What should people learn when explanation, translation, coding, calculation, and factual retrieval are nearly free?
When execution is nearly free, judgment, taste, and problem-formulation rise in relative value — but almost no curriculum has been redesigned around that inversion yet.
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The curriculum conversation converged on an inversion: as AI removes the friction between question and answer, judgment and taste rise in relative value and become the thing worth teaching. The constructive move is to define the new literacy concretely — Anthropic's fluency index operationalizes skillful AI use as observable collaboration behaviors like iteration and refinement, rather than one-shot answer extraction.
85.7% of the conversations in our sample exhibited iteration and refinement: building on previous exchanges to refine the user's work, rather than accepting the first response and moving to a new task.
Operationalizes 'AI fluency' as eleven observable collaboration behaviors, giving the curriculum debate an empirical handle on what skillful AI use actually looks like as opposed to answer extraction.
Judgment matters even more as automation removes the friction between measurement and action that once invited human reconsideration.
Articulates the curriculum inversion: as AI removes execution friction, discernment and taste rise in relative value and become the thing worth cultivating.