Vibe coding - the future of software development?

Mar 27, 2025

6

min read

T

he term "vibe coding" gained significant traction in early 2025, largely due to its popularization by AI expert Andrej Karpathy .

Karpathy initially described vibe coding in somewhat abstract terms, suggesting a state where developers "fully give in to the vibes, embrace exponentials, and forget that the code even exists" .

This definition, while evocative, has evolved in practical application as observed in discussions on platforms like YouTube.

Here, vibe coding is generally understood as the process of using natural language prompts to instruct AI coding assistants to generate software code .

One perspective describes it as allowing AI to take over the coding process based on a given prompt, with the user often simply accepting the AI's suggestions with minimal review .

Another explanation elaborates on this, stating that vibe coding involves developers heavily relying on AI models to generate, refine, and debug code, issuing high-level commands rather than meticulously writing each line .

This approach can be seen as a form of programming by intuition, guided by the AI's "vibes."

However, a more pragmatic definition from Merriam-Webster characterizes vibe coding as writing computer code in a somewhat careless fashion, with AI assistance, where the coder may not fully comprehend the code's functionality and accepts the likelihood of bugs .

The evolution of the term suggests a move from Karpathy's initial, perhaps more aspirational, concept towards a more grounded understanding of its practical implementation.

While the idea of completely disregarding the code might have been intended to highlight the increasing power of AI, the reality appears to involve a more interactive process where human guidance and a degree of technical understanding remain important.

The emergence of vibe coding is not an isolated phenomenon but rather a continuation of a long-term trend in software development towards increasing levels of abstraction .

Over the decades, programmers have sought ways to simplify the process of creating software, moving from the complexities of binary machine code to assembly language in the 1950s, and subsequently to modern high-level programming languages and frameworks .

More recently, the rise of low-code and no-code platforms in the 2010s demonstrated an appetite for even more intuitive ways to build applications, often using visual tools or pre-built templates instead of writing code directly .

However, these platforms typically operated within specific domains and still required users to understand the underlying mechanics of the tools themselves .

By 2025, AI-assisted coding reached a point where its capabilities and awareness within the developer community had significantly increased .

Karpathy's viral post in early 2025, coining the term "vibe coding," effectively crystallized this movement, capturing the imagination of both developers and the broader tech industry .

This period also saw the development of specialized Integrated Development Environments (IDEs) and plugins designed with an AI-first approach, indicating a tangible shift towards the "vibe" style of development .

Developers began experimenting with having AI generate entire modules or even applications through conversational interfaces, marking a significant departure from simply accepting line-by-line code suggestions.

Therefore, while the underlying principle of simplifying software creation has been a long-standing goal, the advent of powerful AI models has ushered in a new era where natural language interaction is becoming a primary means of directing the development process.

Applications and Practical Examples of Vibe Coding:

The initial wave of applications for vibe coding has heavily concentrated on web development, likely due to the vast amount of publicly available code and documentation that AI models can learn from, coupled with the immediate visual feedback that web interfaces provide .

One example illustrates the potential for rapid code generation, where a user prompted Claude 3.7 to create a website on Amazon Web Services (AWS) with only a basic instruction. The AI proceeded to generate 277 lines of code, which the user accepted without review, demonstrating the "just go with the vibes" aspect of this approach .

Another example showcases a more guided application using the platform lovable.dev. Here, the process involved outlining specific steps for the AI to follow, such as displaying product listings from a local file, then integrating with a backend database (Supabase), implementing user authentication, and enabling product purchases using tokens and Stripe for payment processing .

This example highlights that even with vibe coding, a degree of planning and step-by-step direction can be beneficial.

Similarly, the online coding platform Replit allows users to describe the web applications they envision, with its AI features generating the corresponding HTML, CSS, and JavaScript code .

These examples collectively suggest that vibe coding is finding early adoption in web development for tasks ranging from simple website creation to more complex applications with backend integration and user interaction.

