Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2503.14283

Techno-Feudalism and the Rise of AGI: A Future Without Economic Rights?

Pascal Stiefenhofer
View original source ↗Read source document ↗Export metadata ↓

Abstract

The rise of Artificial General Intelligence (AGI) marks an existential rupture in economic and political order, dissolving the historic boundaries between labor and capital. Unlike past technological advancements, AGI is both a worker and an owner, producing economic value while concentrating power in those who control its infrastructure. Left unchecked, this shift risks exacerbating inequality, eroding democratic agency, and entrenching techno-feudalism. The classical Social Contract-rooted in human labor as the foundation of economic participation-must be renegotiated to prevent mass disenfranchisement. This paper calls for a redefined economic framework that ensures AGI-driven prosperity is equitably distributed through mechanisms such as universal AI dividends, progressive taxation, and decentralized governance. The time for intervention is now-before intelligence itself becomes the most exclusive form of capital.

Explore related subjects

econ.GNq-fin.EC

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Pascal Stiefenhofer. 2025-03-18. Techno-Feudalism and the Rise of AGI: A Future Without Economic Rights?. https://arxiv.org/abs/2503.14283

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Computing Endogenous Transformations in Processing Networks: A Dynamic Calibration Approach

Understanding how supply chains endogenously transform requires a parametric model of processing networks with non-neutral substitution elasticities. While the Cascaded CES production function provides a rigorous framework, dynamically calibrating its structural parameters from time-series data constitutes a highly non-convex inverse optimization problem. Since enforcing strict microeconomic concavity renders standard monolithic approaches computationally intractable, we propose a novel structure-exploiting algorithm to bypass this limitation. By leveraging the physical upstreamness topology of the network, our hybrid heuristic effectively breaks the curse of dimensionality inherent in economywide processing networks. Applying this framework to U.S. time-series data, we provide a scalable computational engine to fully endogenize complex supply-chain transformations, ultimately uncovering the elastic origins of asymmetric macroeconomic tail risks.

econ.GN↗
arXiv2026

Access to Live AI Advice and Behavior Under Risk: An Incentivized Experiment

Generative AI has become an everyday advisor, and the systems people consult are live and interactive, not pre-scripted. We ask whether access to such a system changes behavior under risk. In an incentivized experiment (N = 158), participants made lottery choices with an optional decision aid presented as a conventional pre-written tool, a live one-shot AI, or a live interactive AI they could query, with information format held equivalent across conditions. Risk preferences are elicited via DOSE. We find no evidence that access to a live AI advisor changes risk aversion.

econ.GN↗
arXiv2026

Screening Out the Needy: The Effects of SNAP Work Requirements

We examine the effectiveness of work requirements as a screening device in the Supplemental Nutrition Assistance Program (SNAP). Work requirements for "able-bodied adults without dependents" were suspended after the Great Recession and gradually reinstated across counties and states in the 2010s. Using linked administrative SNAP and employment data from five states and a triple-differences design, we find that work requirements reduce SNAP participation by seven percent without increasing labor supply and disproportionately screen out low-income individuals. We develop a welfare framework to interpret these results and find that the social costs of work requirements exceed budget savings.

econ.GN↗