Author: Alan Weissberger
Analysis & Implications of the Communications Cybersecurity Information Sharing and Analysis Center (C2 ISAC)
The Communications Cybersecurity Information Sharing and Analysis Center (C2 ISAC), announced today, is a private sector-only nonprofit dedicated to strengthening defenses across the U.S. telecommunications industry. The founding members of C2 ISAC are: AT&T, Charter Communications, Comcast, Cox Communications, Lumen Technologies, T-Mobile, Verizon, and Zayo. The board of the nonprofit organization will comprise the chief information and security officers from each of the eight network operators, led by AT&T CISO Rich Baich as chairman.
The coalition represents a strategic imperative for major network operators to build a unified, rapid-response network to counter sophisticated, AI-driven infrastructure attacks and state-sponsored espionage. It is a meaningful structural change in how the U.S. telecom sector approaches cybersecurity—especially under pressure from increasingly coordinated, AI-enabled threats and nation-state activity.
Traditional ISACs (Information Sharing and Analysis Centers) already exist across sectors—financial services, energy, healthcare—but telecom has historically been more fragmented in how it shares threat intelligence. Operators often guarded incident data due to regulatory exposure, reputational risk, and competitive sensitivities.
C2 ISAC stands out because it is explicitly private-sector-led, rather than government-anchored or compliance-driven. It focuses on telecom infrastructure itself (RAN, core, transport, signaling systems), not just enterprise IT and aims for real-time operational coordination, not just periodic intelligence reports.
“We’re not going to be operating in silos when a potential event occurs. There’ll be information sharing across all that…[and] coordinated response based on that information sharing,” said Baich. “We could be sharing vulnerabilities that we find to be an issue. We could be sharing information related to different types of cyber techniques that are being utilized. Most importantly, though, it is having that trusted forum and the right relationships that someone can just make a phone call to get an answer,” he added.
In effect, it’s closer to a joint cyber defense grid for carriers than a passive information-sharing forum. Several converging pressures explain why this is happening now:
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AI-enhanced attack capabilities: Adversaries are using AI for automated vulnerability discovery, polymorphic malware, and adaptive intrusion techniques targeting network infrastructure (e.g., signaling exploitation, orchestration layers, and cloud-native cores).
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State-sponsored campaigns: Groups linked to China, Russia, Iran, and DPRK have increasingly targeted telecom networks for espionage, lawful intercept bypass, metadata harvesting, and potential pre-positioning for disruption.
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Soft targets in telecom evolution: The shift to:
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Virtualized RAN (vRAN)
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Open RAN (multi-vendor complexity)
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Cloud-native 5G cores
has expanded the attack surface dramatically, especially at APIs, orchestration layers, and inter-vendor interfaces.
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Regulatory pressure without operational mechanisms: Governments (e.g., via CISA, FCC, NSA advisories) have been urging collaboration, but lacked a low-friction, operator-driven mechanism for tactical data exchange.
Key C2 ISAC functions include:
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Real-time threat intelligence sharing
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Indicators of compromise (IOCs)
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Tactics, techniques, and procedures (TTPs)
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Zero-day exploitation patterns in telecom-specific protocols (e.g., SS7, Diameter, 5G SBA interfaces)
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Coordinated incident response
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Rapid cross-operator alerts when an intrusion is detected
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Shared mitigation playbooks (e.g., blocking malicious signaling traffic patterns)
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Potential “collective defense” actions, like synchronized filtering or patch prioritization
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Infrastructure-specific vulnerability tracking
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Vendor equipment vulnerabilities (RAN, core, routers, optical transport)
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Software supply chain risks (containers, orchestration stacks like Kubernetes in 5G cores)
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Simulation and preparedness
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Joint exercises for large-scale outages or cyber-physical attacks
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Red-teaming of inter-operator dependencies (e.g., roaming, interconnect)
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Why this matters strategically:
This is less about incremental improvement and more about closing a structural asymmetry:
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Attackers collaborate and reuse tooling globally
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Defenders (telecom operators) have historically operated in silos
C2 ISAC is an attempt to match attacker coordination with defender coordination, particularly in a sector that underpins:
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National security communications
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Critical infrastructure interconnectivity
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Emergency services
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Financial transaction networks
In that sense, telecom is closer to energy than to typical enterprise IT—and requires a sector-wide defense posture, not just firm-level security.
Implications for the telecom ecosystem:
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Operators: Likely to gain faster detection and response capabilities, but must overcome internal legal/compliance barriers to share sensitive data.
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Vendors (e.g., Ericsson, Nokia, Cisco): May face stronger pressure for rapid disclosure and coordinated patching, especially if vulnerabilities affect multiple operators simultaneously.
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Cloud providers (AWS, Azure, Google Cloud): Become indirectly implicated, since 5G cores and network functions increasingly run on hyperscaler infrastructure.
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Government: Even though this is private-sector-led, agencies like CISA and NSA will likely act as intelligence feeders and backstops, not primary coordinators.
Risks and limitations:
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Trust barriers: Operators must be willing to share sensitive breach data quickly—historically a weak point.
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Legal liability concerns: Information sharing can expose firms to regulatory or litigation risk unless protected.
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Speed vs. accuracy trade-offs: Real-time sharing increases the risk of false positives propagating across networks.
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Vendor opacity: If equipment/software vendors are slow or incomplete in disclosures, the ISAC’s effectiveness is constrained.
A useful analogy:
C2 ISAC aims to move telecom from a model of independent air traffic control towers to a shared radar network:
Each network operator still controls its own “airspace,” but now they can all see incoming threats earlier and coordinate responses before collisions—or attacks—propagate system-wide.
References:
https://www.lightreading.com/security/eight-big-us-telcos-join-forces-on-network-cybersecurity
Key Differences Between Network Cybersecurity and Control System Cybersecurity & Why It Matters
Cybersecurity threats in telecoms require protection of network infrastructure and availability
WSJ: T-Mobile hacked by cyber-espionage group linked to Chinese Intelligence agency
GSA Meetup: Cyber Security Continues as Major Obstacle for IoT Adoption
Demythifying Cyber security: IEEE ComSocSCV April 19th Meeting Summary
Analysis: Fiber Broadband Association (FBA) whitepaper: Upgrading MSO Networks to Fiber to the Home (FTTH): A Technical Perspective
Executive Summary:
The Fiber Broadband Association (FBA) has published an economic and structural framework for Hybrid Fiber Coax (HFC)-to-Fiber to the Premises (FTTP) [1.] upgrades, viewing that as a ‘strategic imperative’ for cable network operators (MSOs). FBA’s The whitepaper white paper, “Upgrading MSO Networks to Fiber to the Home (FTTH): A Technical Perspective,“ positions fiber as the long-term architectural solution for cable access networks.
The whitepaper evaluates multiple migration paths—including full overbuilds, targeted deployments, and HFC/FTTP coexistence—alongside passive optical network (PON) options and operating models. Economically, the report estimates FTTP operating expenses to be approximately 50% lower than HFC, driven by the elimination of active outside plant components. It outlines deployment options ranging from incremental overlays to full HFC replacement, each with distinct cost-performance trade-offs.
