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.

Key Features and Evolution of APN:
  • 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.
Implementation Progress:
    • 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]

The APN is key to creating a “data-centric” infrastructure where distributed data centers can function as one integrated system. NTT says the APN acts as the bridge that brings optical performance into practical use now, while preparing organizations for deeper photonic integration as the technology matures.  NTT Group, the parent company of NTT DATA, plays a key role in helping to move optical technologies from niche use cases into the mainstream.

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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:

https://uk.nttdata.com/insights/blog/when-networks-hit-the-speed-of-light-why-photonics-is-the-next-big-shift

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

 

Posted in NTT

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.ericsson.com/en/press-releases/7/2026/optus-and-ericsson-achieve-world-first-180mhz-across-2-3ghz-and-3-5ghz-5g-standalone-carrier-aggregation-on-live-network-using-commercial-devices-boosting-5g-customer-experience

https://www.telecoms.com/5g-6g/optus-and-ericsson-use-carrier-aggregation-to-notch-up-3-4-gbps-on-a-live-5g-sa-network

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

 

Extreme Networks deploys Wi‑Fi 7 (IEEE 802.11be) at University of Florida’s “Swamp”

Executive Summary:

Extreme Networks, Inc. today announced the deployment of the first Wi‑Fi 7 network in a collegiate stadium at the University of Florida’s Ben Hill Griffin Stadium, also known as “The Swamp.”  The deployment is engineered to support peak densities approaching 90,000 concurrent users, with an emphasis on low-latency, high-throughput connectivity under extreme load conditions. Client devices associate rapidly via optimized authentication and roaming mechanisms, while high-efficiency scheduling enables uninterrupted uplink/downlink performance for real-time video streaming, social media sharing, and in-venue digital services such as mobile ordering.  Wi‑Fi 7 is based on the IEEE 802.11be standard, which was designed to improve ultra-dense venue wireless network performance.

Wi‑Fi 7 (IEEE 802.11be), improves stadium fan experience by increasing capacity, lowering latency, and making the radio layer more resilient in dense, interference-prone environments. The most relevant features are Multi-Link Operation (MLO)for simultaneous multi-band transmission, 320 MHz channels in 6 GHz, 4K-QAM, puncturing, and enhanced OFDMA/MU-MIMO scheduling.  These features collectively improve spectral efficiency, reduce contention, and sustain deterministic performance in ultra-dense environments. The result is a carrier-grade WLAN fabric that transforms “The Swamp” into a high-capacity, low-latency connectivity domain, establishing a new benchmark for large public venues.

This wireless infrastructure aligns with the University of Florida’s broader stadium modernization program, which includes physical upgrades such as expanded concourses, optimized ingress/egress flows, premium seating enhancements, and next-generation audiovisual systems. The converged digital and physical redesign enables tighter integration between network intelligence and venue operations.

Image Credit: University of Florida

“On game day, The Swamp transforms into one of the most electrifying and densely connected environments in college sports,” said Matt Vincent, Assistant Athletics Director, Information Technology at the University of Florida. “As we continue to invest in the fan experience at Ben Hill Griffin Stadium, adding Wi-Fi 7 allows us to significantly increase capacity while enabling smarter, real-time connectivity that helps everything run smoothly at peak demand. The NIaaS model from Extreme Networks also provides the flexibility to scale as needed without significant upfront investment, allowing our IT team to operate more efficiently while delivering a consistently high-quality digital experience for every fan.”

A New Era of Fan Connectivity:

The new Wi‑Fi 7 (IEEE 802.11be) network from Extreme will deliver:

  • Ultra-fast speeds enabling seamless 4K/8K video streaming, instant social sharing, and real-time stats access.
  • Lower latency for responsive mobile experiences, including in-seat ordering and interactive apps.
  • Improved device capacity supporting tens of thousands of concurrent connections without performance degradation.
  • Consistent coverage across seating bowls, concourses, suites, and outdoor areas.

Key Wi‑Fi 7 (IEEE 802.11be PHY) functions:

  • 320 MHz channels: Double the maximum Wi‑Fi channel width versus Wi‑Fi 6/6E, which increases potential throughput in 6 GHz.
  • 4K-QAM: Packs more bits into each symbol, improving efficiency when signal conditions are good and devices are close to APs, as they often are in under-seat stadium designs.
  • Puncturing: Lets the AP use the clean portion of a wide channel even if part of it is affected by interference, instead of discarding the whole channel.
  • Multi-RU and enhanced OFDMA: Improves how airtime is split among many clients, which is critical when large numbers of fans are active simultaneously.
  • Better MU-MIMO: Helps the AP serve multiple users in parallel, supporting more concurrent sessions without as much contention.

Transforming Stadium Operations:

For fans, the visible benefits are faster onboarding, smoother streaming, and more reliable mobile ordering and payments. For operators, the same network supports staff communications, POS systems, video surveillance, and IoT devices such as sensors and digital signage. Analytics from the WLAN can also reveal crowd flow, dwell time, and concession demand, which helps optimize staffing and sponsorship placement.

Beyond fan-facing services, the Wi‑Fi 7 network underpins mission-critical operational workflows. High-reliability connectivity supports real-time staff communications, accelerates point-of-sale (POS) transaction processing with reduced latency and higher transaction concurrency, and enables high-definition video surveillance integrated with AI/ML-based analytics for threat detection and crowd safety.

The network also functions as an IoT aggregation layer, supporting smart sensors, digital signage, environmental monitoring, and automated control systems via secure segmentation and policy enforcement. Through advanced analytics platforms such as Extreme Analytics, operators gain granular, real-time visibility into user behavior and network performance, including crowd flow dynamics, dwell time distributions, application usage patterns, and concession demand signals.

These data-driven insights enable closed-loop optimization of venue operations, from dynamic staffing and queue management to targeted digital engagement and monetization strategies, including context-aware advertising and sponsorship activation. In aggregate, the deployment represents a shift toward an intent-driven, analytics-centric stadium architecture where connectivity, operations, and revenue generation are tightly coupled.

About Extreme Networks:

Extreme Networks, Inc. (EXTR) is a leader in AI-powered cloud networking, focused on delivering simple and secure solutions that help businesses address challenges and enable connections among devices, applications, and users. We push the boundaries of technology, leveraging the powers of artificial intelligence, analytics, and automation. Tens of thousands of customers globally trust our AI-driven cloud networking solutions and industry-leading support to enable businesses to drive value, foster innovation, and overcome extreme challenges.

