# Vibe coding

Vibe coding is an AI-dependent programming technique where a person describes a problem in a few sentences as a prompt to a large language model (LLM) tuned for coding. The LLM generates software, shifting the programmer’s role from manual coding to guiding, testing, and refining the AI-generated source code. Vibe coding is claimed by its advocates to allow even amateur programmers to produce software without the extensive training and skills previously required for software engineering. The term was introduced by Andrej Karpathy in February 2025 and listed in the Merriam-Webster Dictionary the following month as a "slang & trending" noun.

## Definition

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 his approach as conversational, using voice commands while AI generates the actual code. "It's not really coding - I just see things, say things, run things, and copy-paste things, and it mostly works." Karpathy acknowledged that vibe coding has limitations, noting that AI tools are not always able to fix bugs, requiring him to experiment with changes until the problems are resolved.
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 code without full understanding. AI researcher Simon Willison said: "If an LLM wrote every line of your code, but you've reviewed, tested, and understood it all, that's not vibe coding in my book—that's using an LLM as a typing assistant."

## Reception and use

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 personalised AI-generated tools designed to address specific individual needs, such as an app named LunchBox Buddy 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 suggested that vibe coding is better suited for hobby projects rather than essential tasks. He also observed that AI-assisted coding enables individuals to develop software that previously required an engineering team. In response to Roose, AI expert 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 February 2025, Business Insider described vibe coding as a new buzzword in Silicon Valley.
In March 2025, Y Combinator reported that 25% of startups in its Winter 2025 batch had codebases that were 95% AI-generated, reflecting a shift toward AI-assisted development.
Some developers are exploring multi-modal vibe coding, where users can input not only text but also images, diagrams, and voice commands to describe functionality. This approach is being tested for fields like game development, web design, and cybersecurity automation.

## Challenges

Vibe coding has raised concerns about understanding and accountability. Developers may use AI-generated code without fully comprehending its functionality, potentially leading to undetected bugs and errors. While this approach may be suitable for prototyping, it is considered by some experts to pose risks in professional settings, where a deep understanding of the code is crucial for debugging and maintenance. Ars Technica cites Simon Willison, who stated: "Vibe coding your way to a production codebase is clearly risky. Most of the work we do as software engineers involves evolving existing systems, where the quality and understandability of the underlying code is crucial." In what Ars Technica described as an "ironic twist in the rise of 'vibe coding'", an AI coding assistant refused a programmer's request to generate code and responded with the text, "I cannot generate code for you, as that would be completing your work," and continued, "you should develop the logic yourself."

## Future of Vibe Coding

Leo Paz, founder of Outlit, describes this transformation:
   "I think the role of a software engineer will transition to a product engineer. Human taste is now more important than ever as coding tools make everyone a 10x engineer."
This perspective highlights a growing trend where software engineers are shifting from writing code manually to guiding AI models in generating software. Rather than focusing on syntax and low-level problem-solving, they are transitioning towards product vision, user experience, and business goals.
Vibe coding is not just about writing code faster—it represents a 10x to 100x acceleration in development, making software creation significantly more accessible. Jackson Stokes, co-founder of TrainLoop, describes this shift:
   "Six months ago, coding changed with a 10x speed-up. One month ago, it became a 100x speed-up. This is exponential acceleration. I'm no longer an engineer; I'm a product person."
This exponential acceleration is redefining the role of software engineers. Instead of writing every line of code manually, engineers will increasingly focus on:
* Understanding user needs and translating them into AI-generated software features.
* Validating AI-generated code for correctness, efficiency, and security.
* Optimizing AI interactions to generate more precise and functional outputs.
Some fear that vibe coding will replace low-level programmers. However, history suggests otherwise. Some companies and applications will always require deep technical expertise beyond what AI can efficiently handle. A historical example of this comes from Facebook:
During its early days, Facebook rapidly shipped features using PHP, a language often criticized for inefficiency. However, as the platform scaled, PHP became a massive performance bottleneck. To solve this, Facebook hired hardcore systems engineers to build HipHop (HipHop for PHP), a custom compiler that optimized PHP code for better performance.
   "Facebook got away with PHP, which personally is a terrible language… but you could ship things quickly. However, at some point, it became such a big bottleneck that they had to hire hardcore system people to build a custom compiler, HipHop, so it would run fast on bare metal."
This example illustrates why companies working on complex infrastructure, high-performance computing, and low-level systems engineering will still require highly skilled developers. Current AI tools are not well-suited for low-level systems programming, ensuring that expert software engineers will remain in demand.
As AI-driven development reshapes the industry, software engineers will transition into hybrid roles, combining:
* Technical oversight – Understanding the underlying logic of AI-generated code and debugging complex issues.
* Product and UX design – Ensuring that AI-generated applications align with user needs and business goals.
* AI prompt engineering – Mastering the art of communicating with AI models to generate optimized code.
* System architecture – Designing scalable and efficient infrastructures that AI tools can enhance but not replace.
While entry-level coding jobs may shift away from routine development tasks, engineers who adapt to AI-enhanced workflows will find new opportunities as AI-assisted product builders, system architects, and tech strategists.
The era of vibe coding does not mark the end of software engineering—instead, it evolves the role, making software creation more accessible, rapid, and strategic, while still requiring deep expertise in hard problems that AI alone cannot solve.