While web development appears to be a primary area of initial application, the use of vibe coding in mobile app development presents a more nuanced picture, with both successes and significant challenges reported .

Interestingly, the realm of game development has also seen notable applications of vibe coding, perhaps most famously with the example of Pieter Levels, a well-known figure on X (formerly Twitter), who reportedly built a multiplayer online game reminiscent of Minecraft using Cursor and Claude AI .

This achievement is particularly significant as Levels apparently had no prior experience in game development, suggesting that vibe coding could potentially lower the barrier to entry in specialized areas of software creation for individuals who possess general programming skills but lack specific domain expertise.

The success of this project is often cited as a key moment in popularizing the term "vibe coding," with Levels himself describing his approach as simply "vibing" the entire application into existence .

This example indicates that while challenges exist in areas like mobile app development, vibe coding can indeed be leveraged to create complex applications in domains where the user has a strong conceptual understanding and can effectively guide the AI.

Beyond more complex applications, vibe coding has also found utility in the creation of small, personalized software tools, particularly for non-programmers seeking to automate specific tasks or address individual needs .

A notable example is that of Kevin Roose, a journalist with the New York Times who identifies as a non-coder. Roose experimented with vibe coding to build what he termed "software for one," such as an application named LunchBox Buddy.

This simple tool was designed to analyze the contents of his refrigerator and suggest potential lunch recipes based on the available ingredients .

This application, while perhaps not commercially significant, demonstrates the potential of vibe coding to empower individuals without formal programming training to create functional software solutions tailored to their personal requirements.

The ability to quickly generate code for niche, specific applications that a non-coder would previously have been unable to build independently highlights the democratizing aspect of vibe coding.

It suggests that this approach can bridge the gap between having an idea for a software tool and actually bringing it to life, even without extensive technical skills.

Current Discussions and Trends on X:

Discussions surrounding vibe coding on platforms like X reveal a wide spectrum of opinions, ranging from enthusiastic embrace to deep skepticism .

One notable example of initial enthusiasm comes from a user who claimed to have built an entire Software as a Service (SaaS) application using the Cursor AI editor with absolutely no handwritten code .

This user proclaimed that AI was no longer just an assistant but had become the primary builder of software, encouraging others to adopt this approach. However, this initial excitement was quickly tempered by the user reporting a series of security issues, including unauthorized access to API keys, circumvention of subscription mechanisms, and the creation of random data within the application's database .

The user, admitting a lack of technical expertise, found these issues challenging to resolve and ultimately decided to cease publicly sharing their progress.

This incident serves as a stark reminder of the potential security risks associated with relying solely on AI-generated code without a solid understanding of underlying security principles.

Conversely, a Reddit thread, linked and discussed on X, presented a highly critical view of vibe coding, labeling it a "dangerous fantasy" . This perspective argues that if a person lacks coding knowledge, vibe coding is inherently risky, potentially leading to serious errors and vulnerabilities.

On the other hand, if a person does possess coding skills, vibe coding might simply be an inefficient and frustrating way to develop software compared to more traditional methods. This viewpoint suggests that vibe coding might fall into a paradoxical situation where it is either too dangerous for novices or too cumbersome for experienced developers.

A significant trend emerging from these discussions is the recognition that underlying technical knowledge plays a crucial role in effectively utilizing vibe coding . While the promise of vibe coding might suggest that anyone can create software simply by describing it, practical experiences indicate otherwise.

The discussions on X also highlight the increasing number and variety of tools and platforms that are being used to facilitate vibe coding .

Tools and Platforms Enabling Vibe Coding:

A growing ecosystem of tools and platforms is emerging to support the practice of vibe coding, each offering unique features and targeting different user segments.

Cursor AI: This AI-powered code editor, built as a fork of Visual Studio Code (VS Code), integrates advanced AI capabilities through OpenAI's ChatGPT and Anthropic's Claude models .