Note 1. Fiber optic access networks (sometimes referred to as fiber-to-the-home—FTTH—or fiber-to-the-premises—FTTP) are built to connect homes and businesses to lightning fast Internet connections. The fiber optic cables that make up these networks are the fastest and most reliable broadband technology and are capable of delivering vastly higher bandwidth than traditional copper wires or wireless. All-fiber networks are directly connected from the central office all the way to a subscriber’s building. There is no other technology along the path except fiber optics.
Fiber optic cables are made up of thin strands of glass that carry information by transmitting pulses of light, which are usually created by lasers. The vibrations are turned on and off very quickly. A single fiber can carry multiple streams of information simultaneously over different wavelengths, or colors, of light, enabling more robust video, Internet, and voice services. Fiber cables are capable of transmitting multi-gigabit Internet speeds compared to the mere megabytes typical of copper connections.
Image Credit: Panther Media GmbH/Alamy Stock Photo
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Divergent Cable Operator (MSO) Strategies:
In practice, MSO upgrade strategies remain highly variable, shaped by market competition, plant condition, density, and capital constraints. Most large operators now manage hybrid access portfolios spanning HFC, FTTP, and, in some cases, fixed wireless access (FWA).
Optimum Communications, for example, is deploying FTTP overlays in dense Northeastern markets while relying on DOCSIS 3.1 in rural areas where fiber economics are less favorable.
Comcast and Charter similarly pursue selective FTTP in greenfield and edge-out scenarios but continue to prioritize HFC evolution via DOCSIS 4.0. Both assert that upgraded HFC can deliver symmetrical multi-gigabit services at lower cost. Charter estimates upgrade costs at approximately $100 per home passed (excluding CPE), versus roughly $200 for Comcast. Both operators utilize virtualized CMTS platforms and distributed access architectures (DAA) to support converged HFC/FTTP operations and targeted fiber extensions.
Operational cost differences remain contested. Charter CEO Chris Winfrey characterized the delta as minimal—on the order of one to two dollars per passing—stating, “We’ll take that tradeoff any day.” This reinforces the view that large-scale HFC-to-FTTP overbuilds are unlikely among major incumbents in the near term.
HFC investment also continues. Comcast and Charter are deploying low-latency capabilities based on the IETF Low Latency Low Loss Scalable Throughput (L4S) standard, with Charter already launching in multiple U.S. markets.
Fiber-First Deployments:
Smaller operators are, in some cases, moving more decisively toward FTTP. MCTV, serving approximately 57,000 customers in Ohio and West Virginia, determined that FTTP and DOCSIS 3.1 were cost-comparable and opted for full fiber rebuilds. The operator now passes more than 80% of its footprint with FTTP and has decommissioned roughly two-thirds of its HFC power supplies.
Competitive and Technology Outlook:
The FBA identifies FTTP as the “primary driver” of recent cable subscriber losses, citing “structural limitations” of HFC in symmetry, latency, and scalability “that DOCSIS upgrades can partially address but not fully overcome.” Cable proponents dispute this, pointing to DOCSIS 4.0 and future enhancements.
However, competitive pressure is multi-dimensional. In addition to fiber, FWA is gaining traction, particularly in lower-speed tiers. As a result, operators are increasingly focused on pricing, service bundling, and customer experience, including converged broadband-mobile offerings, rather than peak speeds alone.
On the technology roadmap, CableLabs continues to extend DOCSIS, including exploration of an “operational annex” supporting spectrum up to 3 GHz and downstream rates around 25 Gbit/s, with longer-term targets of 6 GHz and 50 Gbit/s. At the same time, it is expanding work on PON, including coherent PON, reflecting a more access-technology-agnostic posture.
Migration Trajectory:
Some industry veterans align with the FBA’s long-term view. John Chapman has advocated a “fiber-first” strategy, citing projections that access networks may need to support up to 1 Tbit/s by 2040. Rather than abrupt transitions, this approach emphasizes phased migration from HFC to FTTP while leveraging existing infrastructure.
Quotes:
John Chapman, a DOCSIS pioneer and former long-time Cisco Systems engineering exec, suggested last year that the cable industry should adjust its thinking and take a “fiber-first” approach as historical trends indicate that broadband access networks will need to be capable of supporting speeds of 1 Tbit/s by 2040. Rather than doing a quick cutover, he thinks MSO’s should migrate from HFC to FTTP over an extended period. But they should get started now.
“The industry has reached an inflection point between the maturity of DOCSIS and the inevitability of fiber. It’s about coming up with a graceful, pragmatic solution to migrate, to transition from DOCSIS to fiber. And that migration’s going to take 20 years. But we need a strategy that accommodates the two,” he said. “It’s a cultural change.”
Jay Rolls, a former Charter CTO and current CTO of BSP, a company that conducts due diligence on various types of broadband network transactions, also believes that cable operators should be weighing whether FTTP is the right move. His recent analysis suggests that the capital spend on a DOCSIS 4.0 overhaul is comparable to an FTTP rebuild.
“Every network and market is different, but the tipping point is fast arriving where overbuilding fiber makes as much financial sense as upgrading HFC, especially when you consider what comes next,” he said on a Light Reading podcast.
References:
Upgrading MSO Networks to Fiber to the Home (FTTH): A Technical Perspective
https://fiberconnect.fiberbroadband.org/
Fiber Broadband Association Middle Mile WG: how to use “Digital Infrastructure Networks” for coordinated fiber backbone investments
Analysis: AT&T 1Q-2026 results: increased fiber penetration, FWA momentum, D2D deals, and mobile/home internet bundles
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
Fiber Optic Networks & Subsea Cable Systems as the foundation for AI and Cloud services
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Automating Fiber Testing in the Last Mile: An Experiment from the Field
AI wireless and fiber optic network technologies; IMT 2030 “native AI” concept
Dell’Oro: Global RAN market stable (again) in 1Q 2026; top 5 RAN vendors are unchanged
A recently published report from Dell’Oro Group indicates that the stable trends shaping the Radio Access Network (RAN) market in 2025 extended into the first quarter of 2026. Worldwide RAN revenue, excluding services, increased at a low-single-digit year-over-year rate in 1Q 2026, marking the fifth consecutive quarter where the market remained within a relatively narrow range (-4 to +4% year-over-year). However, market fundamentals remain constrained by slower mobile broadband growth.
“This positive start does not alter the fundamentals shaping the growth prospects of this market,” said Stefan Pongratz, Vice President for RAN market research at the Dell’Oro Group. “We attribute the improved conditions primarily to a favorable regional mix and easier comparisons in markets that experienced sharp declines. Meanwhile, RAN remains growth-constrained, and operators are increasingly preparing for a slower mobile broadband growth environment,” Pongratz added.
Additional highlights from the 1Q 2026 RAN report:
- Growth in EMEA and APAC offset weaker activity in North America.
- Revenue rankings were unchanged in 1Q 2026. Based on trailing four-quarter worldwide revenue, the top five RAN suppliers are Huawei, Ericsson, Nokia, ZTE, and Samsung [1.].
- Regional imbalances continue to shape the market recovery trajectory, with APAC excluding China improving while North America and China remain under pressure.
