Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
By Alan J Weissberger
Executive Summary:
The rapid rise of agentic artificial intelligence (AI) is expected to drive material changes across data centers, service provider networks, and the broader telecom ecosystem. As agentic AI moves from chat-oriented interactions to autonomous digital agents, Cisco says that those workloads will not only increase traffic volumes, but also alter traffic characteristics in ways that place new demands on latency, security, orchestration, and distributed compute placement.
“We are entering into a Network Supercycle,” Jeetu Patel, Cisco’s president and chief product officer, said during his opening keynote at Cisco Live in Las Vegas.
As a result, network operators will need more resilient transport, edge compute, and optical capacity to support new traffic patterns and security demands.
Cisco execs pictured (left to right): Jeetu Patel, president and chief product officer; Chuck Robbins, chairman and CEO; Liz Centoni, EVP and chief customer experience officer; and Steven Clayton, SVP and chief communications officer.
Source: Jeff Baumgartner/Light Reading
AI Traffic Impact on Transport Requirements:
From a transport perspective, agentic AI traffic is likely to be more persistent, more interactive, and more latency-sensitive than conventional application traffic. Cisco has said AI-related network traffic is expected to triple over the next three years, with inference flows emerging as a major driver of load growth. That shift could place pressure on transport architectures that were optimized primarily for human-driven web, video, and enterprise application traffic
The implication for service providers is that traffic engineering will need to evolve toward finer-grained path control, stronger telemetry, and improved handling of asymmetric flows. AI sessions that span multiple exchanges between users, applications, and digital agents may also require more sophisticated policy enforcement and security integration across WAN, metro, and access layers.
Edge Compute Needs Grow:
Cisco’s remarks also point to a growing role for edge compute in telecom and cable networks. Some operators are already repurposing legacy central offices and mini data centers to support AI workloads, reflecting a broader shift toward distributed inference close to the user or device.
That architecture matters because many agentic AI use cases will be latency constrained and will not perform efficiently if all processing is centralized in distant cloud regions. Comcast and Charter have both announced AI edge strategies, underscoring how access networks can become part of the compute fabric rather than acting solely as last-mile connectivity.
For network operators, this suggests a new operational model in which compute, storage, and network functions are increasingly coordinated across regional and edge sites. In practical terms, the network becomes part of the application execution environment, not just the transport layer beneath it.
Optical Network Implications:
Optical infrastructure will likely carry much of the burden created by distributed AI deployments. As inference workloads expand across regional hubs, edge sites, and centralized clouds, operators may need higher-capacity optical transport to sustain east-west traffic between distributed compute nodes.
That points to greater demand for dense 400G and 800G interconnects, more flexible wavelength management, and lower-latency optical paths between metro aggregation points and AI facilities. The challenge is not only to scale throughput, but also to preserve path diversity, minimize jitter, and maintain predictable performance for machine-to-machine workloads that are increasingly sensitive to delay.
As AI traffic becomes more dynamic and more operationally critical, optical networks may need to be engineered with the same level of service awareness traditionally associated with enterprise transport and carrier-grade voice or mobile backhaul.
Security is a Top Priority:
Cisco cited security as a serious concern for agentic AI traffic. CEO Chuck Robbins said AI agents designed to help enterprise customers can run roughshod without a proper defense that can quickly detect, intercept and possibly “kill” them before they get out of control. It becomes an even bigger issue when they are built to be nefarious.
“AI changes the speed of defense,” Robbins said. “It’s empowering adversaries at a pace that we haven’t seen in our careers … These [AI] models are as bad as they are ever going to be …They’re only going to get better.”
Anthropic’s new Claude Mythos model, which can auto-detect and possibly exploit software vulnerabilities at scale, is now a “CEO-level discussion,” he added.
“We’re living in a post-Mythos world where security has to be fused and baked into the network,” Patel said, holding that vulnerabilities can now being attacked as soon as they arise.
“We need to reimagine security” in the AI era, Patel said, noting that AI agents will not only handle tasks locally but will be heading outside to connect to third-party agents, servers and various tools.
“Every agentic action is a routing challenge, a trust decision and a telemetry event,” Patel said. The emergence of agentic AI, he said, is shifting the security and permission focus from “access control” (for us humans) to “action control” for agents that will need to be closely monitored, controlled and, if needed, quickly intercepted.
“People don’t trust these agents right now,” Patel said later during a separate discussion with press and analysts.
These concerns also extend to AI agent identity, which Cisco is addressing with its recent agreement to acquire Astrix Security.
This extends to other types of guardrails and observability metrics, too, including the notion of “tokenomics” – essentially keeping tabs on how many tokens an AI agent could consume. If the agent is found to be overspending on tokens, it could be intercepted and shut down.
Patel suggested that, without guardrails, what a company pays for AI tokens for a year could be consumed by an agent in a week. Assessing such AI agent behavior was a key driver of Cisco’s acquisition of Galileo Technologies.
Cisco’s AI Stack:
Cisco is focused on a vertically integrated platform – starting with its Silicon One platform for data centers and enterprise devices, optics, switches, routers and access points, apps and services, and wrapped by a new Cisco Cloud Control platform announced this week. Though Cisco Cloud Control is able to provide unified access to Cisco’s tools, apps and services, such as Meraki, Catalyst and Splunk, Patel stressed that it will also be able to integrate with third parties and support an open ecosystem. Cisco is starting out with support from 52 partners, including AWS, Google Cloud, NetBrain and ServiceNow.
Telecom Market Transition:
Robbins said Cisco used AI to scan 1.8 billion lines of code in 25 different programming languages over the past eight weeks. Without AI models, that would’ve taken eight years, he said.
Patel described the industry as being at a pivotal moment, moving from chat bots to more advanced agents that function as “digital coworkers.” He noted that “These agents are going to be everywhere.”
That transition suggests telecom networks will increasingly support autonomous machine interactions at scale, with implications that extend beyond bandwidth growth into security, policy control, and distributed systems design. For operators and vendors alike, the strategic question is no longer whether AI will affect the network, but how quickly the network architecture can adapt.
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References:
https://www.lightreading.com/ai-machine-learning/cisco-ai-driving-a-network-supercycle-
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
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Hyperscalers Dominance of Subsea Cable Capacity to Increase in the AI Era
Hyperscalers (AWS, Google, Microsoft, Meta/FB) now dominate global subsea cable capacity. Their share of total international bandwidth has surged from negligible levels in 2010 to approximately 75% today. According to data from TeleGeography, hyperscalers are participating in over two-thirds of all planned submarine cable deployments, with Google alone anchoring eight new systems in the Asia-Pacific (APAC) region. Despite this shift, traditional telecommunications operators remain critical to the subsea ecosystem.
Tier-1 telecom carriers provide the deep terrestrial reach and last-mile connectivity that both regional service providers and large content providers require to access edge markets. However, those network operators must increasingly architect their Wide Area Network (WAN) and long-haul transport infrastructure to integrate seamlessly with these massive hyperscale topologies.
