Vibe coding, a burgeoning method utilizing AI to produce code based on natural language descriptions, is being regarded as the forthcoming paradigm of programming. The term was introduced by AI visionary Andrej Karpathy in early 2025, describing the concept of “fully giv[ing] in to the vibes, embrac[ing] exponentials, and forget[ting] that the code even exists.” In reality, vibe coding entails accepting whatever code an AI assistant generates without closely scrutinizing or grasping it, with the belief that the code “mostly works” for intended purposes. Proponents view it as a groundbreaking shortcut: why toil for hours over syntax and debugging when an AI can accomplish it in mere minutes? Nonetheless, the actual situation is more intricate. Vibe coding frequently leads to “shoot-and-forget” code, immediate solutions that function now but turn into a hassle to repair, comprehend, or manage later. This article provides a factual examination of why the “vibes” might not sustain you in the long run of software development, and why traditional coding expertise remains vital in the AI era.
The Appeal of Vibe Coding
It’s clear why vibe coding has captivated the tech community. Instead of scripting every single line, developers (or even non-coders) can articulate their intentions in plain English and let advanced large language models (LLMs) like GPT-5 or Claude generate the necessary code. Karpathy’s viral tweet in February 2025 showcased this attraction: he created a web application by merely instructing an AI on what to do, copy-pasting error messages into it, and accepting the AI’s alterations, all while hardly touching the keyboard. The velocity and convenience are unmatched. Advocates argue this inverts the traditional programming dynamic, positioning humans as high-level “directors” and AI as the diligent “implementers.” Why labor over trivialities when your indefatigable AI partner can bring forth a prototype within hours?
This vision of rapid development has spurred explosive uptake. Within weeks of Karpathy’s tweet, vibe coding graced major media outlets and was readily adopted by startups. Merriam-Webster even included “vibe coding” in its dictionary as a trending term of 2025. Y Combinator noted that a quarter of the startups in its Winter 2025 batch boasted codebases 95% generated by AI. Tech CEOs claim that vibe coding enables “10 engineers to accomplish the work of 100” in feature development. The method also democratizes development, allowing individuals without formal coding training to create functional applications by merely “describing the vibe” of their desires. In theory, anyone can build software without mastering syntax, reducing entry barriers and unlocking fresh creativity.
Commonly cited benefits of vibe coding include:
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Rapid prototyping
Launch a basic app or feature within hours, not weeks. LLMs can generate boilerplate and repetitive code in moments, facilitating quick experimentation. Small teams or individual creators can achieve greater outcomes, realizing ideas that would have been too slow to implement in a conventional workflow. -
Minimized repetitive tasks
Monotonous chores such as writing boilerplate, configuring frameworks, or hunting through Stack Overflow are managed by the AI. This allows developers to concentrate on high-level design and product vision instead of grappling with trivial syntax mistakes. -
Openness for non-developers
With natural language prompts and some guidance, individuals who aren’t professional developers can build functional software. This enables domain experts, designers, or hobbyists to bring their ideas to fruition without first spending years learning to code. -
Boosted productivity for programmers
Even seasoned developers use vibe coding for efficiency enhancements. They can assign routine code creation to AI and dedicate more time to architecture, intricate logic, or creative problem-solving. As one CTO remarked, “I’m spending 80% less time actually typing code… instead I’m contemplating more profoundly on system architecture and user experience, the aspects that truly matter.”
The hype surrounding vibe coding presents it as an engineering utopia, where anyone can develop software by simply “vibing” with an AI collaborator, void of tedious coding. It’s a captivating prospect, and indeed, vibe coding can be transformative for quick successes and ephemeral projects. Karpathy himself found it amusing to allow an AI to execute a “weekend project” and witness creations unveil. For those low-stakes instances, why not give it a whirl?
However, veteran developers and real-world insights expose critical caveats. What transpires after you’ve created that ingenious app? That’s where the vibes frequently dissipate, and genuine software engineering begins. Vibe coding devotees declaring traditional coding abilities are obsolete may face a jarring reality when tasked with maintenance, debugging, and scaling their AI-produced code. Let’s delve into why.
Shoot-and-Forget Code: Present Today, Absent Tomorrow
A primary critique of vibe coding is that it fosters a “fire-and-forget” approach to code. You prompt the AI, receive a working snippet or application, and then proceed without revisiting, effectively embracing a “shoot-and-forget.” The code is seen as a one-off solution: utilize it while it remains functional, but don’t intend to modify or expand it later, for doing so may be a hassle.