Note 1. There were no significant market share shifts quarter-to-quarter for these five vendors whose ranking remains the same. The ongoing war in Iran, with the Strait of Hormuz closed, has disrupted supply chains for specialized components, while higher energy prices are raising operational costs for infrastructure deployment, creating an increasingly complex environment for network equipment suppliers.
Dell’Oro Group’s RAN Quarterly Report offers a complete overview of the RAN industry, with tables covering manufacturers’ and market revenue for multiple RAN segments including 5G NR Sub-7 GHz, 5G NR mmWave, LTE, Macro BTS, small cells, Massive MIMO, and Cloud RAN. The report also tracks the RAN market by region and includes a four-quarter outlook. To purchase this report, please contact us by email at [email protected]
References:
Worldwide RAN Market Remained Stable in 1Q 2026, According to Dell’Oro Group
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
ABI Research: mobile network spending to fall 29% from 2026-to-2031
Dell’Oro: RAN market stable, Mobile Core Network market +14% Y/Y with 72 5G SA core networks deployed
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Mulit-vendor Open RAN stalls as Echostar/Dish shuts down it’s 5G network leaving Mavenir in the lurch
The enterprise network stack is collapsing; AI’s impact; comparison with “Batch Pipelines Break AI Agents”
by Shashi Kiran with Alan J Weissberger, ScD
Abstract:
This article presents the primary author’s point of view on networking technology and market evolution, as experienced it directly with his customers at Nile, where he serves as Chief Marketing Officer (CMO). A key theme is overlaying the impact of AI and its implications for network and network security architecture on a new network stack. We focus specifically on the diverse complexity and heterogeneity of the LAN, while drawing inferences to other areas in the broader enterprise network.
The article draws no information from other publications or references, except for the security breach data points derived from IDC, Gartner, and market surveys. Hence, the References listed at the end of the piece are from related IEEE Techblog posts and Nile press releases chosen by this website’s content manager.
Definitions:
The enterprise network stack is much more than a protocol stack. It is the layered architecture of physical infrastructure, forwarding devices, control protocols, management systems, and security enforcement functions that interconnect users, endpoints, workloads, and cloud services across campus, branch, WAN, data center, and cloud domains. It typically includes access, distribution, core, and edge segments, along with overlay, orchestration, telemetry, identity, and policy planes that govern how traffic is admitted, routed, segmented, monitored, and secured.
A useful way to think about the stack is in terms of planes:
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Data plane: forwards packets, enforces QoS, and applies access-control functions close to the traffic path.
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Control plane: discovers topology and capabilities, computes paths, and reacts to failures.
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Management plane: handles configuration, monitoring, troubleshooting, reporting, and performance management.
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Security stack: includes firewalls, IDS/IPS, secure web gateways, threat intelligence, and related inspection or enforcement tools.
At the device level, the stack typically includes physical media and network hardware such as cabling, Wi-Fi, NICs, switches, routers, gateways, servers, and dedicated security appliances. At higher layers, it includes protocols and services for addressing, routing, transport, application connectivity, identity, and policy enforcement, often mapped loosely to OSI/TCP-IP concepts rather than a strict textbook stack.
In an enterprise environment, the network stack extends across LAN, WAN, data center, cloud, and security domains, so “the stack” is less a single product and more an integrated system of infrastructure, software, telemetry, and policy. That is why discussions of enterprise architecture usually separate forwarding, orchestration, assurance, and security functions even when they are delivered in a unified platform.
Structural Limits of the Enterprise Network Stack:
The enterprise network stack is approaching a structural inflection which may be at a “breaking point.” That’s because what’s failing is structural and architectural, not incremental. The enterprise network stack was architected for a world that no longer exists, and most of the pain organizations feel today is the cost of pretending otherwise. The interesting question isn’t whether it breaks but rather when, and along which seams. Here’s why:
The network stack most enterprises still run was designed around five assumptions that were partly true in 2010 but mostly false in 2026. Users sit at desks on managed devices. Applications live in a corporate data center. Traffic flows north-south through a perimeter. Identity equals a user with a session. Trust derives from network location. Every one of those is gone. Users are hybrid, apps are SaaS and multi-cloud, traffic is increasingly east-west and machine-driven, identity now includes non-human agents acting with delegated authority, and zero trust has formally retired the idea that being inside the network means anything.
So, the enterprise stack isn’t failing because any single piece is bad. Rather, it’s failing because the architecture it was based on no longer matches the workload, the threat model, or the operational reality it’s asked to serve. AI is the forcing function, but the cracks were already there. The choice in front of most enterprises isn’t whether to rebuild but whether to do it deliberately or by accident. Will reinvention and self-disruption be intentional or forced?
Today, many enterprise environments represent layered extensions of legacy architectures rather than cohesive designs. AI acts as an accelerant, exposing pre-existing architectural limitations. The resulting fragmentation increases operational complexity, reduces agility, and amplifies security risk.
Complexity is a Primary Risk Vector:
Complexity has evolved from an operational burden into a primary source of systemic risk. Modern network environments often exceed the capacity for deterministic human understanding, creating conditions where failures and vulnerabilities emerge at the intersections between systems rather than within individual components.
Empirical evidence suggests that many successful breaches exploit misconfigurations and integration gaps rather than novel vulnerabilities. In this context, complexity itself becomes the effective attack surface.
This challenge is particularly acute in the LAN, which often retains legacy architectural elements, heterogeneous device ecosystems, and fragmented management models. Combined with constrained IT resources, this environment can become a disproportionate source of exposure.
Reducing complexity—through architectural simplification, integrated control planes, and automation—is therefore not merely an operational objective but a core security strategy. In AI-driven environments, simplicity directly contributes to resilience and risk reduction.
An Architectural Reset is Needed:
An architectural reset is increasingly necessary. While incremental upgrades remain feasible, their marginal returns are diminishing relative to the growing mismatch between legacy designs and emerging requirements. Many organizations continue to extend existing architectures due to cost constraints or perceived transition risks. However, this approach often compounds technical debt and increases long-term exposure. The more fundamental question is not whether incremental evolution is possible, but whether it represents effective capital allocation in the context of AI-driven workloads and threat models.
Forward-looking architectures are converging around several principles: AI-native workload support, identity-centric security, zero-trust enforcement, and tightly integrated operational models. Organizations that proactively redefine their network architectures around these principles are more likely to achieve sustainable performance, security, and operational efficiency gains.
Here are a couple of conceptual architectural constructs for a unified, secure fabric with AI orchestration, autonomous operation and service delivery, which replaces the fragmented network stack and operations of the traditional/legacy network. The first illustration is more functional; the second is a more theoretical stack. CLICK ON EACH IMAGE TO ENLARGE!
Security and the Network Fabric:
Security is neither fully “moving into” nor “remaining outside of” the network fabric; rather, it is being restructured across distinct functional planes, including identity, policy, enforcement, and detection.
Historically, network-centric security relied on in-path inspection mechanisms (e.g., firewalls, intrusion prevention systems, and proxies). This model proved difficult to scale due to encryption, cloud decentralization, and traffic patterns that bypass centralized inspection points.