References:

https://www.businesswire.com/news/home/20260506829623/en/Extreme-Powers-First-Ever-College-Stadium-WiFi-7-Deployment-at-University-of-Floridas-The-Swamp

Research & Markets: WiFi 6E and WiFi 7 Chipset Market Report; Independent Analysis

Wireless Broadband Alliance Report: WiFi 7, converged Wi-Fi and 5G, AI/Cognitive networks, and OpenRoaming

WiFi 7: Backgrounder and CES 2025 Announcements

WiFi 7 and the controversy over 6 GHz unlicensed vs licensed spectrum

Qualcomm FastConnect 7800 combining WiFi 7 and Bluetooth in single chip

MediaTek to expand chipset portfolio to include WiFi7, smart homes, STBs, telematics and IoT

 

Lumen to acquire Alkira to accelerate its push into multi-cloud and data center interconnect services

Lumen Technologies today announced it would buy cloud ​networking platform company Alkira for $475 million in cash.  The transaction is expected to close in the third quarter of 2026, subject to customary regulatory approvals and closing conditions.  Alkira is a cloud-native, carrier-agnostic networking platform that enables enterprises to design, deploy, and operate connectivity and network services across hybrid and multi-cloud environments. The acquisition is expected ‌to accelerate Lumen’s push into cloud-to-cloud (AKA multi-cloud) and data center interconnect services and expand its total addressable market to about $70 billion through Alkira’s global footprint and cloud-native platform.

Alkira serves enterprise customers – across financial services, retail, technology, healthcare, and manufacturing around the world. Customers use Alkira to manage connectivity across AWS, Azure, Google Cloud, and other environments through a single platform built for enterprise-grade security and compliance.  Lumen said the acquisition will “accelerate its vision with a single control plane that orchestrates connectivity beyond our network – across data centers, multiple clouds, partner ecosystems, and on-premises environments – in one unified system.”

The Programmable Network Imperative:

AI is reshaping how enterprises operate and how their networks must perform. More than half of internet traffic today is automated traffic generated by software systems rather than human users. Networks have to be big enough, fast enough, intelligent enough, and secure enough to keep up. Yet many enterprise networks remain static, manually configured, and fragmented across providers. Lumen is working to define a new category of enterprise networking: one built on world-class physical infrastructure, a programmable network, and a connected ecosystem of clouds, applications, and partners.

Quotes:

Kate Johnson, CEO of Lumen Technologies said:

“For decades, networking ran in the background. Today, it’s the nervous system, determining how fast you can move, how much you spend, and whether your AI investments produce value.  With Alkira, Lumen will pair the trusted network for AI with a cloud-native control plane, which will give customers a programmable network designed for the AI era. It’s what the market needs, and it’s what we’re building at Lumen.”

Lumen President and CFO Chris Stansbury said:

“Strategic revenue now represents more than half of our business revenue, and we are pleased with increasing customer interest in our programmable network solutions. The pending Alkira acquisition reflects a disciplined and opportunistic capital allocation strategy that supports our path to revenue growth outlined at Investor Day, while remaining on track to meet full-year guidance.”

In a letter to its customers, Amir Khan, Co-Founder & CEO, Alkira wrote, in part:

Why we’re excited to join forces with Lumen:

“Looking to the future, we’re thrilled about the powerful combination of Alkira’s on-demand network infrastructure and Lumen’s fiber and AI-ready platform. Lumen brings extensive enterprise reach and a richly connected ecosystem of clouds, applications, and partners. Together, our network infrastructure-as-a-service paired with Lumen’s Connectivity Fabric will deliver the market a connectivity solution purpose-built for the AI era. After close and our planned integration with Lumen, Alkira customers will benefit from deeper integration with Lumen’s network and dramatically broader reach. In the meantime, our priority is unwavering stability and continuity for every customer.”

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Here ​are more details (courtesy of Reuters):
  • According to Lumen, the deal is unlikely to ​have a near-term impact on margins, but is expected to ⁠boost earnings as the digital platform grows, while improving long-term free ​cash flow and lowering buildout costs and risk.
  • “The acquisition of Alkira substantially ​completes the digital platform that we had to build. It accelerates it, it is capex that we do not have to invest now,” CFO Chris Stansbury told Reuters in an interview.
  • Lumen reported revenue ​of $2.9 billion for the first quarter ended March 31, above analysts’ average estimate of $2.83 ​billion, according to data compiled by LSEG.
  • “We had a very strong quarter on private ‌connectivity ⁠fabric (PCF), because we lit up some State of California business,” Stansbury said, adding that PCF growth was in the mid-single digit and Lumen’s digital offerings were a “big piece” of it.
  • The company’s quarterly adjusted loss came ​in at 47 ​cents per share, ⁠compared with expectations of a 13-cent per share loss.
  • Lumen raised its annual free cash flow forecast to a range ​of $1.9 billion to $2.1 billion, from an earlier projection of $1.2 ​billion ⁠to $1.4 billion, as its auditors determined that $729 million of the cash inflows associated with the sale of its consumer fiber operations to AT&T should be ⁠classified ​as operating cash flows.
  • In February, Lumen was ​selected to expand Anthropic’s fiber network across North America, contributing to its nearly $13 billion in total ​PCF contracts.

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About Lumen Technologies:

Over the past 30 years, Lumen Technologies (formerly CenturyLink) has transformed from a regional telco into a global enterprise networking provider through major acquisitions. Key acquisitions include Level 3 Communications ($25B, 2017), Qwest Communications (2011), Embarq (2009), Global Crossing (2011), and Savvis (2011), along with smaller technology firms like NetAura and Cognilytics.
Major Acquisitions (CenturyLink/Lumen Era):
    • Level 3 Communications (2017): A transformative $25 billion deal that brought a massive global fiber network, significantly boosting enterprise capabilities.
    • Qwest Communications International (2011): A major acquisition, adding extensive fiber conduit and expanding the network in the western U.S..
    • Global Crossing (2011): Provided a substantial international footprint and a global network.
    • Savvis (2011): Expanded the company’s capabilities in cloud computing and data center hosting.
    • Embarq (2009): The former landline operations of Sprint Nextel which included metro Ethernet and various data networking services.
    • Centel (1993): An early, foundational acquisition of landline operations. 

Smaller/Strategic Acquisitions:
    • Alkira (2026): Planned acquisition to extend leadership in programmable networking.
    • NetAura (2016): Focused on security services and government customers.
    • Cognilytics (2014): A predictive analytics company.
    • DataGardens, Inc. (2014): A Disaster Recovery as-a-Service (DRaaS) provider.

These acquisitions have significantly broadened Lumen’s fiber assets and shifted the company focus away from residential service towards enterprise, AI-driven networking.

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References:

https://ir.lumen.com/news-section/lumen-to-acquire-alkira/default.aspx

https://www.alkira.com/press-release/lumen-to-acquire-alkira-establishing-the-control-plane-for-cloud-connectivity/

https://ir.lumen.com/news-section/news/news-details/2026/Lumen-Technologies-Reports-Solid-First-Quarter-2026-Results/default.aspx

https://www.reuters.com/business/media-telecom/lumen-beats-quarterly-revenue-estimates-acquire-alkira-475-million-2026-05-05/

Lumen launches Multi-Cloud Gateway (MCGW) and expands metro fiber network after selling consumer FTTH business to AT&T

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Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area

SpaceX’s Starlink low-Earth orbit (LEO) satellite constellation providing high speed internet service is increasingly positioning itself as a scalable broadband access platform within the global telecom ecosystem.  It now has growing relevance for both retail and enterprise connectivity use cases.

Network performance improvements  (see below) have occurred alongside substantial subscriber growth. Starlink’s global user base expanded from approximately 4.6 million at the end of 2024 to over 10 million by early 2026, underscoring the LEO satellite platform’s ability to scale capacity while maintaining service quality.

