By Ben Gilman, with technical insights from Andrew Kulakov
I’ve seen a lot of hype lately around vibe coding—the idea that you can throw a vague prompt at a large language model (LLM), watch it spit out code, and call it a day. Feels like magic. And to be fair—it kind of is.
For the first time, non-technical users can generate working code and prototypes without deep programming knowledge. It’s fast, it’s empowering, and it’s easy to see why it took off. But like all magic tricks, it doesn’t hold up when you turn on the lights.
Vibe coding may work for quick MVPs or internal demos. But when you try to scale, integrate, or build on top of it, things tend to break—right when it matters most. AI-generated code can introduce risks and challenges, especially when used in production environments.
Why Vibe Coding Took Off with AI Tools
With vibe coding, users can create software without needing to know traditional programming languages. You can throw in a rough idea and get back a working prototype. It’s like Google Translate for code—you might not speak the language, but suddenly you’ve “written” something in it.
The problem? Just like a bad translation, you don’t actually know what it says—or how it works.
This kind of guessing works fine for MVPs or demos. But when you try to scale, build on top of it, or integrate it into something more complex, the whole thing starts to fall apart.
The Collapse Point in the Software Development Process
The line between a clever AI-generated result and a production-ready solution is pretty straightforward. The former is a quick win. The latter requires foresight, planning, and intentional architecture.
Vibe-coded systems usually fall apart when the pressure hits: scale, users, integrations, or data volume. Ironically, that’s precisely the moment you can’t afford for things to break—when you’ve found product-market fit and your business has momentum.
And that’s where things get hard. Rebuilding your core system(s) mid-growth is a nightmare—and that’s exactly when vibe-coded foundations tend to crumble.
The Technical Perspective on AI-Assisted Coding
Andrew Kulakov, Solutions Architect, recently ran a series of tests using Claude-4 to clean up technical debt. The outcomes? Mixed at best.
- One-shot prompt = total failure.
- Chain-of-thought prompting = overengineered mess.
- Chain-of-thought + self-evaluation = better structure, still broken.
- Chain-of-thought with complexity classification = solid result in a small repo, but oversimplified.
- Applying that same prompt at scale? Breaks again.
The takeaway: LLMs are decent at generating code snippets—but they struggle with architecture, system design, and long-term planning. As soon as real complexity enters the equation, the cracks show.
That’s why one of the biggest misconceptions we see is the belief that large language models (LLMs) can replace strategic software engineering. They can’t. Think of building software like constructing a house. Artificial intelligence is a powerful crew—it can handle repetitive coding tasks and code generation, but without detailed blueprints, it has no understanding of the overall architecture or system design.
Our Response: The 3PO Approach
At Dualboot, we saw early on that the rise of AI tooling would require more structure—not less. Vibe coding felt exciting, but the risks were real. So instead of riding the hype, we built a response.
3PO was designed to ensure deep clarity before a single line of code is written. It flips the vibe coding process on its head. Instead of figuring things out during development, we treat development as the execution phase—not the discovery phase. We define architecture, validate requirements, and align with business goals up front.
That same philosophy led to DB90: a more efficient, AI-enhanced way of working that allows us to deliver more value to clients in less time. These frameworks weren’t built for flash—they were built for scale, structure, and speed with intention.
We don’t believe in chasing the next trend just because it’s new. We believe in using the right tools with the right process. With 3PO and DB90, we help teams avoid the costly traps of vibe coding by grounding AI development in strategy, clarity, and technical rigor.
We’ve seen where these systems break—and we’ve built a better way forward.
Final Thought
If there’s one mindset shift I hope this piece inspires, it’s this: AI isn’t magic and vibe coding might get you off the ground, but it won’t get you where you need to go. We’re all managers now. Whether you’re a product owner, a developer, or an engineering lead, your job is to guide a team of AI agents—not just execute tasks.
And that means thinking strategically. Building intentionally. And staying grounded—even when the hype says otherwise.