In contemporary architectures, the network fabric is evolving into a high-performance enforcement plane. Policy definition and decision-making are increasingly centralized in identity and control-plane systems, while enforcement is distributed across the network and applied at line rate to identity-associated flows.
This separation of concerns improves scalability and composability. Identity-centric policy models define “who can do what,” while the network enforces those decisions efficiently and locally. The result is a more adaptable and performant security architecture.
However, the effectiveness of this approach depends on architectural discipline. Designs that treat the fabric as one component within a broader, identity-driven security framework tend to reduce complexity. Conversely, attempts to re-centralize security entirely within the network risk recreating earlier limitations in a more complex form.
AI’s Impact on Telecommunications Networks:
Artificial intelligence (AI) is influencing telecom network architectures along two orthogonal dimensions:
1.] AI introduces a new class of workloads that impose stringent and atypical requirements on network infrastructure.
AI workloads fundamentally challenge legacy network design assumptions. Traditional enterprise networks were optimized for north–south traffic patterns, human-driven interactions, and best-effort delivery models. In contrast, AI workloads generate predominantly east–west traffic, operate at machine timescales, and exhibit low tolerance for latency, jitter, and packet loss. Simultaneously, AI-enabled control and management planes enable higher degrees of automation and operational efficiency, particularly in campus and branch environments where autonomous operations are beginning to reduce manual intervention.
2.] AI is increasingly being embedded within the network itself, enhancing operations, optimization, fault diagnosis/recovery and security functions. The interaction between these roles is driving many of the architectural shifts observed today. Today, wide-area networks (WANs) must interconnect AI-intensive data center environments with distributed enterprise domains, effectively bridging heterogeneous traffic models and service requirements.
AI-Driven Changes in Traffic and Risk:
AI is reshaping both the structure of network traffic and its associated risk profile. From a traffic perspective, flows are becoming increasingly east–west, bursty, and machine-generated, with reduced visibility due to encryption and abstraction layers. From a security standpoint, AI introduces new classes of actors (e.g., non-human identities and autonomous agents), as well as new attack vectors, including adversarial AI and data exfiltration via model interactions.
These shifts are tightly coupled. The same properties that define AI-driven traffic—distribution, dynamism, and opacity—also complicate detection and enforcement. As a result, security architectures are evolving toward:
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Identity-centric models that extend zero-trust principles to non-human entities.
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Data loss prevention mechanisms adapted to AI-generated and AI-consumed data flows.
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Fine-grained segmentation within network fabrics, subject to latency constraints.
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Increased reliance on AI-driven detection and response systems to counter AI-enabled threats.
Importantly, these dynamics vary across network domains (LAN, WAN, and data center/cloud), requiring domain-specific adaptations while maintaining consistent policy frameworks.
Alignment with “Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations:”
The key points made in this article are highly consistent with the above referenced IEEE Techblog post written by Shazia Hasnie, Ph.D. Both articles treat AI as an architectural forcing function: Shazia’s article focuses on the data/telemetry layer, while this post extends the same logic to the broader enterprise network stack. The core claim in both pieces is that legacy architectures were built for human-operated, latency-tolerant workflows, not autonomous AI systems. In the Shazia’s article, batch pipelines fail because they deliver stale, incomplete, and inconsistent context to AI agents. Here, the same mismatch appears at the network level, where legacy enterprise designs were optimized for north–south traffic, perimeter trust, and static operational assumptions. Both arguments are fundamentally about architectural mismatch rather than isolated product shortcomings.
A particularly strong point of overlap is the emphasis on real-time context. Shazia’s article argues that AI agents require continuous data freshness and an ordered event stream to function safely, while this piece frames AI networking as a shift toward machine-timescale traffic, streaming telemetry, and identity-aware enforcement. In both cases, the network is no longer just a transport layer; it becomes part of the control loop that determines whether AI decisions are accurate and timely.
The failure models are also similar. Shazia identifies five failure modes of batch-to-agent mismatch: stale data, memory gaps, delete blindness, schema fragility, and coordination failure. While not using that taxonomy explicitly, we share the same underlying diagnosis by arguing that complexity, fragmentation, and legacy operational models are now the primary sources of risk. Our discussion of east–west traffic, non-human identities, zero trust, and observability mirrors Shazia’s broader point that autonomous systems fail when their surrounding infrastructure cannot preserve state, sequence, and policy consistency.
These two articles work well together because they address different layers of the same transition. The first article is mainly about the data plane of AI operations—how telemetry, event streams, and agent inputs must move from batch to streaming to avoid operational failure. This article is about the network and security architecture around that data plane—how the enterprise stack, LAN, WAN, and fabric must evolve to support AI-native workloads and enforcement. Hence, the reader can consider the two articles companion pieces.
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About the Author:
Shashi Kiran has nearly 30 years of experience in network, security and cloud technologies, primarily as an operator and executive in public and private B2B companies, where he has held global product management and marketing positions. He’s adopting a protopian view of AI, while being both fascinated and frightened by it at the same time.
Shashi is currently the CMO at Nile, whose network architecture aligns with what AI-era networks require: identity-centric control, embedded security, and autonomous operations. He previously held executive roles at Cisco, Check Point Software, Broadcom and other venture backed startups, and is based in San Jose, CA. He can be reached at http://www.linkedin.com/in/
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References:
Why Batch Pipelines Break AI Agents: The Case For Streaming-First Network Operations
Nile launches a Generative AI engine (NXI) to proactively detect and resolve enterprise network issues
Fiber Optic Networks & Subsea Cable Systems as the foundation for AI and Cloud services
Dell’Oro: Bright Future for Campus Network As A Service (NaaS) and Public Cloud Managed LAN
Cisco Plus: Network as a Service includes computing and storage too
https://nilesecure.com/press-releases/networking-and-security-in-higher-ed
https://nilesecure.com/press-releases/nile-powers-black-hat-mea-2025-with-zero-reported-incidents
Ookla on the Global D2D Market
Direct-to-device (D2D) satellite connectivity is emerging as a practical extension of non-terrestrial networks (NTNs), enabling standard smartphones to communicate directly with satellite systems without specialized user equipment. Within the 3GPP ecosystem, NTN capabilities were standardized (3GPP specs become standards by being rubber stamped by ETSI and ITU-R) beginning with 3GPP Release 17, establishing a framework for satellite-terrestrial interoperability and expanding the potential reach of mobile broadband beyond the footprint of terrestrial radio access networks.
D2D services could reduce persistent coverage gaps, especially in rural, maritime, and other underserved environments where terrestrial deployment is constrained by economics or geography. However, commercially available services today remain limited, with most deployments focused on messaging and other low-throughput applications rather than full mobile broadband.
From a market perspective, D2D and NTN have broad implications for mobile network operators (MNOs), satellite operators, equipment vendors, and regulators. That strategic importance helps explain why companies such as Apple, Amazon, SpaceX, and AST SpaceMobile are investing in this segment, alongside broader ecosystem activity around 3GPP-based NTN architectures.
Image Credit: Ookla
Ookla® has contributed to the discussion with a high-resolution poster showing global Speedtest® usage data for D2D services, along with a detailed market study on the D2D landscape. The analysis is based on Android devices that register with D2D-capable satellite systems from Starlink, Skylo, and Lynk, providing an early empirical view of how NTN-based connectivity is being used in practice.