Karpathy’s own explanation of vibe coding encapsulates this transient interaction with code. He confessed that the AI-generated “code grows beyond my usual comprehension” and that he ceased reviewing diffs or striving to fully grasp the alterations made. If a bug emerged that the AI couldn’t rectify automatically, he would merely “work around it or request random modifications until it disappears.” This indicates that the approach abandons meticulous debugging in favor of trial-and-error (or as I prefer to term it, “programming by stepwise debugging” [Steve]) until it “mostly functions.” The final result may operate initially, but no one (not even the original creator) genuinely understands how or why. As Karpathy articulated, “I’m constructing a project, but it’s not genuinely coding, I merely observe things, articulate things, execute things, and copy paste things.” It’s a frank acknowledgment that vibe coding yields disposable code: excellent for a rapid demonstration, significantly less so if you need to revisit or manage that initiative.
This shoot-and-forget mindset is acceptable for prototypes or singular tasks, code that is utilized once and cast aside. Indeed, many supporters explicitly recommend confining vibe coding to experimental sandboxes, personal scripts, or proofs-of-concept. “Projects ought to be low-stakes,” counsels AI blogger Simon Willison. If there’s a possibility the code could inflict genuine harm (financial loss, data breach, etc.), vibe coding is ill-advised in that context. Developer Charly Pinsen (in a bluntly titled piece, “Vibe Coding: A Weekend Hack That’s Not Ready for the Real World”) echoes this sentiment: “It’s a tidy gimmick for a swift prototype, but don’t be deceived: it’s far from ready for genuine applications; solid systems and businesses demand more than AI vibes.” He observes that while “the LinkedIn crowd relishes claiming ‘vibe coding’ transforms everyone into a 10x engineer… a prototype isn’t a product, and a product isn’t a business.” Relying on an untested weekend hack to cater to real clients invites trouble.
In our own experience operating an AI-focused blog and developing internal tools, we utilize vibe coding heavily for short-term requirements, e.g., swiftly crafting a custom script or mini-application to automate specific tasks. The productivity increases are undeniable; we might bypass the need for a SaaS subscription by vibe-coding our personalized solution overnight. But we approach this with the mindset that this code has a finite lifespan. We regard it as one-time code: it accomplishes the task today, and if adjustments are needed next week, we’ll probably create a new solution from scratch rather than dive into the tangled internals of the previous one. In other words, we “shoot” (generate the code) and “forget” (discard it once utilized), instead of committing to long-term maintenance. This method is practical when the intention is simply to resolve an immediate issue cost-effectively and swiftly.
However, once your project shifts from a discardable script to something enduring or mission-critical, shoot-and-forget coding becomes hazardous. Valuable software seldom remains unchanged … requirements develop, bugs surface, usage escalates … and that’s where vibe coding’s lack of continuity proves detrimental. Next, we’ll explore why comprehending and upkeeping AI-produced code poses such challenges.
Vibe Debugging: The Horror of Not Understanding Your Own Code
If you’ve ever inherited someone else’s complicated codebase, you know how tedious debugging and extending it can be. With vibe coding, you’re essentially inheritance of code from an AI, a “foreign” creator that may possess an entirely different (or even nonexistent) coding style. You received what you desired (one hopes), but the AI’s solution may diverge substantially from your own coding approach, often surpassing your comfort zone. As Karpathy humorously noted, even within his minor project, the AI’s code became so intricate he “would have to truly read through it for a while” to comprehend it. Many vibe coders don’t dedicate that time; they move ahead without genuinely grasping the code. That’s acceptable until something goes awry.
Now, imagine months later, an error arises or a new feature becomes necessary. The original “vibe” has long since faded, leaving you confronted with hundreds or thousands of lines of AI-generated logic you never thoroughly examined. “Good luck tracking down the bug when you didn’t create it,” quips Pinsen, reflecting on the challenges of debugging AI-generated code. Vibe coding, by its very nature, bypasses the steps of comprehending and reviewing the code, so when an issue occurs, the developer often lacks a mental framework of how the system operates. This encapsulates the essence of “vibe debugging”: attempting to resolve code that feels like someone else’s bewildering enigma. It’s a nightmare scenario for maintainers.
Experts have equated blindly accepting AI code to “driving a car with your eyes shut … it works until it catastrophically doesn’t.” A prevalent notion in software engineering is that you cannot efficiently maintain or extend what you don’t comprehend. Professional developers are trained to prioritize code clarity and explicit knowledge of system behavior. When vibe coding discards this practice, the repercussions manifest later in the development cycle. As the Cloudflare AI team indicates, teams releasing AI-written features with minimal examination can quickly “become unacquainted with their codebases,” making it harder to resolve bugs or vulnerabilities. This is not merely a theoretical issue; it materializes as increased debugging time, a higher incidence of defects, and even an inability to address certain problems without fundamentally rewriting major components.