This evolution is exemplified by T-Mobile’s “SuperBroadband” offering, which integrates 5G fixed wireless access (FWA) with Starlink satellite connectivity to deliver hybrid terrestrial–non-terrestrial network (NTN) solutions for business customers. The viability of such architectures is directly dependent on sustained improvements in satellite network throughput, latency, and service consistency.

Ookla Speedtest® data for the second half of 2025 indicates significant year-over-year improvements in Starlink’s performance across key network metrics. Median download speeds exceeded 100 Mbps in 49 states, compared to 23 states in 2H 2024, reflecting both increased system capacity and improved spectral efficiency. Performance gains were also observed across the lower quartile of users: 25th percentile download speeds improved in 48 states, with the number of states below 50 Mbps declining from eleven to two (Alaska and Florida). This shift indicates not only higher peak throughput but also improved quality of experience (QoE) consistency across the subscriber base.

Latency performance has also trended positively, driven by both constellation densification and architectural enhancements. While Starlink continues to target ~20 ms median latency, the number of states with median multi-server latency below 40 ms increased from one to ten between 2H 2024 and 1H 2025. By 2H 2025, top-performing regions—including New Jersey, Colorado, Arizona, and Washington, D.C.—achieved median latencies of approximately 37 ms, approaching parity with certain terrestrial broadband deployments and enabling latency-sensitive applications.

There has been a rapid expansion of the Starlink constellation and ongoing satellite technology upgrades. As of February 2026, the constellation exceeded 10,000 satellites in orbit, materially increasing aggregate network capacity and reducing cell congestion through greater spatial reuse. The deployment of Generation 3 (V3) satellites—featuring an order-of-magnitude increase (~10×) in downlink capacity relative to prior generations—has further enhanced throughput. Concurrently, upgrades to inter-satellite laser links have enabled more efficient space-based routing, reducing dependency on terrestrial gateway infrastructure, minimizing bottlenecks, and improving end-to-end latency performance.

Notably, these network enhancements have coincided with rapid subscriber growth. Starlink’s global user base expanded from approximately 4.6 million at year-end 2024 to over 10 million by early 2026, demonstrating the platform’s ability to scale capacity in line with demand while maintaining or improving key performance indicators.

Uplink performance has also improved materially, with 22 states achieving median upload speeds ≥20 Mbps in 2H 2025, compared to zero states in the prior-year period. This threshold is aligned with the FCC’s current broadband definition, underscoring Starlink’s increasing capability to meet regulatory benchmarks for two-way broadband services. Nebraska, New Jersey, and Minnesota recorded the largest gains, with Nebraska leading overall at 24.94 Mbps median upload throughput.

However, performance gains remain uneven across certain geographies. States including Connecticut, Hawaii, and New Hampshire exhibited relatively modest uplink improvements, suggesting localized constraints related to capacity allocation, gateway distribution, or demand density. These variances highlight the continued importance of targeted constellation scaling and ground segment optimization to ensure uniform service quality.

In Q4, 44.7% of Starlink’s user base achieved the FCC’s 100/20 Mbps broadband benchmark, signaling the provider’s transition from a niche rural solution to a high-performance market disruptor. By scaling its LEO constellation to over 10,000 nodes and deploying higher-throughput payloads, Starlink has successfully optimized spectral efficiency and reduced latency, maintaining QoS even as its global subscriber base scaled to 10 million.

While the U.S. remains Starlink’s primary market, the competitive landscape is shifting. Amazon’s Project Kuiper faces significant deployment headwinds; despite an FCC mandate to orbit 1,618 satellites by July 2026, the company has only deployed roughly 240 units and has petitioned for a two-year extension due to launch capacity constraints.  This market penetration places legacy GEO operators like Hughesnet and Viasat at a strategic disadvantage. Although these incumbents are leveraging aggressive pricing and CPE (Customer Premises Equipment) refreshes to stem churn, the inherent latency limitations of GEO architecture continue to pose a significant structural barrier to competing with LEO-based performance.

Overall, the data indicates that Starlink is transitioning from a niche rural broadband solution toward a more robust, high-capacity access network capable of supporting hybrid 5G/NTN architectures and enterprise-grade connectivity services.

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Addendum – LEO vs GEO satellite internet:

The technical architectures of Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) systems are fundamentally defined by their orbital altitude, which dictates their latency, link budget, and network complexity.

  • Orbital Mechanics and Altitude:
    • GEO satellites reside at a fixed altitude of approximately 35,786 km. They orbit at the same speed as the Earth’s rotation, appearing stationary from the ground, which allows for simple, fixed-point antenna installations.
    • LEO satellites operate at significantly lower altitudes, typically between 160 km and 2,000 km. Because they are closer to Earth, they must travel at much higher velocities (approx. 28,000 km/h) to maintain orbit, completing a full revolution in about 90–128 minutes.

  • Latency and Propagation Delay:
    • GEO: The extreme distance results in a high propagation delay, with a typical round-trip time (RTT) of 500–600 ms. This is unsuitable for real-time applications like VoIP, gaming, or high-frequency trading.
    • LEO: Proximity to Earth reduces latency to 20–50 ms, making the performance comparable to terrestrial fiber.

  • Link Budget and Power Requirements:
    • GEO: High path loss over 36,000 km requires high-power Traveling Wave Tube Amplifiers (TWTAs) and large, high-gain satellite antennas to maintain signal integrity. However, the terminal transmit power required for low-bitrate applications can actually be lower than LEO due to the stable, optimized architecture of legacy GEO MSS systems.
    • LEO: Lower path loss enables the use of lower-power RF systems. However, the rapid movement requires complex phased array antennas at the user terminal to electronically track satellites and manage seamless handoffs between nodes in the constellation.

  • Network Resilience and Capacity:
    • GEO: A single satellite can cover up to 42% of the Earth’s surface, but capacity is centralized; a single point of failure can impact an entire region.
    • LEO: Resilience is achieved through distributed constellations of thousands of satellites. These systems often utilize Intersatellite Links (ISLs)—optical or RF mesh networks in space—to route data between satellites, reducing the need for local ground gateways.
Comparison Summary

Feature                 LEO Architecture GEO Architecture
Altitude 160 – 2,000 km ~35,786 km
Latency (RTT) 20 – 50 ms 500 – 600 ms
Coverage Regional/Global via large constellation ~1/3 of Earth per satellite
Terminal Type Advanced tracking/Phased array Fixed parabolic dish
Operational Life ~5 years (due to atmospheric drag) ~15 years

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References:

https://www.ookla.com/articles/starlink-hits-new-us-highs

Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way

US Mobile’s new bundle combines its multi-network mobile service with Starlink residential internet

Starlink doubles subscriber base; expands to to 42 new countries, territories & markets

Elon Musk: Starlink could become a global mobile carrier; 2 year timeframe for new smartphones

Direct-to-Device (D2D) satellite network comparison: Starlink V2 (Starlink Mobile) vs “Satellite Connect Europe”

Blue Origin announces TeraWave – satellite internet rival for Starlink and Amazon Leo

Amazon Leo (formerly Project Kuiper) unveils satellite broadband for enterprises; Competitive analysis with Starlink

China ITU filing to put ~200K satellites in low earth orbit while FCC authorizes 7.5K additional Starlink LEO satellites

GEO satellite internet from HughesNet and Viasat can’t compete with LEO Starlink in speed or latency

Blaize and Winmate Forge Strategic Partnership to Accelerate Edge AI Integration in Ruggedized Systems

Bridging the Edge Connectivity Gap:

While modern AI architecture has historically favored centralized data centers, mission-critical applications require real-time inference at the edge. For defense personnel in remote locations, maritime operations, or emergency medical responders, reliance on cloud-based processing is often non-viable due to bandwidth constraints and latency requirements.

