What is vibe coding?
Reviewed by Lokesh DudhatVibe coding is building software by describing what you want to an AI tool (like Cursor, Lovable, or Bolt) in plain language and accepting the code it writes, often without reviewing it. Coined by Andrej Karpathy in February 2025, it's fast and ideal for prototypes, but risky for production without expert review.
Vibe coding describes a way of working where you prompt an AI (Lovable, Cursor, Bolt, Replit, and similar) in everyday language and accept the code it generates, often without reading it line by line. The name comes from a February 2025 remark by Andrej Karpathy, former director of AI at Tesla, about “giving in to the vibes” and forgetting the code even exists.
Why it took off
It collapses the distance between an idea and a working screen. A non-technical founder can go from a sentence to a clickable app in an afternoon, with no team and no setup. For validating a concept, building a demo, or testing a prototype with users, that speed is genuine and useful.
Where it holds up, and where it doesn’t
Vibe coding is dependable for throwaway prototypes, internal tools, and early validation. It becomes risky the moment real users, real data, or real money are involved. The AI optimizes for “does it run,” not “is it secure, will it scale, can anyone maintain it.” Common gaps are missing authentication, no tests, exposed API keys, and structure no other developer can safely change.
Vibe coding vs AI-assisted development
They are not the same. In vibe coding, the AI builds and the human mostly trusts. In AI-assisted development, an experienced engineer directs the AI and reviews everything it produces. The first is for speed; the second is for shipping software people depend on.
Key takeaways
- Vibe coding means generating software by prompting AI in natural language, not by handwriting code.
- The term was coined by Andrej Karpathy in February 2025.
- It is excellent for prototypes and validation but risky for production without review.
- Typical gaps: security, testing, scalability, and long-term maintainability.
- It differs from AI-assisted development, where a senior engineer stays in control.
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Paresh Mayani is the Co-Founder and CEO of SolGuruz, a global custom software development and product engineering company. With over 17+ years of experience in software development, architecture decisions, and technology consulting, he has worked across the full lifecycle of digital products, from early validation to large-scale production systems. He started his career as an Android developer and spent nearly a decade building real-world mobile applications before moving into product strategy, technical consulting, and delivery leadership roles. Paresh works directly with founders, scaleups, and enterprise teams where technology choices influence product viability, scalability, and long-term operational success. He partners closely with founders and cross-functional teams to take early ideas and turn them into scalable digital products. His work revolves around AI integration, agent-driven workflow automation, guiding product discovery, MVP validation, system design, and domain-specific software platforms across industries such as healthcare, fitness, and fintech. Instead of solely focusing on building features, Paresh helps organizations adopt technology in a way that fits business workflows, teams, and growth stages. Beyond delivery, Paresh is also an active tech community contributor and speaker, contributing to global developer ecosystems through Stack Overflow, technical talks, mentorship, and developer community (Google Developers Group Ahmedabad and FlutterFlow Developers Group Ahmedabad) initiatives. He holds more than 120,000 reputation points on Stack Overflow and is one of the top 10 contributors worldwide for the Android tag. His writing explores AI adoption, product engineering strategy, architecture planning, and practical lessons learned from real-world product execution.
