The usual worry is that AI is moving fast. That worry is true and useless: it says nothing about what to do. Transition readiness turns it into a question that can be answered, capability by capability:

What must the ecology already have in place before this AI capability becomes ordinary?

Asked that way, the transition stops being a vague race against time and becomes a list of things to build, each with a deadline set not by a calendar but by how fast a particular capability spreads. Contestability has to exist before automated gatekeeping is everywhere. Limits on what may be inferred about a person have to exist before sensing is ambient. A floor has to exist before work disappears at scale. Each of these is cheap to build early and very expensive, sometimes impossible, to build late.

The pages below

  1. Capability against readiness: why the race is against readiness, not regulation, and why building before is so much cheaper than building after.
  2. The capabilities that change the field: eight AI capabilities, defined by what they do to an ecology, each with the sign that it is becoming ordinary.
  3. The readiness pairs: what must exist before each capability arrives, and why.
  4. Readiness on the ground: what a person, a town, a firm, and a government each need ready.
  5. Measuring readiness without scoring: readiness profiles instead of a readiness index, and the gap between capability and readiness.
  6. When capability outruns readiness: pace applied, domain by domain, and what to do once a window has closed.
  7. Where readiness stands: an assessment of each pair, and which are cheapest to start now.

How it fits the rest

Readiness is the applied companion of commensurate pace: pace buys time, and readiness is what that time is for. It is also a race against leverage half-life, the shrinking window in which public inputs still give the public a say. And it is how the irreversibility register becomes a work plan: every red line is a place where readiness arrived too late.