Vibe-Coding vs. Engineering : Finding the Right Balance in AI-Assisted Development

Why “Just Let the AI Build It” Needs Guardrails—Not Abandonment

By Edward Meyman, FERZ LLC

In early 2025, Andrej Karpathy popularized ‘vibe-coding’—the idea of using AI prompts to spin loose ideas into entire systems. Seductive, yes—but it risks replacing engineering discipline with improvisation dressed up as innovation.

Karpathy’s experiments, like building a menu image generator app entirely through AI prompts, showcase genuine rapid prototyping potential. But as this approach gains broader adoption, it raises critical questions about when convenience crosses into carelessness. The mantra of “let’s just see what happens” might spark innovation in prototyping, but in production systems, it can spark disasters.

But vibe-coding isn’t new—it’s just the latest chapter in a long history of seductive shortcuts.

The Familiar Pattern of Shortcuts

Having spent decades in IT, I’ve witnessed this pattern before—just in different forms. Long before AI, I watched developers jump straight into syntax, crafting loops and conditionals before understanding the algorithmic foundations of their problems. Nothing was more frustrating than seeing people—often those who considered themselves skilled—reach immediately for their IDE and starting to code without first thinking through the system design, the data flows, or the algorithmic approach.

This wasn’t just inefficiency—it was a fundamental misunderstanding of what software engineering actually entails. True talent in development isn’t measured by how quickly you can produce working code, but by how systematically you can think through problems before touching a keyboard. The language syntax, the frameworks, the specific technologies—these are implementation details. The real work happens in the space between problem identification and code generation: the systematic analysis, the algorithmic thinking, the architectural decisions that determine whether a system will scale, maintain, and evolve.

Those who skip this thinking phase aren’t demonstrating talent—they’re demonstrating impatience masquerading as productivity. Vibe-coding, in many ways, represents the evolution of this same problematic impulse. Where developers once jumped prematurely to syntax, they now jump prematurely to prompts. The medium has changed, but the underlying issue remains: the abdication of systematic thinking in favor of immediate gratification.

The Nature of “Vibe”

The term itself is telling. “Vibe” suggests intuition over analysis, feeling over structure, impression over precision. In creative endeavors, vibes can be valuable—they capture something essential about aesthetic direction or emotional resonance. But in engineering disciplines, vibes without structure become recipes for unreliable outcomes.

When we say we’re “following the vibe” in development, we’re essentially admitting to a lack of clarity about what we’re trying to achieve and how we plan to achieve it. It’s development by impression rather than intention, where the goal emerges from the process rather than guiding it.

This represents a fundamental confusion about the role of uncertainty in engineering. Good engineers work with uncertainty constantly—market requirements change, technical constraints emerge, user needs evolve. But they address this uncertainty through systematic approaches: modular design that can accommodate change, abstraction layers that isolate dependencies, testing strategies that validate assumptions.

Vibe-coding treats uncertainty as a feature rather than a challenge to be managed through disciplined thinking.

Understanding Vibe-Coding

At its core, vibe-coding represents the practice of translating conversational ideas directly into functional code through AI assistance. The approach thrives on rapid iteration cycles where developers feed loose concepts to language models and iterate on whatever emerges. This has democratized certain aspects of software creation, enabling faster ideation and empowering domain experts to directly translate concepts into working prototypes.

However, this democratization comes with a fundamental question about the nature of software engineering itself. The practice often bypasses traditional disciplines like system modeling, algorithmic analysis, and architectural planning in favor of experimental discovery. What emerges is frequently functional but architecturally opaque—systems that work but resist explanation, modification, or principled extension.

The Airplane Test

Here’s a thought experiment: would you board an aircraft whose navigation system was vibe-coded? The question isn’t hypothetical posturing—it cuts to the essence of what we expect from engineered systems. In aviation, healthcare, and financial services, “let’s see what happens” isn’t development methodology; it’s a liability waiting for actualization.

