Don’t trade speed for stability as vibe coding emerges

In February 2025, computer scientist Andrej Karpathy coined the term ‘vibe coding’—a radically simplified software development approach where developers express intent in natural language and AI translates that intent into code. “Give in to the vibes,” said Karpathy, encapsulating a vision where the traditional syntax-heavy coding process is replaced by intuitive AI collaboration. With platforms such as Windsurf and Cursor advancing rapidly, vibe coding is more than just a buzzword but a reflection of the shifting relationship between humans and code.

However, the question isn’t whether vibe coding is clever but if it’s production-ready. CTOs and CIOs must evaluate its potential against rising technical debt, security concerns, debugging inefficiencies, and long-term scalability. While the hype is real and the benefits are enticing—faster innovation, leaner teams, and democratized development—vibe coding is not a silver bullet but a paradigm shift in progress, not a fully formed enterprise solution.

Decoding the vibe: A deep dive into what vibe coding really is

Vibe coding represents an evolution of software development, following decades of innovation in low-code and no-code platforms. Instead of dragging and dropping logic blocks, users simply describe what they want to build. Platforms such as Cursor and Windsurf exemplify the principles of vibe coding. These AI-powered development environments allow developers to describe the desired functionalities using natural language, which the system then translates into executable code. Both platforms reflect a broader movement toward intent-driven development, where the focus shifts from manual code writing to guiding AI in achieving the desired outcomes.

The rise of vibe coding is a response to the long-standing limitations in traditional development. Software creation was historically reserved for those with deep expertise in programming languages and architecture. Vibe coding breaks that barrier, allowing anyone with an idea and basic technical understanding to produce functional software. It unlocks software development for non-traditional talent—mathematicians, product managers, even marketers—while enabling professionals to iterate and ship faster.

This democratization is already proving transformative. In Y Combinator’s Winter 2025 batch, 25% of startups report codebases that are 95% AI-generated. Of course, AI-generated code doesn’t always equate to vibe coding; but where the two align, the shift is profound. Vibe coding accelerates prototyping cycles, reduces reliance on larger engineering teams, and fosters a culture of rapid experimentation. It’s also shifting the role of developers from coders to reviewers, managers, and strategists. This may ultimately reshape hiring, onboarding, and workforce development in tech-focused enterprises.

Vibe coding could break more than it builds

Despite the optimism, vibe coding is far from flawless, and understanding its limitations is essential:

• Technical debt: Technical debt is the primary risk with vibe coding, as fast AI-generated code can lack structure, consistency, and documentation, leading to long-term maintenance challenges. Within that, architectural debt can stem from fragmented or poorly planned system designs that make scalability and integration difficult. The speed often bypasses architectural planning, compounding both debts. Without thoughtful oversight, enterprises risk building unstable foundations that become harder and costlier to repair over time.
• Debugging complexities: Debugging AI-generated code remains a significant challenge. Developers may struggle to interpret logic they didn’t write, extending the time needed to locate and fix issues. While AI reasoning models can assist, they too have limitations. The workload is simply shifting from writing code to understanding and reviewing it. Some argue this fresh perspective makes it easier to identify bugs, akin to a peer review. Others claim it reduces clarity, especially when the AI-generated logic becomes too abstract or unfamiliar.
• Security and compliance: Security is another pressing concern. AI-generated code may include unseen vulnerabilities or fail to comply with enterprise-grade security protocols. Ensuring explainability, traceability, and governance becomes critical, particularly in regulated industries such as finance and healthcare. Without proper checks, vibe coding can introduce legal and operational risks, undermining its initial productivity gains.
• Erosion of deep technical skills: While vibe coding opens doors for broader participation, it risks diminishing deep technical knowledge across the workforce. When developers skip the foundational practice of hand-coding, their understanding of core principles may weaken. This could reduce long-term architectural thinking and problem-solving capabilities—skills that remain crucial when AI hits its limits.

Where vibe coding fits into the enterprise software toolkit

Right now, vibe coding’s value may lay in the speed it allows IT teams and business users to collaborate on prototyping and experimenting. It enables teams to rapidly build and iterate on concepts, test functionality with stakeholders, and explore new ideas before committing engineering resources. This makes it ideal for minimal viable product (MVP) development, innovation labs, and other experimental workflows where speed and flexibility matter more than scalability.

That said, vibe coding should be seen as a complement, not a replacement, for more rigorous engineering practices. While it can accelerate development, enterprise software still demands scalability, security, governance, and robust architecture. Traditional engineering disciplines remain vital for production systems. In many cases, no-code and low-code tools, which are trained on internal logic and offer better governance, may be more practical at scale.

However, the rise of vibe coding raises important questions about the future of technical fluency. If AI tools increasingly handle the syntax and structure of code, developers may skip foundational learning entirely. Over time, this could erode critical thinking and system design capabilities—skills essential for project oversight—debugging, and long-term innovation. Enterprises must consider how and where vibe coding can be applied, ensuring that the next generation of engineers still understands the ‘why’ behind the code, not just how to prompt AI to write it.

The Bottom Line: Treat vibe coding as a strategic tool—pilot with purpose and scale with caution.

Vibe coding is a glimpse into the future of software development. But it’s a future that demands careful oversight and proper planning.

Its ability to democratize software creation, optimize teams, and accelerate innovation is real. But so are its flaws: technical debt, debugging bottlenecks, compliance risks, and skill erosion. CTOs who rush in without a plan will likely face setbacks. The smarter move is a measured approach: start with internal pilot use cases, emphasize thoughtful human oversight, and align adoption with internal talent transformation strategies.

Vibe coding is not about killing the vibes but learning how to ride them. For those who balance automation with strategic intent, it offers a path to more agile, creative, and resilient software development. For now, it’s best viewed as a powerful assistant, not a replacement, for core engineering practices that make software reliable and scalable.