Eldorado Hills, CA based Blaize Holdings, Inc. and Winmate Inc. (TAIWAN) have announced a Strategic Partnership Agreement aimed at generating approximately $15 million in business during its inaugural year.  This collaboration integrates Blaize’s high-performance AI accelerators into Winmate’s industrial-grade ruggedized hardware ecosystem—including UAVs, handhelds, vehicle-mounted computers, and embedded systems—designed for mission-critical reliability in high-stress environments. Both organizations anticipate this agreement to be the foundation of a long-term, multi-year technological synergy.

The partnership addresses the “cloud dependency” bottleneck by leveraging Blaize’s GSP® (Graph Streaming Processor) architecture. These chips are engineered to industrial specifications, enabling sophisticated AI workloads to run locally on the device. When paired with Winmate’s ruggedized chassis—built to withstand extreme thermal fluctuations, high-velocity vibration, and dust ingress—the resulting systems provide high-compute AI capabilities in environments where traditional hardware fails.

Target applications:
  • Border security and surveillance: Real-time threat detection and perimeter monitoring
  • Mobile command and control: On-site intelligence and situational awareness for field teams
  • Drones and unmanned systems: Autonomous navigation and mission execution for UAVs and ground vehicles
  • Critical infrastructure: Continuous monitoring and predictive analytics for power, ports, and transportation
  • Maritime domain awareness: Vessel tracking and anomaly detection at sea
  • Field healthcare: Portable diagnostics and decision support in remote and disaster environments

Deal at a glance:

  • First-year revenue: the parties intend to work in good faith to close approximately $15 million in business, expected to scale meaningfully in subsequent years
  • Term: Three-year initial term, with automatic renewal
  • Next steps: Joint engineering, sales, and marketing execution to bring integrated systems to market, with additional opportunities to be added through follow-on programs
Blaize GSP Architecture and Winmate Ruggedization:
The core technical advantage of the Blaize and Winmate partnership lies in the shift from traditional instruction-set architectures to a graph-native processing model. This transition is critical for high-stakes environments like defense and critical infrastructure, where the “cloud round-trip” is an operational liability.
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1. Blaize Graph Streaming Processor (GSP®) Architecture:
Unlike traditional CPUs or GPUs that process tasks sequentially or in massive rigid parallel blocks, the Blaize GSP is purpose-built to execute AI graphs natively in hardware.
    • Task-Level Parallelism: The architecture leverages an on-chip hardware scheduler to analyze data dependencies in real-time. It executes deeper layers of a neural network as soon as previous layers produce sufficient intermediate results, minimizing the “idle time” typical of sequential processing.
    • Performance-to-Power Ratio: The flagship Blaize 1600 SoC features 16 GSP cores delivering 16 TOPS (Tera Operations Per Second) of AI inference within a conservative 7W power envelope.
    • Memory Efficiency: By streaming data through the processor and holding intermediate results in cache, the GSP reduces external DRAM access by up to 50x, which significantly lowers latency and overall system thermal output.
    • Unified Development Platform: All hardware is supported by the Blaize Picasso SDK, which allows developers to port models from standard frameworks (like PyTorch or TensorFlow) into a streaming execution format without requiring low-level hardware manual coding. 

Image Credit: Blaize Holdings

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2. Winmate Rugged Integration:
Winmate embeds these high-efficiency accelerators into “sovereign edge” platforms—hardware that maintains full operational capability without external network reliance. 
  • Pathfinder P1600 SOM: This System-on-Module is the primary vehicle for integration into Winmate’s handhelds and drones. It operates as a standalone unit with dual ARM Cortex-A53 processors and integrated MIPI CSI camera interfaces for real-time sensor fusion.
  • Mission-Ready Durability: These systems are engineered to meet MIL-STD-810H and IP65+ standards, ensuring that Blaize’s AI silicon remains stable under extreme vibration, thermal shock (operating in sub-zero or high-heat field conditions), and high-velocity impacts.
  • Sovereign Edge Computing: By processing sensitive data locally on ruggedized handhelds or vehicle-mounted units, the partnership ensures data sovereignty, preventing critical telemetry or biometric data from ever leaving the device during field operations
Quotes from the CEOs:

“Our customers can’t wait, and they often can’t rely on the cloud. They need AI that runs where the work happens. Winmate makes some of the most capable rugged systems in the industry, and our chips are designed to run AI inside exactly those kinds of devices. This partnership turns a years-long vision into a practical, deployable answer for defense and critical infrastructure operators,” said Dinakar Munagala, CEO of Blaize, Inc.

“Our platforms are deployed on naval vessels, in border outposts, on industrial sites, and in disaster zones – environments where most hardware fails. With Blaize, we can now deliver those same systems with on-device AI built in, giving customers real-time intelligence wherever they operate,” said Ken Lu, Chairman and CEO of Winmate Inc.

Market Outlook: The Shift to On-Device Intelligence:
The demand for localized, secure AI is currently experiencing exponential growth. Market data from BCC Research projects the global edge AI sector to expand from $11.8 billion in 2025 to $56.8 billion by 2030, representing a CAGR of 36.9%. For sectors such as defense, healthcare, and critical infrastructure, the move toward edge AI is driven by two primary imperatives:
    1. Latency: The necessity for near-zero response times in autonomous and diagnostic systems.
    2. Security: The requirement to process sensitive data locally to mitigate the risks associated with transmitting information over public or compromised networks.
By combining low-power, high-efficiency silicon with hardened mechanical engineering, Blaize and Winmate are positioning themselves at the forefront of this industrial shift toward decentralized intelligence.
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About Blaize, Inc.
Blaize delivers a programmable AI platform, purpose-built for AI inference workloads in real-world environments. Its Hybrid AI architecture combines the Blaize GSP (Graph Streaming Processor) with GPU-based infrastructure, enabling AI inference workloads to run across edge, cloud, and data center. Blaize solutions support computer vision, multimodal AI, and sensor-driven applications across smart cities, industrial automation, telecommunications, retail, logistics, and defense. Blaize is headquartered in El Dorado Hills, California, with a global presence across North America, Europe, the Middle East, and Asia. Visit www.blaize.com or follow us on LinkedIn @blaizeinc.

About Winmate Inc.
Winmate Inc. is a publicly traded global leader in rugged computing systems, delivering industrial-grade platforms – including handhelds, tablets, vehicle-mounted units, panel PCs, and embedded modules – for demanding environments across defense, transportation, energy, healthcare, and industrial markets.

