The global race to build AI infrastructure—from hyperscale data centers to national digital sovereignty programs—marks one of the most consequential industrial expansions in decades. While much of the attention remains on GPUs, data center growth, and power availability, connectivity has emerged as both the decisive bottleneck and the most strategic opportunity for service providers. NVIDIA projects that inference demand will rise a billionfold as AI evolves from single-response transactions to continuous reasoning loops, placing unprecedented demands on networks to sustain real-time intelligence flows across globally distributed environments.
Agentic AI represents a fundamental shift from the generative AI wave of 2023–2024. These systems maintain persistent context, orchestrate distributed workflows, and coordinate autonomous actions across infrastructure. As a result, intelligence is moving beyond isolated model analytics toward infrastructure-wide operation, with AI factories, data hubs, and edge environments functioning as a unified reasoning fabric. In this new paradigm, connectivity is no longer a supporting utility—it becomes the coordination layer that enables intelligence production itself.
Traditional network architectures designed for predictable traffic patterns cannot support AI systems that dynamically reconfigure in real time. McKinsey estimates $6.7 trillion in data center investment by 2030, including $5.2 trillion dedicated to AI-ready capacity. Without programmable, automated connectivity, those compute investments risk under-delivering. As enterprises deploy agentic AI at scale, intelligence production increasingly depends on federated connectivity ecosystems rather than isolated provider networks.
This shift gives rise to Network-as-a-Service (NaaS) for AI, a new operating model built around four strategic pillars:
- Connectivity for AI: Carrier Ethernet, wavelengths, Carrier Ethernet over broadband, IP Broadband/DIA, and certified Carrier Ethernet performance profiles designed for AI workloads.
- Automation for AI: Lifecycle Service Orchestration (LSO)-enabled NaaS payloads, Model Context Protocol (MCP) and Agent-to-Agent (A2A) communication, federation frameworks, and LSO automation certification.
- Cybersecurity for AI: Certified Secure Access Service Edge (SASE), Security Service Edge (SSE), Zero Trust architectures, and quantum-safe Carrier Ethernet and wavelength services.
- Revenue Services for AI: GPU-as-a-Service and AI Model-as-a-Service.
Together, these elements position NaaS as an automated network supply chain that unites certified connectivity, lifecycle automation, cybersecurity, and emerging AI revenue services within a standards-based ecosystem. Through Mplify certification programs, service providers can validate the performance, automation, and security capabilities required by agentic AI, distinguishing themselves as strategic infrastructure partners in the multi-trillion-dollar AI buildout.
Mplify’s Market Brief: NaaS: The Automated Network Supply Chain for Agentic AI examines how this global AI infrastructure expansion is transforming networks into programmable, automated supply chains for intelligence production. It defines the emerging NaaS-for-AI model and outlines the standards, certifications, and ecosystem collaboration required to support agentic AI at scale.
Key findings include:
- AI is shifting from single-response transactions to continuous reasoning, driving massive growth in inference across the AI infrastructure landscape.
- Networks are evolving from connectivity utilities into foundational coordination layers for distributed intelligence.
- Agentic AI requires deterministic, programmable, on-demand network fabrics with lifecycle automation across provider boundaries.
- NaaS functions as the automated supply chain uniting certified connectivity, automation, cybersecurity, and new AI-driven revenue services.
- Successful NaaS ecosystems depend on alignment between buyers and sellers through standardized LSO APIs, payloads, and operational processes.
- Mplify certification validates service performance, automation conformance, and security, helping providers differentiate from commoditized offerings.
The transition to NaaS for AI is already underway, and service providers seeking to position themselves as strategic AI infrastructure partners must act now.