Vibe coding in product management: very clever, but it has its limits

Last updated: 28.07.2026 15:00

A feature request during a sprint review, an idea jotted down on a napkin, a “What if…” during a stakeholder call – until now, moments like these often remained just that: ideas. To validate them, product managers had to rely on development teams – which meant waiting weeks. Vibe coding is fundamentally changing that.

Today, product managers can type in what they mean using natural language and get a clickable prototype in minutes. But what exactly is vibe coding? And does it solve all the problems?

Vibe coding bridges the gap between idea and implementation

The term describes a new approach to software development: Instead of writing code line by line, users describe in natural language what they want to create, and an AI model then translates that into executable code. So you describe the “feel” (“vibe”) of a solution, iterate through dialogue with the AI, and gradually get closer to the desired result. For product managers who rarely code themselves, this lowers the key barrier between idea and implementation.

More than just a tool trend

Product management traditionally spends a lot of time translating requirements: into wireframes, user stories, mockups, and briefings for design and engineering. But with every translation, context is lost, and misunderstandings lead to additional iteration cycles. Vibe coding shortens this chain:

• Faster validation of hypotheses:

  Instead of describing an idea, it can be demonstrated – as a working clickable prototype that enables real user feedback.

• Better communication with engineering:

  A clickable design integrated with the appropriate design system via vibe coding also makes requirements concrete and reduces room for interpretation during refinement. A recent case study from Carnegie Mellon University shows that vibe-coded prototypes provide a significantly stronger foundation for communication with engineering teams than traditional wireframes.

• Faster development of internal tools:

  Dashboards, analytics, or small internal tools that replace manual tasks in product management – or for which there was previously no available development time – can now be built by the product management team itself. For example, VIER’s product management team built a tool for automated roadmap generation. It completes in a few minutes what used to take several days – with integration into internal systems and as a self-service option for customer-facing teams, who can customize their individual versions to meet their customers’ needs.

The limits: Vibe coding is no substitute for engineering

Recent studies on the productivity of AI-supported development show significant efficiency gains for routine tasks. For product teams, this means that the time previously spent on organizational tasks is now available for strategy, customer discussions, and prioritization. But as great as the potential is – vibe coding is no substitute for professional software engineering.

The term itself was coined in February 2025 by AI researcher Andrej Karpathy and explicitly describes an experimental, iterative approach to coding – not production-ready development. Prototypes from vibe coding sessions are rarely production-ready. Security considerations, scalability, maintainability, and clean architecture remain areas that require expertise.

According to Communications of the ACM, depending on the study, between 25 and 70 percent of AI-generated code contains vulnerabilities, such as SQL injections. Anyone who overlooks this risks creating a small-scale shadow IT environment: uncontrolled tools that process sensitive data without security or compliance requirements having been verified.

Here’s what to do:

• Clearly define where the line is drawn between a “prototype for validation” and “production-ready code.”

• Involve engineering and security early on, as soon as a prototype leaves the sandbox.

• Use controlled, enterprise-compliant AI environments instead of freely accessible consumer tools to prevent data leakage.

AI provides the tool, but not the judgment!

With vibe coding, the focus of product management shifts even more toward curation and decision-making rather than description. Even if you can build prototypes yourself, you still need to ask the right questions: For whom are we solving which problem? How do we measure success? How does this use case rank in priority compared to others?

AI provides the tool, but not the judgment. Jeff Gothelf, the author of Lean UX, aptly illustrates this using an example from one of his own sessions: According to him, a prototype vibe-coded in an hour looked convincing, but above all raised new questions that could only be answered through product management. A survey conducted by product consultant Saeed Khan also shows that the majority of product managers no longer want to spend time on vibe coding; instead, they want to focus on the classic core tasks of product management.

Conclusion: Prototyping is becoming a core competency in product management

Anyone who wants to “talk” to an AI about architecture, data models, or interfaces will benefit from understanding the basic concepts – not to become a senior engineer themselves, but to formulate meaningful prompts and critically evaluate the results. This means the need for a basic technical understanding is growing.

And: Vibe coding doesn’t turn product managers into developers, but it does make them faster, more precise, and more independent when it comes to early-stage idea validation and other operational tasks. The real added value isn’t that product managers are now writing code themselves, but rather that the gap between an idea and tangible product visions is virtually eliminated.

What remains crucial is how this new speed is integrated, accompanied by clear guidelines on security and production readiness. And with a product management team that continues to focus its time on the truly important questions – not just on the technology itself.

Tips & Recommendations

• Use vibe coding specifically for discovery and validation, not for production-ready features.

• Involve engineering and security as soon as a prototype moves toward live operation.

• Use a secure AI environment that complies with company policies instead of freely accessible tools.

• Build a basic technical understanding within the product team to better interpret AI results.

• Keep the focus on prioritization and customer value – AI provides the tool, not the strategy.