Month: June 2026
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-
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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/
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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
