In a San Francisco co-working space that smells of kombucha and unfulfilled promises, a 22-year-old former philosophy major is excitedly telling his friends about the app he’s building using AI. “I don’t know a single line of code,” he boasts, adjusting his Patagonia vest. “I just tell Cursor what I want, and it builds it for me. It’s called vibe coding. I read about it on Twitter—I mean X. Sorry, I mean whatever Elon’s calling it this week.”
Three weeks later, his startup has raised $2.3 million in pre-seed funding. Three months later, his app has suffered a catastrophic data breach exposing 500,000 users’ personal information. “I didn’t know you were supposed to encrypt user data,” he explains to a TechCrunch journalist. “The AI never mentioned it, and it seemed to work fine in testing.”
Welcome to the brave new world of “vibe coding,” where the vibes are immaculate and the debugger is screaming in existential horror.
The Emperor’s New Development Paradigm
Since Andrej Karpathy coined the term “vibe coding” in February 2025, the tech industry has embraced it with the enthusiasm of a venture capitalist discovering a new way to monetize human insecurity. The premise sounds revolutionary: simply describe what you want your software to do in plain Queen’s English, and AI will handle all that pesky, complicated code for you!
It’s programming democratized! Coding for the masses! No more gatekeeping by computer science graduates who insist on “understanding algorithms” or “knowing what memory leaks are!”
This narrative has spawned an entire ecosystem of AI coding tools like Cursor, Windsurf, and Replit, all promising to transform anyone with the ability to form a coherent sentence into the next technical co-founder. Cursor’s creators have explicitly stated their mission as building “a magical tool that will one day write all the world’s software,” which sounds great until you remember that “magical” is never a word you want associated with your production database.
The reality, however, is somewhat different, as evidenced by the growing number of spectacular vibe-coded app failures making headlines. According to absolutely no formal studies but plenty of Reddit threads, applications built exclusively through vibe coding are 74% more likely to collapse when exposed to real-world conditions, similar to how a child’s drawing of a bridge might look wonderful but wouldn’t survive first contact with physics.
The Three Stages of Vibe Grief
Daniel Bentes, who conducted a 30-day experiment building an application called ObjectiveScope using “99.9% AI-generated code,” identified three distinct phases of the vibe coding experience that the glossy marketing materials somehow fail to mention:
First comes the “Honeymoon Phase,” where AI tools like Claude and Cursor deliver seemingly miraculous results. You describe a feature, and voilà, it materializes! You’re a coding god! Silicon Valley desperate VCs, prepare your checkbooks!
Next arrives the “Context Collapse,” where the AI increasingly loses track of what the hell is happening in the broader system. Features get recreated unnecessarily or broken by seemingly unrelated changes. Your vibe check begins bouncing like it’s written on rubber.
Finally, you enter “Architectural Lock-in,” where those quick-and-dirty early decisions made by the AI become hardwired into your application’s DNA with the permanence of a regrettable tattoo. Unlike traditional development, where refactoring is a standard Tuesday afternoon activity, changing your AI-birthed application’s architecture becomes about as feasible as teaching a goldfish calculus.
Debugging: Where Vibes Go to Die
“Vibe coding is fine. Vibe debugging is a nightmare,” explains Mohit Pandey, a developer who presumably needed several stiff drinks after attempting to fix AI-generated code. “It is 10 times more frustrating than regular debugging. Since AI-generated code doesn’t help form a mental map of how data flows, fixing bugs becomes a never-ending loop of trial and error.”
This perfectly illustrates why experienced developers secretly love vibe coding while publicly expressing concern about its limitations. For seasoned coders who already understand the underlying systems, asking AI to handle boilerplate code is like hiring a butler. For beginners, it’s like hiring a butler who speaks an unknown language and occasionally sets the kitchen on fire.
Jan Flik puts it succinctly: “Most experienced software engineers will tell you that the majority of time they spend is not about creating new code. It is debugging the existing one.” If you can’t debug effectively—and you can’t debug effectively if you don’t understand how code works—then you’re essentially building a house on top of a swamp with no idea how to operate a sump pump.
The Invisible Complexity Gap
What makes vibe coding particularly insidious is what Namanyay Goel calls the “invisible complexity gap”—the difference between “it works on my machine” and “it’s secure in production.”
When helping debug a friend’s AI-generated SaaS for teachers, Goel discovered the application had no rate limiting on login attempts, unsecured API keys, admin functions protected only by frontend routes, and database manipulation directly from the frontend. Yet his friend was genuinely confused by these concerns: “But it works fine! I’ve been testing it for weeks!”
This perfectly encapsulates the problem. Modern software development tools, especially AI assistants, are extraordinarily good at hiding complexity, creating the illusion of competence. It’s like giving someone a pre-built racing drone without explaining that flying too close to a power line will result in a small, expensive fireball.
As the Vibe Coding Framework documentation admits, “Without a structured approach, teams often encounter security vulnerabilities, maintainability issues, and knowledge gaps.” What they don’t explain is that developing this “structured approach” requires exactly the programming expertise that vibe coding supposedly makes obsolete.
The Skills You Didn’t Know You Needed
Matt Palmer, apparently detecting that the vibe was shifting, helpfully outlined five skills necessary for effective vibe coding:
- Procedural thinking: Breaking down your app into logical steps.
