Fiber network scaling to support AI

By Paulo Campos

As AI infrastructure requirements continue to demand high throughput and low latency, networks must scale in capacity without increasing their physical size.

I want to explain some of the considerations related to AI-ready data center networks.

Connectivity best practices for AI

AI workloads involve large-scale data transfers between locations. Distributed edge processing is required to reduce reliance on centralized data centers. AI workloads aren’t just downstream (data to the user); they’re also upstream (user-generated data, edge inference, and IoT sensor inputs). An important requirement of AI is the need to reach and interconnect large data centers with fiber. Low-latency fiber connections are needed to reduce delays, enabling real-time AI inference and decision-making at the edge. This comes with a set of challenges: fiber availability, the possibility of introducing high-count fiber cables, and deployment of those cables.

When specifying and implementing a solution, availability is vital. We recommend working with local fiber manufacturers and stocks—enabling rapid fulfillment, especially for custom-assembled cables, and reducing lead times significantly compared to overseas sourcing. Make sure cables are factory-finished, labeled, and tested, arriving ready for immediate deployment—saving on-field labor and reducing risk of termination errors.  

Indoor/outdoor trunks can reach 864–6,912 fibers; deployments may use 288–6,912 multiple fiber bulk cables per run. Firstly, there needs to be enough space to handle this and avoid blocked airflow, breaches of minimum bend radii, and complicated upgrades. High-density connectors are more susceptible to contamination, micro-scratches, and insertion loss—requiring easy-to-use products and installation, cleaning and inspection expertise. Workforce constraints and permitting delays are cited as top barriers to fiber rollout.

A recent joint study from the Fiber Broadband Association (FBA) and advisory firm Entropy, entitled Accelerating AI with Fiber: Systems and Strategies, argues that fiber broadband forms the foundation of AI at scale—enabling the ultra-fast speeds, ultra‑low latency, and high capacity essential for AI-driven data centers and edge systems. Without increased fiber density, middle‑mile expansion, and next-generation fiber networks, the emerging scale of AI applications—cloud compute, real-time inference, edge AI—will hit network bottlenecks. McKinsey states that AI’s triggering of high data throughput and real-time inferencing needs has driven telcos to invest in distributed GPU-as-a-Service and AI-RAN models, which hinge on fiber’s bandwidth and low latency.

Thanks to fiber, edge nodes can perform meaningful AI inference or even low-latency training without relying on distant core infrastructure. Recent academic research into in-network AI systems shows that embedding computation into the network (supported by fiber's speed) significantly reduces latency and boosts throughput—critical for real-time AI operations.

FTTx and scaling challenges

FTTx networks provide the fast, stable connections needed to ensure smooth transitions in data centers and link edge devices with cloud AI systems. Deploying fiber is capital-intensive, requiring strategic decisions that balance CapEx and OpEx, evaluate return on investment, and consider future demand for AI services as well as the trade-offs between leasing and owning fiber infrastructure. Fiber network scaling is key, but this has to align with data center interconnect (DCI) strategies, support high-bandwidth interlinks such as 400G and 800G optical transport, and ensure redundancy and failover to maintain resilience for mission-critical AI operations.

Traditional networks scale by adding more physical links, switches, and nodes—expanding the network in physical size. However, this introduces latency, inefficiency, and escalating costs. FTTx is vital to achieving high-throughput, low-latency AI infrastructure because it delivers massive bandwidth, minimal signal degradation, and ultra-low latency directly to end points—all without requiring the physical sprawl or energy intensity of legacy network scaling methods. FTTx’s near-light-speed transmission and very low propagation delay far outperform copper, coax, or even wireless last-mile alternatives. What’s more, FTTx is more energy-efficient than legacy copper or wireless solutions over distance. It reduces the need for active electronics in the field and supports passive optical networking (PON), cutting power consumption.

However, scaling fiber infrastructure across the United States presents a range of persistent challenges that are significantly impacting rollout speed and cost-efficiency. High capital expenditure remains a major barrier. This is largely driven by trenching, labor, and permitting—especially in rural and suburban areas where distances are longer, and populations are sparser. Deployment timelines are frequently delayed by fragmented local regulations and lengthy permitting processes, which vary across municipalities and often require extensive negotiation. Compounding these issues is a nationwide shortage of certified fiber technicians and engineers, which limits the pace and scale of installation efforts.

Recommended U.S. solutions

To overcome these barriers and accelerate national fiber deployment, the USA can draw on several proven strategies from European markets. One key recommendation is the standardization of permitting processes at the federal level, similar to the FCC's Dig Once policy, streamlining national guidelines and aligning public-private initiatives to reduce red tape and deployment costs. Expanding infrastructure-sharing mandates for ducts, poles, and rights-of-way can prevent duplication and maximize the use of existing assets. Adopting open-access models, particularly in underserved or highly competitive markets, allows multiple ISPs to operate over a single network, lowering barriers to entry, reducing wasteful overlap, and encouraging competition. Bridging the fiber skills gap is equally vital, and targeted funding for technical training programs can expand the certified labor pool.

Strengthening public-private partnerships offers another powerful lever, especially in rural areas where government co-investment and risk-sharing with telecom operators can make previously unviable deployments feasible. Finally, deploying cost-effective methods like microtrenching or aerial fiber can bypass some of the financial and logistical burdens associated with traditional trenching, offering faster and less disruptive alternatives that maintain high-quality service delivery.

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