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Key Differences Between Network Cybersecurity and Control System Cybersecurity & Why It Matters

By Joe Weiss with Alan J Weissberger

Introduction:

The Operational Technology (OT) [1.] cybersecurity [2.] community continues to ignore control system cyber-incidents [3.] – a governance failure masquerading as a vocabulary issue.

IT and OT network data breaches are documented in multiple sources such as the Verizon Data Breach Report, CISA documents, and others. Palo Alto Networks notes that nearly 70% of industrial firms had an OT cyber-attack last year. Those cyber-attacks were from data breaches – not always causing equipment damage.

Industrial organizations need an integrated and cyber resilient IT-OT framework to address this increasingly sophisticated threat landscape, but it appears they’re not well prepared to defend against network or control system cyberattacks.

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Note 1. Operational Technology refers to the combination of hardware and software designed to directly monitor, control, and manage physical devices, industrial equipment, and critical processes.

Note 2. Cybersecurity can be defined as the practice of protecting people, systems and data from cyberattacks by using various technologies, processes and policies.

Note 3. Cyber-incidents are defined as electronic communications between systems that effects Confidentiality, Integrity, or Availability. This is an IT-centric definition because Safety is not addressed.

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Image Credit: txOne Networks

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There are two communities addressing cybersecurity:

  1. The more prevalent community is the one involved in data security. This includes IT and OT network security and is focused on data breaches.
  2. The second community is focused on engineering security. It is less well-known, but very critical. This discipline is focused on safety, reliability, and productivity.

Professor Ross Anderson stated in his seminal book, “Security Engineering: A Guide to Building Dependable Distributed Systems,” that security engineering is about building systems to remain dependable in the face of malice, error, or mischance.”

The culture gap between network security and engineering organizations will be addressed in the June 2026 issue of IEEE Computer magazine, “Packets and Process: What Network Security and Engineering Get Wrong About Each Other.”

Discussion:

The OT cybersecurity community’s mission is to focus on OT network cyber-attacks. However, its charter does not extend to malicious and unintentional control system cyber incidents involving process sensors, actuators, motors, turbines, transformers, etc.

Importantly, control system cyber incidents can be physics-related rather than network-related. The 2007 Aurora vulnerability test at the Idaho National Laboratory destroyed a 2 MW commercial diesel generator by remotely restarting the generator out- of-phase with the grid. This is a gap in protection of the electric grid and was addressed in the October 2025 IEEE Computer magazine article, “Physics-Based Cyberattacks Against Electric Power Grids and Alternating Current Equipment.”

Idaho National Laboratory ran the Aurora Generator Test in 2007 to demonstrate how a cyberattack could destroy physical components of the electric grid. The diesel generator used in the experiment beginning to smoke as shown below:

Aurora Generator Test. Image Credit: Wikipedia

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Industry and government OT cybersecurity experts continue to downplay the threat of control system cyberattacks and ignore actual control system incidents that do not originate from OT networks by not calling them cyber-related.

There have been more than 20 million control system cyber incidents that have killed more than 30,000 people. Most of these incidents occurred below the IP-Ethernet layers where there is no cyber forensics nor cybersecurity training. As a result, the majority of these incidents were not identified as being cyber-related.

This indicates that control system cyber incidents that are not classified as IP-Ethernet incidents need their own classification as issues to be addressed by cybersecurity policy, especially for critical infrastructure where accidental and/or malicious cyber failures could result in widespread death and destruction.

Given the current geopolitical environment, nation-states are actively reassessing their capabilities to disrupt adversary infrastructure at scale. In this context, dismissing control system cyber incidents solely because they do not originate from traditional IP-based vectors introduces significant risk. Threat actors are increasingly targeting critical infrastructure and associated control systems—spanning both IT and OT domains—leveraging diverse attack surfaces beyond conventional network entry points.

A parallel issue within both the IT and OT security communities is the tendency to classify incidents as “cyber” only when malicious intent is confirmed. This narrow definition is problematic.

For example, the July 2024 CrowdStrike-related outage, which caused global operational disruptions, clearly met the functional criteria of a cyber-incident due to its systemic impact on networked systems. However, its non-malicious origin led some security governance bodies to exclude it from cyber incident classification. Such distinctions can undermine resilience planning, as they fail to account for the full spectrum of cyber-induced operational risk, including software supply chain failures and systemic misconfigurations.

ERPI Focus:

The European Risk Policy Institute (ERPI) was founded by the Australian Risk Policy Institute as part of the Global Risk Policy Network. EPRI Chairman wrote in a blog titled, “Control system cyber incidents and network breaches are apples and oranges”:

“From our ERPI / 3°C World SRP® perspective, Weiss is pointing at a governance failure masquerading as a vocabulary issue: if you define “cyber incident” through an IT breach lens, you will miss (or dismiss) the incidents that actually move risk —those that degrade continuity lifelines by disrupting physical processes. He makes the case that control-system cyber incidents include electronic/automation failures across sensor signals, control logic, firmware and field device communications, and that many are non-malicious yet still produce loss of view, loss of control, equipment damage, and safety/environmental consequences.

What matters strategically is the reporting and response architecture. Breach-centric metrics (and the cultural reflex that “no attack = no incident”) bias organizations toward under-detection, weak root-cause discipline, and false trend comparisons—exactly when coupled infrastructures are most fragile and repair cycles are tight. Weiss’s bridge condition is practical: align engineering and security on a shared incident definition, and train both communities in control-system incident reality so that operational anomalies are treated as cyber-relevant signals, not “maintenance noise.”

If you’re responsible for critical infrastructure, this is a reminder to recalibrate your incident taxonomy and your board narrative: the control-room outcome is the headline, and the network story is only one possible path to it.”

The Crucial Importance of Process Sensors:

Process sensors represent the biggest gap between data security and engineering security. Perplexity.ai explains this gap in detail -see below, but first we distinguish between data security and engineering security:

  • Data security focuses on IP-native devices such as firewalls, routers, switches, etc.
  • Engineering security should be focused on engineering devices and equipment that could cause equipment damage and deaths but have no cybersecurity, authentication, or cyber forensics. This includes process sensors, actuators, motors, transformers, inverters, etc. However, that focus is often not achieved.

Perplexity.ai on the Data vs. Information Security Gap:

Process sensors sit at the junction of physical process integrity and digital telemetry, so they expose a gap that neither data security teams nor engineering security teams fully own today. In practice, security teams tend to focus on protecting data pipelines, identities, and networks, while engineering teams focus on measurement accuracy, calibration, availability, and safety; the sensor itself often falls between those disciplines.

Process sensors are not just data endpoints; they are safety- and control-relevant instruments whose outputs can drive operators, PLCs, DCS logic, and downstream automation. If a sensor is spoofed, degraded, miscalibrated, or manipulated, the result is not only bad data but potentially unsafe or physically damaging control actions.

Traditional data security assumes the main problem is confidentiality, integrity, and availability of information in transit or at rest. But process sensors often use legacy field protocols, serial links, gateways, or embedded devices that were engineered for function and reliability rather than cryptographic assurance, making them difficult to secure using conventional enterprise controls.

Engineering security is usually optimized for process correctness, alarms, redundancy, and fail-safe behavior, not adversarial manipulation of the measurement layer. That means the sensor may be treated as trustworthy instrumentation, even though compromised or false sensor data can undermine control logic, operator decisions, and safety systems.

