The organizational scale requires rebuilding the attention architecture that the old hierarchy provided. The traditional corporate hierarchy was a cascading attention filter — each level condensing information for the level above it. AI has bypassed the filtering by enabling individuals to produce high-quality output that flows directly to decision-makers whose attention was previously protected. The response is not to restore the old hierarchy but to build new filtering mechanisms — vector pods, evaluation gates, protected reflection time — that serve the filtering function in the new cognitive environment.
The educational scale requires shifting pedagogical investment from generation to evaluation. The classical curriculum trains builders to produce: to write code, to draft briefs, to build models. AI has made production abundant. What remains scarce is the evaluative capability that distinguishes good productions from merely functional ones — the pattern libraries, the meta-cognitive skills, the domain expertise that determine whether a bounded mind can assess AI-generated output wisely or only superficially. Building this capability requires pedagogy focused on assessment rather than creation, on judgment rather than execution, on the kind of slow experiential accumulation that deliberate practice requires.
The personal scale requires individual disciplines that impose structure on the AI interaction. The default interaction rewards speed, volume, and seductive acceptance of confident output. The disciplined interaction — setting explicit goals before engaging the AI, imposing evaluation checkpoints, maintaining awareness of the tool's choice architecture, requesting alternatives that the tool did not volunteer — works against the default dynamics and is therefore cognitively costly. The cost is the price of evaluative wisdom in an environment that rewards evaluative speed.
The prescription emerges from Simon's framework applied to contemporary conditions. Simon himself advocated for the design of institutions for bounded agents throughout his career, insisting that the science of the artificial was a discipline as rigorous as any natural science and more urgent for practical outcomes. The AI age has made his prescription impossible to ignore, though whether the response will be adequate to the challenge remains an open question.
The specific prescriptions at each scale are being developed in real time by researchers, practitioners, and institutions responding to the empirical reality of AI-augmented work. The Berkeley study's AI Practice framework represents one version of the organizational prescription. Emerging curriculum reforms at universities that take AI seriously represent early versions of the educational prescription. Individual practitioners experimenting with structured prompting, evaluation disciplines, and reflective pauses represent the personal prescription. None of these is yet mature; all of them are in the phase of productive experimentation that design knowledge requires before it can stabilize into durable practice.
Generation-optimization is obsolete. Structures designed to maximize what builders produce fail in an environment where production is abundant and evaluation is scarce.
Three scales require redesign. Organizational, educational, and personal structures all need to be rebuilt around the bound that remains: bounded evaluative attention.
Evaluation architecture is the new organizational challenge. The filtering function that hierarchy performed must be performed by new structures adapted to the AI environment.
Pedagogy must shift to judgment. Teaching evaluation requires different methods than teaching generation — the pattern libraries and meta-cognitive capacities built through experiential practice.
Personal discipline counteracts defaults. The structured interaction practices that produce wise AI use work against the default dynamics and must be deliberately cultivated.