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Why AI affects both subsea and terrestrial networks
Training and running AI models can move large volumes of data among data centers, cloud regions, and edge locations. Those exchanges may cross a city, a country, or an ocean, so the network path includes more than the long-distance cable: it also depends on metro links, terrestrial backhaul, landing points, and connections to data-center and network hubs.
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The relationship also runs the other way. The International Telecommunication Union’s ION-2030 framework describes optical networks as a foundation for distributed AI training, inference, and cloud-edge data exchange. It also sets out how AI could help design, manage, and optimize optical networks. In other words, AI increases demand for network services while offering tools that may help operators run those networks.
How AI traffic changes network design
Metro networks connect local hubs
Metro networks link data centers, cloud facilities, and other network hubs within and around metropolitan areas. More AI activity at those sites can increase traffic between facilities, not just between a user and an application. Providers therefore need to plan for the location and direction of traffic as well as its total volume.
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Long-haul and subsea links carry traffic farther
Long-haul terrestrial networks connect cities and regions; subsea cables carry traffic between continents. A cross-border AI service can rely on both, plus inland routes that carry traffic from a cable landing point to the relevant data center. A high-capacity ocean segment does not, by itself, guarantee a high-capacity or resilient end-to-end service.
Telstra International said its subsea capacity upgrade was intended to address rising bandwidth consumption, data-center demand, and growth in global backbone networks fueled by cloud and AI applications. The company announced a target of more than 800 Tbps of total lit subsea capacity in 2025. That is a company roadmap statement, not an independently verified measurement of capacity currently in service.
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Capacity figures need their deployment context
Optical upgrades can increase the amount of data carried by a fiber system, but deployment and demonstration figures describe different levels of readiness. Nokia and Telxius reported deployment of 800G coherent pluggable optics across terrestrial transport and a separate demonstration of 400 Gb/s per wavelength over more than 5,600 km on the BRUSA subsea cable in 2026. The latter is a reported trial result on a specific cable, not a claim that all subsea links carry that rate or reach.
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AI for network operations is still a developing capability
Operators and standards bodies are considering AI and machine learning for network planning, monitoring, and control. ITU’s ION-2030 framework names digital twins, multimodal learning, and autonomous control as approaches that could help networks anticipate service needs and address reliability and energy use. These are framework capabilities and directions for development, not proof that every carrier has a self-running network.
Telstra described a five-year implementation plan for AI, machine learning, and cognitive computing to support network self-management. A plan indicates intended work over time; it should not be read as a statement that the planned capabilities are already deployed across the network.
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- 24K gold-plated connectors: Corrosion resistant gold plating keeps connectors clean. And because these cables are fiber optic, they provide 100 % signal transmission with 0 % loss
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ITU’s telecom generative-AI work item discusses network modelling, performance analysis, and automatic decision-making. It also identifies the need for a consolidated way to assess models across different use cases. That distinction matters: a model that performs well for one operational task is not automatically suitable for another, and automated recommendations are not the same as safe, autonomous control in production.
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A Ciena and Heavy Reading survey of 77 communications service providers, fielded globally in February 2025, measured expectations and self-assessments rather than observed future traffic. Its findings were:
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- 29% of respondents expected AI to account for more than half of long-haul traffic within three years.
- 18% expected AI to account for more than half of metro traffic within three years.
- 16% said their optical networks were “very ready” for AI.
The first two figures describe what respondents expected, not a measured share of traffic. The readiness figure is a respondent self-assessment, not an independent audit of network capability. Together, they suggest that providers anticipated substantial demand while relatively few considered their optical networks fully ready.
Resilience depends on the whole route
More capacity on one segment does not necessarily create a resilient service. If an inland backhaul path, landing point, or other shared segment is a single point of failure, a second ocean cable alone may not protect the end-to-end connection.
Google and Telstra described an arrangement combining inter-city terrestrial dark fiber with subsea fiber pairs, saying the integration removes single points of failure. The architectural lesson is that route diversity has to extend across the full path. Paths that look separate on a map may still depend on the same infrastructure; meaningful resilience requires independent failure paths across the segments the service uses.
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What to look for when evaluating an AI-ready network
- Capacity and scope: Check whether a figure refers to total lit capacity, a per-wavelength rate, or a tested result—and whether it is a target, deployment, or demonstration.
- End-to-end topology: Trace the route through metro links, terrestrial backhaul, landing points, subsea systems, and destination hubs.
- Failure independence: Ask whether alternate routes avoid shared bottlenecks and failure points, rather than merely adding a second segment.
- Operational maturity: Separate a standards framework, roadmap, trial, and production deployment. They indicate different levels of capability.
- Evidence for benefits: Treat claims about reliability, energy use, and cost as claims unless measured results and their conditions are reported.
AI is therefore reshaping networks both as a source of traffic and as a prospective operations tool. The strongest evidence today supports rising provider expectations, specific capacity initiatives, and standards work—not a general conclusion that AI has already reduced outages, energy consumption, or network costs.
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