Brian Washburn, Chief Analyst at Omdia’s Telco B2B Solutions Intelligence Service, notes that carriers face intensifying pressure to align their infrastructure with hyperscaler technical requirements. To achieve complete architectural control and establish fully isolated private networks, hyperscalers frequently seek to deploy proprietary optical transport equipment directly within carrier landing stations and co-location facilities. This shift toward self-contained infrastructure creates visibility challenges for the industry. Washburn noted Google’s extensive transpacific cable network as a primary example. Because this hyperscaler traffic is routed over fully private, dark fiber subsea segments, it remains entirely invisible to carrier networks and traditional traffic-modeling metrics, rendering these massive data volumes completely opaque.
TeleGeography’s interactive submarine cable map shows the majority of active and planned international submarine cable systems and their landing stations. Selecting a cable route on the map provides access to data about the cable, including the cable’s name, ready-for-service (RFS) date, length, owners, website, and landing points. Selecting a landing point provides a list of all submarine cables landing at that station.
From a macro perspective, the deployment of next-generation physical infrastructure is increasingly tied to the rollout of raw, rack-scale data center capacity to support emerging AI workloads. Matt Walker, Chief Analyst at MTN Consulting, indicates that while Tier-1 US operators anticipate near-term traffic growth from centralized AI training models, they maintain a cautious, wait-and-see outlook regarding long-term network demand and the broader monetization of distributed inference at the edge. “With agentic, the potential for rapid growth in unexpected parts of the network is real, and it’s not clear how to plan for this,” he said. Operators are worried they will be stuck with the network costs to support “these pricey new AI-enabled services,” he also noted. Telco’s lack of visibility becomes a problem here. Walker stated in his research report: “The industry is flying partially blind. No comprehensive public study of AI traffic volumes, patterns, or growth exists. Nokia, Ericsson, and a handful of others have made partial contributions, but hyperscalers don’t share traffic data. For an industry spending over $600 billion in capex this year, this is a significant planning liability.”
MTN also revealed that telco capex remained subdued in 4Q2025, rising just 0.2% YoY to $86.6B as operators prioritized capital discipline, AI-enabled efficiency, and monetization of prior 5G investments. On an annualized basis, capex declined 0.9% to $295.7B, remaining below the $300B threshold for a second consecutive year. The strongest annualized capex growth rates were recorded by Swisscom (40.7%), Etisalat (40.5%), Airtel (24.4%), SoftBank (10.5%), and Deutsche Telekom (10.3%). The steepest capex declines came from China Telecom (-13.6%), Telefonica (-12.3%), China Unicom (-11.5%), Reliance Jio (-10.8%), and China Mobile (-8.1%).
Regionally, the Americas strengthened its lead in 4Q2025, accounting for 36.5% of global telecom revenues and 36.3% of capex, supported by resilient performance from T-Mobile US, AT&T, and Verizon. Asia’s revenue share moderated to 35.6% and capex share fell to 32.4%. This is notable given that Chinese telcos have been ramping AI and data center spending, while overall capex continues to decline as cuts to radio/hardware spending post-5G more than offset these gains.
References:
https://www.lightreading.com/ai-machine-learning/ai-is-going-to-transform-our-networks
https://www.submarinecablemap.com/
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
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SK Telecom applies digital twins to SK Hynix semiconductor fabs using NVIDIA Omniverse libraries
SK Telecom (SKT) announced today that it has applied digital twins to SK Hynix semiconductor fabs [1.] using NVIDIA Omniverse libraries, optimizing the technology for complex, large-scale manufacturing environments. Digital twins recreate actual factories and equipment in virtual environments, enabling companies to simulate and verify the impact of process changes and equipment layout adjustments in advance. By enabling simulation of a wide range of scenarios in virtual environments, digital twins are gaining attention as a core physical AI technology that reduces trial and error while supporting data-driven decision-making. Last year, SKT completed a proof of concept (PoC) for applying digital twin technology to SK Hynix semiconductor fab. The company plans to proceed with commercialization in phases, aligning with SK Hynix’s roadmap to establish an “Autonomous Fab” by 2030.
Note 1. SK Hynix operates major semiconductor fabrication and packaging sites across South Korea and China, with new multibillion-dollar facilities under development in South Korea and the United States. While its core, multi-billion-dollar fabs are dedicated entirely to semiconductor memory production (DRAM, HBM, and NAND Flash), the company also operates a dedicated, separate pure-play foundry business that manufactures non-memory logic chips for external contract clients. : The main facilities in Icheon, Cheongju, and Yongin are specialized strictly for SK Hynix’s high-volume memory products like High-Bandwidth Memory (HBM), standard DRAM, and NAND flash. These massive facilities do not accept contract manufacturing orders for logic chips from external companies.
The Contract Foundry Business (External Clients): SK Hynix operates a wholly-owned subsidiary called SK Hynix System IC. This arm acts as a dedicated foundry for fabless semiconductor clients.
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Using the NVIDIA Agent Toolkit, SKT has also developed “Agentic Digital Twin Modeling” technology, which automates and intelligently processes diverse data—such as equipment and spatial structures at manufacturing sites—for digital twin environments. This technology enhances the efficiency of data conversion, scene optimization, and performance improvement tasks that arise during the development and operation of digital twins in manufacturing environments.
A virtual factory implementation using SK Telecom’s digital twin platform. /Courtesy of SK Telecom
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SKT is enhancing its platform by integrating NVIDIA Omniverse libraries to improve the loading speed of large-scale Open USD-based 3D scenes, execution performance, and GPU and memory usage efficiency. Through this, the company plans to implement a stable and scalable digital twin environment even in complex manufacturing environments with massive data volumes, such as semiconductor fabs.
“Semiconductor fabs are among the most challenging manufacturing environments, combining massive amounts of 3D data, complex equipment structures, and the need for high-level optimization,” said Mike Geyer, head of industrial digital twins at NVIDIA. “SKT has demonstrated a high level of technical capability in applying and validating NVIDIA Omniverse libraries, as well as the NVIDIA Agent Toolkit in real-world industrial settings within this environment.”
“Through our collaboration with NVIDIA, we have validated that manufacturing digital twins can evolve beyond simple 3D visualization into a physical AI platform capable of understanding and optimizing large-scale 3D manufacturing data,” said Cho Ik-hwan, Head of Physical AI at SKT. “Going forward, SKT will continue to expand its role as a physical AI technology partner with NVIDIA across various industrial sectors, including semiconductors.”
As a network provider equipped with end-to-end AI solutions — from AI infrastructure and models to services — SK Telecom plans to expand and strengthen its business targeting the enterprise and public sectors.