Looking ahead, continued investment in larger satellite constellations and additional spectrum holdings should improve D2D capacity, coverage, and service robustness. As the technology matures, the industry is likely to move from narrowband messaging toward richer data services, with 3GPP NTN providing the standardization path for broader ecosystem scale-up.
For mobile network operators, the long-term effect could be a rebalancing of investment priorities at the edge of network coverage, particularly in sparsely populated regions. That may reduce the incentive for some rural tower builds and alter the demand outlook for parts of the RAN infrastructure supply chain.
Looking ahead, continued investment in next-generation satellite constellations, coupled with expanded spectrum access, is expected to enhance D2D performance and capacity. Key players—including Starlink, AST SpaceMobile, and Amazon’s Project Kuiper—are targeting higher data rates and broader service capabilities, with the objective of extending beyond narrowband messaging to support more data-intensive applications.
For MNOs, the evolution of D2D introduces potential shifts in network planning and capital allocation, particularly at the margins of coverage. Satellite-based augmentation could reduce the economic rationale for terrestrial infrastructure deployment in sparsely populated areas, with downstream implications for tower companies and certain segments of the radio access network (RAN) supply chain.
From a policy perspective, D2D also has the potential to reshape universal service frameworks and coverage obligations. Regulators seeking to expand connectivity may increasingly incorporate NTN-based solutions into their policy toolkits, prompting a reassessment of long-standing assumptions regarding the role of terrestrial infrastructure in achieving nationwide coverage. In that sense, D2D is not just a satellite story. It is becoming a broader telecom architecture shaped by 3GPP specifications and the convergence of terrestrial and non-terrestrial mobile networks.
References:
Analyst firms wide forecasts for the LEO satellite direct-to-device (D2D) market
Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way
Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area
GSA: 5G Non Terrestrial Networks, 5G SA and 5G Advanced gain momentum
Analysis: Amazon <- Globalstar – a strategic move for D2D and spectrum parity
Direct-to-Device (D2D) satellite network comparison: Starlink V2 (Starlink Mobile) vs “Satellite Connect Europe”
Deutsche Telekom selects Iridium for NB-IoT direct-to-device (D2D) connectivity
Standards are the key requirement for telco/satellite integration: D2D and satellite-based mobile backhaul
MTN Consulting: Satellite network operators to focus on Direct-to-device (D2D), Internet of Things (IoT), and cloud-based services
Merry-go-round of dog chasing his tail: relationship between U.S. hyperscalers and private Gen AI companies
1. Hyperscalers’ earnings growth this quarter was boosted by an unusually large contribution from “other income,” which was actually mark-ups of their equity stakes in private Gen AI companies. For example:
- Nearly half of Alphabet’s (Google) record $62.6 billion profit—about $28.7 billion—did not come from search ads, cloud services or any of its products at all. It came from Alphabet updating the value of the equity it owns in private AI companies, primarily Anthropic. Alphabet holds a 14% stake before the announcement of an additional $40 billion commitment last week.
- Amazon’s earnings release stated that first-quarter net income “includes pre-tax gains of $16.8 billion included in non-operating income from our investments in Anthropic”—more than half of Amazon’s pre-tax income (or profit) for the quarter.
- Alphabet and Amazon generated “other income” totaling $53 billion in Q1 2026, which accounted for nearly 60% of those two companies’ total net income in Q1 and 34% of the total $155 billion in income this quarter. Of this $53 billion in “other income,” $49 billion was explicitly due to equity stakes in private AI companies.
- Microsoft reported “only” $942mn of other income in the first three months of the year, but this line item has now made $7.2bn over the past nine months.
- Under U.S. accounting rules, publicly traded firms must adjust and report the assessed value of their private equity holdings every quarter. Because private AI start-ups like Anthropic experienced meteoric valuation updates (e.g., Anthropic climbing to an estimated $380 billion), both Alphabet and Amazon were required to record those massive “on-paper” gains directly to their bottom-line net income.
- When the AI bubble finally bursts (and it will) the private AI companies assessed market value will collapse, resulting in “impairment write-downs” and huge earnings declines for the hyperscalers, e.g. Amazon, Google/Alphabet, Microsoft, FB/Meta, and Oracle.
2. Now here’s the merry-go-round/ dog chasing its tail relationship:
Not only have private investments and increasingly engorged funding rounds become a meaningful driver of the hyperscalers’ aggregate earnings, but the money the hyperscalers have pumped into the likes of Anthropic and OpenAI has allowed those private AI companies to sign huge computing deals with Alphabet’s Google Cloud, Microsoft’s Azure and Amazon Web Services (AWS). OpenAI and Anthropic now make up about half of the entire cloud computing order books at Oracle, Alphabet, Amazon and Microsoft!
Indeed, AI startups have loaded up hyperscalers with unprecedented long-term financial commitments.
–>OpenAI and Anthropic make up over $1 trillion of the estimated $2 trillion cumulative revenue backlog currently held by major cloud service providers!
- OpenAI to Microsoft Azure: Internal documents show OpenAI’s massive server rentals have generated more than $23 billion in direct cloud spending for Microsoft.
- Anthropic to Google Cloud: Anthropic signed a contract committing to spend $200 billion over five years on Google’s cloud infrastructure and TPU chips.
- Anthropic to AWS: In tandem with a fresh $5 billion investment from Amazon, Anthropic committed to spend over $100 billion over the next decade on AWS technologies.
Image Generated by Chat GPT
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- Backlog Percentage: Over 40%. Anthropic‘s $200 billion Multi-Year Commitment accounts for nearly half of Google Cloud’s total disclosed $240 billion revenue backlog.
- Current Revenue Share: Estimated 12% to 15% of its current $20 billion quarterly revenue run-rate is driven directly by AI infrastructure consumption from startups (both frontier labs and over 40 mid-tier AI companies built on Google Cloud Vertex AI).
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- Current Revenue Share: Estimated 15% to 18%. Microsoft’s annualized AI revenue run-rate hit $37 billion. A massive chunk of Azure’s overall 40% growth rate is anchored directly by OpenAI’s compute demands and the commercialization of OpenAI-tied products.
- Current Revenue Share: Estimated 6% to 8%. While AWS has the largest overall cloud scale ($150 billion annual run rate), its revenue is traditionally diversified across enterprise SaaS and retail. However, Anthropic’s new $100 billion infrastructure commitment means AWS’s revenue mix is aggressively shifting toward AI startups. [1, 2, 3, 4]
–>This is another sign of just how incestuously codependent the big tech industry is to astronomically valued private AI start-ups.
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4. Another example of this codependency is Oracle and OpenAI’s massive, debt-fueled financial loop. In September 2025, the two companies signed a staggering five-year, $300 billion cloud-computing contract. This single deal radically transformed both companies’ financial profiles, binding their survival together as inextricably tied.
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- For Oracle: The $300 billion contract instantly added to Oracle’s Remaining Performance Obligations (RPO), which skyrocketed 359% to $455 billion. This accounting metric allowed Oracle to position itself as a dominant “hyperscaler,” pushing its market cap upward.
