Table of Contents
Operators are pushing toward 400G, 800G and 1.6T optical networking, but AI workloads are also increasing the importance of deterministic performance, power efficiency, telemetry and automation
The first requirement AI places on optical networks is obviously more capacity. Connecting increasingly large data centers, distributing compute across multiple sites and moving enormous datasets among clouds and enterprises are driving traffic growth that conventional network expansion cycles were not designed to accommodate. Operators are responding with new fiber, higher-capacity wavelengths and more efficient coherent optics.
But simply adding bandwidth will not be enough. AI workloads are increasingly important to business operations, and their performance can depend on more than whether sufficient aggregate capacity exists. Latency, jitter, availability and consistency all become important when networks connect distributed compute resources or support real-time inference.
That shift is reflected in service provider priorities. In Ciena’s latest global survey, 35% of respondents identified network consistency, including guaranteed performance stability and low jitter, as the leading opportunity for monetizing premium connectivity. And 96% said advanced network automation, including agentic AI, will be important to capturing AI-driven network opportunities.
The emerging optical investment cycle is about simultaneously increasing capacity, efficiency and intelligence.The capacity transition is already visible in commercial networks. AT&T, for instance, has expanded 400G wavelength connectivity to more than 40 U.S. metros, with 400G handoff capability enabled across 440,000 properties serving more than 2.3 million business tenants. The operator explicitly positions that expansion around distributed AI, cloud and analytics workloads that require high throughput and predictable performance between locations where data is created, processed and stored.
The technology roadmap is moving quickly beyond 400G. Verizon has validated 1.6 Tbps transmission over a live metro network using Ciena’s WaveLogic 6 Extreme coherent optics. The trial demonstrated a single 1.6 Tbps wavelength operating through a dense ROADM environment, while the technology is also designed to extend 800G connectivity across substantially longer network distances.
Lumen is deploying the same generation of coherent technology as it expands infrastructure for AI workloads. Beyond increasing raw capacity, Ciena says WaveLogic 6 Extreme can double capacity within the same space and power envelope, reducing space and power consumption per bit by approximately 50%. Lumen is also using Ciena’s network-control software to automate management of its expanding fiber assets.
Those efficiency improvements are important because AI is putting pressure on more than bandwidth. Power and physical space are constrained throughout digital infrastructure, meaning the economics of optical upgrades increasingly depend on how much capacity operators can extract from existing fiber, racks and power envelopes.
The next challenge is operational. Traditional optical networks have largely been engineered around relatively predictable traffic requirements and long-lived circuits. AI introduces a more dynamic environment. Workloads can move between clouds and data centers, compute availability can change, and application requirements can vary considerably in latency, capacity and resilience. That puts greater value on telemetry and automation.
Service providers in Ciena’s survey increasingly favor dynamic connectivity models: 56% identified multi-cloud connectivity management as an important revenue driver and 50% pointed to consumption-based bandwidth services, compared with 38% for traditional fixed-capacity services. Nearly half expect movement of large AI datasets between clouds to increase demand for flexible, high-capacity connectivity.
Optical vendors are consequently optimizing not just transmission rates but the surrounding system. Nokia, for example, is developing application-specific coherent solutions spanning metro through subsea networks, alongside denser amplification systems intended to light large numbers of fiber pairs with lower power and footprint requirements.
The direction of travel is clear. AI is accelerating the industry’s move from 400G toward 800G and 1.6T connectivity. But the more consequential transition may be from optical networks designed primarily to transport capacity toward networks able to expose, optimize and automate that capacity according to changing application requirements.
For operators, the AI-era optical network will need to be faster, more predictable, more efficient and considerably more programmable.
