Sufficient Statistics
B_H values horizon progress, B_F values reliability in unforgiving environments, and C measures the value mass where both constraints bind.
Horizon, forgiveness, and directed AI innovation. The paper studies why the profitable direction of AI progress changes across task regions.
AI progress is not one scalar quality ladder. Tasks fail for different reasons. Some tasks require long chains of coordinated action. Other tasks can be short but unforgiving because a local error is costly or irreversible. The model turns that distinction into sufficient statistics for valuing frontier movement and project direction.
B_H values horizon progress, B_F values reliability in unforgiving environments, and C measures the value mass where both constraints bind.
The same project menu can favor runtime, tools, or search in one task region and reliability or verification in another. The regional ratio B_H^R/B_F^R determines the direction.
A one-dimensional quality ladder cannot generate common-project reversals without adding the missing task heterogeneity back through project effects or costs.
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