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

2026-05-13

AI Summary

Current AI services rely heavily on centralized cloud computing, suffering from high latency, network congestion, inefficient resource utilization, and privacy risks. This work proposes the Intelligent Delivery Network (IDN), which, for the first time, abstracts AI capabilities as network primitives to enable on-demand deployment and dynamic coordination across cloud, regional, edge, and local tiers. By integrating core techniques—including capability abstraction, heterogeneous compute fusion, demand-driven scheduling, service routing, state-aware caching, and trust management—IDN establishes an efficient, trustworthy, and low-latency distributed architecture for AI services. This approach significantly enhances the accessibility, responsiveness, and resource efficiency of AI service delivery, offering a new paradigm for Internet infrastructure in the AI era.

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.

Problem

• AI services
• cloud-centric deployment
• latency
• privacy concerns
• heterogeneous compute resources

Innovation

• Intelligence Delivery Network
• distributed AI
• edge intelligence
• capability abstraction
• trust management