References:
https://www.thelec.net/news/articleView.html?idxno=10930
Inside Amazon’s new data center network architecture: quasi random network topology and passive optical devices
Amazon Web Services (AWS) claims it recently achieved a major breakthrough in Data Center Network (DCN) architecture and has been quietly deploying the new technology in its data centers since late last year. Amazon detailed its new networking design in a paper published May 21st titled “RNG: Flat Data Center Networks at Scale.” RNG, or “resilient network graphs,” is built around a quasi-random topology and new passive optical hardware. It’s a “quasi-random” design that combines elements of traditional, structured data networks with the performance advantages of more random architectures.
The goal is to move off conventional hierarchical “fat-tree” designs toward a flatter, more mesh-like fabric that uses far fewer routers and switches, offers more parallel paths, and therefore delivers higher effective throughput at lower power and capex.
“By essentially flattening the network, we eliminated the bottlenecks that come with traditional networking designs,” Matt Rehder, vice president of AWS Network Engineering, said in an exclusive interview with WIRED. “We think we’re the only ones who have done this at scale. RNG is a great fit for our core demands, but AI training data patterns are far more coordinated and centrally orchestrated, so they don’t approximate a random graph.”
The fact that Amazon is using this in the real world is “remarkable,” said Brighten Godfrey, a computer science professor at the University of Illinois Urbana-Champaign and an expert in networking, who was not involved in Amazon’s research. Godfrey coauthored a seminal 2012 paper on random network graphs, which he says are a “mind-bending problem to solve, in general.”
Classic cloud DCNs use structured topologies (Clos/fat-tree) where paths are highly regular and layered (Top of Rack (ToR)–aggregation–core). By contrast, random-graph theory says the most efficient routing networks are flat random graphs: each node connects to a small random subset of others, creating many short, diverse paths and graceful degradation under failures. The problem has always been practical: random cabling at scale is unmanageable, and routing across a huge random graph is nontrivial.
AWS’s “quasi-random” design essentially mixes determinism with randomness: key structural elements are fixed to keep the cabling and deployment manageable, while enough randomness is retained in the interconnect pattern to get the performance and resilience benefits of random graphs. The physical enabler is a new passive optical device called a ShuffleBox that standardizes how switches connect and internally permutes links so that, when many ShuffleBoxes are wired together, the resulting global topology is quasi-random without having to hand-design every link.
Image Credit: Amazon
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Key architectural pieces and claimed gains:
AWS reports that RNG-based fabrics now serve as the default network architecture for most new AWS data centers, after initial deployments beginning in 2024. The company claims the design:
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Uses roughly 69% fewer routers/switches than traditional fat-tree DCNs, because the network is flatter and relies more on passive optical fanout.
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Delivers up to about 33% higher throughput, due to more independent paths and better load spreading.
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Cuts network equipment power consumption by on the order of 40%, with associated reductions in cooling and operational overhead.
On the control-plane side, AWS developed a routing scheme called Spraypoint. Instead of always following a strict shortest path from source to destination, Spraypoint first “sprays” traffic randomly to neighbors, then directs it via preselected “waypoints” using more conventional shortest-path routing. This hybrid behavior exploits the quasi-random topology to open many more independent paths than standard ECMP-style shortest-path routing would, which in turn improves utilization and resilience under congestion or failures.
Strategic implications:
For AWS’s cloud and AI build-out, this is positioned as a foundational infrastructure advantage: higher bisection bandwidth and lower network energy per bit directly benefit large-scale AI training clusters, storage backends, and multi-tenant cloud workloads. Fewer active devices and more passive optics also translate into lower capex and opex at hyperscale, so AWS is framing this as both a performance and cost/sustainability play that could save billions of dollars and reduce CO₂ emissions over time.
From a networking-theory standpoint, this is notable as one of the first reported at-scale, production deployments of a flat random-graph-inspired topology in a hyperscale DCN, rather than a purely academic or lab system.
In a quasi-random topology like AWS’s RNG fabric, the impact on latency and jitter comes from three main effects: path length distribution, load spreading, and failure behavior.
Baseline latency: path lengths and device count:
In a traditional Clos/fat-tree, average latency is dominated by a fixed number of stages (ToR → agg → core → agg → ToR), so hop count is tightly controlled but you pay for many active devices. A quasi-random, flat graph replaces that rigid hierarchy with many short, irregular paths; on average, shortest paths between any two switches are similar or slightly shorter in hop count than in a fat-tree, and there are fewer active routers in the path because the architecture offloads fanout to passive optics. That tends to keep or slightly reduce median/mean latency per flow, especially under moderate load, because packets traverse fewer serialized queueing points even if the physical graph looks “messier.”
Jitter: congestion and path diversity:
Jitter is driven much more by variable queueing delay than by fixed propagation or serialization. In a quasi-random fabric with many alternate paths and a load-balancing scheme like Spraypoint (random spray + waypoint-based shortest paths), flows can be spread more evenly across the network, reducing hot spots and thus reducing the variance of queueing delay across packets. That can lower jitter compared with a Clos under the same aggregate load, because the system is less likely to funnel many flows through the same few congested uplinks or spine devices.
However, because the routing intentionally uses many different paths, per-flow packet reordering becomes more likely unless constrained by per-flow hashing or waypointing, which can show up as effective jitter at higher layers. AWS’s description of Spraypoint suggests they mitigate this by using waypoints and policy to preserve some path structure, so you get the diversity benefits without unconstrained per-packet spraying.
Under failure and high load:
Where quasi-random really helps latency/jitter is under failure and partial congestion. In a Clos, link or spine failures can force large sets of flows to converge on a smaller subset of remaining equal-cost paths, driving up queueing delay and jitter nonlinearly. In a resilient random-graph-style fabric, node/edge failures simply remove a few edges from a highly connected graph; there are typically many alternative short paths, so the increase in hop count and queueing pressure is smaller and more diffuse. That tends to keep tail latency and jitter (P99, P99.9) better behaved, even if median latency looks similar to a Clos at low load.
So, qualitatively: median latency is roughly comparable to a well-designed Clos, sometimes better due to fewer active stages; jitter and tail latency should improve under realistic, bursty load and failure scenarios, provided the routing stack is designed to limit packet reordering.
Summary and Conclusions:
Quasi-random data center topologies like AWS’s RNG fabric replace rigid Clos/fat-tree hierarchies with a flatter, graph-like network that preserves short path lengths while dramatically increasing path diversity, which tends to hold median latency roughly steady or slightly better by reducing the number of active, queueing devices per path and offloading fanout to passive optics. They primarily improve jitter and tail latency by spreading flows across many alternative routes so congestion is less concentrated, making queueing delays less bursty and keeping P99/P99.9 behavior more stable under failures and hot spots, provided the routing layer (for example, AWS’s Spraypoint approach) constrains packet reordering through way pointing or per-flow consistency.