It aims to simplify software development by offering intelligent code suggestions, automated error detection, and dynamic code optimization.

Key features include inline code generation, where users can prompt the AI to generate code directly within the editor; chat with code functionality, allowing for conversational interaction with the AI about specific code segments; and AI-powered autocompletion, which suggests code completions based on context and natural language input .

While Cursor AI has shown strengths in small-scale projects and rapid scripting tasks, it reportedly faces limitations when dealing with larger, more complex projects involving multiple files and intricate dependencies .

To address these challenges, the community is exploring solutions like AISpec, a proposed specification layer designed to make AI coding with Cursor more reliable and structured by ensuring adherence to project architecture and coding patterns .

Despite these limitations in complex scenarios, Cursor AI is recognized for its ability to understand the context of the codebase and make comprehensive changes based on user commands , and it is considered a tool that is likely to produce good quality code, albeit potentially requiring a more technical user .

Replit: This online coding platform has embraced AI-assisted development, with its CEO noting that a significant portion of Replit users never write a single line of code manually, likely leveraging the platform's AI features like Ghostwriter AI .

Replit provides an AI chat interface directly within its IDE, enabling users to generate code, receive explanations of existing code, and even deploy applications based on simple natural language prompts .

A key advantage of Replit is its fully hosted environment, allowing users to describe their desired application, have the AI build it, and deploy it to the cloud instantly .

This streamlined approach makes Replit particularly user-friendly for individuals who may not have a traditional development environment set up or who have limited coding experience .

While Replit's vibe coding capabilities are powerful, some suggest that its interface and the level of interaction with the underlying code might still feel closer to traditional coding compared to more abstract no-code solutions .

For those new to programming, platforms like Lovable might offer an even more intuitive first step into building web applications .

Anthropic Claude (and Claude Code): Anthropic's Claude is a large language model that has gained recognition for its ability to handle very large prompts, making it suitable for coding tasks where a significant amount of context might be necessary .

Claude 3.7 is noted for its reasoning capabilities and has been specifically recommended for extended coding projects . Building on this foundation, Anthropic introduced Claude Code, a terminal-based AI assistant designed to integrate seamlessly with existing development workflows .

Unlike some cloud-based alternatives, Claude Code operates directly in the terminal, connecting to Anthropic's API without intermediate servers handling code, which emphasizes security .

It aims to understand the entire codebase holistically, assisting with tasks beyond simple code completion, such as automated Git operations, testing and debugging, architecture understanding, and cross-file refactoring .

While Claude Code shows promise as a "supervised coding agent," reports indicate that its output quality can be inconsistent across different programming languages .

Nonetheless, Claude, particularly the Claude 3.7 Sonnet model, is positioned as a sophisticated AI partner for software teams, capable of understanding codebase architecture and reasoning through complex problems . The recent release of Claude Code further solidifies Anthropic's commitment to providing advanced AI tools for vibe coding, albeit with a terminal-based interface that might present a technical barrier for some users .

GitHub Copilot: Developed by GitHub and OpenAI, Copilot functions as an AI pair programmer integrated directly into popular code editors like VS Code .

It primarily focuses on autocompleting code as the developer types but has evolved to include a chat mode (Copilot Chat) where users can ask coding questions in natural language .

Copilot's capabilities extend to generating entire functions or modules based on comments written in the code, effectively bridging the gap towards vibe coding . Its strength lies in assisting developers during the coding process by constantly suggesting potential implementations based on the context of their work .

While Copilot can be a valuable tool for tech-savvy individuals with limited coding experience, its integration into the coding environment suggests that it is primarily intended for those who are already engaged in writing code, rather than serving as a completely no-code solution .

ChatGPT and Other LLMs: General-purpose large language models like OpenAI's ChatGPT and Anthropic's Claude can also be utilized for vibe coding, even though they are not specifically designed as coding IDEs .