Several typical issues render vibe-coded projects especially challenging to debug and maintain:
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Chaotic or non-standard code: AI generators may produce technically correct but peculiar solutions, utilizing unusual libraries, outdated patterns, or just logically convoluted methods that a human wouldn’t typically devise. The code frequently lacks the cohesive architecture or style that characterizes human design. Developers have reported AI-generated codebases morphing into a tangled mess. As one critic noted, large LLM-written programs can devolve into “LLM mush”, devoid of the organized structure required for scaling and upkeep. This mush becomes challenging to analyze when issues arise.
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Absence of documentation and clarity: A vibe-coded project generally contains scant comments or documentation (unless you specifically prompt the AI to incorporate them). It may also exhibit inconsistent naming conventions or abstract logic that wasn’t evident in the prompt description. Combined with the absence of code review, this results in future developers (or your future self) possessing little guidance. Consequently, team cooperation and onboarding new developers become hindered. Others may find the code opaque or baffling without someone who truly comprehends it.
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Fragile “glue” fixes: The vibe coding workflow promotes a quick-fix mindset: copy error message -> paste to AI -> accept the revision, repeat. This iterative patching can culminate in a heap of band-aid solutions that render the overall code weak. The AI might address a symptom without tackling the underlying cause, or circumvent a bug in a haphazard manner that incurs technical debt. Over time, these layers of AI-generated fixes create a house-of-cards codebase: one that “works… until it doesn’t.”
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Neglected edge-case logic: Unless expressly instructed, an AI might generate the simplest code that passes the prompt scenario, neglecting to consider edge cases or atypical inputs. Human developers instinctively contemplate edge cases and error management while coding; an AI has no future stake, fulfilling the prompt as straightforwardly as possible. This means many vibe-coded applications function in standard situations but fail in less conventional conditions. Troubleshooting those corner cases later proves difficult, particularly if the error appears far from the immediate cause (which you may not grasp due to the aforementioned reasons).
In summary, vibe coding can trade off short-term convenience for long-term struggles. It front-loads all the ease of “writing” the code, while back-loading the challenges to the maintenance stage. Traditional development distributes the effort more uniformly: you invest time initially in design, comprehension, and careful coding, which makes subsequent debugging manageable. Vibe coding saves initial time, but that debt frequently comes due with interest during debugging.
A particularly illustrative anecdote from Charly Pinsen states: “I once inherited a codebase so tangled it took weeks to unwind, AI-generated code feels like that on steroids.” In other words, if you find deciphering another human’s disjointed code challenging, deciphering an AI’s code (which could be entirely logical to the machine but incomprehensible to any human) is exponentially harder. Pinsen also observes that merely “tweaking until it works” isn’t engineering; it’s a gamble. When that gamble fails, you might be left without the skills or knowledge to resolve the issue, unless you essentially re-engineer the component from scratch.
Hidden Costs: Risks to Security and Reliability
Another significant reality check for vibe coding emerges in the form of security and reliability. AI-generated code may function, but is it safe and stable? Often the response is no, at least without a meticulous review by an informed developer. Default vibe-coded applications carry substantial hidden risks that may not be apparent to the individual who prompted the coding.
Security vulnerabilities represent a foremost concern. Research indicates that a considerable percentage of AI-generated code contains serious security flaws. The Cloud Security Alliance cautions that even elite LLMs often neglect critical security protocols; indeed, “the top foundational models generate at least 36% insecure code” when evaluated on security-sensitive tasks. In straightforward terms, more than one in three pieces of code that an AI produces could potentially be a ticking time bomb, susceptible to exploits. These encompass classic vulnerabilities such as SQL injection, cross-site scripting (XSS), hardcoded secrets, weak authentication, and more. A human developer trained in security might identify and avert such errors, but an AI will produce them without specific direction to the contrary. Deploying AI-generated code into production without a thorough security examination is akin to gambling with potential breaches.
Real-world incidents corroborate this. In one instance, an AI-generated snippet for a web application indiscriminately exposed API keys to the public, resulting in an immediate security breach. Another vibe-coded SaaS application showcased on social media was “immediately compromised” by researchers, according to MIT Technology Review. The simplicity and rapid nature of vibe coding can deceivingly coax developers into bypassing the rigorous testing typically employed to identify such vulnerabilities. When an issue is later uncovered, the previously mentioned lack of understanding resurfaces; those security challenges are “harder to address if development teams are unfamiliar with the problem code.” In critical systems, that poses a nightmare scenario.
