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It was named Collins Dictionary's Word of the Year for 2025.
Vibe coding describes a chatbot-based approach to creating software where the developer describes a project or task to a large language model (LLM), which generates code based on the prompt. The developer does not review or edit the code, but solely uses tools and execution results to evaluate it and ask the LLM for improvements. Unlike traditional AI-assisted coding or pair programming, the human developer avoids examination of the code, accepts AI-suggested completions without human review, and focuses more on iterative experimentation than code correctness or structure.
Advocates of vibe coding say that it allows even amateur programmers to produce software without the extensive training and skills required for software engineering. Critics point out a lack of accountability, maintainability, and the increased risk of introducing security vulnerabilities in the resulting software.
Computer scientist Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla, introduced the term vibe coding in February 2025. The concept refers to a coding approach that relies on LLMs, allowing programmers to generate working code by providing natural language descriptions rather than manually writing it.
Karpathy described it as "fully giv[ing] into the vibes, embrac[ing] exponentials, and forget[ing] that the code even exists".
He used the method to build prototypes like MenuGen, letting LLMs generate all code, while he provided goals, examples, and feedback via natural language instructions. The programmer shifts from manual coding to guiding, testing, and giving feedback about the AI-generated source code.
The concept of vibe coding elaborates on Karpathy's claim from 2023 that "the hottest new programming language is English", meaning that the capabilities of LLMs were such that humans would no longer need to learn specific programming languages to command computers.
A key part of the definition of vibe coding is that the user accepts AI-generated code without fully understanding it. Programmer Simon Willison said, "If an LLM wrote every line of your code, but you have reviewed, tested, and understood it all, that is not vibe coding in my book - that's using an LLM as a typing assistant".
In February 2025, New York Times journalist Kevin Roose, who is not a professional coder, experimented with vibe coding to create several small-scale applications. He described these as "software for one", referring to personalized AI-generated tools designed to address specific individual needs, such as an app that analyzed his fridge contents to suggest items for a packed lunch. Roose noted that while vibe coding enables non-programmers to generate functional software, the results are often limited and prone to errors.
In one case, the AI-generated code fabricated fake reviews for an e-commerce site. He also observed that AI-assisted coding enables individuals to develop software that previously required an engineering team. In response to Roose, cognitive scientist Gary Marcus said that the algorithm that generated Roose's LunchBox Buddy app had presumably been trained on existing code for similar tasks. Marcus said that Roose's enthusiasm stemmed from reproduction, not originality.
In March 2025, Y Combinator reported that 25% of startup companies in its Winter 2025 batch had codebases that were 95% AI-generated, reflecting a shift towards AI-assisted development within newer startups. The question asked was about AI-generated code in general, and not specifically about Vibed code.
Three engineers interviewed by IEEE Spectrum agreed that vibe coding is a way for programmers to learn languages and technologies they are not yet familiar with.
Andrew Ng has taken issue with the team, saying that it misleads people into assuming that software engineers just "go with the vibes" when using AI tools to create applications.
Vibe coding has raised concerns about understanding and accountability. Developers may use AI-generated code without fully comprehending its functionality, leading to undetected bugs, errors, or security vulnerabilities. While this approach may be suitable for prototyping or "throwaway weekend projects" as Karpathy originally envisioned, it is considered by some experts to pose risks in professional settings, where a deep understanding of the code is crucial for debugging, maintenance, and security.
In May 2025, Lovable, a Swedish vibe coding app, was reported to have security vulnerabilities in the code it generated, with 170 out of 1,645 Lovable-created web applications having an issue that would allow personal information to be accessed by anyone.
Generative AI is highly capable of handling simple tasks like basic algorithms. However, such systems struggle with more novel, complex coding problems like projects involving multiple files, poorly documented libraries, or critical code that has real-world impacts.
LLMs generate code dynamically, and the structure of such code may be subject to variation. In addition, since the developer did not write the code, they may struggle to understand syntax/concepts that they themselves have not used.
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