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
- Capability against readiness: why the race is against readiness, not regulation, and why building before is so much cheaper than building after.
- 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.
- The readiness pairs: what must exist before each capability arrives, and why.
- Readiness on the ground: what a person, a town, a firm, and a government each need ready.
- Measuring readiness without scoring: readiness profiles instead of a readiness index, and the gap between capability and readiness.
- When capability outruns readiness: pace applied, domain by domain, and what to do once a window has closed.
- 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.