The Discipline of Deliberate Building
Building is getting cheap. Deciding what to build isn't.
AI is making software much cheaper to build. That won’t make product judgment less important; it will make judgment the scarce resource.
For a long time, engineering capacity protected companies from themselves. A team might have ten ideas but time to build only two, so most had to survive serious debate. The constraint killed some good ideas, but it also stopped many mediocre ones from reaching users.
That protection is disappearing. When an agent can turn almost any idea into a working prototype, the fact that something can be built starts to feel like a reason to build it. We are about to get very good at shipping bad ideas quickly.
So ruthless prioritization matters more, not less. Every feature creates complexity even when the code is free: users have to understand it, the company has to maintain it, and the product has to absorb it without becoming harder to use. AI reduces the cost of adding features, not the cost of having them.
Each new feature is cheap to add, but it can interact, conflict, or compete for attention with every feature already there.
Taste follows the same logic. When you can build almost anything, you need a stronger sense of what belongs. When you can optimize a hundred metrics, you need to know which results matter. Knowing when to leave the product alone may become the most valuable product skill.
The software industry offers an enormous amount to learn from, with thousands of companies constantly testing ideas in public. But the useful lesson is rarely the visible feature. What matters is the mechanism underneath it and whether the same logic applies to your product.
AI will make those lessons easier to find and cheaper to test. It can spot patterns, build prototypes, and measure what changes. But measurement cannot tell you which changes are worth caring about or whether they make the product better.
The best companies will get exceptionally good at moving through this learning loop: notice something that works, understand why, form a hypothesis about its relevance, build the smallest useful test, put it in front of users, measure what changes, and keep the lesson whether it succeeds or fails.
As that loop accelerates, the advantage will not come from building more. It will come from learning quickly while making every addition earn its place.