- For OpenAI: The contract allowed OpenAI to claim it had secured the long-term compute capacity needed to achieve Artificial General Intelligence (AGI). This backed up its massive valuations, enabling OpenAI to close a historic $122 billion funding round in March 2026 at an $852 billion valuation.
- Oracle is a Financial Proxy for OpenAI: If OpenAI faces a “credit event” or cash crunch, Oracle’s stock directly plummets. Critics note that Oracle signed a contract with a startup that historically burns far more cash than it takes in, making OpenAI’s ability to actually pay the $300 billion highly volatile.
- The Debt Spiral: To physically fulfill OpenAI’s compute demands, Oracle has gone on a massive, debt-fueled construction spree. Oracle raised $18 billion in bonds in late 2025 and an additional $30 billion in early 2026. Its capital expenditures have eclipsed operating cash flows, leading to deeply negative free cash flow and over $134 billion in total corporate debt.
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- Project Finance Bottlenecks: Major commercial banks have struggled to syndicate the massive multi-billion-dollar construction loans Oracle needs to build out the required data centers (such as its 4.5-gigawatt capacity goals).
- Bank Limits: The sheer volume of debt concentrated around this single enterprise relationship has pushed several Wall Street institutions against their regulatory exposure limits for a single corporate partnership.
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References:
https://www.ft.com/content/be97df0a-76b1-4cb0-9ba4-d1117d8d1450
https://fortune.com/2026/04/30/google-amazon-ai-profits-anthropic-stake-bubble-earnings-2026/
https://finance.yahoo.com/sectors/technology/articles/google-amazon-biggest-profit-driver-170449859.html
AI infrastructure spending boom: a path towards AGI or speculative bubble?
Expose: AI is more than a bubble; it’s a data center debt bomb
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
China’s open source AI models to capture a larger share of 2026 global AI market
OpenAI and Broadcom in $10B deal to make custom AI chips
Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight
Analyst firms wide forecasts for the LEO satellite direct-to-device (D2D) market
LEO satellite direct-to-device (D2D) technology looks promising. Telecom analyst firms see D2D as a fast-growing but still early-stage market, with forecasts ranging from roughly 22% to 49% revenue CAGR depending on scope and whether they are measuring total D2D services or smartphone satellite D2D specifically. But that’s not happening now. T-Mobile chief Srini Gopalan, who said the service so far had generated “a lot less usage” than anticipated.
The most common near-term view is that basic D2D will add modest operator revenue at first, but the long-term market could become multi-billion-dollar as broadband and richer services mature. Here are a few analyst forecasts:
- MarketsandMarkets projects the D2D market to rise from USD 0.57 billion in 2025 to USD 2.64 billion by 2030, a 35.6% CAGR.
- Mordor Intelligence projects the direct-to-device satellite connectivity market from USD 4.08 billion in 2025 to USD 13.80 billion by 2031, a 22.37% CAGR.
- Omdia forecasts smartphone satellite D2D revenue to reach USD 11.99 billion by 2030, with a 49.4% revenue CAGR from 2026 to 2030.
- Counterpoint Research expects 46% of all smartphones shipped by 2030 to be D2D-capable. That implies D2D is moving from a niche satellite feature toward a mainstream handset capability, driven by chipset integration and broader device support.
- Juniper Research thinks the number of monthly active users will top 150 million by 2031. The analyst firm suggests a temporary access model, similar to roaming or travel eSIMs, where consumers purchase access in a particular area for a set period. Juniper thinks connectivity alone won’t be enough to attract consumers. It believes operators will have to bundle the satellite service into rewards programs or roaming access.
- Analysys Mason expects operators launching D2D in 2026 to see about a 1% annual revenue uplift from basic services alone, with much larger upside once broadband D2D becomes available.
- TelecomTV reports a similar view from Analyst Brad Grivner, who says D2D could give MNOs around a 1% annual revenue uplift and also improve retention and upsell opportunities.
The spread in forecasts mostly reflects different definitions of the market, different start dates, and whether the analyst counts only current narrowband services or also future broadband D2D. In practical terms, the consensus is that D2D will start as a coverage and messaging feature, then evolve into a broader connectivity platform as device support and satellite capacity scale.
Analysts consistently point to 3GPP NTN standardization (rubber stamped by ETSI and ITU-R), more satellite-ready smartphones, and large-scale LEO deployments as the main catalysts. They also emphasize emergency messaging, rural coverage, IoT, industrial connectivity, and enterprise resilience as the first meaningful demand pools. D2D market growth is being driven by a mix of coverage gaps, new device support, and expanding enterprise use cases. The strongest themes across analyst and industry reports are universal connectivity, IoT demand, LEO satellite buildout, and 3GPP NTN standardization.
Image Credit: Digital Regulation Platform
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Main D2D growth drivers:
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Coverage expansion. Analysts say D2D is filling a major gap in rural, remote, maritime, and disaster-prone areas where terrestrial networks are weak or unavailable.
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3GPP NTN standards. Standardized non-terrestrial networking is making satellite connectivity more practical for mainstream devices and accelerating ecosystem adoption.
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LEO constellation growth. More low-Earth-orbit satellites, along with falling launch costs and better satellite economics, are increasing capacity and improving latency.
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Smartphone integration. As more phones become satellite-capable, D2D can move beyond niche emergency features into broader consumer usage.
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Enterprise IoT demand. Logistics, mining, agriculture, utilities, and energy firms want reliable connectivity for remote assets, monitoring, and worker safety.
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Disaster resilience. Climate-related outages and emergency-response needs are pushing governments and operators toward backup connectivity solutions.
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Carrier-satellite partnerships. Cooperation between MNOs and satellite operators is speeding commercialization and helping services reach scale.
The D2Dmarket is still starting with messaging, emergency connectivity, and narrowband IoT, but analysts expect growth to broaden as device support and satellite capacity improve. In short, D2D grows fastest where it solves a clear pain point: no coverage, weak resilience, or expensive remote connectivity.
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References:
https://www.lightreading.com/satellite/making-the-most-of-satellite-d2d
Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way
Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area
GSA: 5G Non Terrestrial Networks, 5G SA and 5G Advanced gain momentum
Analysis: Amazon <- Globalstar – a strategic move for D2D and spectrum parity
Direct-to-Device (D2D) satellite network comparison: Starlink V2 (Starlink Mobile) vs “Satellite Connect Europe”
Deutsche Telekom selects Iridium for NB-IoT direct-to-device (D2D) connectivity
Standards are the key requirement for telco/satellite integration: D2D and satellite-based mobile backhaul
MTN Consulting: Satellite network operators to focus on Direct-to-device (D2D), Internet of Things (IoT), and cloud-based services
Nvidia strategic partnership with IREN targets 5G Watts AI infrastructure buildout + $2.1B investment option
Nvidia has announced a strategic partnership with cloud AI data center operator IREN [1.] to deploy up to 5G Watts (5GW) of AI infrastructure, driven by a $3.4 billion services contract and a $2.1 billion investment option for Nvidia. This collaboration aims to secure critical, high-density data center capacity for AI workloads while accelerating IREN’s transition into a major AI infrastructure provider. This strategic expansion targets up to 5GW of NVIDIA DSX-aligned AI infrastructure across IREN’s global pipeline. The roadmap centers on the 2GW Sweetwater campus in Texas, positioned to be the flagship deployment of NVIDIA’s DSX factory architecture. This integrated model synergizes NVIDIA’s reference designs with IREN’s core competencies in utility-scale power procurement, site development, and full-stack GPU cloud operations.
