Introduction

The traditional product development process is being completely disrupted by Vibe Coding. This AI-based development model allows you to describe functional scenarios in natural language, enabling AI to automatically generate runnable code and complete acceptance testing. This article uses real cases to explain how product managers can transition from requirement translators to AI commanders, mastering the new core competencies of atmosphere creation and precise definition.

When you are in a meeting discussing the timeline for the next version with the development team, a runnable demo may have quietly been generated in the AI’s “atmosphere”.

“This requirement is quite simple; it’s just a pop-up after user login to display the pending work orders.”

“Okay, let me evaluate. The front-end pop-up component, connecting to the work order API, handling loading and empty states… It should be ready for launch in about two weeks.”

This classic dialogue between product and development is familiar and accurately depicts the standard path for building a product feature: PRD, review, scheduling, development, testing, and launch—a long and rigorous chain.

But what if I told you that this decades-long path might be completely overturned by something called “Vibe Coding”?

01 Conceptual Innovation: Generating Code by Speaking

Don’t be intimidated by the word “Coding”; the essence of Vibe Coding is not about writing code.

You can think of it as: “Speak naturally, get code.”

You no longer need to fill out prototypes in a PRD or define technical solutions for every field. You only need to describe the desired functional scenario, user experience, and business objectives in clear language—this is referred to as creating an “atmosphere” (Vibe).

Then, AI (such as GPT-4, Claude, etc., powered intelligent IDEs) will generate runnable application code based on the “atmosphere” you provide.

This is not just a simple tool upgrade; it represents a complete “paradigm shift” in work.

For product managers, this means a subtle yet fundamental shift in core value: from “drawing blueprints and supervising construction” to “describing visions and accurately accepting AI-generated results.”

The core contradiction in development shifts from “how to implement” to “what exactly is needed” and “how to describe it accurately.”

02 Practical Case: Experiencing the Future of AI Workflows

Talking about concepts is too abstract; let’s get practical. Let’s take the earlier requirement of “a pop-up to display pending work orders after user login” and see what happens under Vibe Coding.

Traditional Process: You write a PRD, hold review meetings, wait for scheduling, track progress, conduct acceptance testing… After a series of efforts, you’re exhausted.

Vibe Coding Process (illustrative, but the actual implementation will be more complex):

1. Your input changes: You open an AI programming tool like Cursor or Windsurf, not writing a PRD but inputting a structured “atmosphere description”:

   “We need a global pop-up that triggers only after successful user login. The core is to display the list of pending items synchronized from the work order system, showing the work order title, urgency level, and last updated time. Visually friendly, but not too flashy; there should be a warm prompt when data is empty. The pop-up should automatically close after 5 seconds, but the user can also close it manually.”

2. AI intelligent workflow starts (this is key, and the future):

   A branch (Builder): The AI analyzes your description and begins to automatically generate the front-end pop-up component (possibly React/Vue code) while also generating the backend logic to call the “pending work order API”.

   B branch (Quality Inspector): Meanwhile, another AI agent will derive an acceptance criteria checklist from the same description: “Does the pop-up only trigger upon successful login?”, “Is the data mapping correct? (title, urgency icon, time format)”, “Is the empty state prompt displayed?”, “Are the auto-close and manual close functions working?”

3. Convergence and automated acceptance: At a certain “convergence point”, the acceptance checklist from branch B will verify the code generated by branch A like an automated testing script, producing a scored acceptance report.

4. Your core work: What you receive is no longer code but this report. You no longer need to worry about whether the if-else statements are correct; instead, you make the final judgment based on your product intuition and business knowledge: Is the business logic 100% accurate? Is the user experience process smooth enough? What did the AI misunderstand that requires further clarification or “human intervention”?

In this process, you transform from a “process promoter” and “requirement translator” into an “AI trainer” and “quality auditor”. You focus on strategy and experience while leaving the heavy lifting of implementation to tireless AI.

03 Role Evolution: Your New Ace from “Writing Requirements” to “Tuning Models”

Hearing this, you might feel a bit anxious: does this mean product managers will be replaced?

On the contrary, I believe product managers will not be replaced but must evolve. The mechanical transmission of requirements and prototyping functions will indeed be greatly enhanced or even replaced by AI. However, our true irreplaceability is accelerating its shift from “transmitting requirements” to “defining the boundaries of problems” and “accepting the rationality of generated results”.

To fulfill the new role of “AI commander”, our capability pyramid needs to be restructured:

Top-level capability (new ace): Atmosphere creation and precise definition

This is the most core new skill. It’s no longer about writing lengthy documents but about transforming vague business demands and user insights into structured, unambiguous, AI-executable “prompts” or “atmosphere documents”. This essentially reflects super logical thinking and abstract capability.

Mid-level capability (key skills): AI process design and result evaluation

Just like in the previous case, you need to design the collaboration process with AI as you would design product features. Should you use one model for everything, or design multiple AI agents to collaborate (e.g., A writes code, B writes tests)?

More importantly, you need to establish a set of quality standards for evaluating AI outputs, which includes not just whether the function works, but also: “Is the code structure clear and easy for future human maintenance?”, “Does it comply with our technical architecture specifications?”

Bottom-level capability (still important): Technical understanding and business insight

You don’t need to know how to reverse a linked list, but you must understand interfaces, data flows, basic component concepts, and system architecture. Only then can you communicate on the same channel with AI and development colleagues, accurately pinpointing issues. Deep business insight will always be the ultimate basis for your decision-making.

04 Action Guide: Three Things You Can Do Now

The future is here; it’s just not evenly distributed yet. Instead of worrying, take action:

1. Get hands-on experience: Open tools like Cursor or Lovable now. Try describing a small feature in your product in natural language, such as “generate a user list page with filtering functionality”, and see what the AI produces. The shock of hitting the “generate” button is far greater than reading ten articles.
2. Cognitive exercise, shift perspectives: During your next PRD or requirement card writing, consider a thought experiment: “If I were to feed this requirement directly to AI, is my description precise and unambiguous enough? What boundary conditions and exceptional scenarios have I overlooked?” This exercise will greatly enhance your requirement refinement skills.
3. Initiate discussions, explore processes: Within your team, proactively initiate a discussion: “If we attempt to use AI to assist in generating requirements going forward, what new steps should we add to our review process? Do we need an ‘AI-generated code review checklist’?” This will help your team smoothly transition to the new norm of human-AI collaboration.

The storm brought by Vibe Coding is superficially a revolution in developer efficiency, but at a deeper level, it is a liberation movement that “unburdens” and “empowers” product managers. It frees us from cumbersome processes and details, allowing us to focus more on the initial and core proposition: understanding users, defining value, and ensuring it is perfectly realized.

So, don’t just focus on prototyping tools and project management software. The core competency of the next generation of product managers may well be the ability to “train AI”.

Finally, I’d like to pose a question to everyone and welcome discussion in the comments:

In the approaching era of Vibe Coding, what do you think is the most important core competency for product managers?