In conclusion, quasi-random fabrics are less about shaving a few microseconds off baseline latency and more about delivering more predictable end-to-end performance—especially for east–west, latency-sensitive cloud and AI workloads—by trading rigid structure for statistically robust, highly connected graphs that degrade more gracefully when links, nodes, or traffic patterns become pathological.
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References:
https://arxiv.org/pdf/2604.15261
https://www.wired.com/story/amazon-aws-ceo-matt-garman-ai-agents/
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Network X Americas: AT&T and Comcast reveal huge AI impact on network operations
Echoing a recent Cisco report, telecom leaders at the Network X Americas conference (held in Irving, TX last week) noted that AI is fundamentally shifting traffic patterns while having a very positive impact on network operations. With billions of connected sensors and devices (like autonomous vehicles generating 20GB of data per day), operators are forced to prioritize uplink capacity and low latency over traditional consumer downlink traffic.
AT&T’s network CTO, Yigal Elbaz, cited the robo-taxi as a bellwether for how AI is affecting network traffic. Each Waymo vehicle generates about 20 gigabytes of data per day, roughly 30 times the amount a typical mobile user consumes. Most of that traffic flows from the car to the cloud. “Every other week,” Elbaz noted, “a new flavor of a frontier AI model drops on us.”
“We already have about 700,000 changes on a daily basis in our network made by AI,” said Elbaz, noting that AT&T has built a proprietary foundation AI model because standard large language models (LLMs) don’t understand KPIs, network alarms or fiber deployment specifics. He cited a 20-25% cost reduction and 12-15% better results than general-purpose models.
In his keynote speech, Comcast EVP and Chief Network Officer Elad Nafshi described 200 edge compute centers capable of self-healing 77% of network events. He touted AI chipsets close enough to customers’ homes to pinpoint outside plant faults with 99.2% precision, and a partnership with Nvidia to push that edge platform further.
Nafshi highlighted the gap in network provider promises vs delivery with a hypothetical small-business use case example. A pizza shop operator, could materially change workflow and productivity if the service provider delivered an AI-enabled concierge—built on a task-optimized small language model—to manage order intake and customer interaction. In that scenario, the network evolves from a passive access pipe into an application-aware platform that augments business operations. The concept is credible from a technical standpoint, but remains largely theoretical until operators can effectively reach and educate SMB customers who still perceive connectivity as a fixed monthly expense.
Both AT&T and Comcast Israeli executives said this was more than modernization and discussed the changes in what a network does. The network is now a platform, not a pipe. Today’s network learns, adapts and increasingly acts on behalf of its customers. But I can’t help but wonder if the customers know… or if that network value will ever trickle down to the customers who need it most.
In a keynote panel session titled, ” Convergence in action – Competing, scaling and winning in the AI-driven connectivity market,” Josh Goodell, AT&T’s VP of Broadband and Converged Product Development, framed the company’s objective as becoming “the greatest simplifier of our customers’ lives” while instilling “connectivity confidence.” That positioning is notable for a sector that has historically under-communicated its value proposition beyond basic service metrics.
The broader industry narrative appears to be shifting. Historically, go-to-market strategies emphasized throughput benchmarks and promotional pricing. As Omdia’s Ruth Brown (panel session moderator) observed, packaging has been largely defensive, optimized around billing constructs rather than differentiated user experience. The emerging model instead centers on networks that operate contextually and autonomously—delivering value in ways that are largely invisible to the end user.
Derek Peterson, CTO of Boingo Wireless, articulated a parallel issue in venue networks, describing the “stadium problem.” Operators dimension infrastructure for peak ingress and then underutilize that capacity once users are inside the venue. The architectural question is no longer solely about capacity provisioning, but about service-layer innovation on top of that capacity. At Petco Park, Boingo leveraged existing network assets to enable pre-entry commerce, driving incremental revenue before fans pass through the gates. The infrastructure was not the constraint; the limiting factor was identifying and executing on higher-order use cases.
A similar disconnect persists in the industry’s framing of the digital divide. AT&T’s John Stankey and others have suggested the gap is nearing closure, citing expanded fiber footprints and fixed wireless access. While coverage metrics have improved, the divide has never been purely a function of infrastructure availability. Adoption is equally constrained by affordability and, critically, by perceived value. If connectivity continues to be positioned as a commoditized utility, the most economically vulnerable segments—those with the greatest need for digital enablement—remain the least likely to engage.
This is particularly relevant in an AI-driven economy. The users and small enterprises that could benefit most from intelligent, network-delivered services are often those least exposed to the evolving capabilities of the platform. The industry risks over-indexing on measurable deployment milestones while under-communicating the functional value of next-generation networks.
The Network X keynotes underscored that the technical roadmap is largely in place. Network operators are advancing toward networks capable of real-time traffic learning, proactive cybersecurity at the edge, and highly personalized in-home connectivity experiences. These capabilities represent a more compelling value proposition than traditional service tier comparisons.
However, the central challenge remains go-to-market execution. The industry has demonstrated that it can architect and deploy these capabilities at scale. It has yet to establish a clear, effective framework for articulating that value to end users and enterprises in a way that drives adoption.
As a final observation, the broader telecom ecosystem—illustrated by developments such as autonomous vehicle platforms—already depends on AI-enabled, highly distributed network intelligence. While the underlying infrastructure is incrementally aligning with these requirements, the industry dialogue around its broader economic and societal implications remains underdeveloped.
References:
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
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Hurricane Electric establishes carrier neutral PoP at Lincoln Data Centers, Nebraska
Fremont, CA headquartered Hurricane Electric is a leading Internet backbone [1.] and colocation provider specializing in colocation, dedicated servers, direct Internet connections and web hosting. Hurricane Electric operates its own global network, running multiple OC192s, OC48s and Gigabit Ethernet. The ISP offer the following services:
- IP Transit [2.]: Wholesale internet connectivity ranging from 100 Mbps to massive network speeds over IPv4 and IPv6.
- Colocation: Physical rack and cabinet space in their carrier-neutral data centers (primarily in Fremont and San Jose, California) for customer-owned servers.
- Dedicated Servers: Single-tenant servers for businesses seeking dedicated safety, hardware, and performance.
- Web Hosting: Virtual hosting accounts for running and maintaining websites.
Note 1. Hurricane Electric claims to have the world’s largest IPv6-native Internet backbone. President Mike Leber founded Hurricane Electric in a garage in 1994. Hurricane Electric now operates an international backbone network and owns several datacenters, including a new 200,000 square-foot Fremont 2 colocation facility.
Note 2. IP transit is a commercial, wholesale service where an upstream Internet Service Provider (ISP) allows network traffic to travel through its backbone infrastructure to reach the rest of the global internet
Image Credit: Hurricane Electric
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Today, the company announced that it has established a new Point of Presence (PoP) at Lincoln Data Centers. The new PoP is located at 206 South 13th Street, Lincoln NE.
