Vibe coding represents a fundamental shift in how software is created, where developers leverage AI to generate code from natural language prompts rather than writing it manually. This emerging approach is rapidly transforming the software development landscape, promising increased productivity while simultaneously raising questions about code quality and developer expertise. Since its introduction in early 2025, vibe coding has quickly gained traction in tech circles, with Silicon Valley insiders declaring it "the dominant way to code."
Vibe coding was formally introduced by Andrej Karpathy, a co-founder of OpenAI and former AI leader at Tesla, in February 2025. The term was subsequently listed in the Merriam-Webster Dictionary as a "slang & trending" noun shortly after its introduction. According to Karpathy's original definition, vibe coding is where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists".
At its core, 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 then generates software, shifting the programmer's role from manual coding to guiding, testing, and refining the AI-generated source code. This approach claims to allow even amateur programmers to produce software without the extensive training and skills previously required for software engineering.
The concept elaborates on Karpathy's earlier claim from 2023 that "the hottest new programming language is English," suggesting that LLMs had reached capabilities where 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. As AI researcher Simon Willison put it: "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"[1]. This distinction is important—vibe coding specifically refers to accepting AI-generated code without thorough review, rather than all forms of AI-assisted programming.
The vibe coding process revolves around a distinct workflow that fundamentally differs from traditional programming approaches. Understanding this process helps illuminate both its appeal and its potential pitfalls.
Vibe coding is highly iterative. After the AI generates code based on an initial prompt, the developer tests or runs it to see the outcome. If there are errors or the functionality isn't as expected, the developer feeds that information back to the AI in natural language.
A common technique is simply copy-pasting error messages or exceptions into the chat with the AI and asking it to fix the problem. Karpathy notes that when he gets an error, he'll drop the error message into the AI with no additional comment, and "usually that fixes it". The AI analyzes the error and adjusts the code accordingly, enabling even those with minimal coding experience to troubleshoot.
One striking aspect of vibe coding is a tendency to trust the AI and accept its suggestions wholesale. Karpathy mentioned he often clicks "Accept All" on the changes the AI proposes without even reading the diffs line by line. This highlights a key cultural difference: vibe coders are comfortable letting the AI make broad changes across the codebase in seconds—changes that a human would normally carefully review.
The rationale is that if something breaks, the AI can fix it in the next round. This approach sacrifices some precision and understanding for speed and convenience, characterizing the "move fast and fix things on the fly" ethos of vibe coding.
The rapid adoption of vibe coding across various sectors stems from several compelling benefits that address longstanding challenges in software development.
Developers can build functioning software much faster by offloading laborious coding tasks to AI. Complex boilerplate or repetitive code can be generated in seconds, leading to exponential productivity gains measured in orders of magnitude. A venture capitalist noted that using AI in this way lets you get "the first 75% [of a feature] trivially, and it's amazing".
This acceleration means prototypes that might have taken days or weeks to code by hand can be built in hours. For example, a hobbyist reported creating a restaurant menu translator app in a single evening by continuously prompting an AI for each feature, something that would have been much slower with manual coding.
Because coding is done in natural language, people with little to no programming experience can create software. AI researcher Harry Law observes that "for a total beginner… it can be incredibly satisfying to build something that works in the space of an hour" using these tools.
This democratizes development – entrepreneurs, designers, or domain experts who aren't fluent in programming can still turn their ideas into working apps by describing their needs. Entire applications can be built with zero handwritten code, as evidenced by Replit's finding that a majority of their users' projects involve no direct coding by the user at all.
Many find that removing syntax barriers unleashes creativity. The ability to experiment with ideas rapidly because the AI takes care of details can be very satisfying. This immediate translation of concept to working code can spur further ideas and lead to higher motivation and a fun, iterative design process.
Vibe coding excels at handling repetitive or formulaic tasks that many developers find tedious: writing boilerplate code, setting up project configurations, or converting data formats[4]. These are precisely the kinds of tasks where AI can save developers significant time, allowing them to focus their mental energy on core logic or user experience.
Despite its promising benefits, vibe coding presents several significant challenges that must be addressed for its responsible and effective implementation.
For beginners, using AI to handle all coding can become a crutch. While they may get quick results, they might skip learning fundamental concepts of computer science. "Beginners can make fast progress, but it might prevent them from learning about system architecture or performance," warns Harry Law of Cambridge.
In traditional settings, struggling through coding tasks teaches important lessons about how and why code works. Vibe coding bypasses much of that struggle, potentially producing a generation of developers who can prompt models but lack deeper coding skills to troubleshoot or optimize code when AI falls short.
AI-generated code might not adhere to best practices or optimal design, especially if the user isn't guiding it carefully. Karpathy admitted that his AI-written code "grows beyond my usual comprehension," and he would have to read through it for a while to fully grasp it.
This hints at a maintainability issue: if code becomes too large or convoluted (because the AI kept appending fixes and features without refactoring properly), it can be hard for humans to manage later. Without diligent code review, "security vulnerabilities may also slip through," Law notes.
