Look at vibe coding through Architects Lens

What Is “Vibe Coding”?

“Vibe coding” refers to a new approach to software development where the programmer leans heavily on AI code generation, essentially coding by describing what they want and letting an AI produce the source code . Instead of manually writing every line, the developer provides prompts or instructions (even via voice) to a large language model (LLM) specialized for coding, and the LLM generates the code to implement those ideas . In this paradigm, one “surrenders to the flow,” focusing on high-level intentions while the AI does the heavy lifting of actual coding . The term vibe coding was popularized by AI researcher Andrej Karpathy in early 2025 to describe how advanced coding assistants allow developers to “fully give in to the vibes… and forget that the code even exists” . It’s not a formal methodology like Agile; rather, it’s a slang term for an informal, conversational style of programming with AI – a cultural phenomenon born out of the recent leaps in code-generating AI tools .

Example of an AI assistant (ChatGPT) generating code based on a natural-language prompt. The user requests a JavaScript function to shuffle a deck of cards, and the AI produces a complete solution with explanations. This illustrates the essence of vibe coding: describe the desired outcome, and let the AI handle the implementation.

Under vibe coding, the developer’s role shifts from writing syntax to guiding and refining the AI’s output . Instead of crafting every algorithm by hand, you might say, “I need a web form with a sidebar, decrease the padding by half”, and accept the changes the AI suggests . Errors are handled by simply feeding the error messages back into the AI and asking it to fix them . In Andrej Karpathy’s words, “I just see stuff, say stuff, run stuff, and copy-paste stuff, and it mostly works” – it doesn’t feel like traditional coding at all . Essentially, vibe coding turns programming into a collaborative dialogue between human and machine. This approach has been made viable only recently, as modern code-generating AIs (like OpenAI’s Codex/ChatGPT, Anthropic’s Claude with coding abilities, or tools like Replit’s AI) have become sophisticated enough to produce substantial blocks of correct code from a simple prompt .

Origins and Recent Trends

Vibe coding emerged from the confluence of AI advancements and developer experimentation. Karpathy (OpenAI co-founder and former Tesla AI lead) jokingly coined the term around February 2025 , after observing that new AI pair-programmers were “getting too good” and enabled a very hands-off style of coding . What started somewhat tongue-in-cheek caught on rapidly in developer communities. Within weeks, the concept went viral: major tech media covered it , Merriam-Webster added “vibe coding” as a trending term, and forums like Reddit lit up with debates on this practice . Y Combinator even released a 30-minute video explainer titled “Vibe Coding Is the Future,” indicating how seriously the startup world is taking this trend .

Enthusiasm for vibe coding is fueled by eye-opening early results. In Y Combinator’s Winter 2025 cohort, fully 25% of startups had codebases that were ~95% AI-generated, an astonishing adoption of AI-driven development . Replit (an online IDE) reports that “75% of Replit customers never write a single line of code”, a statistic CEO Amjad Masad shared to illustrate that many users rely entirely on high-level prompts or AI assistance instead . In other words, a majority of a popular coding platform’s users are effectively vibe coding – building apps by instructing the computer what to do in plain language. Tech entrepreneurs are embracing the approach: for example, one startup founder said that with vibe coding, “if you have an idea, you’re only a few prompts away from a product.” This captures the current vibe (pun intended) in Silicon Valley: turning ideas into working software faster than ever before by partnering with AI.

Developer social media is rife with “vibe coding” anecdotes. Some programmers have even adopted this style in personal projects for years. One developer wrote that since 2023 he has “AI handle about 90% of the code” for projects, using GPT-4 and custom tools to feed entire project context to the AI . He successfully launched small web apps and bots with minimal manual coding, essentially acting as a project manager gluing together AI-generated pieces. Teams are experimenting as well – for instance, Menlo Park Lab, a generative AI startup, is “all in on vibe coding” as a core development practice . Even large enterprises are paying attention. The trend has spawned solutions like TurinTech’s “Artemis”, an AI platform to optimize and clean up AI-written code, backed by $20M in funding to address the inefficiencies that vibe coding can introduce . Early adopters of such tools reportedly include big banks and blue-chip companies looking to harness AI-generated code without its downsides . This flurry of activity shows that vibe coding has moved from a fringe experiment to a mainstream discussion in the software industry in a very short time.

