AI workloads are changing the way fiber networks are being designed, deployed, and upgraded; they also change how we should assess them. No longer just about reach and speed, connectivity now requires control, scalability, and proximity to data and compute resources. This first instalment of a 20-point checklist examines the backbone, access, interconnect, and optical capabilities that determine whether a fiber provider is truly AI-ready.
In sum – what to know:
Backbone coverage – terrestrial, metro, and subsea fiber footprints define national and global AI reach, impacting latency, resilience, and performance for distributed workloads.
Access and interconnect – on-net buildings, data centre proximity, IXs, and hyperscale interconnects determine how efficiently AI data can flow from source to compute.
Optical capability – dark fiber, wavelength services, and next-generation high-capacity optics (400G+) give enterprises control, scalability, and the bandwidth needed for large-scale AI deployments.
Artificial intelligence, as we all know (or as we keep hearing, anyway) is redefining what ‘connectivity’ means. The movement of data is not just about bandwidth, anymore, but about the ability to connect, control, process, and scale distributed AI workloads across a digital fabric. Fiber providers, once ranked purely for the reach and speed of their network footprints, are now being assessed for how smartly and sustainably they serve the new AI economy.
Here is a 20-point framework – or just a useful list – to evaluate AI readiness in fiber infrastructure, or fiber readiness in AI infrastructure, if you prefer. It goes from physical scale, per the old days, to optical capability, automation and programmability, ecosystem integration, and service transparency. But quick word on the structure, here. The order is not arbitrary; it follows a logical progression around how fiber infrastructure supports AI workloads.
As such, it goes from local to global, and from physical reach to logical capability. Which explains, say, why metro reach comes after terrestrial footprint and before subsea footprint – and why on-net buildings, data centre proximity, IX interconnects, and hyperscale interconnects come after. Because it describes the backbone layer (national, first; local, second, global, third) and then the access and interconnect layers around points of attachment and exchange.
So the narrative goes: how far the fiber reaches in a country, how deeply it reaches in cities, how globally it connects; and then where to plug into it at one end, and how to connect into the cloud ecosystem at the other. It is supposed to follow how an AI network architect might evaluate fiber companies. Note: RCR is not an AI architect, by any stretch, but the logic seems plain: can they reach my locations, can they connect my workloads, can retrieve my AI insights?
And so on and so forth. Note as well, this article (with 20 ‘checklist’ items) has been split into two parts, just because it quickly gets long (and because RCR has other work to do besides). So here are points one-to-10 on the list; the second instalment, 11-to-20, considering other AI-readiness aspects (programmability & intelligence, sustainability & edge, governance & openness, innovation & assurance), will appear in due course.
Backbone & footprint (for national and international reach)
1 | Terrestrial fiber footprint
The headline measure, traditionally – which tends to place telcos, particularly Chinese ones, nearer the top of the pile. The numbers are big ones – well over a million kilometres, in some cases – and are worth looking up just to get a sense of the total extent of a provider’s land-based fiber routes, connecting cities, regions, and enterprise clusters. Terrestrial fiber defines their core coverage area, typically regional or national, where an operator can offer end-to-end control over performance and resilience. AI workloads – especially those involving large-scale model training or inference across edge sites – rely on predictable, low-latency terrestrial links. For enterprises looking to support rising AI workloads, it makes sense to map data flows against terrestrial footprints to know where to expect single-network performance, and where peering or backhaul partners may be required.
2 | Metro reach
Another backbone measure; a provider’s metro reach gives a nominal measure of how densely and widely a provider’s fiber penetrates metropolitan and suburban areas. AI applications increasingly depend on distributed edge processing – in retail, healthcare, logistics, and manufacturing – and deep metro coverage shortens the distance between compute and data sources, improving responsiveness and cost efficiency. Again, organisations deploying edge AI should prioritise providers with strong metro reach in their operational regions to minimise latency and interconnection costs.
3 | Subsea fiber footprint
Where we get into (or under) international waters; fiber providers variously give kilometre measures about the extent of their undersea cable systems, interconnecting continents and regions. Important – clearly, despite the push on sovereignty, as the global AWS outage last month proved – on the grounds that cloud compute engines remain generally centralised, and enterprise AI models and datasets often cross borders. Global fiber diversity ensures resilience, capacity, and compliance across different countries and regions. And of course, subsea reach directly impacts replication speed, cloud interconnectivity, and failover performance. For global AI strategies, enterprises should look for providers with multiple subsea routes, diverse landing points, and proper ownership stakes – not just capacity leases – for better control and redundancy.
Access / interconnect density (to connect to the backbone)
4 | On-net buildings
Generally available from providers, this is a measure of the assorted buildings, campuses, and other facilities that are directly connected to their fiber network. It matters because being ‘on-net’ simplifies provisioning and lowers latency – as there’s no need for third-party access circuits. For enterprises deploying AI clusters or data-processing nodes, direct connectivity accelerates rollouts and guarantees consistent performance. Note to enterprises: assess how many key sites are already on-net with a potential provider, and prioritise those providers that can extend fiber to critical AI nodes with minimal lead times.
