Stop Writing PRDs. The Interface Just Changed.

You’ve probably done this recently. You fed your requirements into an AI, asked it to write a PRD, and watched it produce something that looked… almost right. Professional structure. Clean formatting. Reasonable logic flow. Then you handed it to your dev team, and within ten minutes the questions started rolling in: What about edge cases? What happens when the API fails? What’s the fallback? The document was full of holes you’d have to fill yourself. So you concluded what everyone’s concluding: AI isn’t ready for real product work.

That conclusion is wrong.

It’s not that AI can’t write requirements. It’s that you’re shoving a floppy disk into a USB port and blaming the disk.

The PRD — that beautiful, narrative-rich, human-optimized document you’ve been perfecting for years — was never designed for AI consumption. It was designed for developers who bring imagination, industry context, and the ability to read between the lines. AI brings none of that. AI executes exactly what you write and nothing more. The interface has fundamentally changed, and most product managers haven’t noticed.

Here’s what’s actually happening: the consumer of your requirement documents has quietly switched from human to machine. And when the consumer changes, the format has to change with it.

Three Ways to Ship Requirements, and Why Only One Survives

Let’s lay out the three approaches side by side, because the differences become obvious immediately.

Approach 1: Traditional PRD. You spend one to two days writing a comprehensive document — background, goals, user stories, feature lists, interaction prototypes, exception flows. You hand it to a developer. The developer reads it, fills in the gaps using experience and common sense, asks questions in a review meeting, and eventually translates it into code. The document’s reader is a human. The document is designed for human consumption, which means it’s full of “you know what I mean” spaces. This works because humans are excellent at pattern completion. It’s slow because everything runs through human bandwidth.

Approach 2: Vibe Coding. This is the Andrej Karpathy approach — you describe what you want in natural language, AI generates code, you iterate. It sounds liberating. In practice, it’s a wild horse. AI without boundary constraints will produce something, but whether that something matches your intent is pure luck. The more complex the requirement, the higher the probability of spectacular drift. You end up spending more time correcting course than you would have spent writing the thing yourself. Vibe Coding is great for personal demos. It’s a disaster for serious production pipelines.

Approach 3: Spec Coding. This is the one that’s spreading through engineering teams at companies like AWS, GitHub, Google DeepMind, and Tencent like wildfire. Instead of writing a PRD, you write a SPEC — a document optimized not for human comprehension but for AI execution. It contains only what AI needs to generate working code: core logic, business flows, boundary conditions, acceptance criteria, error definitions. Prototypes aren’t embedded as images; they’re linked as HTML files that AI can parse structurally. The developer reviews the SPEC, approves it, and feeds it directly to AI for code generation. No translation layer. No human-to-AI interpretation gap.

The difference between these three approaches isn’t tool sophistication. It’s who the document is written for.

The Consumer Changed. The Format Didn’t. That’s the Problem.

Here’s the judgment that most product managers haven’t made yet: the downstream consumer of your requirements has changed species.

For over a decade, your PRD was consumed by a human developer — someone with imagination, industry experience, and contextual understanding. You could write something slightly ambiguous, and the developer would fill in the blanks. You’d align in a meeting, clarify over Slack, and converge through conversation. The PRD was a collaboration document, not an execution contract.

Now developers are increasingly feeding requirements directly into AI for code generation. The ultimate executor of your requirements is no longer human. It’s machine. And machines don’t do pattern completion. They don’t bring industry context. They don’t read between the lines. A human reads a document to understand it. An AI reads a document to execute it. These are fundamentally different acts requiring fundamentally different documents.

This is why your AI-generated PRDs keep failing. You’re writing in human-optimized format, then expecting AI to both decode human nuance and produce precise code. That’s a contradiction. You’re asking for poetry comprehension and compiler precision from the same input. It doesn’t work.

PRD vs SPEC: Not a Rename, a Paradigm Shift

If you’re thinking “SPEC is just a more structured PRD with clearer boundaries and a new name,” stop. The underlying logic is completely different.

A PRD’s default assumption is that the reader is an experienced professional. You write what to build. You don’t write what not to build. You don’t enumerate every exception. You don’t define every error code. The developer fills in those gaps using common sense. That’s why PRD review meetings exist — they’re essentially gap-filling sessions where developers ask about boundaries, exceptions, and special cases that the PRD left for human imagination to complete. This isn’t laziness. It’s the PRD format working as designed. It’s a collaboration document.

