Vibe Coding: What It Is, Where It Breaks, and Where It Works

The term "vibe coding" took off in early 2025 when Andrej Karpathy described a coding style where you "fully give in to the vibes, embrace exponentials, and forget that the code even exists." You describe what you want to an AI tool, it generates the code, you run it, and if it works, you move on. If it does not, you describe the error and let the AI fix it.

That is vibe coding in its purest form. You are not reading the code. You are not reasoning about data structures, edge cases, or architecture. You are vibing. The AI is the developer. You are the product manager.

This is not inherently bad. That is the first thing worth saying, because the conversation around vibe coding tends to split into two camps: people who think it is the future of software, and people who think it is an embarrassment. Both are wrong.

Vibe coding is a tool. Like any tool, it has situations where it shines and situations where it will hurt you. The problem is not vibe coding itself. The problem is people using it in contexts where it does not belong, and not realizing the difference until something breaks in production at 2 AM on a Friday.

Let us be fair about this. There are situations where vibe coding is genuinely the right approach, and dismissing it entirely means missing out on real productivity gains.

Prototyping and proof of concepts. If you need to test whether an idea is worth building, vibe coding gets you there in hours instead of days. You are not shipping this code. You are showing it to a co-founder, an investor, or your team to validate the concept. The code quality does not matter because the code is disposable. What matters is speed, and vibe coding delivers that.

Hackathons and demo days. You have 24 hours to build something impressive. Nobody is going to audit your code. Nobody is going to maintain it. The entire point is to ship fast, show the demo, and win the prize. Vibe coding was practically designed for this context. Some of the best hackathon projects at McTaba Labs demo days were built this way, and there is nothing wrong with that.

Personal projects and learning experiments. If you are building a personal budget tracker or a script to organize your photos, the stakes are low. If it breaks, you are the only person affected. Vibe coding lets you build things you would never have built otherwise, simply because the barrier to entry is lower. That is a net positive for the world.

Automating repetitive tasks. Writing a script to rename 500 files, scrape a website, or convert data between formats? These are single-use scripts. Vibe code them, run them, move on. Spending three hours writing tests for a script you will run once is a waste of your time.

Learning new technologies. Vibe coding can actually be a useful exploration tool. If you want to understand what a new framework feels like, having AI generate a small app lets you see the patterns quickly. The key is that this is the starting point of learning, not the end of it. You still need to go back and understand what was generated.

Now the other side. These are the situations where vibe coding will get you into real trouble.

Production applications with real users. The moment your code serves real people, you become responsible for it. When a bug shows up (and it will), you need to diagnose it, understand the root cause, and fix it without breaking something else. If you vibe-coded the codebase, you are staring at code you do not understand, trying to fix a problem you cannot diagnose. This is not a theoretical risk. We have seen developers at McTaba Labs inherit vibe-coded prototypes that got pushed to production, and the time spent understanding the existing code exceeded the time it would have taken to write it properly from scratch.

Security-sensitive code. AI models generate code that works. They do not reliably generate code that is secure. Authentication flows, payment processing, data encryption, input validation: these are areas where subtle mistakes create real vulnerabilities. A vibe-coded auth system might look correct in the demo. It might even pass basic testing. But if the AI used a deprecated hashing algorithm, stored tokens insecurely, or missed an injection vector, you have a security hole that you do not know exists because you never read the code. In markets like Kenya where M-Pesa integration handles real money, this is not theoretical. It is dangerous.

Anything you need to maintain for more than a month. Software is not a one-time activity. It is an ongoing relationship with a codebase. You will need to add features, fix bugs, update dependencies, and respond to changing requirements. If you vibe-coded the original system, every change becomes a gamble. You are asking the AI to modify code that neither you nor it fully understands (the AI has no memory of what it generated last month). Each change introduces the risk of subtle regressions that you cannot catch because you do not understand the architecture.

Team projects. When multiple developers work on a codebase, shared understanding is essential. Code reviews, architectural discussions, and debugging sessions all assume that the author of the code can explain why they made specific choices. If the honest answer is "the AI wrote it and I accepted it," the team cannot function effectively. You become a bottleneck for your own code because you cannot explain it, modify it, or defend the decisions embedded in it.

The most dangerous thing about vibe coding is not the bad code. It is the false confidence it creates.

When you build something with AI and it works, you feel like you built it. The dopamine hit is real. You have a working application. You can show it to people. It does the thing. The problem is that "it works" and "I understand it" are completely different statements, and vibe coding makes them feel identical.

This false confidence shows up in job interviews. We have seen candidates at McTaba Labs who can show impressive portfolio projects but cannot answer basic questions about how their own code works. "How does your auth flow handle token refresh?" Silence. "What happens if this API call fails?" Silence. "Why did you choose this database structure?" Silence. The project looks great. The understanding is not there.