“AI factories are becoming foundational infrastructure for the global economy,” said Jensen Huang, founder and CEO of Nvidia. “Deploying these systems at scale requires deep integration across the full stack — compute, networking, software, power and operations. IREN brings the scale and infrastructure expertise to help accelerate the buildout of next-generation AI infrastructure globally. Together, we are building for the age of AI,” he added. Future deployments are expected to focus on IREN’s 2-gigawatt Sweetwater campus in Texas, which the companies expect to serve as a flagship deployment for Nvidia’s DSX architecture.
“This partnership combines NVIDIA’s AI systems and architecture leadership with IREN’s expertise across power, land, data centers, GPU deployment and infrastructure operations,” said Daniel Roberts, cofounder and co-CEO of IREN. “Together, we believe we can accelerate deployment of AI infrastructure and expand access to compute for AI-native and enterprise customers globally.”
References:
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Expose: AI is more than a bubble; it’s a data center debt bomb
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
NTT’s IOWN is (finally) evolving to an All Photonics Network (APN); Physics based AI for enterprise OT
Like all South Korea telecoms, NTT has revised its mid-term business strategy to center on AI infrastructure, data centers, and “value domains.” This shift follows a slowdown in its traditional telecoms “cash cow” business and aims to reorient the group toward higher growth areas. The company is prioritizing AI-related services, overseas data centers, and its vision for an IOWN [1.] based connectivity platform built for GPU, network, and power-heavy workloads.
Note 1. IOWN is NTT’s Innovative Optical and Wireless Network initiative, with a photonics optical network being at its core. An All-Photonics Network (APN) is NTT’s vision for a next-generation network that uses laser generated light, rather than electronic conversion, to move data across compute, storage, and transport layers. It is NTT’s bet on a much faster, lower-latency, and more energy-efficient network architecture for AI, data centers, and advanced telecom services.
–>The all optical network was promised by many new age telcos in the late 1990s- early 2000s but it has never seen the light of day (no pun intended)
Image Credit: NTT
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Benefits of an all photonics network:
Today, continuous, high-volume AI data flows across clouds, data centers and edge environments rely on stable, low-latency pathways. Yet networks that rely on optical to electrical to optical (OEO) conversion cannot provide this consistently. Even small variations in routing, buffering and electrical switching reduce the predictability that AI needs. Adding bandwidth can delay the symptoms but doesn’t address the architectural challenges these networks face as data movement intensifies.
At the leading edge of this shift is the All-Photonics Network (APN), developed by the IOWN Global Forum. It’s an architectural breakthrough and a practical step to rearchitecting how data moves, designed for a world where AI is changing the rules entirely. The APN introduces a new way of architecting and operationalizing photonic transport so organizations can use it without having to manage the underlying optical engineering. Instead of relying on electrical conversions at every stage, it extends optical communication to the transport layers that connect sites, regions and data centers. That results in far more consistent network performance. It reduces jitter significantly and improves throughput by avoiding repeated processing overhead.
The IOWN Global Forum outlines a future where optical-first infrastructure delivers (see image below):
- Up to 100 times lower power consumption
- More than 120 times greater transmission capacity
- A reduction in end-to-end latency by as much as 200 times
NTT wants to combine AI with IOWN’s photonics-based networking to better support AI-era compute, data center, and transport demands. AIOWN is meant to solve the bottlenecks created by AI workloads, where power, latency, and bandwidth are becoming as important as raw compute.
NTT is positioning it as infrastructure for the AI era, not just as a telecom upgrade, so it sits at the center of the company’s broader shift toward AI infrastructure and data centers. Instead of relying mainly on conventional electronic networking, the pure optical IOWN aims to connect data centers and networks with photonics-based transport that can reduce energy use and improve performance. That makes it especially relevant for GPU clusters, AI cloud environments, and high-capacity backbone links.
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NTT says the traditional telecom environment is getting tougher, with stronger competition and rising traffic demands pressuring its core business. In response, it is shifting emphasis to three growth areas: AI services for corporate clients, global data center expansion, and adjacent financial services, while also reframing its network layer for the AI era through IOWN.
The “value domains” framing is basically NTT’s way of saying it wants to move up the stack into higher-margin, customer-specific businesses rather than remain mostly a utility-like connectivity provider. In practice, that means selling integrated AI, data center, and industry solutions where NTT can capture more of the economic value than in wholesale telecom alone. NTT believes telecom cash flows will grow more slowly than AI infrastructure demand and they are likely correct. AIOWN is especially important because it ties together compute, networks, and power, which are becoming the real bottlenecks in AI deployments. The strategy also aligns with NTT’s broader enterprise AI positioning, where it can monetize infrastructure and services together rather than betting only on model development.
- Commercial Evolution (APN1.0 to APN2.0): NTT launched APN1.0 in March 2023, offering dedicated wavelength services with 1/200th the latency of conventional networks. Evolution includes the introduction of Open APN (Open All-Photonic Network) standards for interoperability.
- Performance Targets (2030): The APN aims to achieve \(100 \times\) higher power efficiency, \(125 \times\) greater capacity, and \(1/200\) end-to-end latency compared to traditional, electronics-based networks.
- Photonics-Electronics Convergence (PEC): By using light instead of electricity in network devices and servers, the APN eliminates costly, slow optical-electrical-optical conversions.
- Service Expansion: APN services are expanding to support high-demand applications like 5G/6G mobile fronthaul, remote medical services, remote construction, and AI video analysis.
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- 2025 Milestones: NTT utilized APN for the Expo 2025 Osaka to connect pavilions and demonstrated 1Tbps-class optical paths at OFC2025.
- 2026 Developments: At MWC Barcelona 2026, NTT showcased APN-facilitated AI video analysis, in-network computing, and improved AI inference processing.
- Open Standardization: NTT is collaborating with partners (e.g., IOWN Global Forum) to develop open specifications for multi-vendor interoperability. [1, 2, 3]
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Most Operational Technology (OT) environments remain stuck with legacy systems, creating a gap between modern enterprise capabilities and industrial operations. NTT is addressing this enterprise OT gap caused by legacy system stagnation by implementing private 5G networks and edge computing, allowing for modernization without full system overhauls. This approach utilizes physics-based AI to provide secure, real-time insights on-premises, overcoming challenges in visibility and standardization.
