The moment you define an axis, you have already made a value judgment.

That is the core claim behind “Domestication Space.” A toy field of traits may look technical, but the important choice is not the rendering. It is the act of declaring which dimensions matter, how they should be combined, and what kind of target counts as success.

This is why the essay is really about optimization systems more broadly. Once a story becomes a coordinate system, geometry begins to do ethical work. A metric turns distance into preference. A threshold turns similarity into grouping. A target turns aspiration into ranking.

None of those moves are neutral.

The public lesson is not about finding the “perfect” metric. It is about making the metric visible enough to contest. If one axis is weighted more heavily than another, the ranking changes. If the target shifts from a point to a region, the definition of success changes. If similarity is computed differently, the resulting structure changes with it.

That pattern repeats across many AI systems. Hidden objectives become visible only through their downstream effects: what gets ranked highly, what gets generated more often, what gets filtered out, and whose interests are quietly encoded in the coefficients.

The useful norm, then, is simple:

Name the dimensions.
Make the objective inspectable.
Allow perturbation of the metric.
Treat ranking and generation as accountable choices rather than neutral output.

In that sense, “Domestication Space” is less a product idea than a public argument for legibility. If a system optimizes, the objective should be visible enough to question. If a system ranks, the metric should be open enough to test. That is the minimum viable honesty of an optimization interface.