The core mismatch: The real issue is that data security protects the pipeline, while engineering security protects the process, but process sensors belong to both domains. Because ownership is split, sensor trust, authentication, anomaly detection, and physical tamper resistance are often addressed inconsistently or not at all, creating a blind spot at the boundary between cyber and physical risk.

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Highlights of Sensors Converge Conference Presentation:

To address these important issues and gaps, I will be presenting at the Sensors Converge conference in Santa Clara, CA on May 7, 2026. The title of my talk is, “Process Sensor Monitoring for Cybersecurity, Reliability, and Safety.” The presentation will include the following topics:

  • Process sensors (Level 0 devices) are inherently cyber vulnerable yet remain largely unrecognized by cybersecurity organizations.
  • Process sensor incidents, both malicious and unintentional, have caused catastrophic and fatal cyber/operational events across multiple sectors, but were not identified as being cyber-related.
  • Fatalities have occurred in every decade since the 1980s, including this decade.
  • Monitoring process sensors at the physics level can materially improve reliability, safety, and cybersecurity.
  • A discussion of what a process sensor cybersecurity program should include and what organizations should be involved.
  • The implications of process sensors which are not cyber-secure, because they don’t meet U.S. and/or EU cybersecurity requirements.

Nation-state actors, including Russia, China, and Iran, understand Level 0 cyber deficiencies. In sharp contrast, most cyber defenders do not and won’t identify process sensor incidents as being cyber-related. This gap helps explain why process sensor cybersecurity remains largely absent from OT security forums and RSA Conference discussions. It may also explain why government OT cybersecurity advisories don’t include insecure Level 0 devices, even though process sensors provide the trusted input to controllers and SCADA/DCS systems.

Conclusions:

Network cybersecurity functions across IT and OT domains, and control system engineering organizations, operate with fundamentally different objectives, taxonomies, and thresholds for identifying and classifying cyber incidents. This divergence has led to a persistent disconnect in how incidents affecting control systems are recognized and addressed within broader network security governance frameworks. Dismissing control system cyber events because they fall outside narrow, IT-centric definitions is not merely a semantic issue—it reflects a structural governance gap with direct implications for critical infrastructure resilience.

To address this, industry and government stakeholders must converge on a harmonized definition of cyber incidents that encompasses both network-centric and control system–centric perspectives. This alignment should be supported by cross-domain training, ensuring that both network security practitioners and engineering teams possess sufficient understanding of control system architectures, threat models, and failure modes. Without such integration, efforts to compare incident frequency, severity, and systemic impact across IT networks and control systems will remain inconsistent and misleading. More critically, this fragmentation will continue to obscure systemic risk, leaving essential infrastructure sectors exposed to increasingly sophisticated and multi-domain cyber threats.

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About Joe Weiss:

Joe Weiss is an expert on control system cyber security. He authored the 2010 book, “Protecting Industrial Control Systems from Electronic Threats.”

Joe is an ISA Fellow, Emeritus Managing Director of ISA99, an IEEE Senior Member, has patents on instrumentation, control systems, and OT networks. He is a professional engineer with CISM and CRISC certifications and is a member of Control Process Automation Hall of Fame.

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References:

https://www.paloaltonetworks.com/resources/research/state-of-ot-security-report

OT Cybersecurity: The Guide to Securing Industrial Systems

https://www.controlglobal.com/blogs/unfettered/blog/55360358/control-system-cyber-incidents-are-not-the-same-as-network-breaches

Mouse click could plunge city into darkness, experts say: https://www.cnn.com/2007/US/09/27/power.at.risk/index.html

IoT Sensor Standards Are Absolutely Essential for Security

Verizon Business sees escalating risks in mobile and IoT security

 

Cybersecurity to be a top priority for telcos in 2023

Anthropic’s Project Glasswing aims to reshape IT cybersecurity

Emerging Cybersecurity Risks in Modern Manufacturing Factory Networks

Cybersecurity threats in telecoms require protection of network infrastructure and availability

StrandConsult Analysis: European Commission second 5G Cybersecurity Toolbox report

IEEE/SCU SoE Virtual Event: May 26, 2022- Critical Cybersecurity Issues for Cellular Networks (3G/4G, 5G), IoT, and Cloud Resident Data Centers

Orange, Nokia, Nvidia, and Intel debate: ASICs vs. GPUs vs. General-Purpose CPUs for RAN Baseband Processing

For Orange CTO Laurent Leboucher, the main attraction of AI today lies in its potential to improve the efficiency of 5G radio access networks (RANs). That helps explain Orange’s recent collaboration with Nokia and Nvidia. Orange already deploys Nokia’s purpose-built 5G network equipment and software at mobile sites in France and other markets. Until recently, it had little obvious need for Nvidia, the U.S. chip making king best known for the graphics processing units (GPUs) used to train large language models. But Nokia and Nvidia became closely aligned last October, when Nvidia took a 3% stake in Nokia as part of a $1 billion investment. Nokia is now developing AI RAN software designed to run on GPUs.

Leboucher’s interest is driven in part by concerns over the cost of custom silicon — the application-specific integrated circuits (ASICs) used in purpose-built 5G networks. “It creates an opportunity to bring a general-purpose chipset instead of an ASIC implementation,” he told Light Reading at last week’s FutureNet World event in London. “I think we could, at some point, benefit from the economies of scale of new chipsets. That could be Nvidia.”

The rationale is much easier to understand than arguments about 5G for autonomous vehicles. Chip manufacturing is already expensive, and both Nokia and Ericsson expect component costs to rise further this year amid relentless AI demand. At the same time, the RAN market remains relatively small and has contracted. According to market research firm Omdia, telco spending fell from $45 billion in 2022 to $35 billion last year and is expected to stay at that level. In that context, it is increasingly difficult to justify designing high-cost chips with limited reuse outside telecom.

Image Credit: Orange

Last year, Nvidia spent about $18.5 billion on research and development, generated nearly $216 billion in revenue, and reported a gross margin of more than 70%. Its financial strength is not in question. If telecom operators can use its GPUs for RAN software, they may face less pressure to secure the long-term economics of 5G and 6G development. That alone could be enough to support the case for Nvidia. The counterarguments are cost and power consumption. By design, custom silicon is optimized for a specific workload and will always outperform a more general-purpose processor at that task. An Nvidia GPU in the RAN could therefore be seen as excessive — like using a crop duster to water a hanging basket.

Leboucher, believes that Nokia and Nvidia are developing something far more compact than a typical data-center deployment. “It is not a Blackwell GPU,” he said, referring to Nvidia’s current hyperscaler-class product line. “I have an understanding it’s something which is a little bit smaller.” One of the first GPU-based products is expected to come on a card that Orange can insert into an existing Nokia AirScale chassis.

He is also interested in replacing traditional RAN algorithms with AI to improve spectral efficiency and overall performance. Through trials with Nokia and Nvidia, Orange wants to determine whether a GPU is actually required to capture the full benefit. “We can completely rethink the way we are doing algorithms today, using AI for the radio Layer 1,” he said, referring to the most compute-intensive part of the RAN software stack. Some of the “AI-RAN” narrative still sounds “a little bit like science fiction,” Leboucher admitted. “But I think there are some very interesting ideas behind that. We want to understand where we are.”