Users can simply describe the program or functionality they desire in natural language, and the LLM can generate the corresponding code, often providing it in formatted blocks that can be copied and pasted into a development environment .

These models can even explain the generated code if asked, making them useful for learning and understanding new concepts .

However, a key limitation is that ChatGPT and similar models do not directly run or test the generated code, requiring the user to handle this step separately .

Despite this, the accessibility and versatility of these LLMs make them a convenient starting point for many exploring AI-assisted coding, serving as a readily available assistant for brainstorming, code generation, and debugging .

Other Emerging Platforms: The landscape of vibe coding tools is rapidly expanding, with new platforms and services constantly appearing .

Examples include Pythagora, Bolt, and Lovable, which are explicitly built for vibe coding. Lovable, for instance, offers a no-code interface where users can describe features and see them appear in a live preview .

Bolt allows users to start coding projects directly in the browser with zero setup . Windsurf is another AI-powered coding environment that enables users to build applications through natural language prompts, offering an integrated terminal and support for various technologies .

These emerging platforms highlight the growing interest and innovation in the field of vibe coding, catering to a diverse range of user needs and technical skill levels.

Benefits and Advantages of Vibe Coding:

One of the most frequently cited benefits of vibe coding is its potential to significantly increase the speed and productivity of software development .

By automating the generation of boilerplate code and suggesting solutions based on natural language prompts, AI-assisted coding can drastically reduce the time spent on routine tasks.

In some instances, developers have reported experiencing substantial speed increases, with one founder even claiming a 100x acceleration in their development process .

This enhanced productivity can potentially lead to smaller development teams being able to accomplish more in the same amount of time .

Surveys also indicate that a significant majority of developers who utilize AI coding tools perceive an advantage in completing their tasks more efficiently .

This ability to rapidly generate code and automate repetitive processes allows developers to focus their efforts on more complex and strategic aspects of their projects, ultimately leading to faster feature additions, bug fixes, and overall project delivery.

Vibe coding also holds the promise of making software development more accessible to individuals who do not possess extensive programming knowledge, thereby democratizing the process of software creation .

Several examples illustrate this potential, with non-coders successfully building simple personal tools and applications using vibe coding platforms .

These tools enable users to translate their ideas into functional software by simply describing what they want in natural language, without needing to grapple with the intricacies of programming syntax or complex development environments .

This increased accessibility can empower a wider range of individuals, from hobbyists to professionals in non-technical fields, to create custom software solutions that address their specific needs, potentially fostering innovation from diverse perspectives and backgrounds.

The speed and ease of use associated with vibe coding make it particularly well-suited for rapid prototyping and experimentation .

Developers can quickly generate initial versions of their applications or test out new features and ideas without investing significant time and effort in manual coding.

This allows for faster iteration and validation of concepts, enabling teams to rapidly explore different approaches and refine their solutions based on early feedback.

The ability to quickly translate an idea into a working prototype can significantly accelerate the innovation cycle, allowing for more agile development processes and a greater willingness to experiment with novel concepts.

Furthermore, by automating many of the more mundane and repetitive coding tasks, vibe coding can free up skilled developers to concentrate on higher-level problem-solving and creative aspects of software development .

This shift in focus allows experienced programmers to dedicate their expertise to architectural planning, system integration, and tackling complex challenges that require human ingenuity and a deep understanding of software design principles.

By offloading routine coding to AI assistants, developers can engage in more stimulating and intellectually demanding work, potentially leading to higher-quality software and more innovative solutions.

This also allows them to leverage their unique human skills in areas like strategic decision-making and creative problem-solving, ultimately enhancing the value they bring to their projects and organizations.

Limitations, Challenges, and Criticisms of Vibe Coding:

Despite the potential benefits, vibe coding also faces significant limitations, challenges, and criticisms within the software development community. A primary concern revolves around the quality and reliability of the code generated by AI .

