Vibe Coding, Broken by Design

Vibe coding promised to reinvent how we build software. Describe what you want, and an AI system writes the code, designs the interface, connects the backend, and deploys the app. It was supposed to be software development without the overhead typically associated with it.

The idea is seductive. Prototypes appear in minutes. You can test an idea without hiring a team or even knowing how to code. But somewhere along the way, a fantasy took hold: that you could build a company, a real one with users, revenue, and compliance requirements, with nothing more than a prompt. That story sold a dream. What we are now seeing is the reality catching up. The issue is not that vibe coding is useless. It has been misrepresented. The tools work well for what they are meant for: exploration, validation, and iteration. They do not replace the deeper craft of building production systems that are secure, scalable, and maintainable.

It is time to reframe what vibe coding really means.

What Vibe Coding Really Is (and Isn’t)

Vibe coding is an intent-driven way to build software. You describe what you want, and AI generates the code. It feels effortless because it turns ideas into working interfaces in hours instead of weeks. That speed is valuable for prototypes, internal tools, and quick experiments where the goal is to learn fast, not to scale.

The confusion comes from treating vibe coding as a full replacement for engineering. The term now covers everything from playful prototyping to production systems, which makes it unclear where it actually fits. In reality, vibe coding is a creative flow, a fast and intuitive way to explore ideas, not an engineering discipline.

A working demo is not a working system. Real products still need architecture, testing, observability, and security. Without these fundamentals, AI-built code becomes fragile and difficult to maintain.

Used by skilled engineers, vibe coding can speed up delivery without losing quality. Used without understanding the craft, it produces quick wins that collapse under pressure. The key is intention: know when you are experimenting and when you are building something meant to last.

Reality Check

In late 2024 and early 2025, a new generation of AI-assisted coding platforms exploded in popularity. Tools like Lovable, v0, Bolt.new, Base44, and Replit dominated the conversation. Traffic soared. Social feeds are filled with screenshots of “apps built in a weekend.”

But by mid-2025, the trend shifted. According to Similarweb’s global sector data and analysis from FinalRoundAI, traffic to these platforms began to fall sharply.

Source: Similarweb Global Sector Trends on Generative AI

Across the DevOps and code completion categories, traffic peaked in the spring and has been declining steadily since. Lovable, v0, and Base44, once breakout names, have all seen drops of more than 50 percent in a matter of months. Even Replit, with an established user base, has cooled off.

A closer look at search interest tells the same story.

Source: FinalRoundAI - The Vibe Coding Bubble

Lovable and v0 spiked in July and August, then fell rapidly in September. Bolt.new and Base44 show similar curves. The pattern is consistent: initial excitement, rapid adoption, then a visible pullback. Even executives from within the space have acknowledged the problem. When hype meets friction, curiosity fades. People try, encounter limits, and move on.

This is not a sign of failure. It is the normal correction that happens when a technology matures. The market is discovering what these tools are actually good for and what they are not.

The Illusion of Done

To a non-technical eye, an AI-generated app looks complete. The interface renders. Buttons work. Forms send data. A database shows up somewhere. It feels finished.

Engineers see something else. They look for the layers that make software safe and durable, and they often find them missing. Vibe coding platforms are brilliant at removing the barrier to experimentation. They let anyone move from concept to interface almost instantly, which is a genuine breakthrough. But the same qualities that make them fast also make them fragile once real users arrive.

Weak Security. Many AI-built apps rely on client-side checks or single-layer authentication, which are easy to bypass. The FinalRoundAI report cites the Tea App incident, where a vibe-coded project exposed more than 70,000 identity images with location data. The system “worked” until someone looked closer.
Fragile Data Foundations. Early prototypes often use simple, single-tenant databases. Once real data flows in, performance collapses, queries slow down, and migrations become nearly impossible without rewriting the core.
Unstable Under Load. Generated APIs rarely include rate limiting, caching, or error handling. When multiple users act at once, apps hang, duplicate requests, or corrupt data.
Zero Observability. Most vibe-coded projects have no logs, tests, or deployment automation. When failures occur, there is no way to trace what broke or roll back safely.
Hard to Maintain. AI can produce code fast but not cleanly. The output often lacks structure or consistency, making it harder and costlier for engineers to maintain or extend later.

These are not small problems. They are the difference between a product that runs and one that lasts. The issue is not that AI is unreliable. It is that real-world systems are complex. Without architecture, testing, and observability, any tool, AI or human, will produce fragile results.

Turning Demos into Discipline

Vibe coding should not mean skipping the fundamentals. It should mean using AI to accelerate them. The healthiest way to see it is as a continuum:

Idea stage: AI helps visualise and test concepts quickly.
Prototype stage: AI accelerates building MVPs and internal tools.
Production stage: Engineering practices take over, including security, testing, performance, and compliance.

The next phase of AI development will not be about speed alone. It will be about stability, finding ways to move fast while maintaining the integrity of the systems we build.

The teams that will thrive are those that treat AI as leverage, not as a replacement. They will use it to accelerate the first 80 percent of creation, then apply engineering discipline for the final 20 percent that makes software resilient, observable, and compliant.

Used responsibly, vibe coding becomes a creative accelerator that helps teams explore ideas faster and hand off stronger foundations to engineers. Used carelessly, it produces shallow architecture and code that cannot evolve.

That is the model we believe in at kosuke.ai: AI for speed, engineering for strength. We use AI to compress the early build cycle, then apply senior engineering rigor to secure, audit, and scale the system. The goal is not to build faster alone, but to build better and make speed sustainable.

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Reframing Vibe Coding