What is Vibe Coding?

Vibe Coding is a programming technique that uses artificial intelligence, particularly large language models (LLMs), to create software based on simple descriptions in everyday language. This means you don’t need to know how to code; you just guide, test, and refine the AI-generated code. It’s like telling a smart assistant what app you need, and it builds it for you.

Vibe Coding is an AI-dependent programming technique where users describe a problem or software idea in natural language, typically in a few sentences, as a prompt to a large language model (LLM) tuned for coding. The LLM then generates the software, shifting the traditional programmer’s role from manual coding to guiding, testing, and refining the AI-generated source code. A key characteristic is that users often accept the code without fully understanding it, relying on the AI’s capabilities.

The term was coined by Andrej Karpathy, a prominent computer scientist and co-founder of OpenAI, in an X post on February 6, 2025. You can watch his Youtube. Karpathy, also known for his role as Tesla’s former AI leader, described Vibe Coding as a process where “you fully give in to the vibes, embrace exponentials, and forget that the code even exists.” He highlighted its conversational nature, noting he uses voice commands with tools like Cursor Composer and SuperWhisper, barely touching the keyboard. This approach elaborates on his 2023 claim that “the hottest new programming language is English,” suggesting LLMs have advanced to the point where traditional coding skills may become less necessary.

By March 2025, Vibe Coding was listed in the Merriam-Webster Dictionary as a “slang & trending” noun, reflecting its rapid rise in tech discourse. It’s seen as a method that allows even amateur programmers to produce software without the extensive training previously required, potentially revolutionizing the field.

Vibe Coding relies on advanced LLMs, such as those from OpenAI, Anthropic, and tools like Cursor, GitHub Copilot, and Replit Agent. Users interact with these AI systems through chat interfaces, describing their needs in plain English. For instance, a user might say, “Create a React app that takes a photo of a restaurant menu, translates it, and shows a list of food with images,” and the AI generates the code structure, often producing a working prototype with minimal tweaks.

The process is iterative: if errors occur, users feed them back to the AI, accept changes, and repeat until it works. Karpathy noted that when bugs persist, he experiments with random changes, describing it as “not too bad for throwaway weekend projects” but “quite amusing.” This casual approach distinguishes Vibe Coding from traditional programming, which emphasizes precision and understanding.

Reasons for the Hype

The hype surrounding Vibe Coding is driven by several factors, reflecting its potential to transform software development:

1. Democratization of Coding: By removing the need for coding expertise, Vibe Coding opens software creation to non-programmers, potentially fostering innovation from diverse backgrounds. For example, New York Times journalist Kevin Roose, not a coder by trade, used Vibe Coding to create “software for one”—personalized tools like LunchBox Buddy, which analyzes fridge contents to suggest school lunch ideas. This accessibility is seen as a way to empower creators who previously lacked technical skills.
2. Speed and Efficiency: Vibe Coding accelerates development, enabling rapid prototyping. Reports indicate that experienced programmers can complete tasks in minutes that would have taken 20-30 minutes of traditional coding, freeing up time for more complex work. This speed is particularly appealing for startups and hobbyists, with one developer noting it made a project feasible during a presentation rather than after work.
3. Startup Adoption and Ecosystem Growth: The adoption rate is notable, with TechCrunch reporting in March 2025 that 25% of Y Combinator’s Winter 2025 cohort had codebases that were almost entirely AI-generated. This suggests Vibe Coding is reshaping how new tech companies build software, potentially reducing reliance on traditional engineering teams.
4. Cultural Shift in Tech: Silicon Valley has embraced Vibe Coding as a buzzword, with Business Insider describing it as a movement where “seasoned engineers and people with zero coding experience are relying on AI to write code.” This cultural shift is fueled by the promise of faster, more inclusive development, aligning with broader AI trends.

Examples and Case Studies

Several examples illustrate Vibe Coding in action:

• Kevin Roose’s Projects: Roose created multiple small-scale applications, including LunchBox Buddy, which he detailed in a February 2025 New York Times article (A.I. and Vibecoding Helped Me to Create My Own Software). He also built tools to transcribe podcasts, organize social media bookmarks, and check furniture fit for his car trunk, highlighting its utility for personal, niche needs.
• React App Prototype: A blogger shared creating a React app via Vibe Coding, prompting an AI with, “Create me a React app from scratch… an app which can take a photo of a menu in a restaurant, translate it, then show a list of food with images… It must be a PWA with a camera button.” The AI-generated a working prototype, though it required minor tweaks, demonstrating its capability for complex projects (Vibe Coding. AI-Assisted Coding for Non-Developers).
• DIY Drawing Robot App: Another example involved an enthusiast using Vibe Coding to build a web app for a DIY drawing robot, showcasing its potential for hobbyist projects (Vibe Coding. AI-Assisted Coding for Non-Developers).

These cases underline Vibe Coding’s versatility, though they also reveal limitations, such as initial code messiness and the need for refinement.

Limitations and Concerns

Despite the hype, Vibe Coding faces significant challenges:

• Code Quality and Bugs: AI-generated code can be error-prone, with Karpathy noting that LLMs sometimes fail to fix bugs, requiring users to experiment with changes. This can lead to inefficient or incorrect code, as seen in Roose’s experience where an app fabricated fake reviews for an e-commerce site.
• Security and Privacy: There are concerns about security vulnerabilities, especially for end-user applications. Hacker News discussions highlight that vibe-coders might not be aware of security exploits, potentially increasing demand for professional services to handle maintenance (What is vibe coding? How creators are building software with no coding knowledge).
• Maintainability: Since users may not understand the generated code, maintaining or updating it can be difficult. This is particularly problematic for long-term projects, with some predicting a “pile of crappy prototypes” in production, potentially giving Vibe Coding a negative connotation (Vibe Coding — Beer + GenAI = a whole useful app in an evening. How I now write my prototypes and weekend projects).
• Complexity Limitations: AI tools excel at standard patterns but struggle with highly specialized or novel functionality, requiring more traditional coding for advanced features (What is Vibe Coding? How Creators Can Build Software Without Writing Code).

Broader Implications and Controversy

Vibe Coding’s rise has sparked debate within the tech community. Proponents argue it’s a natural evolution, aligning with AI’s growing role in automation. Critics, however, worry it could devalue traditional programming skills, with some predicting increased demand for professional developers to clean up AI-generated code. AI researcher Simon Willison clarified that if code is reviewed, tested, and understood, it’s not Vibe Coding but using an LLM as a typing assistant, highlighting a distinction in intent (Vibe coding - Wikipedia).

The controversy extends to its suitability for production environments. While it’s praised for hobby projects and rapid prototyping, Ars Technica notes questions about reliability for real-world applications, suggesting it may be “gnarly or reckless” for critical systems (Will the future of software development run on vibes?).

Future Outlook

The future of Vibe Coding seems tied to AI advancements. As LLMs improve, they may address current limitations, such as better handling of complex tasks and enhanced security. However, the tech community’s response will likely shape its trajectory, with the potential for both widespread adoption and backlash against its perceived risks. Predictions include a shift toward more disposable, personalized software, with less reliance on open-source libraries, as noted by Andrew Chen (Vibe coding, some thoughts, and predictions).

In conclusion, Vibe Coding’s hype is fueled by its transformative potential, but its success will depend on balancing accessibility with reliability, a challenge that will define its role in the evolving landscape of software development as of March 24, 2025.