Reports indicate that AI-generated code can often lack the structure, maintainability, and efficiency of code written by experienced human developers . Because these AI models are trained on existing code, they may not always produce output that adheres to best practices or considers edge cases effectively .

This can lead to code that is riddled with bugs, difficult to understand and modify, and ultimately requires significant review and potential rewriting by human developers to ensure its quality and reliability .

Debugging code produced through vibe coding can also present considerable challenges . Unlike traditional coding where developers have a clear understanding of the logic they have implemented, with AI-generated code, the underlying reasoning behind certain choices might be opaque.

This can make it significantly harder to identify and fix errors, as the AI may not provide clear explanations for its output .

In some cases, even the AI itself might struggle to resolve bugs in the code it has generated, leading to developers resorting to workarounds or making random changes in an attempt to fix the issues .

This difficulty in debugging can potentially negate some of the time saved during the initial code generation phase.

Furthermore, the notion that vibe coding entirely removes the need for technical expertise is largely contested .

While it might enable individuals with limited coding knowledge to create very simple applications, effectively guiding AI coding assistants and validating their output often requires a foundational understanding of programming principles .

Users may struggle to formulate effective prompts that yield the desired results or to interpret the generated code to identify potential issues . Additionally, mastering the art of prompt engineering itself becomes a critical skill.

Simply asking an AI to "build an app" is unlikely to produce a robust and functional product. Instead, users need to learn how to provide clear, specific, and detailed instructions to guide the AI towards the desired outcome.

Current vibe coding tools also appear to have limitations when dealing with complex software projects that involve multiple files, intricate architectures, and numerous dependencies .

Maintaining consistency and ensuring that different parts of the codebase interact correctly can be challenging for AI models that may struggle to grasp the overall structure and relationships within a large project .

This suggests that while vibe coding might be useful for smaller, more self-contained applications or individual modules, it may not yet be well-suited for large-scale enterprise-level development without significant human oversight and architectural guidance.

There is also a concern that the rapid generation of code through vibe coding, if not carefully managed, could lead to an accumulation of technical debt .

Code that is quickly produced without proper attention to structure, documentation, and adherence to coding standards can create maintainability challenges in the long run.

If developers rely too heavily on AI-generated code without fully understanding its implications or refactoring it for clarity and efficiency, it could increase the cost and difficulty of future modifications and updates to the software .

Tools and Platforms Enabling Vibe Coding

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Ethical Considerations and Potential Risks:

The increasing use of AI in code generation through vibe coding also brings forth several important ethical considerations and potential risks that need to be carefully addressed.

One significant concern is the potential for bias and discrimination in AI-generated code .

AI models are trained on vast datasets of existing code and data, which may contain biases reflecting societal inequalities.

As a result, these biases can be inadvertently incorporated into the code generated by the AI, potentially leading to discriminatory outcomes in applications built using vibe coding.

For example, an AI trained on biased hiring data might generate code for a resume screening tool that unfairly disadvantages certain demographic groups.

It is therefore crucial to be aware of these potential biases and to implement strategies for evaluating and mitigating them in AI-assisted development processes.

Security vulnerabilities represent another serious ethical risk associated with vibe coding .

As highlighted by the example of the SaaS application that was quickly compromised after being built with AI , relying solely on AI-generated code without a strong understanding of security best practices can lead to significant weaknesses in the software.

AI models might not always prioritize security considerations or generate code that is resilient to common attack vectors like SQL injection or cross-site scripting .

Developers using vibe coding need to be vigilant in reviewing and testing AI-generated code for potential security flaws, especially when dealing with sensitive data or critical functionalities.

Copyright and intellectual property issues also pose ethical and legal challenges . AI models are trained on massive amounts of code, some of which may be protected by copyright.

There is a risk that these models could inadvertently reproduce or closely resemble copyrighted code in their output, potentially leading to legal disputes over intellectual property rights .

Unlike human developers who are expected to understand and respect copyright laws, AI models lack this inherent understanding.