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References:
The All-Photonics Network Enables the Next-Generation Digital Economy
https://www.rd.ntt/e/research/JN202203_17536.html
https://www.nttdata.com/global/en/insights/focus/2025/039
https://www.enterprisetimes.co.uk/2026/05/08/ntts-edge-strategy-overcomes-ot-stagnation/
NTT’s IOWN provides ultra low latency and energy efficiency in Japan and Hong Kong
NTT pins growth on IOWN (Innovative Optical and Wireless Network)
Sony and NTT (with IOWN) collaborate on remote broadcast production platform
NTT to offer optical technology-based next-generation network services under IOWN initiative; 6G to follow
NTT to launch 25 Gps FTTH service in Tokyo starting March 2026
NTT DOCOMO successful outdoor trial of AI-driven wireless interface with 3 partners
Optus and Ericsson achieve 180MHz across 2.3GHz and 3.5GHz bands using carrier aggregation on a live 5G SA network
Australian telco Optus has demonstrated advanced 5G NR carrier aggregation (5G NR-CA) performance on its 5G standalone (SA) network by implementing four-component carrier aggregation (4CC CA) across low-, mid-, and upper-mid-band spectrum. Using Ericsson 5G SA network equipment and software, the configuration aggregates FDD bands at 900 MHz (Band n8) and 2.1 GHz (Band n1) with TDD bands at 2.3 GHz (Band n40) and 3.5 GHz (Band n78). Two-Component Carrier (2CC CA) uplink aggregation
This combined Optus’ unique two mid-band TDD spectrum holdings across 2.3GHz and 3.5GHz, achieving a record 180MHz TDD spectrum aggregation. In particular:
- Four-Component Carrier aggregation enabled 220MHz downlink bandwidth, leveraging spectrum across four different bands of 900MHz, 2.1GHz, 2.3GHz and 3.5GHz
- Two-Component Carrier uplink aggregation combined one Frequency Division Duplex (FDD) band from 900MHz and 2.1GHz with one TDD band from 2.3GHz and 3.5GHz
- Achieved peak speeds of 3.4Gbps (downlink) and 200Mbps (uplink) in a live network site with commercial devices, including the Samsung Galaxy S26 Ultra
The demonstration aligns with 3GPP Release 16 and Release 17 5G NR-CA enhancements (TS 38.300, TS 38.101-1/2), which extend carrier aggregation capabilities across heterogeneous duplex modes (FDD+TDD) and multiple frequency ranges within FR1. The downlink configuration leverages cross-band scheduling and advanced MIMO layers (likely up to 4×4 or higher per component carrier, depending on band support) to maximize spectral efficiency across aggregated carriers.
On the uplink, Optus and Ericsson reported 200 Mbps throughput using two-component carrier aggregation (2CC CA), combining FDD (n8/n1) and TDD (n40/n78) spectrum. This implementation is consistent with 3GPP Release 16 uplink enhancements, including uplink carrier aggregation and transmit (Tx) switching (TS 38.213), which enables efficient utilization of UE power resources across multiple uplink carriers, particularly in mixed duplex scenarios.
All results were achieved on a live commercial 5G SA network at Optus’ Sydney campus using commercial off-the-shelf (COTS) user equipment, including the Samsung Galaxy S26 Ultra. This indicates full compliance with 3GPP-defined UE capability signaling (TS 38.306) and the availability of device-side support for complex NR-CA band combinations, including inter-band and cross-duplex aggregation.
“This achievement demonstrates how we are translating cutting-edge 5G technology into meaningful benefits for customers in real-world environments. Through our ongoing collaboration with Ericsson, we are unlocking greater capacity and performance across our 5G network, enabling faster speeds and more reliable connectivity,” said Optus CTO Sri Amirthalingam. “This milestone marks an important step in our network evolution towards 5G Advanced, reinforcing our commitment to remain at the forefront of innovation and to deliver tangible value for our customers.”
Ludvig Landgren, head of Ericsson Australia and New Zealand operations said: “Optus continues to demonstrate strong leadership in adopting advanced 5G capabilities, and this milestone highlights the strength of our partnership. By expanding and combining multiple spectrum assets with Ericsson technology, we are helping Optus deliver meaningful performance improvements that translate directly into better everyday experiences for their customers.”
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From a broader industry perspective, these results build on ongoing 5G NR-CA advancements. T-Mobile US has demonstrated approximately 6 Gbps downlink throughput using six aggregated carriers in FR1, as well as 550 Mbps uplink throughput leveraging uplink Tx switching across sub-6 GHz bands. In Europe, Vodafone and MediaTek achieved 277 Mbps uplink throughput using NR uplink CA, while Elisa, Ericsson, and MediaTek demonstrated 12CC aggregation reaching 8 Gbps downlink—highlighting the scalability of NR-CA as defined in 3GPP Release 17 and evolving into Release 18 (5G-Advanced).
Within Australia, Telstra has deployed Ericsson’s automated carrier aggregation (CA) optimization solution across more than 50 live 5G Advanced sites, leveraging dynamic CA configuration and traffic-aware scheduling—capabilities aligned with 3GPP Release 18 objectives for AI-assisted RAN optimization.
A notable aspect of the Optus/Ericsson demonstration is the aggregation of 180 MHz of mid-band spectrum across n40 (2.3 GHz) and n78 (3.5 GHz). While not a headline peak-rate milestone, this represents a first in terms of contiguous mid-band NR-CA deployment at this bandwidth scale. Mid-band aggregation is particularly significant within the 3.3–4.2 GHz “golden band” range defined in global 5G spectrum harmonization efforts, as it offers an optimal balance between coverage and capacity.
Operationally, this configuration is expected to deliver immediate gains in high-traffic scenarios—such as dense urban environments, transport hubs, and large venues—by increasing available cell throughput and improving user-level quality of service (QoS). Furthermore, the expanded mid-band capacity directly benefits fixed wireless access (FWA) deployments, where sustained throughput and cell-edge performance are critical. Because the demonstrated CA combinations are already supported by commercial UE categories, deployment can proceed without requiring new device classes, accelerating time-to-impact.
Ericsson was recently selected to modernize and expand SoftBank’s core networks, as well as accelerate the Japanese giant’s 5G SA adoption. Expanding on a previous 5G SA deal centered around its radio access network (RAN) products, Ericsson is providing SoftBank with its Core Networks’ portfolio, including a dual-mode 5G Core solution running on Ericsson’s Cloud Native Infrastructure Solution (CNIS).
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References:
https://www.sdxcentral.com/news/ericsson-and-optus-claim-5g-sa-world-first/
https://www.ericsson.com/en/press-releases/7/2026/optus-and-ericsson-trial-ai-to-boost-5g-downlink
https://www.nokia.com/mobile-networks/ran/carrier-aggregation/5g-carrier-aggregation-explained/
China Unicom-Beijing and Huawei build “5.5G network” using 3 component carrier aggregation (3CC)
Nokia, BT Group & Qualcomm achieve enhanced 5G SA downlink speeds using 5G Carrier Aggregation with 5 Component Carriers
Finland’s Elisa, Ericsson and Qualcomm test uplink carrier aggregation on 5G SA network
T-Mobile US, Ericsson, and Qualcomm test 5G carrier aggregation with 6 component carriers
Ericsson and MediaTek set new 5G uplink speed record using Uplink Carrier Aggregation
BT tests 4CC Carrier Aggregation over a standalone 5G network using Nokia equipment
T-Mobile US achieves speeds over 3 Gbps using 5G Carrier Aggregation on its 5G SA network