Lincoln Data Centers provides a carrier-neutral interconnection and colocation environment purpose-built for organizations with expanding connectivity requirements. The facility combines diverse fiber infrastructure, access to regional and long-haul carriers, low-friction interconnection through meet-me-room capabilities, and flexible deployment options that support scalable growth. Positioned in the geographic center of the United States, Lincoln Data Centers serves as an efficient regional hub for enterprises, cloud platforms, content providers, and network operators seeking resilient, low-latency connectivity across the Midwest and beyond.
The central United States continues to play an increasingly important role in digital infrastructure development due to its geographic advantages, expanding fiber ecosystems, growing enterprise technology adoption, and proximity to major population and business centers. Nebraska’s favorable business environment and central location make Lincoln an attractive market for organizations seeking resilient, low-latency connectivity and diversified network routes.
The new PoP improves fault tolerance, load balancing, and congestion management for next-generation IP connectivity services throughout the region. Customers of Lincoln Data Centers can now access Hurricane Electric’s extensive IPv4 and IPv6 backbone through 100GE (100 Gigabit Ethernet), 10GE (10 Gigabit Ethernet), and GigE (1 Gigabit Ethernet) ports.
“We are pleased to expand Hurricane Electric’s presence in the Midwest with this new Point of Presence at Lincoln Data Centers,” said Mike Leber, President of Hurricane Electric. “Lincoln’s central location, strong business climate, and growing digital infrastructure ecosystem make it an ideal site to support customers requiring reliable, high-capacity Internet connectivity across the region.”
With this deployment, organizations in and around Lincoln can exchange IP traffic directly with Hurricane Electric’s vast global network, which supports more than 40,000 BGP sessions with over 10,500 networks across more than 320 major exchange points worldwide.
The addition of this PoP reflects Hurricane Electric’s ongoing investment in expanding connectivity throughout North America and its commitment to delivering low-latency, highly resilient Internet connectivity for enterprises, cloud providers, research institutions, content platforms, and service providers.
About Hurricane Electric:
Fremont, California-based Hurricane Electric operates its own global IPv4 and IPv6 network and is considered the largest IPv6 backbone in the world. Within its global network, Hurricane Electric is connected to more than 320 major exchange points and exchanges traffic directly with more than 10,500 different networks. Employing a resilient fiber-optic topology, Hurricane Electric has five redundant 100G paths crossing North America, four separate 100G paths between the U.S. and Europe, and 100G rings in Europe, Australia and Asia. Hurricane also has a ring around Africa, and a PoP in Auckland, NZ. Hurricane Electric offers IPv4 and IPv6 transit solutions over the same connection. Connection speeds available include 100GE (100 gigabits/second), 10GE, and gigabit ethernet. Additional information can be found at http://he.net.
References:
Broadcom with Samsung Electronics: Integrated 5G and Wi-Fi 8 FWA Platform
Broadcom has announced a collaboration with Samsung Electronics Co., Ltd. to develop a reference platform for fixed wireless access (FWA) deployments, combining Broadcom’s BCM6776 Wi-Fi system-on-chip (SoC) with Samsung’s B1320 5G modem. The platform is designed to integrate 3GPP Release 17 5G connectivity with emerging IEEE 802.11bn (Wi-Fi 8) capabilities, supporting convergence between wide-area and local-area broadband technologies.
The reference design targets global FWA use cases, where operators seek to deliver high-throughput broadband services using 5G radio access in conjunction with advanced in-home wireless distribution. By aligning 5G and Wi-Fi 8 performance characteristics, the platform addresses requirements for sustained throughput, low latency, and reliability under variable radio conditions. The design also emphasizes scalability for high-volume deployments, with integration intended to reduce system complexity and cost.
The Broadcom BCM6776 is a tri-band Wi-Fi 8 SoC designed for residential and small enterprise access points. It integrates a quad-core Arm-based network processor with Wi-Fi 8 radio functionality in a single device. The SoC supports 2-stream operation with 40 MHz channels in the 2.4 GHz band, and 4-stream operation with up to 160 MHz channels in the 5 GHz and 6 GHz bands. This configuration enables multi-gigabit aggregate throughput while maintaining compatibility with evolving IEEE 802.11bn features.
Integration of compute and radio subsystems within a single SoC reduces bill of materials (BOM) requirements and simplifies hardware design. Power efficiency is also improved relative to prior architectures that relied on discrete components, supporting deployment in thermally constrained residential environments.
Image Credit: Broadcom
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The Samsung B1320 modem is a 5 nm-class integrated 5G chipset compliant with 3GPP Release 17. It supports peak downlink throughput of up to 3.43 Gbps and uplink throughput of up to 1.17 Gbps, depending on deployment configuration. The modem incorporates a quad-core Arm CPU, RF transceiver, power management functions, and a global navigation satellite system (GNSS) receiver.
The platform further supports non-terrestrial network (NTN) operation, including both NR-NTN and NB-NTN modes, enabling compatibility with satellite-based extensions of 5G coverage.
The combined architecture is designed to sustain end-to-end throughput between the 5G access link and the in-home Wi-Fi network, minimizing bottlenecks between the wide-area and local domains. This is particularly relevant for FWA deployments, where performance is constrained by both radio access conditions and in-premises distribution efficiency.
By providing a pre-integrated reference design, the platform enables original equipment manufacturers (OEMs) and operators to accelerate development cycles and standardize system performance across deployments. This approach supports broader adoption of FWA as a complement to fixed broadband infrastructure, particularly in scenarios where fiber deployment is limited or economically constrained.
“At Computex 2026, we are highlighting that the future of home internet can be both accessible and affordable,” said Joonsuk Kim, Executive Vice President and Head of CP Development at Samsung Electronics. “This platform is designed to deliver reliable performance across a wide range of environments, helping operators bring high-quality connectivity experiences to subscribers.”
“Broadcom is proud to lead the Wi-Fi 8 transition alongside Samsung and our valued ODM partners,” said Vijay Nagarajan, Vice President of Marketing, Wireless and Broadband Communications Division at Broadcom. “This partnership is a game-changer for the FWA market. The combination of Wi-Fi 8 and 5G prioritizes coordinated reliability, giving operators a tool that delivers a consistent experience to every corner of the home.”