How Vibe Coding Works in Practice

In practical terms, vibe coding means working with AI as a co-developer throughout the software creation process. A typical vibe coding workflow might look like this:

• Describe the Goal: The developer starts by describing the feature or problem in natural language. This could be done via text prompts or even voice commands. For example: “Build a simple TODO app with user login and a task list.” The key is that the prompt is specific about what is needed, but the developer does not manually write the solution – they delegate to the AI.
• AI Generates Code: An AI coding assistant (such as ChatGPT, GitHub Copilot, Replit’s Ghostwriter/Agent, or Cursor) takes the prompt and produces code (or config, or other artifacts) that attempts to fulfill the request. The AI might create multiple files, functions, or classes as required. At this stage, the human acts more like a requester or tester than an author.
• Review and Refinement: The initial AI output is rarely perfect. The developer examines what was generated (at least at a high level or by running it) to see if it meets the need. They might refine the prompt or provide additional instructions to adjust the result . For example, if the UI isn’t quite right, the developer might say, “Now make the login form green and add a remember-me checkbox.” The AI will tweak the code accordingly. This iterative prompt-response loop continues until the software behaves as desired.
• Testing and Fixing: The developer runs the code. If there are errors or bugs, instead of diving into the code logic directly, a vibe coder will often copy-paste error messages into the AI and ask for a fix . The AI debugs its own code or suggests workarounds. The human might also ask for improvements (e.g., “Optimize this function” or “Simplify this code”). Essentially, the coder and AI pair-program the bug fixes.
• Deployment and Cleanup: Once the application works, the developer may do a final review or light cleanup. In ideal vibe coding, the motto is “Accept All changes, don’t read diffs” – meaning the coder trusts the AI’s changes without painstaking verification. In practice, for anything non-trivial, an experienced developer will at least sanity-check the critical parts before deploying. The code can then be deployed or handed off, with the knowledge that much of it was machine-generated.

Orchestrating the solution

This workflow highlights that vibe coding is highly conversational and iterative. It blurs the line between coding and talking. As IBM’s technical strategists describe it, it’s like taking inspiration and “convert[ing] it into something” tangible via AI . Developers with strong skills can leverage vibe coding to get a running prototype in hours, by focusing on what the software should do rather than how every line should be written. For instance, IBM’s engineers have used this approach to quickly prototype an app for financial planning just by formulating a good prompt and letting the AI build the first draft . The result: coding feels more like directing or orchestrating the solution, and less like grinding through boilerplate.

no real coding needed

However, it’s important to note that vibe coding in its purest form (as originally defined) implies a somewhat reckless abandon of strict oversight. Karpathy’s own example involved never manually searching through code or carefully reading AI-generated diffs, which he admits leads to code that “grows beyond my usual comprehension” in complexity . In professional settings, most developers won’t go that far – they will still do code reviews or add tests for AI-written code. In fact, as one AI engineer put it, if you are reviewing and testing all AI-produced code until you understand it, “that’s not vibe coding, it’s just software development (with AI assistance)” . This underscores that vibe coding as a “no real coding needed” ideal is mostly applied to rapid prototypes or low-stakes projects. In day-to-day team development, AI code generation is increasingly used, but usually under the umbrella of normal engineering rigor (design reviews, testing, etc.). Next, we’ll examine the key benefits and drawbacks of vibe coding that practitioners and observers have noted.

Benefits of Vibe Coding

Vibe coding has risen in popularity because it offers several compelling advantages, especially when used in the right context. Some of the notable pros include:

Speed and Productivity

Perhaps the biggest draw is the dramatic acceleration in development speed. Seasoned programmers have found that an LLM can produce code “an order of magnitude faster” than a human in many cases . Routine tasks that might take hours can be done in minutes by simply prompting the AI. This enables rapid prototyping and iteration. Teams can go from idea to a functional demo at unprecedented pace – as one developer noted, “if you have an idea, you’re only a few prompts away from a product.” By offloading grunt work to the AI, developers free up time to build more features or try more ideas in the same timeframe. For businesses, this faster time-to-market can be a significant competitive advantage.

