Beyond the Sum: Unlocking AI Agents Potential Through Market Forces

Fuente: arXiv
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Autores principales: Sanabria, Jordi Montes, Vecino, Pol Alvarez
Formato: Preprint
Publicado: 2024
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author Sanabria, Jordi Montes
Vecino, Pol Alvarez
author_facet Sanabria, Jordi Montes
Vecino, Pol Alvarez
contents The emergence of Large Language Models has fundamentally transformed the capabilities of AI agents, enabling a new class of autonomous agents capable of interacting with their environment through dynamic code generation and execution. These agents possess the theoretical capacity to operate as independent economic actors within digital markets, offering unprecedented potential for value creation through their distinct advantages in operational continuity, perfect replication, and distributed learning capabilities. However, contemporary digital infrastructure, architected primarily for human interaction, presents significant barriers to their participation. This work presents a systematic analysis of the infrastructure requirements necessary for AI agents to function as autonomous participants in digital markets. We examine four key areas - identity and authorization, service discovery, interfaces, and payment systems - to show how existing infrastructure actively impedes agent participation. We argue that addressing these infrastructure challenges represents more than a technical imperative; it constitutes a fundamental step toward enabling new forms of economic organization. Much as traditional markets enable human intelligence to coordinate complex activities beyond individual capability, markets incorporating AI agents could dramatically enhance economic efficiency through continuous operation, perfect information sharing, and rapid adaptation to changing conditions. The infrastructure challenges identified in this work represent key barriers to realizing this potential.
format Preprint
id arxiv_https___arxiv_org_abs_2501_10388
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Beyond the Sum: Unlocking AI Agents Potential Through Market Forces
Sanabria, Jordi Montes
Vecino, Pol Alvarez
Computers and Society
Artificial Intelligence
Computation and Language
Computer Science and Game Theory
Multiagent Systems
I.2.2; I.2.7; I.2.11; J.4; K.4.4
The emergence of Large Language Models has fundamentally transformed the capabilities of AI agents, enabling a new class of autonomous agents capable of interacting with their environment through dynamic code generation and execution. These agents possess the theoretical capacity to operate as independent economic actors within digital markets, offering unprecedented potential for value creation through their distinct advantages in operational continuity, perfect replication, and distributed learning capabilities. However, contemporary digital infrastructure, architected primarily for human interaction, presents significant barriers to their participation. This work presents a systematic analysis of the infrastructure requirements necessary for AI agents to function as autonomous participants in digital markets. We examine four key areas - identity and authorization, service discovery, interfaces, and payment systems - to show how existing infrastructure actively impedes agent participation. We argue that addressing these infrastructure challenges represents more than a technical imperative; it constitutes a fundamental step toward enabling new forms of economic organization. Much as traditional markets enable human intelligence to coordinate complex activities beyond individual capability, markets incorporating AI agents could dramatically enhance economic efficiency through continuous operation, perfect information sharing, and rapid adaptation to changing conditions. The infrastructure challenges identified in this work represent key barriers to realizing this potential.
title Beyond the Sum: Unlocking AI Agents Potential Through Market Forces
topic Computers and Society
Artificial Intelligence
Computation and Language
Computer Science and Game Theory
Multiagent Systems
I.2.2; I.2.7; I.2.11; J.4; K.4.4
url https://arxiv.org/abs/2501.10388