From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place
Abstract
Artificial intelligence feels omnipresent, yet the disruption many expect has not fully arrived. The main reason is not model capability, nor even the tools built to harness those models. Rather, most organizations are still using AI to accelerate workflows designed for a pre-AI world. We offer a three-stage lens: Augmentation, Automation, and Reconstruction, and argue that the most consequential disruption resides in the third stage where workflows and markets are rebuilt around delegation, machine-to-machine interaction, continuous monitoring, and auditable constraints. Achieving this system-level transformation takes time: it requires trust and accountability infrastructure, machine-legible and interoperable data and interfaces, the design and adoption of these new workflows, and economic incentives that favor reconstruction rather than local optimization: the complementary investments that produce the familiar "productivity J-curve" of general-purpose technologies. We illustrate this transition through examples in consumer markets, education, news, and coding. Finally, we emphasize a normative point: the agentic future is not predetermined. Leaders must both skate to where the puck is going and actively steer it toward a good place, ensuring innovation delivers welfare gains felt by businesses and consumers around the world.
Cite
@article{arxiv.2605.29207,
title = {From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place},
author = {David M. Rothschild and Jake M. Hofman and Markus Mobius and Brendan Lucier and Eleanor Dillon and Daniel G. Goldstein and Nicole Immorlica and Aleksandrs Slivkins},
journal= {arXiv preprint arXiv:2605.29207},
year = {2026}
}
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5 Pages, 0 Figures