Lower Barrier to Entry

Vibe coding opens the door for those with minimal coding experience to create software. Because the approach relies on describing what you want in plain language, even “amateur programmers” or people who aren’t professional developers can get results without deep knowledge of algorithms or syntax . As The New York Times quipped, with modern AI “just having an idea can be enough” to start programming . This democratization means domain experts or designers with ideas can prototype solutions themselves, rather than needing to hand off to a software engineer for every new concept. It can also accelerate onboarding of junior devs – they can produce useful code via AI while still learning the deeper concepts in parallel. Overall, vibe coding can make software development more inclusive and broaden who contributes to coding.

Focus on Higher-Level Design

Because the AI handles the repetitive boilerplate and intricate details, developers can spend more mental energy on high-level architecture, user experience, and problem-solving. An IBM AI advocate observed that developers can now concentrate on “solving real-world complex problems… designing efficient architecture… and fostering innovation rather than routine tasks” . In vibe coding mode, you think about what the software should do, not how to write every piece – which aligns programming more closely with the abstract thinking of an architect or product designer. This shift can increase developer satisfaction by reducing tedious work and allowing them to exercise creativity and big-picture thinking. It’s essentially a move toward a “problem-first” approach, where you let the implementation details emerge dynamically via AI .

Rapid Prototyping & Innovation

Vibe coding is particularly powerful for quickly experimenting with ideas. Because it’s so quick to get a working prototype, teams can cheaply test out features or even whole product concepts. This encourages innovation and risk-taking: you can try something, and if it doesn’t work, you haven’t lost much time. Industry observers note that this ability to “progress with a minimum viable product (MVP), cheaply experiment… and adapt based on feedback” reduces sunk costs and business risk . In other words, vibe coding can function like an innovation sandbox – enabling a “fail fast” mentality where the cost of failure is low. Enterprise architects value this because it means more ideas can be explored without lengthy development cycles or large teams. It also allows for quicker pivots since the initial investment in any single approach is smaller.

Developer Enjoyment and Inspiration

Many who have tried vibe coding describe it as fun and empowering. Karpathy – an expert programmer – said it was “quite amusing” to build a weekend project this way . Senior devs who don’t need AI still enjoy using it to “try out wild new ideas” at high speed, just to see what’s possible . Some liken the experience to having a tireless pair-programmer or an “AI intern” who can generate ideas that you might refine. This can boost developer morale and satisfaction, since they spend more time in creative exploration and less on plumbing code. One IBM engineer remarked that “vibe coding is a thing… you can take inspiration and convert it into something”, implying it can be a very stimulating way to build, turning imaginative prompts into tangible results . For seasoned engineers, it’s a refreshing change of pace; for beginners, it’s incredibly motivating to see immediate results, which can encourage them to learn more.

Drawbacks and Risks of Vibe Coding

Despite its promise, vibe coding also comes with significant challenges and caveats. Tech leaders and developers have been quick to point out the downsides that become apparent especially as projects grow. Key concerns include:

Code Quality and Correctness

AI-generated code is not guaranteed to be good code. Often the initial output is “basic and imperfect” – it may work for a simple case but lack the polish or efficiency a human engineer would aim for. Without careful review, vibe coding can produce solutions that are functionally correct but suboptimal or even flawed. For example, large language models might write an algorithm that is much less efficient than a well-informed human solution, or they might use outdated libraries and bad practices. One startup found that as you generate a lot of code via AI, you also generate “a lot of inefficiencies”, requiring later optimization to improve performance and resource usage . In critical systems, these inefficiencies or hidden bugs can be costly. Therefore, while vibe coding speeds up initial development, teams often must budget additional time for debugging, profiling, and refactoring the AI-produced code to meet production standards.