It is therefore important for developers using vibe coding to be aware of this risk and to employ tools and practices that can help ensure the code they are using is compliant with licensing and intellectual property regulations.

The lack of transparency and accountability in some AI systems can also raise ethical concerns.

If developers do not fully understand how an AI model arrived at a particular piece of code, it can be difficult to identify potential issues, debug errors, or assign responsibility in case of failures or vulnerabilities.

The "black box" nature of certain AI algorithms can make it challenging to trace the origins of the generated code or to understand its underlying logic, which can be problematic in critical applications where transparency and explainability are essential .

Furthermore, there is a risk of developers becoming overly dependent on AI tools, potentially hindering the development of their own fundamental programming skills.

If developers rely too heavily on AI to generate code without fully understanding the underlying principles, their ability to solve problems independently or to maintain and extend code without AI assistance might diminish over time.

This dependency could also lead to challenges if the AI tools become unavailable or are not suitable for certain tasks.

Finally, the potential for AI to be used for harmful purposes, including the generation of malicious code, is an ethical consideration that warrants attention.

While current AI models often have safeguards in place to prevent the generation of harmful content, the ongoing development of more sophisticated AI raises concerns about the potential for misuse.

It is important for the industry to continue to develop ethical guidelines and safeguards to mitigate this risk and to ensure that AI tools are used responsibly.

The Impact of Vibe Coding on the Software Development Job Market:

The emergence of vibe coding and AI-assisted coding is poised to have a significant impact on the software development job market, leading to both transformations in existing roles and the creation of new opportunities .

One of the most anticipated effects is the automation of routine and repetitive coding tasks, which could potentially reduce the need for large teams focused primarily on mundane implementation .

As AI tools become more adept at generating boilerplate code and handling basic programming tasks, the roles of software developers are likely to shift towards more architectural design, complex problem-solving, and the oversight and integration of AI-generated code .

This transition suggests a future where developers spend less time writing basic code and more time on higher-level strategic thinking and innovation.

Concurrently, the increasing adoption of AI in software development is expected to drive a higher demand for professionals with expertise in artificial intelligence, machine learning, and related fields .

Roles at the intersection of software engineering and AI, such as machine learning engineers, data scientists, and specialists in AI frameworks, are already experiencing significant growth in demand .

As organizations seek to leverage AI tools effectively, the need for individuals who can build, train, and integrate AI models into software development workflows will continue to rise, creating new and specialized job opportunities.

Interestingly, there are indications that the impact of AI-assisted coding might be more pronounced at the entry-level tier of the software development job market.

With AI capable of handling some of the tasks traditionally assigned to junior developers, such as basic coding and debugging, there has been a reported decrease in job postings for entry-level positions in some areas.

This suggests that new entrants to the field might need to focus on acquiring more specialized skills and a deeper understanding of software development principles to remain competitive in an AI-driven landscape.

Despite these shifts and potential challenges, the prevailing view within the industry is that AI will primarily serve to augment the capabilities of human developers rather than completely replace them.

While AI can undoubtedly enhance productivity and automate certain tasks, the complexity, creativity, and critical thinking required for many aspects of software development are likely to continue to necessitate human expertise.

The ability to understand user needs, design innovative solutions, and ensure the quality, security, and ethical implications of software remains largely within the domain of human developers.

To navigate this evolving landscape, adaptability and continuous upskilling will be crucial for software engineers . Experienced developers are encouraged to embrace AI tools and learn how to integrate them into their workflows.

Familiarity with AI coding assistants and the ability to effectively utilize prompt engineering techniques are rapidly becoming essential skills.

By proactively acquiring knowledge in areas like AI and machine learning, developers can position themselves to take advantage of the new opportunities that arise from the increasing integration of AI into software development.

The long-term success of software professionals in an AI-driven world will likely depend on their ability to adapt to these changes and to leverage AI as a powerful tool to enhance their skills and productivity.