Capability discovery
The first phase of generative AI, when attention went to finding out what models could do. The next phase, boundary design, is about where they should act and with what authority.
Capability discovery describes how the first phase of generative AI worked. Models were new, so the main activity was finding out what they could do: write, summarise, code, translate, reason a little, use tools. Demos, benchmarks and hype followed the capability frontier.
That phase asked "what can it do?". It rewarded breadth and surprise, and it shaped many of the questions people still ask: Which model is best? Can AI reason? Is this AGI? Those questions are about the abstract, not about a system in a workflow.
What replaces it
The next phase is dominated by boundary design: deciding what a particular system may do, under which permissions, with which failure modes and recovery. Capability is no longer the scarce thing. Authority, supervision, cost and recovery are. Capability is not authority.
Read more in The wrong questions about AI right now.
Related terms
Boundary design
Designing where an AI system's authority starts and stops: which actions it may take, with what data and permissions, and where it must hand back to a person or recover.
Capability is not authority
A design principle for AI agents: being technically able to perform an action does not mean the agent should be permitted to perform it. Delegation needs gradients of authority.
Capability readiness
How ready an organization's actions are to be used by AI agents: whether each action is a real business capability, exposed cleanly, with clear permissions, side effects and recovery.