It also shows up when things go wrong. A developer who understands their code can debug systematically. They know the data flow, the state management, the likely failure points. A developer who vibe-coded their project can only do one thing when it breaks: paste the error into the AI and hope for the best. Sometimes that works. Sometimes it starts a spiral where each AI fix introduces a new bug, and the codebase drifts further from anything coherent.

The fix is simple, but it requires discipline. When you vibe code something, treat the AI output as a first draft, not a finished product. Read every line. Ask yourself what each function does, why the data flows this way, and what would happen if this input were malformed. If you cannot answer those questions, you do not own the code yet. You are borrowing it.

The answer is not "never vibe code." The answer is to vibe code intentionally, with a clear understanding of what you are doing and what you are trading off.

Rule 1: Know which mode you are in. Before you start a coding session, decide whether this is a vibe coding session (speed matters, code is disposable) or a real engineering session (quality matters, code must last). Do not let yourself drift between the two. The worst outcomes happen when people start vibe coding "just to get the prototype working" and then ship the prototype to production because it looked done.

Rule 2: Vibe code the first draft, then understand every line. This is the best use of AI for professional developers. Let the AI generate a quick implementation, then go through it line by line. Refactor the parts you do not like. Ask the AI to explain the parts you do not understand. Delete the parts that are unnecessary. What you end up with is code you wrote (with AI assistance) that you fully understand. This is faster than writing from scratch but does not sacrifice understanding.

Rule 3: Never vibe code the parts that matter most. Authentication, payments, data validation, error handling. These are the areas where bugs are most expensive and most dangerous. Even if you vibe code the UI and the basic CRUD operations, write the critical paths yourself (or at minimum, review the AI output with paranoid attention to detail).

Rule 4: If you cannot explain it, you cannot ship it. Before merging any AI-generated code, ask yourself: "Could I explain this to a teammate during a code review?" If the answer is no, you have more work to do. This single rule prevents most of the problems that vibe coding creates.

Rule 5: Keep your fundamentals sharp. Vibe coding is most dangerous for developers who do not have strong fundamentals to fall back on. If you understand HTTP, databases, authentication patterns, and error handling at a deep level, you can catch the mistakes AI makes. If you do not have those fundamentals, you cannot tell good AI output from bad AI output, and that is when things go wrong.

If you are job hunting in 2026, you need to know that hiring managers are aware of vibe coding, and most of them view it with skepticism. Not because they think AI tools are bad, but because they have already been burned by candidates whose skills did not match their portfolios.

The pattern is obvious from the interviewer's side. A candidate submits a portfolio with three polished projects. The code is clean, the architecture is solid, the README is perfect. Then the technical interview starts, and the candidate cannot implement a basic function without AI assistance. Cannot explain their own project's data model. Cannot debug a simple error when you take away the AI tools. This happens frequently enough that many companies now specifically test for it.

What hiring managers actually want to see is developers who use AI as a multiplier for real skills, not as a replacement for them. They want someone who can use Cursor or Claude Code to move fast, but who can also whiteboard an architecture, reason about trade-offs, and debug problems independently. The combination of AI fluency and genuine engineering skill is extremely valuable. Either one alone is not enough.

If you have been relying heavily on vibe coding, the best thing you can do is start building one project the hard way. Pick something meaningful, write the core logic yourself, use AI for boilerplate and repetitive tasks, but make sure you can explain every architectural decision. That project, with its messy git history and real problem-solving, will be worth more in interviews than five vibe-coded portfolio pieces.

AI coding tools are getting better fast. The code that Claude, GPT, and other models generate in 2026 is significantly better than what they produced in 2024. So does that mean vibe coding will eventually be safe for everything?

Probably not. Here is why. Even as AI gets better at generating correct code, the fundamental problem remains: if you do not understand the code, you cannot maintain it, debug it, or evolve it when requirements change. Better AI output raises the floor (fewer obvious bugs) but does not eliminate the need for human understanding of the systems being built.

What will change is the boundary between "vibe-codeable" and "needs real engineering." As AI gets more reliable, that boundary will shift. Things that require deep understanding today might be safely delegated to AI in a few years. But the boundary will always exist, and the most valuable developers will be the ones who know where it is.

The practical takeaway: learn to use AI tools aggressively. Vibe code when the context is right. But invest equally in understanding the fundamentals that let you evaluate AI output, catch its mistakes, and take over when the AI is not enough. That combination of AI fluency and engineering depth is the most future-proof skill set a developer can have in 2026 and beyond.

At McTaba Labs, our cohort members use AI tools every day. But they also write algorithms by hand, debug without AI assistance, and build systems they can explain line by line. That balance is intentional, and it is what makes them employable in a market that is increasingly skeptical of AI-generated everything.