Product Features:
The Samsung B1320 is a broadband-optimized 5G platform with the following features:
- 3GPP Release 17
- 4Rx/2Tx radio chain support
- Power Class 1.5 support (TDD bands)
- LPDDR4x / LPDDR5x support
- 1.6 GHz quad-core ARM Cortex-A55 CPU
- 5 Gbps USXGMII, PCIe Gen 3, USB 2.0
- GNSS
- NR-NTN and NB-NTN support for n255 and n256 (L- and S-bands)
The Broadcom BCM6776 is a single-chip Wi-Fi SoC and multi-band radio supporting the following:
- High performance quad-core CPU complex
- Dedicated network processing engine freeing the CPU complex for operator-specific applications and utilities
- Integrated 2×2 2.4 GHz and 4×4 5 GHz and 6 GHz Wi-Fi 8 MAC/PHY/Radio functionality, simplifying system design and lowering cost
- On-chip 2.4 GHz power amplifiers (PAs) and support for third-generation digital pre-distortion for reduced external components and improved RF efficiency
- Versatile memory controller supporting DDR4, LPDDR4, DDR5, and LPDDR5
- Dual PCIe Gen3 controllers to enable simultaneous tri-band applications with a single additional chip
- Integrated multi-gig PHY
A Global Ecosystem of Support:
The launch is supported by the world’s leading original equipment manufacturers (OEMs), who are already integrating the B1320 / BCM6776 platform into their next-generation gateway portfolios.
“HUMAX Networks is delighted to pioneer the next-generation 5G CPE market alongside global technology leaders Broadcom and Samsung. At the recent MWC 2026, we successfully showcased the industry’s first Wi-Fi 8 solution, which integrates Samsung’s cutting-edge 5G technology with Broadcom’s next-generation silicon. Through our ongoing partnership, we remain committed to driving market innovation and consistently delivering top-tier experiences and innovative devices to our global customers,” said Jerry Lee, CEO of Humax Networks.
“We are delighted to collaborate with Broadcom and Samsung to develop our next generation Wi-Fi 8 gateway addressing MSO CBU/FWA market. This solution is capable of delivering a smarter, more secure, and future-ready network optimized solution to meet MSO/FWA customers’ increasing demands of cost competitive 5G NR connectivity,” said Johnson Hsu, SVP & GM of WNC’s Connectivity & Solutions BG.
Availability:
Global carrier trials and OEM sampling of the Samsung B1320 / Broadcom BCM6776 FWA platform are underway.
About Broadcom:
Broadcom Inc. (NASDAQ: AVGO) is a technology leader that designs, develops, and supplies semiconductors and infrastructure software for global organizations’ complex, mission-critical needs. Broadcom combines long-term R&D investment with superb execution to deliver the best technology, at scale. Broadcom is a Delaware corporation headquartered in Palo Alto, CA. For more information, visit www.broadcom.com.
Broadcom, the pulse logo, and Connecting everything are among the trademarks of Broadcom. The term “Broadcom” refers to Broadcom Inc., and/or its subsidiaries. Other trademarks are the property of their respective owners.
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References:
Extreme Networks deploys Wi‑Fi 7 (IEEE 802.11be) at University of Florida’s “Swamp”
Ookla: FWA Speed Test Results for big 3 U.S. Carriers & Wireless Connectivity Performance at Busy Airports
Aviat Networks and Intracom Telecom partner to deliver 5G mmWave FWA in North America
T-Mobile’s growth trajectory increases: 5G FWA, Metronet acquisition and MVNO deals with Charter & Comcast
Analysis: AT&T 1Q-2026 results: increased fiber penetration, FWA momentum, D2D deals, and mobile/home internet bundles
Latest Ericsson Mobility Report talks up 5G SA networks and FWA
Analysis: Broadcom’s end-to-end 50G PON Edge AI portfolio with WiFi 8 support
Broadcom has announced the BCM68850, a 50G ITU-T PON home gateway system-on-chip (SoC) that integrates a neural processing unit (NPU) and provides native support for emerging Wi-Fi 8 (IEEE 802.11bn) capabilities. The device extends the evolution of broadband access silicon toward higher-capacity passive optical network (PON) standards while maintaining alignment with next-generation in-home wireless technologies. Broadcom is currently sampling the BCM68850 and BCM55050 to its early access customers and partners. Please contact your local Broadcom sales representative for samples and pricing.
The integration of NPU functionality within the gateway reflects an architectural trend toward distributing compute resources closer to the network edge. This enables localized processing of AI-driven workloads within customer premises equipment (CPE), which can reduce upstream bandwidth demand and improve responsiveness for latency-sensitive applications.
Migration to 50G PON, as defined within ongoing ITU-T standardization efforts (e.g., Higher Speed PON), provides increased access capacity and improved latency characteristics relative to earlier generations such as XGS-PON. These enhancements support more deterministic service delivery, particularly in environments where traffic patterns are becoming increasingly burst-oriented and driven by compute-intensive applications.
Image Credit: ADTRAN
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In residential networks, traffic is expected to increasingly consist of short-duration, high-throughput bursts associated with edge processing, real-time analytics, and interactive services. A 50G PON gateway can accommodate these patterns by transmitting high-density payloads over sub-millisecond intervals, after which shared channel resources are rapidly released for other users. This behavior contributes to improved utilization efficiency on shared fiber infrastructure.
Low-latency and low-jitter performance are important for emerging application classes, including distributed AI inference, synchronized edge workloads, and multi-stream ultra-high-definition media. These requirements extend across both the access network and the in-home wireless domain, reinforcing the need for coordinated evolution of PON and Wi-Fi technologies.
From a deployment perspective, introduction of 50G-capable CPE provides operators with additional capacity headroom and supports alignment with future service requirements. Coupled with advancements in IEEE 802.11bn, this approach enables continued scaling of residential broadband performance while maintaining consistency across access and local network segments.
BCM68850 – 50G PON Edge AI Gateway SoC:
The BCM68850 is a standalone 50G PON Gateway SoC that provides an industry-standard ITU-T path for operators to future-proof their networks. The device features:
- High-Performance Application Engine: A dedicated CPU for third-party and operator applications leveraging industry available middleware.
- Integrated Neural Engine: A dedicated NPU that accelerates Edge AI inference, reducing cloud latency and enhancing data privacy by keeping sensitive information on premises.
- Symmetric 50G Performance: Delivers full 50G throughput to meet the insatiable appetite for reliable, multi-gigabit bandwidth.
- Wi-Fi 8 Ready: Native compatibility with Wi-Fi 8 standards to ensure the highest reliability and real-world consistency at the broadband edge.
- Intelligent Self-Healing: Enables operators to implement real-time anomaly detection and predictive bandwidth optimization, reducing OpEx and improving ARPU.
- Advanced Security: Incorporates enhanced security algorithms, including Post-Quantum Cryptography (PQC).
“The BCM68850 is a defining milestone for global fiber networks; we are reshaping the broadband edge as the central intelligence hub of the home,” said Philip Radtke, vice president of product marketing for Broadcom’s Wireless and Broadband Communications Division. “This flagship SoC joins our established lineup of NPU-accelerated fiber, cable, set-top box, and Wi-Fi solutions, ensuring operators can efficiently deploy edge-intelligent broadband regardless of the access medium and extend that intelligence all the way to the edge.”
