AI in Networks Market
SK Telecom forms AI CIC in-house company to pursue internal AI innovation
SK Telecom (SKT) is establishing an in-house independent company (CIC) that consolidates its artificial intelligence (AI) capabilities. Through AI CIC, SK Telecom plans to invest approximately 5 trillion won (US$3.5 billion) in AI over the next five years and achieve annual sales of over 5 trillion won ($3.5 billion) by 2030.
On September 25th, SK Telecom CEO Ryu Young-sang held a town hall meeting for all employees at the SKT Tower Supex Hall in Jung-gu, Seoul, announcing the launch of AI CIC to pursue rapid AI innovation. Ryu will concurrently serve as the CEO of SK CIC. SK Telecom plans to unveil detailed organizational restructuring plans for AI CIC at the end of October this year.
“We are launching AI CIC, a streamlined organizational structure, and will simultaneously pursue internal AI innovation, including internal systems, organizational culture, and enhancing employees’ AI capabilities. We will grow AI CIC to be the main driver of SK’s AI business and, furthermore, the core that leads the AI business for the entire SK Group. The AI CIC will establish itself as South Korea’s leading AI business operator in all fields of AI, including services, platforms, AI data centers and proprietary foundation models,” Ryu said.
The newly established AI CIC will be responsible for all the company’s AI-related functions and businesses. It is expected that SK Telecom’s business will be divided into mobile network operations (MNO) and AI, with AI CIC consolidating related businesses to enhance operational efficiency. Furthermore, AI CIC will actively participate in government-led AI projects, contributing to the establishment of a government-driven AI ecosystem. SKT said that reorganizing its services under one umbrella will “drive AI innovation that enhance business productivity and efficiency.”
“Through this (AI CIC), we will play a central role in building a domestic AI-related ecosystem and become a company that contributes to the success of the national AI strategy,” Ryu said.
By integrating and consolidating dispersed AI technology assets, SKT plans to strengthen the role of the “AI platform” that supports AI technology/operations across the entire SK Group, including SKT, and also pursue a strategy to secure a flexible “AI model” to respond to the diverse AI needs of the government, industry, and private sectors.
In addition, SKT will accelerate the development of future growth areas (R&D) such as digital twins and robots, and the expansion of domestic and international partnerships based on AI full-stack capabilities.
Ryu Young-sang, CEO of SK Telecom, unveils the plans for the AI CIC
CEO Ryu said, “SK Telecom has secured various achievements such as securing 10 million Adot (AI enabled) subscribers, selecting an independent AI foundation model, launching the Ulsan AI DC, and establishing global partnerships through its transformation into an AI company over the past three years, and has laid the foundation for future leaps forward. We will achieve another AI innovation centered around the AI CIC to restore the trust of customers and the market and advance into a global AI company.”
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References:
https://www.businesskorea.co.kr/news/articleView.html?idxno=253124
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Lumen: “We’re Building the Backbone for the AI Economy” – NaaS platform to be available to more customers
“Lumen is determined to lead the transformation of our industry to meet the demands of the AI economy,” said Lumen Technologies CEO Kate Johnson. “With ubiquitous reach and a digital-first platform, we are positioned to deliver next-gen connectivity, power enterprise innovation, and secure our own growth. This is how we build the trusted network for AI and deliver exceptional value to our customers and shareholders.”
Highlights included keynote remarks from Johnson, who outlined the three pillars of the company’s strategy:
- Building the backbone for the AI economy with a physical network designed for scale, speed, and security – delivering connectivity anywhere and for everything customers want to do.
- Cloudifying and agentifying telecom to reduce complexity and simplify the network for customers as an intelligent, on-demand, consumption-based digital platform.
- Creating a connected ecosystem with partnerships that extend Lumen’s reach, accelerate customer-first, AI-driven innovation, and unlock new opportunities across industries.
Johnson noted how Lumen’s growth is powered by a set of unique enablers that turn the company’s network into a true digital platform. With near-term product launches like self-service digital portal Lumen Connect, a universal Fabric Port, and new innovations in development that extend intelligence into the network edge, Lumen is making connectivity programmable and effortless. Combined with the company’s Network-as-a-Service business model and a connected ecosystem of data centers, hyper-scalers and technology partners, these enablers give customers the speed, security, and simplicity they need to thrive in the AI economy.
Lumen Technologies CEO Kate Johnson spotlights the company’s bold strategy, financial progress, and early look at product roadmap to reimagine digital networking for the AI economy at a gathering of industry analysts.
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Chief Financial Officer Chris Stansbury said 2026 is expected to mark an inflection point as new digital revenues, growth in IP and Wavelengths, and long-term hyper-scaler contracts begin to outpace legacy declines – setting up what he called a “trampoline moment” for expansion. Lumen projects business segment revenue growth in 2028 and a return to overall top-line growth in 2029, establishing a clear path from stabilization to value creation.
With a strengthened balance sheet and greater financial freedom, executives highlighted the bold investment in the company’s three strategic pillars, each designed to accelerate innovation and position Lumen for long-term industry leadership.
