A Model of Artificial Jagged Intelligence

TL;DR

This paper introduces an economic model of Artificial Jagged Intelligence (AJI), explaining uneven AI performance and how user calibration and scaling influence adoption and reliability in AI systems.

Contribution

It develops a tractable model of AJI that incorporates local reliability, user calibration, and scaling effects, providing insights into AI performance variability and adoption thresholds.

Findings

Local error is quantified by posterior variance.

Calibration and mastery improve AI adoption and reliability.

Scaling can enhance or obscure AI performance gains.

Abstract

Generative AI systems often display highly uneven performance across tasks that appear ``nearby'': they can be excellent on one prompt and confidently wrong on another with only small changes in wording or context. We call this phenomenon Artificial Jagged Intelligence (AJI). This paper develops a tractable economic model of AJI that treats adoption as an information problem: users care about \emph{local} reliability, but typically observe only coarse, global quality signals. In a baseline one-dimensional landscape, truth is a rough Brownian process, and the model ``knows'' scattered points drawn from a Poisson process. The model interpolates optimally, and the local error is measured by posterior variance. We derive an adoption threshold for a blind user, show that experienced errors are amplified by the inspection paradox, and interpret scaling laws as denser coverage that improves average quality without eliminating jaggedness. We then study mastery and calibration: a calibrated user who can condition on local uncertainty enjoys positive expected value even in domains that fail the blind adoption test. Modelling mastery as learning a reliability map via Gaussian process regression yields a learning-rate bound driven by information gain, clarifying when discovering ``where the model works'' is slow. Finally, we study how scaling interacts with discoverability: when calibrated signals and user mastery accelerate the harvesting of scale improvements, and when opacity can make gains from scaling effectively invisible.