Maintainability and Technical Debt

Maintainability is a major worry with vibe-coded projects. What happens after the AI pumps out thousands of lines of code? If developers have not kept up with understanding that code, it can become a black box that is hard to maintain. Seasoned engineers warn that “LLMs are great for one-off tasks but not good at maintaining or extending projects” . An AI might introduce convoluted logic or inconsistent coding patterns that make the codebase difficult for humans to navigate later. Over-reliance on AI without refactoring can thus accumulate technical debt – messy, opaque code that “could become unmanageable during scaling or debugging” down the line . This is particularly problematic in a team setting: if one developer vibe-coded a feature and then leaves, the next maintainer might struggle to decipher how it works if no one fully understood it in the first place. In short, the ease of producing code is a double-edged sword – it’s easy to create a large system quickly, but that system might lack the clear structure and documentation that normally comes from a thoughtful design process . Enterprises must be mindful that quick gains in development speed could be offset by long-term maintenance costs if vibe coding is not disciplined.

Loss of Architecture and Skills

Because vibe coding bypasses a lot of manual effort, there’s a risk that developers (especially less experienced ones) won’t learn important software engineering principles. One expert noted that the “ease of use is a double-edged sword… beginners can make fast progress, but it might prevent them from learning about system architecture or performance.” In traditional development, struggling through designing modules or optimizing code teaches valuable lessons; an AI that magically handles it might leave a knowledge gap. From a team perspective, if junior developers rely too much on AI, they may not develop the deep expertise needed to make wise decisions when the AI falls short. Over time, an organization could lose engineering skills or have a false sense of competence. Moreover, the codebases generated might lack a coherent architecture. AI tends to solve locally what the prompt asks for, which might lead to a patchwork design unless a human constantly guides it. Large systems typically need a unifying vision (for scalability, modularity, etc.), and that is something vibe coding doesn’t inherently provide. As a result, teams might end up refactoring an AI-generated prototype significantly to impose a proper architecture after the fact .

Debugging and Trust Issues

While vibe coding makes it easy to get something working quickly, debugging those AI-written sections can be challenging. Developers remark that when an AI produces code you don’t fully understand, tracking down the cause of a bug feels like navigating someone else’s unfamiliar code – except that “someone else” might not have followed logical patterns a human would. The code can be correct in syntax but wrong in logic, or have subtle errors. IBM’s analysis pointed out that AI code can be “dynamic and lacks architectural structure,” making bugs hard to pinpoint . When an error arises, the vibe coding approach of feeding it back to the AI may fix it, but if it doesn’t, the human has to dive into code that they didn’t write. This can be frustrating and time-consuming, potentially eroding the productivity gains. There’s also an inherent trust issue: without reading through AI-generated code, can you be confident it’s doing the right thing (and only the right thing)? Professional developers are trained to be skeptical; many will not deploy code they haven’t reviewed. Simon Willison, an advocate for responsible AI coding, argues that if you don’t review what the LLM wrote, you’re taking a gamble – one he refuses to take for production code . In critical applications, blindly trusting AI output is obviously dangerous. Thus, for serious projects, vibe coding often needs to be tempered with additional verification steps, which reduces some of the speed benefit.

Security and Compliance Risks

Skipping code reviews and bypassing a deep understanding of code can lead to security vulnerabilities slipping through. This is a pointed concern in vibe coding. If the AI uses an insecure function or leaves input validation out, a human may not notice if they’re in “accept all” mode. One engineer cautioned that “security vulnerabilities may also slip through without proper code review” in the vibe coding process . Additionally, using AI tools raises issues of data privacy and licensing – prompts might send proprietary code to an external service, or the AI might generate code that is inadvertently copied from licensed sources. Enterprise IT leaders have to ensure that vibe coding practices comply with their security policies (for example, by using self-hosted or privacy-compliant AI models, and by instituting human review for any AI-generated code that goes into production). In summary, the convenience of vibe coding has to be balanced with traditional software governance: testing, security auditing, and compliance checks remain essential and might even need to be enhanced to catch AI-introduced flaws .