“With ever increasing consumer and enterprise demand for bandwidth and ultra-reliable connectivity, operators are upgrading the Central Office and End Points with 50G PON capability. Next-generation solutions such as Broadcom’s BCM68850 SoC are critical to unlocking the value of this investment by future-proofing the network edge and ensuring high service levels at every node and premise,” said Jaimie Lenderman, practice leader for Optical, IP, and Broadband Infrastructure market research at Omdia.”By establishing a true end-to-end 50G pipe, operators can deliver the massive capacity and deterministic low latency required to support the rigors of the imminent Wi-Fi 8 deployment cycle.”
This end-to-end 50G offering completes the path from Broadcom’s BCM68660 OLT to the edge, providing a seamless and technically robust ecosystem comprising the BCM55050 ONT or the BCM68850 CPE gateway. This architecture introduces a new level of efficiency by optimizing CPU and memory resources for the AI era, ensuring that the home gateway can handle the massive data pipes required for the next decade of digital innovation.
About Broadcom:
Broadcom Inc. (NASDAQ: AVGO) is a technology leader that designs, develops, and supplies semiconductors and infrastructure software for global organizations’ complex, mission-critical needs. Broadcom combines long-term R&D investment with superb execution to deliver the best technology, at scale. Broadcom is a Delaware corporation headquartered in Palo Alto, CA. For more information, visit www.broadcom.com.
Broadcom, the pulse logo, and Connecting Everything are among the trademarks of Broadcom. The term “Broadcom” refers to Broadcom Inc., and/or its subsidiaries. Other trademarks are the property of their respective owners.
References:
https://www.broadcom.com/company/news/product-releases/64341
2026 Fiber Connect Keynote: “The Future of Fiber Optics: AI and the Quantum”
Nokia and Google Fiber trial 50G PON – first in the U.S.
HKT is first to deploy 50G PON technology in Hong Kong
Türk Telekom and ZTE trial 50G PON, but commercial deployment is not imminent
Ooredoo Qatar is first operator in the world to deploy 50G PON
Highlights of FiberConnect 2024: PON-related products dominate
Fiber Connect 2023: Telcos vs Cablecos; fiber symmetric speeds vs. DOCSIS 4.0?
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Executive Summary:
Consumer-driven AI traffic [1.] currently represents a marginal share of aggregate Internet traffic. However, accelerating adoption of agentic AI is expected to materially reshape traffic composition over the next decade. In its “AI Impact on Wide Area Networks” report, Cisco projects that AI will emerge as the dominant driver of network traffic growth. As consumer AI adoption approaches “near-universal usage,” AI and agentic AI are forecast to increase consumer-driven network traffic by approximately 6.6× by the mid-2030s (see chart below).
Cisco estimates that this AI expansion will account for roughly 63% of incremental traffic growth relative to non-AI scenarios. The study focuses specifically on WAN implications, rather than data center or GPU infrastructure, and provides guidance on network design and capacity planning. Methodologically, the report integrates real-world traffic observations (via Cisco Crosswork Assurance User Experience), third-party industry datasets, and controlled laboratory evaluations of AI agents to characterize how AI-generated traffic diverges from conventional web traffic patterns.
Token-consumption data shows nearly 10x year-over-year growth, while in some service provider measurements Cisco is seeing ~4x growth in just eight months. Sustained growth at these rates means AI traffic will become a meaningful component of overall network traffic by 2035.
Note 1. Consumer AI traffic has a few defining technical traits: it is still dominated by short text-based exchanges, but it is becoming more stateful, more upstream-heavy, and more latency-sensitive as users move from simple prompts to agentic workflows and multimodal interactions. Today’s consumer AI traffic is still overwhelmingly text-oriented, which is one reason the aggregate bandwidth impact remains modest despite rapid adoption. Comcast’s network observation is a useful real-world proxy: 97.1% of AI traffic was text-based, while images accounted for 2.6% and video only 0.3%. The key technical implication is that current traffic volumes are often limited more by conversation frequency and session behavior than by very large payloads, though that changes quickly as users adopt image, audio, and video generation.
Although AI inference traffic is currently “negligible” relative to dominant categories such as video streaming, Cisco projects it will comprise approximately 25% of total network traffic by 2035 (see chart below). At that point, AI traffic is expected to represent a “meaningful component” of overall network load. Importantly, AI-generated traffic exhibits distinct characteristics: inference flows are approximately twice the duration of typical web transactions, demonstrate higher upstream bandwidth demand, and operate at “software speed” rather than human interaction rates.
The emergence of AI agents as “power users” further amplifies these dynamics. Cisco notes that agent-executed tasks can generate up to 450% more traffic per task compared to human-driven interactions. This shift is expected to drive operator adoption of “flow-aware network and security systems” as traffic patterns become increasingly machine-driven and less predictable.
Cisco’s broader framing is that AI traffic “isn’t just adding traffic,” but is changing the shape of traffic, with inference flows running about twice as long as typical web transactions and, in some cases, generating up to 450% more traffic per task when an agent executes the workload. AI inference sessions tend to hold resources longer, create more sustained flows, and push operators to think in terms of flow-aware behavior rather than only peak-throughput sizing. Cisco also notes that about 9% of AI inference flows carry more upstream than downstream traffic, versus about 0.5% for typical web traffic, which is a meaningful shift for access and broadband networks. Cisco reports that approximately 9% of AI inference flows are upstream-dominant, compared to roughly 0.5% for traditional web traffic, with this divergence expected to widen alongside increased agentic AI utilization. In parallel, latency sensitivity is anticipated to become a more critical performance parameter for AI-driven applications.
Latency and symmetry:
AI traffic is also more sensitive to latency than many ordinary consumer web transactions because the user experience is often conversational and interactive, with the expectation of near-immediate turn-taking. Cisco describes AI inference as operating at “software speed” rather than human speed, which means small delays can be more noticeable and operationally important. At the same time, upstream demand becomes more significant because prompts, context, attachments, and agent-generated actions can increase return-path traffic, especially as multimodal inputs and agentic tool use expand.
Multimodal growth:
The biggest step-up in technical impact comes when consumer AI shifts from text-only prompting to multimodal generation and agent-driven workflows. In those cases, each task can involve multiple model calls, retrieval steps, tool invocations, and richer media payloads, which expands both flow count and bytes per session. Cisco’s study suggests that this is why AI traffic will increasingly require “flow-aware network and security systems,” because the traffic profile is not just larger, but structurally different from conventional browsing.
Infrastructure Implications:
Telecom infrastructure is becoming “increasingly intertwined with hyperscale infrastructure, not because operators are leading AI investment, but because they are becoming part of the ecosystem that supports it,” analyst firm MTN Consulting said in an April 27th research note. “Demand for optical transport, data-center interconnect, and edge infrastructure is rising as telecom networks carry growing volumes of cloud and AI-driven traffic,” the firm said.