This is not the first time the industry has debated a shift from ASICs to general-purpose processors for RAN equipment. Alongside its purpose-built 5G portfolio, Ericsson already offers cloud RAN products based on Intel CPUs. Samsung is now focused on Intel-based virtual RAN and has recently predicted the end of purpose-built 5G. Even so, cloud and virtual RAN still account for only a small share of live 5G deployments. Huawei and Ericsson, the two largest RAN vendors, remain committed to custom silicon development.

Nvidia’s entry into the market has clearly given Leboucher and his team more to evaluate as RAN technology becomes more sophisticated. “We are introducing new requirements for radio networks, typically for beamforming, and we have to consider the need for quite powerful chipsets,” he said. “Whether the best way to keep going is using ASICs or a general-purpose architecture – I think this is a good time to ask the question. Before, it was too early.”

The answer could shape Orange’s next major RAN decisions. The operator is preparing for what Leboucher describes as a “refresh” of RAN equipment across several countries ahead of the expected 6G launch in 2030. For the first time, he said, Orange will include cloud RAN as a “major option” in its request for proposal.

The concern around Intel as an alternative to Nvidia is its still-fragile financial position. Before December, Intel had been trying to spin off its network and edge group (NEX), which develops RAN chips. Those plans were later shelved, but the company’s net loss widened to about $4.3 billion in the most recent first quarter, from $887 million a year earlier, while revenue rose only 7% year over year to $13.6 billion. Cristina Rodriguez, who had led NEX, left this month to join Coherent, and Intel has not yet named a successor.  “The shares jumped 28% in after-hours trading, taking Intel firmly into meme-stock territory,” said Radio Free Mobile analyst Richard Windsor in a blog published after results came out on April 23. “I say meme-stock because there is no other way to describe it when the shares are on a 2026 PER [price-to-earnings ratio] of 137x, and its technology looks obsolete.”

Orange places significant value on separating hardware from software, allowing the same RAN software to run across multiple hardware platforms. Ericsson and Samsung both say the virtual RAN software they have built for Intel CPUs could, with relatively modest changes, be ported to AMD silicon using the same x86 architecture or to Arm-based CPUs.

By contrast, Layer 1 code written for Nvidia GPUs and the CUDA software stack would not be portable to other platforms, according to Ericsson. “I think the main challenge we see with that is we are trying very hard to keep our stack portable, to give hardware options,” Michael Begley, Ericsson’s head of RAN compute, told Light Reading at MWC Barcelona this year. “If you go all in on one, it’s great, but you’re all in on one, and you can’t offer those other options to the operators or the ecosystem.”

Leboucher acknowledges that risk. “The risk of lock-in exists, definitely,” he said. “We really want to stay open. At the same time, we know that benefiting from a very, very large-scale general-purpose architecture should improve the TCO [total cost of ownership]. At the end of the day, it will be a trade-off. But we would welcome an architecture where we have the capacity at some point to decide to swap if we need to swap.”

Nokia’s hope is that much of the Layer 1 software written for Nvidia GPUs will eventually be deployable on other GPU platforms. But Nvidia’s near-monopoly in that segment leaves the industry with few alternatives for now. There is also optimism inside Nokia that GPU-based code could later be adapted for capable CPUs, although Ericsson’s comments suggest that would be much harder. For telecom executives, the choices made over the next couple of years may be pivotal as 6G approaches.

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References:

https://www.lightreading.com/5g/orange-weighs-nvidia-against-intel-for-5g-chips-ahead-of-new-rfp

RAN Silicon Rethink- Part II; vRAN and General-Purpose Compute

RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN

T-Mobile expands FTTH footprint via 50-50 JVs with Oak Hill Capital and Wren House

T-Mobile US is expanding its fiber-to-the-home (FTTH) footprint by investing ~$2.7 billion in two new 50-50 joint ventures (JVs) with Oak Hill Capital ($2 billion for  GoNetspeed and Greenlight Networks) and Wren House ($700 million for  i3 Broadband). These partnerships aim to pass around 1.8 million homes, largely in the northeastern U.S., accelerating T-Mobile’s fixed broadband expansion alongside their 5G network. Those deals are expected to close in the first half of 2027. T-Mobile, which markets fiber services under the brand name “T-Fiber,” said the deals are part of a plan to serve 18 million to 19 million total broadband customers – including 3 million to 4 million fiber customers – by the end of 2030.

  • GoNetspeed offers voice and broadband services to residential and business customers (including multiple-dwelling units, or MDUs) in parts of Alabama, Connecticut, Maine, Massachusetts, Missouri, New York, Pennsylvania and Vermont, with plans to light up networks in cities in New Jersey and Rhode Island. GoNetspeed sells a handful of fiber-fed broadband tiers up to 6 Gbit/s and offers DSL in some areas.
  • Greenlight Networks, founded in 2011, supports speeds up to 10 Gbit/s for residential and business customers in New York (Rochester, Buffalo, Binghamton, Capital Region and Hudson Valley), Pennsylvania (Scranton, Wilkes-Barre and Lehigh Valley), and Baltimore, Maryland. It serves about 225,000 homes and nearly 10,000 small businesses.
  • i3 Broadband serves parts of Illinois and Missouri with broadband and voice services.

T-Mobile said GoNetspeed and Greenlight are expected to pass a combined 1.3 million households by the end of 2026, with i3 Broadband expected to pass roughly 500,000 households by that time. As it is with T-Mobile’s prior fiber JVs, the service providers involved in this new pair of transactions will operate under wholesale models that enable T-Mobile to offer “simple” plans with no annual service contracts.

Key Details of the Expansion – Total Investment: USD 2.7 billion:
  • Target: ~1.8 million new homes passed, primarily in the Northeast.
  • Partners: Joint ventures with investment firms Oak Hill Capital and Wren House.
  • Strategic Goal: Deepen fiber footprint to support a target of 18-19 million broadband customers by 2030, with 3-4 million on fiber.
Additional Strategic Moves (April 2026):
  • Starlink Business Backup: T-Mobile is introducing a Starlink-powered backup option to provide comprehensive, resilient connectivity for business customers, enhancing their “SuperBroadband” offerings.
  • Broadband Strategy: This move follows earlier 2025 moves, including the joint venture with EQT to acquire Lumos and the takeover of Metronet, strengthening T-Mobile’s position as a major fiber competitor.

Image Credit:  Panther Media GmbH/Alamy Stock Photo

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New Street Research analysts David Barden and Vikash Harlalka (via Light Reading) said GoNetspeed passed about 770,000 locations in June 2025, with 725,000 of them passed with fiber, and the rest passed by copper and hybrid fiber/coax (HFC). They also estimate that Greenlight passed about 330,000 locations and i3 Broadband passed roughly 370,000 with fiber as of June 2025. Combined, the three operators involved in the proposed T-Mobile JVs pass nearly 1.5 million total locations, including 1.4 million fiber locations, according to NSR.

