Prototyping isn't thinking

... and other AI shortcuts that are making you a worse PM

I often see the same pattern play out.

Somebody excitedly shares how they prototyped a new feature in two hours. They show their team a working demo. Everyone’s impressed by the speed. Three weeks later, the feature is quietly shelved because it solved the wrong problem.

AI gave them speed. But speed masked that they never stopped to think.

In my last article, I talked about how AI removes or automates many of the busywork of product development. But some shortcuts don’t save time. They skip essential work that looks slow but is actually the work.

This is the new trap for product managers. You might actually be atrophying your critical thinking muscle by outsourcing too much to AI. Some examples:

When AI prototypes replace real thinking

The shortcut is seductive: spin up a working prototype in hours. Show stakeholders something tangible. Watch them get excited. Feel like you’re making progress.

But you’ve just anchored everyone on a solution before confirming the problem is worth solving. You’ve optimized the easy part — making something — and skipped the hardest part: making sure it matters.

The best companies in the world like Meta, Shopify, and Amazon share one thing in common: a culture of writing. I’m a strong believer that writing is thinking — when you write a narrative document, you untangle a woolly set of insights and complaints into a clear problem definition. You sketch on whiteboards. You debate with engineers. You sit with the discomfort of not knowing the answer yet. This is how you actually build your product sense.

When you skip the heavylifting and go straight to prototyping, you never develop the muscle. I’m not saying you should skip AI prototyping entirely — it’s to be careful when you use it.

Use AI for research synthesis first — summarize user interviews, identify patterns in feedback, map out the problem space. Prototype on paper to keep it ugly enough that it invites real critique. Only reach for AI prototyping after you’ve validated two things: the problem is actually valid, and your solution direction is roughly right.

Before you prompt an AI prototyping tool, ask yourself: “What evidence do I have that this deserves a solution?” If the answer is thin, prototyping is the wrong tool. You’re using a screwdriver to hammer a nail.

When AI “customer interviews” replace real conversations

The five-minute shortcut: ask ChatGPT to role-play as your target customer. Generate persona descriptions. Get “insights” about their pain points without leaving your desk.

This is probably the biggest crime you can commit as a product manager.

AI generates statistically average responses. It smooths over the weird, specific, surprising details that actually matter. The marketing manager who prints out every dashboard on Friday afternoons because she doesn’t trust digital-only charts. The small business owner who uses your software in ways you never designed for because it solves a problem you didn’t know existed. The user who almost churned three times but stayed because of one specific feature you nearly deprecated.

These are the insights that shape great products. AI can’t give them to you because they’re not average. They’re outliers, edge cases, surprising behaviors that reveal what really matters.

You’re also training yourself to accept synthetic empathy instead of building real understanding. Real customer conversations have texture. You hear the hesitation in someone’s voice when they say something is “fine.” You notice what they don’t say. You pick up on the problem behind the problem because you asked a follow-up question to something unexpected.

The thoughtful alternative is to use AI as a multiplier, not a replacement. Use it to prepare for customer conversations — schedule interviews, draft interview questions, synthesize past research, identify gaps in your understanding. Use it to analyze interviews after you’ve done them: transcription, theme extraction, and clustering similar quotes across multiple conversations to spot patterns you might have missed.

When AI gives you an illusion of validation

AI can make any idea sound viable. Ask it if your concept makes sense and it will confidently articulate benefits, edge cases, risks, and user motivations. It will explain your idea back to you in a way that feels polished and persuasive.

But it’s doing is only pattern-matching from the entire internet and giving you a coherent narrative.

I once heard this saying: AI makes mediocre PMs faster at being mediocre. This is one sure way to achieve it. If you’re using AI to confirm rather than challenge, you’re just accelerating bad judgment. Great PMs use AI to stress-test their thinking, not rubber-stamp it.

The thoughtful alternative is to flip how you use AI in validation. Don’t ask it to confirm your hypothesis. Ask it to destroy it.

Use AI as a devil’s advocate: “What’s wrong with this idea? What am I missing? What would make this fail?” Use it to generate counter-hypotheses, not just validate yours. Use it to identify what you should validate with real users, not to replace that validation.

When AI shortcuts create shallow strategy

AI is very good at structure.It can generate crisp strategy documents, articulate goals, and outline roadmaps with almost eerie confidence.

Feed AI your team’s OKRs, last quarter’s retrospective, and some market context. Ask it to write your next quarter’s strategy and roadmap.

The output looks right. It reads like strategy. It contains the words we associate with strategy.

But real strategy is choice. It’s about what you won’t do, which users you won’t serve, and what success won’t look like. It’s about constraints and conscious trade-offs. It is about how the pieces fit together, not how the document reads.

The thoughtful alternative is to use AI for the inputs to strategy, not the strategy itself. Use it to research the competitive landscape, synthesize market trends, pull together data on user behavior.

And then, once you’ve written your first strategy draft, use it as a sparring partner to stress-test your thinking: “What would a skeptic say? What am I under-weighting?”

If you’re using AI to write your strategy, congrats, you’ve just abdicated the most important part of your job.

To tie it all together…

The best product managers I know are leaning into AI hard — but they’re doing it selectively. They’re using it to eliminate the parts of their job that don’t require judgment so they have more time for the parts that do. They’re getting faster at execution without getting sloppier at thinking.

It’s all about recognizing where shortcuts help you, and where they slowly erode your craft.