Vibe coding often results in code that works on the surface but may hide problems underneath. Since developers might accept AI-written code without fully reading or understanding it, they risk incorporating inefficient or error-prone code.
The phrase "it mostly works" appears repeatedly in discussions of vibe coding, highlighting a core concern: surface-level functionality without depth of quality or resilience. When bugs do appear, debugging becomes especially challenging if you didn't write or understand the code in the first place.
Working by "vibe" can significantly affect teamwork, communication, and coordination in development teams. When developers code primarily by intuition without clear planning, knowledge tends to remain siloed.
Limited documentation on planning, scope, and requirements reduces communication on how a solution is going to work, making it harder for teammates to understand each other's code or the overall direction. The team can become dangerously dependent on individual developers because the code's structure and rationale live mostly in the original coder's head.
Vibe coding is being deployed across diverse sectors, demonstrating its versatility and potential for widespread impact.
Government agencies often struggle with limited resources, complex bureaucracy, and the need for quick responses. Vibe coding helps by enabling employees to create custom apps that improve efficiency and service delivery.
Applications in government include process automation (automating routine tasks like data entry, scheduling, and document processing), citizen engagement (developing interactive platforms to improve communication between government agencies and citizens), data analysis and visualization (creating tools for analyzing large datasets), and training and education (developing custom e-learning platforms to train government staff in new policies, regulations, or procedures).
The speed advantages of vibe coding make it particularly valuable for startups and entrepreneurs looking to quickly validate ideas. Being able to go from concept to working prototype in hours rather than weeks gives startups a significant competitive advantage. This rapid iteration allows more ideas to be tested in less time, potentially accelerating innovation.
Karpathy himself noted that vibe coding is "not too bad for throwaway weekend projects," highlighting its utility for personal or hobby programming. The low barrier to entry means that individuals can create tools, games, or applications for personal use without needing to invest significant time in learning programming languages.
The adoption of vibe coding is spreading rapidly across the technology industry, with significant implications for how companies develop software.
According to Y Combinator executives, vibe coding has overtaken the industry surprisingly quickly. Garry Tan of Y Combinator stated: "I think our sense right now is this isn't a fad. This isn't going away. This is actually the dominant way to code, and if you're not doing it, you might be left behind. This is here to stay".
A recent email from Google co-founder Sergey Brin to DeepMind engineers emphasized principles aligned with vibe coding. Brin encouraged engineers to integrate AI into their workflow to reduce time spent on coding, stating that "code matters most" and advising that if it's simpler to prompt an AI for a solution, then that's preferable to training an entirely new model.
Brin's message focused on simplicity and speed, writing: "Simplicity — Let's use simple solutions where we can. Eg if prompting works, just do that, don't posttrain a separate model" and "Speed — we need our products, models, internal tools to be fast". These principles align closely with the vibe coding approach of prioritizing speed, simplicity, and AI-driven development.
As vibe coding continues to evolve, several trends are emerging that will shape its future development and adoption.
There's growing recognition of the need for better safety measures and tools around vibe coding. Simon Willison suggests that "safe vibe coding for complete beginners starts with a sandbox" to prevent people from causing harm with their projects.
Willison anticipates a "cambrian explosion in tooling to help people build their own custom tools as productively and safely as possible". This suggests a future where vibe coding becomes more accessible while also incorporating guardrails to prevent misuse or security issues.
Critics argue that the next generation of vibe coding tools should optimize for "sustainable comprehension" rather than just immediate gratification. The most revolutionary aspect of AI coding tools may not be that they let developers skip understanding, but that they compress years of learning into months.
The future of vibe coding likely involves finding the right balance between leveraging AI for speed and efficiency while still ensuring developers understand enough of what they're building to maintain it effectively over time.
Rather than replacing traditional software development entirely, vibe coding is likely to be integrated into existing workflows, with teams adopting a hybrid approach that combines AI-generated code with human oversight and traditional software engineering practices.
Enterprises with higher standards for code maintainability and reliability may adopt vibe coding more selectively, using it primarily for prototyping or for generating specific components while maintaining more traditional approaches for critical systems.
Vibe coding represents a paradigm shift in how software is created, moving from manual coding to a collaborative process between humans and AI. Introduced by Andrej Karpathy in early 2025, it has rapidly gained momentum, particularly in Silicon Valley, where industry leaders consider it "the dominant way to code."
The approach offers compelling advantages in speed, accessibility, and creativity, potentially democratizing software development by allowing people with minimal programming experience to create functional applications. However, it also presents significant challenges related to code quality, maintainability, and the potential erosion of fundamental programming skills.
As vibe coding continues to evolve, the focus is likely to shift toward creating safer tools and finding the right balance between leveraging AI for efficiency while ensuring developers maintain sufficient understanding of their code. The future may not be about choosing between vibe coding and traditional development, but rather about finding ways to integrate them effectively, combining the speed and accessibility of AI with the rigor and deeper understanding of conventional software engineering.
The rise of vibe coding doesn't signal the end of programming expertise, but rather its transformation. As one observer noted, the future belongs to those who use AI as a powerful tool, not a replacement for knowledge—"not just vibes, vibes plus knowledge".