Beyond security, reliability and scalability also present challenges for vibe-coded projects. Code that works for a basic demo may falter under realistic circumstances: high load, complex user behavior, integration with other systems, etc. AI code creators don’t inherently optimize for performance or stability unless prompted. They often produce the simplest solution meeting the prompt example, not the robust one that considers scalability or failure modes. Consequently, vibe-coded applications may function adequately for one user and a minimal dataset but struggle or crash when scaled to thousands of users or large data sets. Performance tuning and resource management are aspects that arise with engineering experience; an AI does not intuitively incorporate those concerns.
Maintaining state and consistency throughout a large codebase presents another obstacle. Vibe coding sessions generally target resolving individual prompts; they don’t always result in a coherently designed system-wide structure. This can lead to fragile integration points. For instance, two features developed on separate occasions might utilize slightly dissimilar data schemas or assumptions, because the developer (and the AI) failed to monitor the overall coherence. In a human-led endeavor, the developer’s mental model of the system prevents such inconsistencies; in vibe coding, that framework may be absent. Scaling such a makeshift system or incorporating new features can resemble playing Jenga: one wrong move, and everything collapses.
To be fair, many of these challenges can be mitigated. Responsible employment of AI coding assistants involves precisely what vibe coding in its purest form avoids: reviewing, testing, and comprehending the AI’s outputs. If you treat the AI as a competent junior developer, reviewing its work and training it to amend its errors, you can identify vulnerabilities and design issues. However, that diverges from the essence of vibe coding. As Simon Willison emphasizes, if you thoroughly review and test AI-generated code to the extent that you understand it, “that’s not vibe coding; it’s software development.” Genuine vibe coding, as defined by Karpathy, suggests not undertaking those due diligence tasks. It “deliberately sacrifices comprehensive understanding for speed and accessibility,” making it inherently perilous for anything beyond provisional projects.
In conclusion, vibe coding can significantly accelerate development dramatically, yet it also accelerates you right past the safety nets and best practices that traditional development employs to guarantee quality. This is why many seasoned engineers tread cautiously with the method. They aren’t merely being stubborn traditionalists; their concerns echo “legitimate worries regarding quality, security, and sustainability.” There’s no such thing as a freebie: any time saved by forgoing testing or thoughtful consideration of the code is generally repaid (with interest) when issues arise.
Traditional Coding vs. Vibe Coding: Why Skills Remain Essential
Amid the enthusiasm surrounding vibe coding, there exists a narrative in certain circles that “coding skills are now obsolete.” If an AI can create code for you, why bother studying algorithms, data structures, or debugging strategies? This perspective is short-sighted. In truth, strong programming and software engineering capabilities are more critical than ever for effectively leveraging AI and constructing dependable software.
First, it’s vital to clarify that vibe coding does not wholly replace programming; it’s an evolution of tools. Consider it this way: high-level languages and frameworks didn’t eliminate the necessity of grasping programming; they abstracted specific details, but developers continued to require an understanding of what the code is doing conceptually. Likewise, using an AI to generate code elevates human involvement to a higher level of abstraction; you’re crafting prompts instead of syntax, but you still need to know what to request, how to assess the results, and how to integrate the components. A vibe coder lacking fundamental coding knowledge is navigating blind. They may achieve functional code through trial-and-error, yet they won’t genuinely know if the solution is accurate, efficient, or secure.
As one front-end developer remarked, vibe coding “does not negate expertise; it enhances it”. The AI can manage mundane coding tasks, but the developer must still steer it appropriately and detect its errors. Understanding code structure and architecture is crucial to prompt the AI effectively (garbage in, garbage out). Debugging proficiency is necessary to identify when the AI has fabricated a nonexistent function or utilized a subpar technique, and then guide it to a superior solution. Performance optimization, security scrutinization, and adjusting code to handle edge cases all demand classic engineering thought. Vibe coding offloads some implementation labor to machines, yet it doesn’t diminish the importance of the engineer’s role; if anything, it accentuates its higher-level facets.
Simon Willison differentiates between “AI-assisted programming” and genuine vibe coding. Numerous developers already utilize tools like GitHub Copilot or ChatGPT as smart autocompletes or soundboards; they still meticulously review and integrate suggestions. This can indeed enhance productivity while maintaining code quality. In those instances, coding expertise remains fundamental; the AI serves merely as a powerful aid, akin to an advanced IDE. Extreme vibe coding (no review, no understanding) represents a deviation, beneficial in restricted scenarios but not advisable as a standard practice for professionals.
