Nov 3, 2025
Economic Agency
Elsa Donnat, Shannon Yang, Alex Woodruff
The rapid adoption of autonomous AI agents is expected to give rise to new economic layers where agents transact and coordinate without human oversight (Hadfiled et al., 2025). The emergence of virtual agent economies presents a range of wide-reaching impacts to human economies, including several undesirable systemic risks such as reduced resource access, market distortion and job displacement. We further identify gradual disempowerment as a major multi-faceted risk to human actors.
In the literature, permeability between AI agent economies and human economies is presented as a key factor that will either enable risks to materialise, or serve as a protective barrier to human economies. A permeable economy is defined as one that allows for porous interaction and transaction with it by external actors. Conversely, an impermeable economy has boundaries that hermetically seal it off, insulating other economies from its influence. A recent paper by Google DeepMind called such an impermeable AI agent economy a ‘sandbox economy’.
Despite being a principal variable of influence between human and AI agent economies, permeability is not well-defined or understood in the literature. We have designed a conceptual framework to understand permeability - risks, affected objects, gates of permeability, levers to influence permeability.
The capabilities of AI agents to operate as economic actors will shape their roles in AI agent economies and human economies, which both affect human economies. On top of foundational model capabilities, an AI agent that exerts high economic influence can be expected to utilise capabilities across three dimensions: Generality, Autonomy, and Agency. We think it is crucial to intentionally design and measure AI agents in these dimensions, though we encountered difficulties based on inconsistent and overlapping definitions of Autonomy and Agency in the literature.
We present taxonomies to map the spectrum of Autonomy and Agency, offering insights into which the levels in chains of action AIs may be handed control from humans.
Props for the clearly written and well-structured manuscript! The topic is very relevant and cutting-edge. The two frameworks you provide are both interesting, albeit a bit generic at times.
It’s unclear to me how useful these frameworks will be. Maybe the paper could benefit from more explicitly tracking how different governance mechanisms map onto the different “gates” and “objects”, to make the relevance of the framework clearer.
The second framework feels more useful to me - I especially like separating autonomy and agency. But it could have benefitted from engaging more deeply with recent research on characterizing AI agents (see for example Kasirzadeh & Gabriel 2025, Characterizing ai agents for alignment and governance). The framework feels quite disjoint from the first one. Some discussion of how these two frameworks interact, if at all, would be beneficial.
Forecasting relevance is a bit thin, although operationalizing permeability and AI agents is a first step towards being able to forecast it. More discussion of relevance to forecasting would have been good.
Cite this work
@misc {
title={
(HckPrj) Economic Agency
},
author={
Elsa Donnat, Shannon Yang, Alex Woodruff
},
date={
11/3/25
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
