# Boutique software at scale

AI may change the economics of specificity. Empathy and feedback still decide whether the result is useful.

## A thesis about specificity

Mass production made many goods more available and affordable. Standardization was often the route to scale. A highly specific solution could be expensive to create and maintain, so customers accepted something closer to the average.

AI may change part of that calculation for software. If creating, adapting, and explaining a tool becomes less expensive, a more specific answer may become practical for a smaller audience.

That is a thesis to test. It is not proof that every personalized product will succeed, or that the costs of reliability, support, and maintenance disappear.

## Generating more is not understanding more

A system can produce a great deal of software without understanding why anyone would choose to use it.

The hard question remains: what is the person trying to accomplish, what makes that difficult, and what would count as an improvement? Faster implementation helps only after you can answer those questions well enough to test something.

That is why dogfooding and empathy may become more valuable as production gets cheaper. They supply the judgment that generation alone does not provide.

## Keep the craft where it matters

A useful product can share a reliable foundation while adapting the parts that affect the user’s task. It does not need an entirely separate codebase for every preference.

For a learning tool, the important specificity might be the feedback. For a workspace, it might be how related people, notes, and tasks retain their context. For a calculation, it might be the way the assumptions are visible and easy to change.

The craft is in selecting the detail that changes the outcome. Adding options without judgment can move work onto the user instead of helping them.

## The test is an actual choice

Give somebody the tool for a real task. Let them keep their alternatives. Observe which one they choose, where they hesitate, and what they have to repair after using it.

Measure the result and the effort it takes to get there. Include mistakes and maintenance. A compelling first demonstration is a beginning; a product people return to is stronger evidence.

If the result is not good enough for you, find out why. If you love it but everyone else struggles, find out which part of your understanding the interface requires them to supply.

## What we are building toward

Project Dogfood supplies a feedback loop. Raise the Floor asks whether useful starting points can reach more people. Raise the Roof asks what more demanding work becomes possible. Top Gun asks whether a specific result can be better than a strong alternative.

The bet is that these aims reinforce each other. The work on this site will have to show where that bet succeeds and where it needs revision.

> Cheaper production expands what is possible. Understanding the person decides what is worth making.