The airplane analogy illuminates why good software engineering transcends working code. Aircraft systems embody principled design with fail-safes built into their structure, modular components that can be tested independently, and predictable behavior under various conditions. These aren’t luxuries—they’re necessities when failure has consequences beyond inconvenience.

More importantly, aircraft engineers don’t start with cockpit layouts or wing materials. They begin with aerodynamic principles, weight calculations, and system requirements. The specific implementation choices flow from these fundamental analyses, not the other way around.

Vibe-coded systems invert this process. They begin with implementation attempts and hope that systematic properties emerge organically—an approach that might occasionally produce working systems but will consistently produce systems that resist systematic understanding or improvement.

AI-Assisted vs. AI-Substituted Thinking

This distinction is crucial because it separates legitimate AI assistance from intellectual abdication. AI-assisted development treats artificial intelligence as a sophisticated implementation tool—capable of generating code, optimizing algorithms, suggesting refactoring approaches, and even identifying potential edge cases. But the human engineer remains responsible for the systematic thinking that precedes and guides these implementations.

In AI-assisted development, you start with algorithmic thinking. You analyze the problem space, consider different approaches, evaluate trade-offs, and design the system architecture. Then you use AI to help implement these well-considered designs more efficiently and accurately. The AI accelerates execution but doesn’t replace analysis.

Vibe-coding represents AI-substituted thinking. The AI becomes responsible not just for implementation but for system design, architectural decisions, and even problem definition. The human provides high-level intentions and curates outputs, but the fundamental engineering thinking gets outsourced to systems that, however sophisticated, lack the contextual understanding and judgment that complex systems require.

The Systematic Alternative

The alternative isn’t to reject AI tools—it’s to use them within a framework of disciplined thinking. This means beginning every significant development effort with systematic analysis: What problem are we actually solving? What are the constraints and requirements? What are the different algorithmic approaches we might take? What are the trade-offs between them?

At FERZ, we see vibe-coding as another example of probability masquerading as engineering. The solution isn’t abandoning AI, but embedding it within deterministic guardrails—structures of validation, governance, and accountability.

Only after working through these questions should we reach for implementation tools—whether those tools are traditional programming languages or AI assistance. The specific technology becomes a means to an end rather than a substitute for understanding the end itself.

This approach doesn’t eliminate uncertainty or creativity—it channels them productively. Creative solutions emerge from deep understanding of problem constraints, not from avoiding those constraints. Innovation happens when you understand the rules well enough to know which ones can be bent and which ones cannot.

When Vibe-Coding Works

To be fair, vibe-coding has legitimate applications where systematic overhead exceeds systematic benefit. For rapid prototyping, educational exploration, and creative projects where experimentation is the primary goal, the approach can accelerate learning and iteration. Weekend projects, proof-of-concept demonstrations, and exploratory tools benefit from reduced friction between idea and implementation.

The challenge arises when these exploratory approaches migrate to production environments without appropriate transformation, or when they become the default approach even for complex systems that demand systematic thinking.

The Long-Term Consequences

The deeper concern extends beyond immediate technical debt to the cultivation of engineering practices. Just as jumping directly to syntax prevented developers from developing strong algorithmic thinking skills, relying primarily on vibe-coding risks creating practitioners who never develop the systematic analysis capabilities that complex systems require.

These skills aren’t just technical—they’re cognitive frameworks that enable engineers to reason about complex systems, anticipate failure modes, and design for evolution over time. Programming languages and AI models will continue evolving, but the ability to think systematically about complex problems remains the core competency that distinguishes engineering from experimentation.

Moving Forward

The future of AI-assisted development lies in combining artificial intelligence’s implementation speed with human systematic thinking. This means treating AI as a powerful tool for executing well-considered designs rather than a replacement for the design thinking itself.

After all, no one would board a vibe-coded airplane. Why trust vibe-coded systems with your business, your data, or your future? The future of software depends on whether we engineer with discipline—or gamble with vibes.

 


Edward Meyman is the founder of FERZ LLC, a consultancy focused on deterministic AI governance and assurance-first architectures.