Limits to Usefulness on Complex Projects

Vibe coding works best for relatively self-contained tasks or well-trodden domains (like building a standard web CRUD app or using common frameworks). Its efficacy drops when faced with truly novel or complex software engineering problems. As observed in industry discussions, current LLMs “get lost in the requirements” when projects become large or highly intricate, and can “generate a lot of nonsense content” beyond a certain complexity . In other words, they might do a decent job on the first 70–80% of a typical app (the generic parts), but then struggle with the last mile that involves nuanced business logic, tricky integrations, or performance tuning . Andrej Karpathy himself noted that sometimes the AI “can’t fix a bug” or handle a particular request, and he resorted to applying “random changes” until the problem went away – clearly not a systematic approach you’d want in mission-critical code! Venture capitalist Andrew Chen summed up his experience by saying using the latest AI tools for vibe coding was “both brilliant, and enormously frustrating”, because “You can get the first 75% [of a project] trivially… Then try to make changes and iterate, and it’s like you…” hit a wall .

Teams adopting vibe coding report similar friction: the initial scaffolding is easy, but extending the system with new requirements can confuse the AI or require ever more complex prompts. Thus, vibe coding is not a silver bullet for all programming – for deep algorithmic work, highly optimized systems (e.g. a new database engine), or long-lived software that undergoes many changes, the traditional skills and thoughtful coding are still irreplaceable. In fact, the consensus in the developer community is that vibe coding is great for quick demos and drafts, but delivering maintainable, robust software products still requires human engineering expertise at the helm .

Impact on Teams and Development Culture

The rise of vibe coding is prompting a re-examination of developer roles, team workflows, and the culture of software development:

Changing Developer Roles

As vibe coding tools become commonplace, the role of a developer may shift more towards a curator or architect of AI-generated code. Developers might spend less time typing out boilerplate and more time specifying requirements, integrating components, and verifying outputs. In enterprise settings, we may see new norms where senior engineers act as “editors” of AI-produced code – guiding the AI with better prompts, then reviewing and refining the results for quality . Junior developers could ramp up faster by using AI to handle routine tasks, while they observe and learn from the suggestions. However, there’s also potential for a skill gap to widen: the best developers will be those who not only code well but can also harness AI effectively, knowing when to trust it and when to intervene. This could elevate the importance of software architecture and conceptual design skills over syntax trivia. Some have compared this to managing an “AI pair programmer” – the human must still provide vision and critical thinking.

Team Collaboration and Workflow

Vibe coding introduces new dynamics in collaboration. On one hand, non-engineering team members (like designers or product managers) might be able to prototype ideas themselves with AI, which can then be handed to engineers – this can improve collaboration by giving everyone a more direct creative tool. On the other hand, within a development team, if one person is vibe coding heavily, others need to be brought into the loop on what the AI produced. Code reviews become part detective work to ensure nothing was missed. Teams may establish guidelines, such as: “AI-generated code must be commented or explained by the prompter,” or mandatory peer review for any AI-written module, to maintain transparency. There’s also the aspect of version control and diff management – AI might introduce large changes that are hard to manually inspect. Some teams use tests as a communication mechanism: as in, “if the AI code passes all our tests and linters, we accept it.” In summary, collaboration can remain strong, but processes may adjust: think shorter development cycles (since AI produces code quickly) but possibly longer code review or testing phases to compensate. In large organizations, internal “AI coding” champions or centers of excellence might form to share best practices so that vibe coding is used consistently and safely across teams.

Developer Satisfaction and Culture

The cultural impact of vibe coding is nuanced. Many developers get genuinely excited by the possibilities – it feels like having superpowers or an ever-helpful assistant. This can boost morale, as engineers can accomplish more with less tedium. It also fosters a culture of experimentation: developers might be encouraged to spike out ideas with AI and show results, leading to a more innovative atmosphere. However, there can be negative feelings as well. Some engineers worry that reliance on AI could deskill the profession, turning coding into a commodity or reducing the artistry of it. There’s pride and enjoyment in crafting clean code; if one’s job shifts to just gluing AI outputs, not everyone will find that fulfilling. There can also be frustration when the AI doesn’t do what you want – a feeling of wrestling an unpredictable collaborator. Andrew Chen’s remark about the process being “enormously frustrating” beyond the initial success resonates with many who have tried to build something substantial with AI . In an enterprise context, leaders will need to manage these cultural factors: encouraging use of AI tools but also training developers to maintain their skills and not become overly dependent. Done right, vibe coding can improve developer happiness by removing drudgery; done poorly, it could alienate developers who feel their craftsmanship is being sidelined by “autocomplete on steroids.”

