When AI Makes Building Easy, Product Thinking Gets Harder

Over the past year, something fundamental shifted.

With AI, vibe-coding, and increasingly capable assistants, building software has become dramatically easier. I’ve seen it firsthand while working on EcoMoat and with my customers and partners while practicing hands on advising. I can prototype in hours. I can refactor architecture faster than ever. I can spin up new endpoints, experiments, even entire flows in a fraction of the time it used to take to teams.

Execution friction is collapsing, but something unexpected happened.

Product thinking became harder.

The New Bottleneck: Judgment

When building was slow, execution itself was the constraint. You had to fight for engineering time. You had to justify features because shipping them was expensive.

Now? You can build almost anything.

The bottleneck is no longer “Can we build this?”

It’s “Should we build this?”

That’s a very different skill. AI can:

• Write code

• Refactor code

• Generate specs

• Suggest improvements

• Draft UI patterns

• Summarize user feedback

But it cannot decide what truly matters. That’s judgment.

And judgment doesn’t scale the same way code does.

Feature Velocity Is Becoming Irrelevant

There was a time when product orgs celebrated feature velocity. More releases. More experiments. Faster cycles. But when AI lets you ship five features in a week instead of one, velocity stops being a competitive advantage.

In fact, it becomes dangerous. If you choose the wrong direction, you’re now wrong at 5x speed. I’ve felt this very directly.

While iterating on the EcoMoat engine, the admin, and our mini-RAG pipelines, I’ve had AI rewrite working code in ways that subtly broke dependencies, introduce refresh loops that looked elegant but created instability, and “optimize” architecture in ways that created long-term friction.

It was fast. It was impressive. It was productive.

And sometimes, it quietly made things worse.

What’s even more interesting is how quickly the landscape shifts. Problems that felt painful six months ago are almost gone today. New model capabilities solved entire classes of issues overnight. But each new capability also introduced new edge cases, new abstractions, new failure modes.

AI doesn’t just accelerate building. It accelerates change. Which means clarity becomes more important than ever.

• If your architecture isn’t modular, AI will amplify the mess.

• If your priorities aren’t sharp, AI will help you scale the wrong thing.

Speed is no longer the hard part. Direction is.

AI Changed My Workflow

Working with AI forced me to become more structured:

• I document decisions more clearly.

• I break systems into smaller, well-defined services.

• I isolate responsibilities.

• I think in contracts and interfaces, not just features.

Because when you let AI operate inside your system, ambiguity becomes expensive.

The cleaner your thinking, the better the output.

AI exposed something uncomfortable: many of our inefficiencies weren’t technical. They were conceptual.

Strategic Clarity Becomes the Moat

If building becomes commoditized, what remains scarce? Clarity.

The ability to say:

• No.

• Not now.

• This is the real problem.

• This is the bet.

When you can build anything, prioritization becomes existential.

For EcoMoat, this meant:

• Saying no to many features.

• Focusing on moat rating quality, not UI noise.

• Investing in structured data and reliability instead of surface-level growth hacks.

Ironically, AI made me more disciplined.

Because it made it easier to be distracted.

The PM Skill Stack Is Shifting

AI fluency is now table stakes.

But the differentiator is:

• Conviction

• Narrative clarity

• System thinking

• Modular architecture thinking

• The courage to choose direction under uncertainty

Execution used to separate good PMs from average ones.

Now? Judgment does.

Final Thought

AI removed friction from building, but it amplified the cost of wrong decisions.

When you can ship five features in a week, choosing the wrong one is five times more expensive.

In an AI-native world, the moat isn’t speed. It’s strategic clarity.

And that’s much harder to automate.