Intelligence Delivery Network: Toward an Internet Architecture for the AI Age

TL;DR

The paper proposes the Intelligence Delivery Network (IDN), an Internet architecture designed to deliver AI capabilities efficiently across distributed environments, addressing latency, privacy, and resource utilization issues in current cloud-centric paradigms.

Contribution

It introduces IDN, a novel architecture that enables flexible, demand-driven deployment and management of AI services across cloud, edge, and local environments.

Findings

IDN improves AI service accessibility and responsiveness.

IDN reduces latency and bandwidth usage for AI applications.

IDN enhances privacy and trust in AI service delivery.

Abstract

The rapid emergence of AI-powered applications is reshaping the role of the Internet. Users increasingly rely on the network to obtain intelligence services derived from large foundation models, rather than merely to reach remote endpoints or retrieve specific content. Today's dominant deployment paradigm for AI services remains cloud-centric, where user requests are transmitted to remote data centers for centralized inference. Although operationally convenient, this paradigm suffers from latency and jitter, heavy wide-area traffic, limited utilization of distributed heterogeneous compute resources, and growing privacy and governance concerns. In this paper, we propose the Intelligence Delivery Network (IDN), an Internet architecture that treats AI capabilities as deliverable network services. The key idea is to position, select, reuse, and verify intelligence across cloud, regional, edge, and local environments according to demand locality, resource availability, and policy constraints. We present the system assumptions of IDN, define its core architectural mechanisms, and discuss how capability abstraction, compute resource integration, demand-driven deployment, service routing, state-aware caching, and trust management can jointly support distributed AI services. We believe that IDN provides a practical path toward an Internet architecture for the AI age, making AI capabilities more accessible, efficient, trustworthy, and responsive to diverse application needs.