5 | Data centre proximity
A gateway measure, somewhere between access and interconnect (but mostly about access); this describes how physically (and also optically; as in the geographic distance and length of fiber light) close a network provider’s fiber routes or physical points-of-presence (PoPs) are to key colocation and hyperscale data centres. In other words, it’s about the ease and speed of connecting into compute, storage, and cloud ecosystems that live in those facilities. As a measure, it shows how ‘ready’ a fiber network is to deliver or collect traffic from the places where digital workloads actually reside. Point is, AI workloads are bandwidth-heavy and latency-sensitive, and so proximity to data centres – particularly GPU-dense campuses – minimises transport costs and latency between compute zones. So it is worth selecting providers with fiber routes that pass through / by to preferred colocation or hyperscale campuses – to simplify private interconnects and enable scalable capacity on demand.
6 | IX interconnects
Internet exchange (IX) interconnects provide shared connectivity to multiple networks at a neutral point, like ISPs, CDNs, or cloud providers). Where data centre proximity (above) says where you can physically and optically access a facility and hyperscale interconnects (below) provide direct links to private hyperscale clouds, IXs fit in between the physical access and private interconnects as a shared layer, effectively, that provide additional or alternative network paths to reach endpoints that might otherwise be out of range. Basically, if your AI workload needs data from a third-party provider or cloud, the IX reduces the number of hops and provides a lower-cost path; it’s useful for model collaboration. So the message goes: check your provider is present in key IXs near your compute hubs. At the same time, if you want guaranteed, high-performance connectivity, then look for…
7 | Hyperscale interconnects
A step further, hyperscale interconnects (typically given in the affirmative, rather than as a count, or a who/where measure) imply a provider owns dedicated high-capacity circuits that plug directly into a hyperscaler’s private network inside its data centres. Large hyperscalers own or lease their own long-haul fiber networks, as well, connecting multiple cloud regions and zones; a hyperscale interconnect can give direct access into their private backbone, also. Which is relevant for high-volume AI workloads that span multiple regions or require cross-region replication. Point is that for big AI workloads, being near a data centre (proximity or internet exchange) is necessary but probably not enough. You need a direct hyperscale interconnect if you are to avoid slower, shared, or congested routes. So check which hyperscaler regions a provider connects to directly – and whether those connections support elastic bandwidth or pre-provisioned wavelength services for AI data exchange.
Optical / transport capability (to gauge network performance)
8 | Dark fiber footprint
Dark fiber refers to unused or unlit fiber strands that a provider makes available for enterprises to lease or manage directly. Unlike standard managed wavelength services, dark fiber gives full control over optical performance, capacity, and security, allowing enterprises to build private, end-to-end connections between sites or data centres. Which is particularly valuable for AI workloads with predictable, high-volume traffic, where control and encryption are the key principles. Leasing dark fiber enables enterprises to deploy their own optical equipment and scale capacity independently, rather than relying on shared circuits. When evaluating providers, enterprises should consider the extent of their dark fiber footprint along key routes, lead times for deployment, and whether the network allows flexible reconfiguration to accommodate evolving AI data flows.
9 | Wavelength services
Where dark fiber gives the enterprise full flexibility and control, wavelength services are managed by the provider instead. In this case, the provider ‘lights’ the fiber and operates the optical equipment – and so the enterprise gets a dedicated high-end fiber circuit without having to manage their own optical gear. These services are ideal for AI workloads with high but variable traffic – such as linking GPU clusters, moving training datasets, or connecting to cloud ingress points. Wavelengths offer a balance between operational simplicity and performance control: enterprises get guaranteed bandwidth, low latency, and reliability, while avoiding the complexity of managing the optical layer themselves. Consider where wavelength services are available, the supported capacities (see below), and whether circuits can be provisioned dynamically or pre-planned to match AI data flow patterns.
10 | 800G / 1T fiber footprint
Where dark fiber and wavelength services describe how to light and manage optical connectivity, some kind of indicator of a provider’s technical capability is also important. New optical upgrades – from 100G to 400G to 800G to 1T, eventually (and the rest) – define how ‘AI-ready’ a network backbone really is. High-capacity optics enable dense data movement between AI data centres with lower power per bit and higher spectral efficiency. It should be available as another kilometre measure, indicating the number of fiber routes that can carry next-generation high-capacity wavelengths. Unlike wavelength services, which can be provisioned on demand over smaller capacity circuits, the 400G+ backbone represents the provider’s underlying capacity and ability to scale for ultra high-throughput traffic. As such, and to avoid future AI bottlenecks, consider both the geographic extent of these high-speed links and the max capacities available on key routes.
This article is to be continued, including explanation of the following:
Programmability & intelligence (for automation, efficiency, control)
Sustainability & edge (for energy and compute integration)
Governance & openness (for policy, ecosystem, collaboration enablers)
Innovation & assurance (for future-proofing and trust metrics)
