A Model of Artificial Jagged Intelligence
Joshua Gans
TL;DR
This paper introduces Artificial Jagged Intelligence (AJI), an economics-inclined model where AI reliability varies locally across tasks due to uneven coverage of knowledge. By modeling knowledge points as a Poisson process and truth as Brownian motion, it derives a tractable framework in which the posterior variance $\\sigma^2(x)$ governs local error and adoption decisions, and where the inspection paradox elevates experienced risk relative to benchmark averages. It shows that scaling improves average reliability but leaves jaggedness largely intact; calibration and mastery offer pathways to convert jagged reliability into value, with calibration enabling selective delegation and mastery shaping how quickly users learn reliable regions. The work highlights complementarities and tradeoffs among scaling, regularity, and calibration, and it proposes usage-weighted evaluation and tail-risk reporting to better predict real-world adoption and welfare. Overall, AJI provides a coherent lens to forecast adoption, design interfaces, and guide investments in scalability, calibration, and mastery to maximize welfare under local, task-specific uncertainty.
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.