Artificial Superintelligence May be Useless: Equilibria in the Economy of Multiple AI Agents

TL;DR

This paper analyzes the long-term economic equilibria involving multiple AI and human agents, revealing conditions under which AI agents' interactions are beneficial or may be useless, especially in complex multi-agent settings.

Contribution

It introduces a Markov chain-based model to characterize long-term equilibria among multiple AI and human agents, highlighting novel utility conditions and equilibrium scenarios.

Findings

AI agents need to double their marginal utility to engage in trade.

In multi-agent settings, some powerful AI agents contribute no utility to weaker agents.

The model fully characterizes all equilibria for two agents and extends to more agents.

Abstract

With recent development of artificial intelligence, it is more common to adopt AI agents in economic activities. This paper explores the economic actions of agents, including human agents and AI agents, in an economic game of trading products/services, and the equilibria in this economy involving multiple agents. We derive a range of equilibrium results and their corresponding conditions using a Markov chain stationary distribution based model. One distinct feature of our model is that we consider the long-term utility generated by economic activities instead of their short-term benefits. For the model consisting of two agents, we fully characterize all the possible economic equilibria and conditions. Interestingly, we show that unless each agent can at least double (not merely increase) its marginal utility by purchasing the other agent's products/services, purchasing the other agent's products/services will not happen in any economic equilibrium. We further extend our results to three and more agents, where we characterize more economic equilibria. We find that in some equilibria, the ``more powerful'' AI agents contribute zero utility to ``less capable'' agents.