The tools being used to write software are fundamentally different from what they were just two years ago. A new approach called vibe coding is letting people describe what they want in plain language and letting artificial intelligence handle the code. Whether you are a seasoned developer or someone who has never opened a code editor in your life, vibe coding is worth understanding. It has changed who can build software, how fast it gets built, and what risks come with it.
What Is Vibe Coding?
Vibe coding is a way of building software by describing what you want to an AI tool in everyday language and letting it generate the code for you. Instead of writing the technical instructions yourself, you hold a conversation with the AI. You describe what you need, the AI produces the code, you test it, and you guide it with follow-up prompts. The process repeats until the result works the way you intended.
The term was coined in February 2025 by Andrej Karpathy, an AI researcher and founding member of OpenAI. In a post on X (formerly Twitter), he described a new style of development where you “fully give in to the vibes, embrace exponentials, and forget that the code even exists.” The phrase caught on immediately. Within weeks, Merriam-Webster had listed it as a slang and trending expression. By the end of 2025, Collins English Dictionary named it their Word of the Year.
The core idea is straightforward: your role shifts from writing code to directing an AI that writes it for you.
Key Facts About Vibe Coding
Here is a quick snapshot of what you need to know before going further.
- Andrej Karpathy introduced the concept in February 2025, and it moved from niche discussion to mainstream practice within months.
- Collins English Dictionary named “vibe coding” its Word of the Year for 2025, cementing its place in both tech and general culture.
- A significant share of early-stage startups have adopted AI-generated codebases as a core part of their development workflow.
- Major technology companies have openly acknowledged using AI tools to generate a meaningful portion of their new code.
- Popular vibe coding platforms include Cursor, Replit, Lovable, Bolt.new, and GitHub Copilot.
- Linux creator Linus Torvalds used vibe coding in early 2026 to build a component of a personal audio tool, a notable milestone for a figure known for his exacting standards in software quality.
- The practice has moved well beyond hobbyist projects and is now part of professional engineering workflows at companies of all sizes.
How Does Vibe Coding Work?
The workflow feels more like a conversation than a traditional coding session. Here is the basic loop most vibe coders follow.
- Describe your idea. You type a prompt in plain language, such as “Build me a login page with an email field, a password field, and a Google sign-in button.”
- The AI generates the code. The tool translates your description into working code, often within seconds.
- You run it and observe. You check whether it behaves as intended.
- You refine with more prompts. If something is off, you tell the AI what to change: “Move the button to the bottom of the form” or “Add an error message if the password field is empty.”
- Repeat until done. The loop continues until the result matches what you had in mind.
Modern AI tools do not merely paste together code snippets. Large language models now understand software architecture, design patterns, and common failure points. At its best, the experience feels less like programming and more like working alongside a very fast, very tireless engineering colleague.
Key Highlights
What Makes Vibe Coding Different from Regular AI-Assisted Coding
Not every developer who uses an AI tool is vibe coding. The distinction matters, and it has been debated seriously since the term first appeared.
Developer and writer Simon Willison drew the line clearly: if an AI wrote every line of your code but you reviewed, tested, and understood all of it, that is not vibe coding. That is using AI as a capable typing assistant. Vibe coding, in its original sense, means accepting AI output without closely reviewing or fully understanding it, and trusting the results to hold up.
Karpathy himself acknowledged the limits of this approach in his original post. When the AI cannot resolve a bug, he described his instinct as follows: he would “just work around it or ask for random changes until it goes away.” That freewheeling, trust-the-output mindset is exactly what distinguishes vibe coding from careful, deliberate AI-assisted engineering.
Who Is Using It and Why
Vibe coding is no longer confined to hobbyists experimenting on weekends. Its reach spans a wide and growing range of users.
- Complete beginners are using it to build their first functional apps without any prior programming knowledge.
- Startup founders are shipping early prototypes in days rather than months, compressing timelines that once required a full engineering team.
- Senior developers are using it to accelerate repetitive or low-stakes work, freeing their attention for harder problems.
- Large technology companies, including some of the most prominent names in the industry, are using AI code generation at scale as a standard part of their development process.
- Educators have embraced it as a teaching tool. Karen Brennan, a professor at Harvard Graduate School of Education, described it as something that changes “the economics of experimentation,” letting students build things quickly just to understand them.
New York Times journalist Kevin Roose, who has no background in programming, used vibe coding in early 2025 to build several small personal applications. He described them as “software for one,” meaning tools built entirely around his own needs, the kind that would never justify hiring a developer but are now well within reach of a well-prompted AI.
The Best Use Cases
Vibe coding delivers the most value in situations where speed matters more than long-term maintainability. Here is where it tends to shine.
- Personal tools and utilities that only you will use and that do not handle sensitive data.
- Rapid prototypes built to test an idea before committing real resources to it.
- Learning experiments where the goal is to see what something could look like, not to ship a production product.
- Simple internal tools for small teams that do not need to scale or connect to complex systems.
- Weekend and side projects where creative momentum matters more than architectural perfection.
The Real Risks You Should Know
Vibe coding has genuine weaknesses that are worth understanding before you rely on it for anything consequential.
