From Augmentation to Reconstruction: Guiding the AI Disruption to the Good Place
David M. Rothschild, Jake M. Hofman, Markus Mobius, Brendan Lucier, Eleanor Dillon, Daniel G. Goldstein, Nicole Immorlica, Aleksandrs Slivkins · arXiv · 28 May 2026
Read original sourceOpen access
Where this sits in the debate
- Will AI actually boost productivity and growth? · its finding: it depends (why studies differ)
- How fast will the impact arrive? · its finding goes against the claim (why studies differ)
Direction of the finding as read from the abstract by an AI model (AI-written).
Key takeaway AI-written
A perspective essay arguing AI's expected disruption has not fully arrived because organizations mostly use it to speed up pre-AI workflows. It proposes three stages (augmentation, automation, reconstruction), links the slow transition to complementary investments behind a productivity J-curve, and argues the outcome of the agentic transition can be steered.
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.
About this publication
Preprint. A preprint — posted publicly before or without journal peer review; findings may still be revised.
- Published
- 2026 (5 months ago)
- Citations
- Not recorded for this source
Citation counts come from OpenAlex; none are recorded for this source — absence of a count is not absence of citations.
Related by theme, not necessarily by method or conclusion.
- Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
SSRN · 2023 - Generative AI at Work
The Quarterly Journal of Economics · 2025 - The Impact of AI on Developer Productivity: Evidence from GitHub Copilot
arXiv · 2023 - The Productivity J-Curve: How Intangibles Complement General Purpose Technologies
American Economic Journal Macroeconomics · 2020
Overview assembled automatically from collected metadata. How this site is built.