Based on an assumption that each fiber network operator has achieved penetration levels of about 25%, New Street said this implies that the Oak Hill JV has about 275,000 customers while the Wren House JV has about 75,000. At that level, they said that means T-Mobile is paying about $725 million for customers from the Oak Hill JV and $250 million for customers from the Wren House JV.  The New Street analysts  said today’s announcement shows that T-Mobile continues to have interest in acquiring “pure-play fiber operators.” As such, they also believe that the odds of a reported T-Mobile-Uniti deal have dropped.

The analysts also believe that the new fiber-focused JVs will also lower the odds of a potential combination with a major US cable operators such as Charter Communications. “A larger fiber footprint also makes it more difficult to get a deal approved by regulators,” they explained.

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Even with the two new JV’s, T-Mobile’s fiber footprint will still be dwarfed by those of AT&T and Verizon,

  • AT&T is targeting a 60 million fiber-to-the-premises (FTTP) footprint by 2030, leveraging joint ventures to accelerate deployment.
  • Verizon, following acquisitions of Frontier and Eaton Fiber, projects 32 million fiber passings by 2026, with plans to reach 40–50 million via further partnerships and inorganic growth. Verizon, which also struck a deal to acquire Eaton Fiber last fall, is on track to end 2026 with more than 32 million fiber passings. CEO Dan Schulman reiterated that Verizon plans to broaden its fiber footprint to 40 million-50 million “over the medium term,” but did not provide a more specific timeframe.  “There’s no question that fiber is a key differentiator … against competitors that don’t have it,” Schulman said, noting that the attachment rate of Verizon mobile customers who also get broadband from Verizon is hovering at about 55%.

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References:

https://www.lightreading.com/broadband/t-mobile-s-new-jvs-fixate-on-fiber

https://www.lightreading.com/broadband/verizon-surpasses-6m-fwa-subs-as-priority-shifts-to-fiber

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Big Tech AI spending binge results in massive job cuts!

Executive Summary:

The tech industry is undergoing a massive structural realignment. Hyperscalers, Software as a Service (SaaS) vendors, and telecom network and equipment providers are aggressively slashing workforces to reallocate capital toward massive AI infrastructure investments.  Alphabet, Meta, Amazon, and Microsoft are projected to spend a collective $674 billion in 2026—over double their 2024 levels.  Most of that spending is AI related.

From the referenced WSJ article:

“Tech companies are in effect playing a game of chicken with each other on capital-spending plans. They are shelling out as much as they can—more than their rivals, they hope—on AI chips and data centers that could put them in the lead in a race they feel they can’t afford to lose. That in turn is heightening competition over who can use AI to help do more with a lot less, freeing up money to spend on expensive chips.”

Hyperscalers, such as Microsoft and Meta Platforms (Meta), are the latest to  their significantly reduce their workforces to scale AI-driven operations. Meta is reportedly reducing its headcount by approximately 8,000, while Microsoft has initiated a “voluntary retirement program” (aka a buyout) targeting 7% of its U.S. workforce—a strategic move to trim payroll before resorting to involuntary layoffs.

This trend is industry-wide: Oracle and Snap have executed significant reductions, while Block announced plans to cut 40% of its staff (over 4,000 employees).  March 2026 represented a two-year peak in tech industry contraction, with Layoffs.fyi reporting 45,800 tech job reductions.

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Source:  Layoffs.fyi
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The AI Transformation Narrative vs. Financial Reality:

Executive leadership is framing these cuts as a strategic pivot toward an AI-native future where automated workflows replace legacy human-centric processes. While CEOs like Block’s Jack Dorsey insist these decisions aren’t driven by distress, a “game of chicken” is unfolding in capital planning.

Companies are locked in an escalating race to secure AI silicon (GPUs), High Bandwidth Memory (HBM) and expand Data Center footprints, creating a massive drain on liquidity.  This heightens the pressure to achieve “doing more with less”—using AI to automate internal functions and free up the capital necessary for expensive infrastructure. However, in many cases, these cuts are simply corrective measures for pandemic-era overhiring or efforts to normalize efficiency metrics:

  • Oracle: Annual revenue per employee remains significantly below industry leaders like Microsoft.
  • Snap: Headcount remains 65% above pre-COVID levels despite consistent operating losses.

Strategic Risks and “Off-Balance-Sheet” Engineering:

While slashing headcounts improves Revenue Per Employee (RPE)—a key KPI for Wall Street—it introduces significant long-term risks:

  • Talent Attrition & Brain Drain: Aggressive layoffs degrade morale and may drive elite engineering talent toward startups, potentially creating new competitors.
  • Governance & Safety: Reducing human oversight during AI deployment could lead to safety and business model integration failures.
  • Regulatory & Public Backlash: The “AI as a job killer” narrative is fueling community opposition to massive data center builds, complicating infrastructure rollouts.

The CAPEX Burden:

The financial strain is becoming evident even for “Deep Pocket” firms. Alphabet, Meta, Amazon, and Microsoft are projected to spend $674 billion in CAPEX this year—more than double their 2022 spend.

  • Amazon is projected to be cash-flow negative this year.
  • Meta’s CAPEX is set to exceed 50% of its annual revenue, with its debt-to-equity ratio climbing to 39% (up from 8% five years ago).
  • Some firms are reportedly utilizing “off-balance-sheet financial wizardry” to maintain their AI compute growth without alarming debt markets.

Verdict of the Market?

Markets are sending mixed signals. While analysts are obsessed with efficiency metrics (questions about efficiency on earnings calls have tripled in two years), they are becoming “skittish” regarding unbridled spending. Tesla (TSLA), for instance, saw a 4% stock dip after raising its spending target to $25 billion.

Ultimately, tech giants—who already average $2M in annual revenue per employee—are betting that further workforce reductions will juice efficiency and fund the AI arms race. The trade-off remains whether these “leaner” organizations can maintain the innovation and safety standards required to lead the next technological cycle.

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The telecom sector is particularly vulnerable, as AI-native “zero-touch” operations begin to replace legacy roles permanently.

  • Network Operators:BT has announced plans to replace up to 10,000 roles with AI by 2030, specifically targeting network management and customer service.
  • Network Equipment Vendors: Equipment giants Ericsson and Nokia have collectively shed over 36,000 roles in recent years, pivoting from traditional hardware to AI-optimized software and networking.
  • Integrators:Accenture and IBM are utilizing AI to automate junior-level coding and back-office HR tasks, signaling that AI reskilling is now a prerequisite for workforce retention.

Strategic Outlook – Monetization and the “RPE” Battle:   

For both MNOs and tech giants, the coming years are about monetization. Investors have shifted from cheering bold AI visions to demanding tangible results, with a heavy focus on Revenue Per Employee (RPE)—a metric that workforce reductions are designed to “juice.”

That “Great Realignment” is a high-stakes gamble, in this author’s opinion.  The firms that successfully bridge the gap between massive infrastructure investments and scalable, profitable AI-native services will lead the next generation of global technology. Those that fail to balance efficiency with talent retention may find themselves outpaced by leaner, AI-native startups born from the very talent they have released.

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References:

https://www.wsj.com/tech/ai/the-ai-splurge-is-costing-big-tech-its-workforce-34a88e68

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