Security is the most serious concern. When developers accept AI-generated code without reading it carefully, security vulnerabilities can slip through undetected. Industry researchers and security firms have documented meaningful rates of flawed or insecure code coming out of AI-assisted workflows, particularly in web applications handling user data. The problem is not that AI cannot write secure code. The problem is that vibe coding, by design, involves trusting the output rather than scrutinising it.
Bug density rises without review. Analysis of AI-assisted codebases has consistently shown that logic errors, incorrect dependencies, and configuration mistakes appear more frequently in code that was generated and accepted without thorough review. These are exactly the kinds of bugs that cause problems in production and are hardest to trace after the fact.
Knowledge gaps grow over time. When developers ship code they have never read, they lose the ability to debug it when things break. Industry surveys have repeatedly flagged that many developers, particularly those earlier in their careers, acknowledge deploying AI-generated code they do not fully understand. This creates systems that work until they do not, and then no one knows why.
Enterprise adoption carries additional risk. Raymond Kok, CEO of Mendix, a Siemens company, has been direct in his assessment: “Vibe coding in the world of enterprise software will need to understand the notion of non-functional software requirements, governance, and control. Until then, it’s a short-term con with limited long-term gains.” His point is that enterprise software must satisfy requirements around reliability, security, compliance, and maintainability that go far beyond whether the code runs correctly on the first test.
Speed gains are not guaranteed. Counterintuitively, research has found that experienced developers working on complex tasks can sometimes move more slowly with AI tools than without them. The overhead of prompting, reviewing, correcting, and re-prompting adds up on tasks that require deep technical judgment. Vibe coding accelerates certain tasks significantly while slowing others down.
Tools to Know
Several platforms have become reliable starting points for anyone exploring vibe coding. Here is a brief overview.
- Cursor is a code editor rebuilt from the ground up around AI. It allows developers to move fluidly between natural language descriptions and working directly in the code itself.
- Replit is a browser-based platform that lets users build and deploy applications entirely through prompts, with no local development environment required.
- Lovable is widely used for building web applications from descriptions, though it drew attention in 2025 after security researchers identified vulnerabilities in a number of the apps it had generated.
- Bolt.new is a fast tool suited to generating front-end web projects quickly.
- GitHub Copilot is Microsoft’s AI coding assistant, used by professional developers as an intelligent suggestion and autocomplete engine within their existing workflow.
- Google AI Studio and Firebase Studio are Google’s offerings, designed to suit different experience levels from enthusiastic beginners to professional developers building production applications.
Responsible Vibe Coding: How to Use It Well
Vibe coding does not have to mean insecure or unreliable software. Used with discipline, it is a genuinely powerful way to move faster. Here is how to approach it responsibly.
- Always review code before deploying it, especially anything that touches user data, payments, or authentication. Reading the output is not optional in these contexts.
- Use security scanning tools such as Snyk or Semgrep to check AI-generated code for known vulnerabilities before it reaches production.
- Build incrementally. Generate one component, understand it, test it, and move to the next. Trying to prompt an entire application into existence at once produces systems that are difficult to debug and harder to maintain.
- Be honest about what you understand. If you cannot explain what a piece of code does, that is a meaningful risk signal, not just a gap in curiosity.
- Match the approach to the stakes. A personal weekend project and a system handling customer financial data are not the same kind of work, and they should not be built the same way.
What the Future Looks Like
The initial burst of enthusiasm around vibe coding has settled into something more considered. By late 2025, senior engineers were publicly describing the difficulties of maintaining and scaling AI-generated codebases, a moment some in the tech press called the “vibe coding hangover.”
Andrew Ng, one of the most respected voices in AI, pushed back on the term itself in mid-2025. He argued that it misleads people into thinking professional developers using AI tools are simply going with the flow, when the reality is far more deliberate and skill-dependent.
Karpathy himself revisited the concept in early 2026. Reflecting on his original post, he introduced a new term, “agentic engineering,” to describe where the practice had matured: a workflow where developers orchestrate AI agents with oversight and judgment, rather than simply accepting whatever the AI produces. The shift from vibe coding to agentic engineering is the shift from trusting the output to supervising it.
The developers thriving in this environment are not those who reject AI tools outright or those who ship AI output without review. They are the ones who have developed taste: knowing when to trust the AI, when to interrogate it, and how to break hard problems into pieces the AI can handle reliably.
Conclusion
Vibe coding marks a real turning point in who can build software and how quickly it gets done. It has opened the door to non-programmers, accelerated prototyping for founders and engineers alike, and introduced a genuinely new way of thinking about what software development looks like. It has also brought real risks around security, code quality, and long-term maintainability that are not going away on their own.
Put plainly: vibe coding is not magic, and it is not a substitute for understanding what you are building. It is a powerful tool that works best when matched to the right task, used with the right level of oversight, and treated as a starting point rather than a finished answer. Used that way, it opens up creative and practical possibilities that would have felt out of reach not long ago.
Disclaimer: This article is intended for general informational purposes only. Technology tools, platforms, and best practices in AI-assisted development evolve rapidly, so always verify current capabilities and limitations with official sources before making any decisions based on this content.