Quality Assurance and Governance

From an IT leadership perspective, vibe coding necessitates updated governance. Continuous integration pipelines may incorporate AI code scanners or additional static analysis to catch issues from AI contributions. Organizations might establish rules about where AI can be used (e.g., prototyping vs production code) and require documentation for AI-generated components – essentially treating the AI like an external contractor whose work needs review. Education is key: developers should be trained in prompt engineering (to get better results) and in reviewing AI code. Some companies even maintain an internal library of approved prompts or use in-house LLMs to keep sensitive code in a secure environment. As Gartner predicts, by 2028 75% of enterprise software engineers will use AI code assistants in their work , so clearly the industry is heading toward widespread adoption. The organizations that thrive will be those that integrate these tools in a way that maintains high standards. Notably, vibe coding does not eliminate the need for disciplines like testing, DevOps, threat modeling, etc. – if anything, those become even more important to verify the deluge of code AI can generate. Enterprise architects should ensure that adopting vibe coding doesn’t bypass the checkpoints that ensure systems are reliable, secure, and aligned with business requirements.

Few words at the end…

(yes, I know… it is lengthy… )

Vibe coding has quickly moved from a buzzword to a real influence on how software is built in 2025. It represents a shift toward more natural and rapid development, where telling the computer what you want takes precedence over hand-crafting how it’s done. This AI-powered coding style offers exhilarating speed and creative freedom, enabling even novices (or simply the time-constrained) to turn ideas into working software with unprecedented ease . For enterprise software leaders, vibe coding promises faster prototyping, increased productivity, and the ability to tackle more projects with the same resources – a compelling proposition in today’s competitive environment. It aligns with the trend of developers focusing on higher-order problems while automation handles routine implementation details .

However, along with these benefits come very real trade-offs. Without prudent controls, vibe coding can lead to bloated, fragile codebases and security or maintainability headaches down the road . The informality that makes it attractive for a quick win is exactly what can make it risky for long-term, collaborative software development. Therefore, the consensus among thought leaders is to approach vibe coding as a tool best used in moderation and with oversight. It’s excellent for hackathons, early-stage prototypes, and accelerating low-stakes tasks. In those scenarios, the ability to “just vibe” and let the AI fill in the blanks can significantly boost innovation and developer enthusiasm. But when it comes to mission-critical products, teams are finding that traditional software engineering rigor must still apply: AI-generated code should be tested, reviewed, and integrated into a well-thought-out architecture – essentially guided by experienced developers to ensure quality .

In summary, vibe coding today is less a strict methodology than a cultural shift in programming. It’s a reflection of how far AI assistance has come, altering the developer experience and workflow. Companies that adopt vibe coding practices stand to gain in agility, but they should do so deliberately: provide training, set clear guidelines (e.g. when to use vibe coding vs. when to write code manually), and leverage tools to mitigate its weaknesses (such as AI code validators and security scanners). By finding the right balance, organizations can harness the “vibes” to their advantage – speeding up development and empowering their teams – without getting lost in the flow. The code may practically write itself now, but the responsibility for delivering maintainable, robust software still firmly rests with us humans . With a healthy mix of excitement and caution, enterprise architects and senior developers can guide their teams through this new era of AI-assisted development, making the most of vibe coding while upholding the standards that professional software demands.

Sources: The information above is synthesized from recent discussions and analyses of vibe coding across industry publications and developer communities, including Ars Technica , Business Insider , New York Times , TechCrunch , IBM’s technical blogs , and first-hand developer accounts , among others. These sources reflect the state of vibe coding as of 2025, capturing both the enthusiasm and the critical lessons learned as this trend unfolds.