities include selecting AI tools, designing prompt strategies, maintaining AGENTS.md files, and establishing quality standards.

Prompt Engineers specialize in optimizing human-AI communication. Unlike AI Architects focused on systems, Prompt Engineers work directly with domain experts to translate requirements into effective AI instructions.

Vibe Engineers—term proposed by Simon Willison—maintain disciplined vibecoding practices. They combine conversational development with testing rigor, ensuring velocity doesn’t compromise quality.

| Role | Primary Focus | Typical Background | |||-| | AI Architect | Systems, tools, standards | Senior engineer, architect | | Prompt Engineer | Requirements translation | Technical writer, domain expert | | Vibe Engineer | Quality in AI workflows | Full-stack developer |

What Tools Define the Vibecoding Ecosystem?

Emergent leads in full-stack application generation, supporting web and mobile from natural language descriptions. The platform raised $70M in January 2026 and claims 5 million users.

Cursor functions as an AI-first IDE, integrating code generation directly into development workflow. Developers report using Cursor for daily coding tasks with AI assistance inline rather than separate prompting.

Replit provides collaborative coding environment with AI assistance built in. The platform targets educational and prototyping use cases, with millions of users experimenting with AI-generated code.

Lovable competes directly with Emergent in the full-stack vibecoding space, focusing on consumer applications and rapid MVP development.

ICSE 2026: Academic Validation of Vibe Engineering

ICSE 2026, the International Conference on Software Engineering held April 12-18 in Rio de Janeiro, Brazil, featured technical briefings on “Vibe Engineering.” The inclusion marks formal academic recognition of AI-assisted development as a legitimate research domain.

Conference materials distinguish between casual vibecoding and engineered approaches requiring validation, testing, and methodological rigor. Presentations covered SE4SM framework, empirical studies of AI-assisted productivity, and tools for maintaining quality in AI-generated code.

The Future: From Vibecoding to Autonomous Software Creation

Human-guided vibecoding represents the first phase of AI-assisted development. Current workflows require humans to initiate, review, and integrate AI contributions.

Agentic engineering introduces structured oversight—tests, reviews, architectural constraints—while maintaining conversational velocity. This phase dominates current enterprise adoption.

Autonomous software creation will emerge when multi-agent teams handle complete development lifecycles with minimal human intervention. Addy Osmani projects this timeline at 3-5 years [inferred] based on current capability growth rates.

The trajectory follows pattern recognition: human prompts → human-guided agents → autonomous agent teams. Each phase increases abstraction while maintaining human oversight at decreasing granularity.

FAQ: Vibecoding and AI-Assisted Development