A SPEC’s default assumption is the opposite. The reader is AI, and AI doesn’t fill gaps. Every boundary, every exception, every constraint must be locked down in the document. What to do in each scenario. What not to do. Input definitions. Output definitions. Error codes. All of it, explicit. A合格的 SPEC leaves zero ambiguity. If AI reads it and still has questions, the SPEC is defective.

A PRD is a product manual written for humans. A SPEC is an execution contract written for machines. One seeks to be understood; the other seeks to be executed without interpretation.

This produces a counterintuitive result: a SPEC looks simpler than a PRD because it strips out descriptive content, background narrative, and interaction explanations. But its information density is actually higher, because every sentence is hard execution data that AI needs. No padding. No context-setting. No “as a user, I want to…” storytelling. Just logic, flow, and boundaries.

I Finished Five Days of Work in One Morning

Let me tell you what happened when I went full SPEC mode.

I had five requirements to process. In the old world, each one would take a full day —梳理 business logic, write the PRD, design the prototype, review, revise. Five requirements, five days. Each requirement represented roughly 32 developer-hours of implementation work.

I finished all five in one morning.

Here’s how: I’d already built a domain knowledge base for my product area. When the requirements came in, I had a solution shape in my head. I opened five parallel AI conversations. In each window, I provided context, confirmed the approach, and let AI generate both the SPEC document and the HTML prototype. My only job was judgment — is the direction right? Does the output match expectations?

The two most time-consuming steps — writing documents and drawing prototypes — were entirely eliminated from my workflow.

The cost? About $50 in API calls for the morning. Compare that to a week of senior product manager salary. The ROI isn’t just good. It’s a different order of magnitude.

When you stop doing the mechanical labor of documentation, you discover you have enormous bandwidth for the work that actually matters: deciding what to build, what not to build, and whether the approach is right.

AI Is Forcing Product Managers Back to Their Real Job

For years, a product manager’s competitive advantage was expression-layer competence: writing clean documents, drawing规范 prototypes, organizing clear flows. These are the skills of translating requirements into a format developers can consume.

AI can now do all of that faster and more consistently than you can.

So will product managers be replaced? Here’s my判断: product managers whose only value is expression-layer execution will struggle. But product managers whose value is judgment will see their influence amplified, because AI removes the execution bottleneck and lets judgment scale.

What’s the judgment layer? It’s answering these questions: What’s the essential nature of this requirement? Where’s the real user pain? Which scenarios are worth building and which look exciting but deliver no value? Where are the boundaries — what do we build and what do we refuse to build? How do we validate assumptions cheaply and ship fast?

AI can’t answer any of these. AI doesn’t know what your users actually need. AI doesn’t know which feature will move your business metrics. AI doesn’t know when to say no. AI takes over the expression layer. The judgment layer was always where real product managers earned their money. The difference is that now there’s no place to hide behind beautiful documents.

Two traits will define product managers who thrive in the AI era. The first is curiosity — your willingness to adopt new tools and methods determines how far you can push AI’s capabilities. While others are still hand-writing PRDs, you’re parallel-processing five requirements with SPECs. That’s not incremental improvement. That’s a different league. The second is domain expertise — your depth of understanding of the business determines the quality of your judgment. AI executes. You decide what’s worth executing.

The Game Rules Changed. Adapt or Eat Dust.

Most discussions about AI’s impact on product management swing between two extremes: “PMs are obsolete” or “AI is just a tool, humans still matter.” Both miss the point.

What’s actually happening is simpler and more brutal than either take. The rules of the game changed. The old rules rewarded whoever could express requirements most clearly, most规范, most beautifully. The new rules reward whoever can fastest identify valuable requirements, most accurately judge direction, and most efficiently deploy AI to ship.

The old method isn’t dead. PRDs will limp along for a while, the way horses didn’t vanish the day cars appeared. But you know where the future belongs. The interface changed. The earlier you adapt to the new interface, the earlier you capture the upside.

Expression is no longer the moat. Judgment is. The question isn’t whether AI will replace you — it’s whether you’ll stop doing robot work and start doing the work only humans can do.