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As AI infrastructure spreads beyond the data center, optical network upgrades are necessary to support distributed training, inference and critical AI workloads
The first wave of AI infrastructure investment was overwhelmingly a compute story. Hyperscalers and neocloud providers raced to assemble GPUs into increasingly large AI factories, while networking investment concentrated on the scale-up and scale-out fabrics needed to keep those accelerators working efficiently. Now the effects are spreading beyond the data center.
As AI infrastructure becomes larger and more distributed — and as the industry shifts from training toward widespread inference — the networks connecting data centers, clouds, enterprises and eventually edge locations are becoming part of the AI infrastructure equation. That transition is beginning to produce a new investment cycle in fiber and optical networking.
The market data is increasingly difficult to dismiss. Dell’Oro Group reported that the global optical transport equipment market grew 15% year-over-year in the second quarter of 2026, while data center interconnect revenue from IP-over-DWDM and traditional WDM systems increased 45%. Dell’Oro expects the overall optical transport market to grow 16% this year and exceed $18 billion in manufacturer revenue, citing AI data center construction as a central demand driver.
Service providers see the same trajectory from the demand side. New research commissioned by Ciena found that 90% of more than 1,200 telecom, wholesale fiber and regional service provider respondents expect high-capacity AI network services to drive revenue growth during the next three to five years. More tellingly, 88% expressed a strong sense of urgency around optical network upgrades needed to support premium enterprise AI connectivity, while just 11% believe routine upgrades will be sufficient.
Some of the immediate pressure comes from a familiar networking problem at an unfamiliar scale. Power, real estate and cooling constraints increasingly make it difficult to expand the largest AI clusters indefinitely within a single facility or campus. The result is greater interest in scale-across architectures that connect separate compute domains and allow them to function more like a larger distributed system.
Side note: We recently published a report examining multi-dimensional scaling in the shift from training to inference; download “Building AI-era network fabrics”.
The networking requirements can be extreme. Ciena’s research notes that distributed synchronous training of frontier models can require communications measured in tens of petabits per second, potentially involving hundreds of fiber pairs operating in parallel. Some 95% of surveyed providers expect hyperscaler scale-across requirements to contribute substantially to wholesale revenue growth.
But training is only part of the story. Nearly half of the surveyed service providers expect the growth of AI inference data centers to create new DCI revenue opportunities. As inference becomes more geographically distributed, network requirements expand beyond raw capacity to include latency, reliability, jitter and the ability to connect workloads dynamically across multiple infrastructure domains.
Operators are already responding. AT&T, for example, has expanded 400G wavelength services to more than 40 U.S. metros and enabled 400G handoff capability across 440,000 properties serving more than 2.3 million business tenants. The operator is also increasing capacities toward 1.6 Tbps across key metro and long-haul routes as it positions its network for AI and other data-intensive applications.
The broader implication is that AI is moving the networking bottleneck outward. Accelerators remain scarce and expensive, but compute alone has diminishing value if models, datasets and inference requests cannot move efficiently among locations. Fiber availability, optical capacity, route diversity and predictable network performance therefore become increasingly important components of AI infrastructure.
That creates an opportunity for network operators, but also an investment requirement. The next phase of the AI buildout will not happen entirely inside AI factories. It will increasingly happen between them, and across the networks connecting compute to clouds, enterprises, devices and users. For optical networking, the AI infrastructure cycle is only beginning.