“AI network traffic is already reshaping infrastructure needs. What we are seeing is clear: AI isn’t just adding traffic. It’s changing the shape of traffic,” Javier Antich, principal product management engineer in the CTO office of Cisco’s provider connectivity group, and Gurudatt Shenoy, SVP, product management, provider connectivity, explained in this blog post.
These shifts are beginning to influence access network evolution. Fiber networks already provide relatively symmetric throughput and low latency, while cable operators are advancing similar capabilities through DOCSIS upgrades. Mid-split and high-split architectures increase upstream spectrum allocation, enabling more balanced capacity profiles. Concurrently, Tier 1 operators such as Comcast and Charter Communications are introducing low-latency enhancements within DOCSIS networks.
Operational data reflects early-stage impacts. Comcast Chief Network Officer Elad Nafshi noted at the Cable Next-Gen event in March that approximately 97.1% of AI traffic on Comcast’s network remains text-based, with images accounting for 2.6% and video just 0.3%, indicating that bandwidth-intensive multimodal AI traffic has yet to scale materially.
Network design impact:
For broadband and access networks, the immediate engineering issues are upstream traffic capacity, queue behavior, and latency consistency rather than raw total throughput alone. Symmetry upgrades (such as DOCSIS mid-split and high-split for MSOs), along with low-latency capabilities, are relevant because consumer AI creates more return-path pressure and more time-sensitive sessions. In other words, the challenge is not simply to carry more bytes; it is to carry more interactive sessions with predictable performance, especially as multimodal and agentic usage scales.
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References:
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency
Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance
Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips
CES 2025: Intel announces edge compute processors with AI inferencing capabilities
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
- Gigawatt-Scale Power and Liquid Cooling: Next-generation AI clusters require unprecedented power density, often exceeding 40kW to 100kW per rack. Telcos cannot simply drop these into existing facilities; they require entirely new or heavily retrofitted data centers featuring advanced liquid cooling architectures to prevent thermal throttling.
- The Fragmented Edge vs. Centralized Fortresses: Operators are realizing that centralized hyperscale data centers (like AWS or Azure clusters in Virginia) cannot support latency-sensitive “Physical AI” or real-time agentic workflows. To make AI-native networking work, carriers must deploy high-density compute racks directly at the network edge, a highly complex and capital-intensive roll-out.
- Neutral Interconnection Hubs: Multi-cloud setups and distributed training workloads are putting immense pressure on backbones. The expansion rate of neutral interconnect hubs (like Equinix and Digital Realty) is directly gating how fast enterprises and telcos can orchestrate data between fragmented training clusters and edge inference nodes.
- Rack-scale architecture is rapidly emerging as the primary deployment unit as enterprises transition from discrete servers to fully integrated systems capable of supporting the power density, thermal constraints, and interconnect requirements of production-scale AI workloads.
Image Credit: AMD
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AI data centers supporting telecom networks require fundamentally different power and cooling infrastructure compared to legacy enterprise facilities. The transition to generative AI and real-time edge processing has pushed power density per rack from an average of 5–10 kW up to 40–100+ kW.
Dell Technologies Inc. has been strategically aligning its portfolio to this shift, and at Dell Technologies World 2026, the company introduced an expanded PowerRack portfolio that integrates compute, networking, and storage within a unified rack-scale platform. This evolution underscores a broader transition in system design priorities—from server-centric architectures to tightly coupled, rack-level systems—driven by the escalating demands of AI infrastructure. As Arun Narayanan, senior vice president of compute and networking product management at Dell, indicated, increasing power density and system complexity are making rack-level architectural optimization not just advantageous, but essential.
“Go back two years ago, the largest, most powerful rack was 80 kilowatts,” Narayanan said. “Come to Vera Rubin, you’re going to get racks of 235 kilowatts, and then get to the next generation of Rubin Ultra and Kyber, you’re going to very quickly get to one megawatt racks. You have to fundamentally redesign everything from power distribution to cooling.”
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- Medium-Voltage Power Distribution: Traditional facilities step utility power down to 480V AC far from the rack. High-density AI data centers run medium-voltage or power directly down to the row or container level before stepping down. This minimizes conduction losses through the heavy copper busbars.
- The Move to 48V DC Busbars: Within the server chassis, power shelf architectures are shifting from traditional 12V DC distribution to DC busbars. A delivery architecture reduces the current required to deliver the same wattage by a factor of four. Resistive power loss occurs when electrical energy is converted into heat due to the inherent opposition to current flow in a conductor. The formula (P{loss} = I^2 R dictates that this power dissipation is highly sensitive to current changes. Therefore, cutting the current to one-fourth reduces internal rack heat and conduction power losses by 93.75%
- Grid Interconnection and Substation Constraints: A single rack-scale AI cluster (such as a cluster of 32 or 64 interconnected nodes) can easily pull 2 to 3 Megawatts (MW). Operators are bypassing traditional local distribution grids entirely. They are building dedicated on-site substations tied directly to transmission-level lines to guarantee upstream capacity.
[ Liquid Cooling Architectures for AI Racks ]
┌───────────────────────────┐ ┌───────────────────────────┐
│ Direct-to-Chip │ │ Immersion Cooling │
├───────────────────────────┤ ├───────────────────────────┤
│ Closed loop micro-channels│ │ Entire server submerged │
│ bolted directly onto GPUs │ │ in dielectric fluid tank │
│ │ │ │
│ [ GPU ] ──► [ Liquid] │ │ ┌───┐ ┌───┐ ┌───┐ │
│ Cold Plate Coolant │ │ │GPU│ │CPU│ │RAM│ │
│ Circuit Circuit │ │ └───┴─┴───┴─┴───┘ │
└───────────────────────────┘ └───────────────────────────┘
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- Direct-to-Chip (Cold Plate) Cooling: This is the primary architecture for 2026 deployments. A closed-loop copper block with micro-channels is bolted directly onto high-thermal-flux components like the GPU or CPU. A specialized dielectric or water-glycol fluid circulates through the block. This absorbs heat directly from the silicon via conduction and pumps it away to a secondary heat exchanger.
- Immersion Cooling (Single-Phase and Two-Phase):
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- Single-Phase: The entire server blade is submerged in a bath of non-conductive, hydrocarbon- or synthetic-based dielectric fluid. The fluid circulates through the chassis via natural convection or pumps to remove heat.
- Two-Phase: The dielectric fluid has a low boiling point (\(50^{\circ }\text{C}\)). The heat from the chips boils the fluid into a vapor. The vapor rises to a condenser coil at the top of the sealed tank, condenses back into liquid, and falls back into the pool. This utilizes the latent heat of vaporization, making it highly efficient.
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- Cooling Distribution Units (CDUs): High-density loops rely on CDUs to act as the barrier between the internal facility water loops (which can be lower quality) and the ultra-pure, treated water circuit flowing directly through the server cold plates.
References:
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Lumen Technologies to connect Prometheus Hyperscale’s energy efficient AI data centers