- Framework knowledge: Knowing what tools exist for specific tasks.
- Checkpoints: Building in discrete steps and saving working versions.
- Debugging: Methodically identifying and fixing errors.
- Context management: Selectively providing relevant information to AI.
If this list sounds suspiciously like “things experienced software developers already know how to do,” that’s because it is. In fact, it’s essentially a checklist of fundamental programming skills dressed up in vibey language to make them sound more accessible. It’s like claiming anyone can perform surgery because instead of “scalpel,” we’re now calling it a “healing pointer.”
Zubin Pratap puts it bluntly: “For novice engineers, vibe coding is a siren’s song—alluring but very treacherous. It’s like giving a novice driver the keys to a Formula 1 car. The power is intoxicating, but without the foundational skills, it’s a recipe for disaster.”
The Great Vibe Marketing Swindle
The most successful con artists know that the key to a great swindle is making the mark feel special, chosen, and uniquely capable. The vibe coding movement has mastered this technique, presenting itself as a democratizing force while actually creating a more treacherous landscape for beginners.
AI coding tool marketers have created a brilliant double-bind: if you succeed with their tool, it proves the tool works. If you fail catastrophically, it proves you didn’t use it correctly or didn’t have the right “vibe skills.” Either way, they win, and either way, they keep your subscription fee.
The VIBE workflow, which stands for Verbalize, Instruct, Build, and Evaluate, sounds wonderfully accessible until you realize it requires you to “use RICE-Q for clear prompts, tools with MCP for coding, and MCP features for task management and documentation.” If you know what any of those acronyms mean without Googling them, congratulations—you’re probably already a software programmer.
This marketing sleight-of-hand lets companies position their tools as “coding for everyone” while quietly requiring extensive technical knowledge to use them effectively. It’s like advertising a “guitar for people who can’t play guitar,” then including in the fine print that you need to understand music theory, chord progressions, and have callused fingertips.
The Silicon Valley Self-Selection Algorithm
Here’s what’s really happening: Silicon Valley has created the perfect self-selection algorithm. Those who already have software programming experience can leverage vibe coding to become more productive. Those who don’t will either:
A) Realize they need to learn fundamental programming concepts, essentially putting them on the traditional learning path.
B) Create applications with critical security flaws, performance issues, and maintenance nightmares, then either fail publicly or become case studies for why vibe coding “isn’t for everyone.”
C) Succeed through sheer tenacity and luck, then be held up as the exception that proves the rule, fueling more marketing materials.
Meanwhile, bootcamps and online courses are already pivoting to offer “Vibe Coding Fundamentals” at $12,000 per six-week program, which—surprise!—end up teaching many of the same programming concepts they were teaching before, just with trendier terminology and the occasional Claude prompt.
The Debugging Dystopia
The final piece of evidence that vibe coding secretly favors experienced developers comes from Jan Flik’s experiment with debugging, where even advanced AI models struggled to fix their own code. When given simple debugging prompts, AI “claims that it need to fix code inside branch of ‘if’ condition even there was no way that path was executed.”
This reminds us of a fundamental truth that Silicon Valley periodically forgets: programming isn’t about writing code; it’s about solving problems. The code is just the medium through which solutions are expressed. If you can’t understand the problem and recognize when a solution is incorrect, all the AI assistance in the world won’t help you.
As Andrej Karpathy himself admitted, vibe coding is fine for “throwaway weekend projects, but not so much for serious or complex work.” The fact that this crucial caveat doesn’t appear in any of the marketing materials is surely just an oversight.
The Future of Vibing
Despite these challenges, vibe coding isn’t going away. In fact, it’s becoming more sophisticated and potentially even more deceptive in its accessibility claims. The latest models like Claude 3.7 and Gemini 2.5 are being integrated into development environments in ways that make them feel like “an ever-ready junior developer on the team.”
But here’s the thing about junior developers: they require supervision, guidance, and correction from senior developers. An AI can write code all day long, but without someone who understands programming to review it, refine it, and catch its inevitable mistakes, you’re essentially playing Russian roulette with your users’ data.
What we’re witnessing isn’t the democratization of coding—it’s the creation of a new, more subtle form of technical hierarchy. Instead of “those who can code” versus “those who cannot,” we now have “those who understand enough about coding to effectively use AI” versus “those who are completely at the mercy of whatever the AI produces.”
The brutal irony is that to truly benefit from vibe coding, you need precisely the skills that vibe coding supposedly makes unnecessary. It’s like claiming you don’t need to know how to drive because you have a self-driving car—right up until the moment the self-driving system fails and hands control back to you at 120 mph.
In the end, the most successful vibe coders will be those who approach AI as a tool to enhance their existing skills rather than replace them—which is exactly what experienced developers are already doing. As Zubin Pratap explains, for seasoned pros, it’s like “you’re the chef and you’ve got a savant sous-chef”—someone who can execute your vision but still needs your expertise and direction.
For everyone else? Well, there’s always another round of funding for “AI-powered coding that really works this time, we promise.”
Have you tried vibe coding yourself? Did you find it empowering or frustrating? Are you an experienced developer secretly delighted that AI tools have made your skills more valuable while appearing to make them obsolete? Share your coding horror stories or success tales in the comments below—and please format them in plain English, as our commenting system hasn’t been taught to vibe debug yet.
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