Lumen’s strategy begins with the physical network, which carries a significant portion of the world’s internet traffic. With construction underway coast-to-coast, the company is executing a multi-billion-dollar program to expand its intercity and metro fiber backbone:
- Adding 34 million new fiber miles by the end of 2028 for a total of 47 million intercity and metro miles.
- Connecting data centers, clouds, edge, and enterprise locations in any combination.
- Delivering 400G today and plans to scale to 1.6 terabits in the future.
Lumen’s substantial investments to expand high-speed connectivity ensures customers have the network scale, speed, and reliability to confidently innovate and grow without constraints.
The rise of AI is driving unprecedented demands for a new, Cloud 2.0 architecture with distributed, low-latency, high-bandwidth networks that can move and process massive amounts of data across multi-cloud, edge, and enterprise locations. Lumen is meeting this challenge by cloudifying and agentifying telecom, turning its expansive fiber footprint into a programmable digital platform that strips away the complexity of legacy networking.
Lumen plans to make its network-as-a-service (NaaS) platform [1.] available to more customers, regardless of their existing internet connection. At the company’s Analyst Forum, The NaaS platform includes new innovations like Lumen Fabric Port (Q4 2025), Lumen Multi-Cloud Gateway (Q4 2025), and Lumen Connect (Q1 2026). Together, these technologies digitize the entire service lifecycle, so customers can provision, manage, and scale thousands of services across thousands of locations, within minutes.
Note 1. Network as a Service (NaaS) is a cloud-based model that allows businesses to rent networking services from a provider on a subscription or pay-per-use basis, instead of building and maintaining their own network infrastructure. NaaS provides scalable and flexible network capabilities, shifting the cost from a capital expense (CapEx) to an operational expense (OpEx). NaaS functions by using a virtualized, software-defined network, meaning the network capabilities are abstracted from the physical hardware. Businesses access and manage their network resources through a web-based interface or portal, and the NaaS provider manages the underlying infrastructure, including hardware, software, updates, and troubleshooting.
Lumen CTO Dave Ward unveiled “Project Berkeley,” a network interface device that essentially expands the company’s NaaS services, like on-demand internet, Ethernet and IP VPN, to off-net sites using any access type. Those access types can be 5G, fiber, copper, fixed wireless access, satellite and more. Project Berkeley leverages digital twin technology, which lets Lumen have “a full replicate understanding of exactly what’s going on in this device running out of our cloud.”
Ward said on the company’s website:
“Lumen is taking the network out of its hardware box and transforming it into a true digital platform. Technology and Product Officer Dave Ward. “By cloudifying our fiber assets into software and disrupting cloud economics, we’re giving customers the ability to turn up services within minutes, scale as their AI workloads demand, and innovate at cloud speed. This is what the future of digital networking should deliver.”
Lumen has been growing its NaaS platform for some time. It launched its first offering in 2023 and now counts over 1,000 enterprise NaaS customers. The company now plans to bring its connectivity products to over 10 million off-net buildings, said Ward. The device will also allow hyper-scalers to integrate and sell these products in their respective marketplaces.
In closing the Analyst session, CEO Johnson underscored that Lumen’s strategies are the foundation of the company’s momentum today – transforming the industry with innovation to fuel growth, strengthening financial performance, and positioning the company as a critical enabler in the digital economy.
“We’re thrilled by the energy and engagement we’ve seen from the analyst community. The discussions around how Lumen is delivering an expansive network, digital platform, connected ecosystem and winning culture to meet the exponential enterprise demands of AI demonstrate the urgent need for innovation in our industry, and we’re proud to be at the forefront of that conversation.”
About Lumen Technologies:
Lumen is unleashing the world’s digital potential. We ignite business growth by connecting people, data, and applications – quickly, securely, and effortlessly. As the trusted network for AI, Lumen uses the scale of our network to help companies realize AI’s full potential. From metro connectivity to long-haul data transport to our edge cloud, security, managed service, and digital platform capabilities, we meet our customers’ needs today and as they build for tomorrow.
For news and insights visit news.lumen.com, LinkedIn: /lumentechnologies, X: lumentechco, Facebook: /lumentechnologies, Instagram: @lumentechnologies and YouTube: /lumentechnologies. Lumen and Lumen Technologies are registered trademarks of Lumen Technologies LLC in the United States. Lumen Technologies LLC is a wholly owned affiliate of Lumen Technologies, Inc.
References:
For a replay of the webcast, visit Lumen’s investor website
https://www.fierce-network.com/broadband/lumen-says-its-taking-its-naas-new-level
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Ciena to acquire Nubis Communications for high performance optical and electrical interconnects to support AI workloads
New Ciena Acquisition:
Today, Ciena announced it will acquire electronics startup Nubis Communications, a privately-held company headquartered in New Providence, New Jersey for $270 million. Nubis specializes in high-performance, ultra-compact, low-power optical and electrical interconnects tailored to support AI workloads. The Nubis acquisition will give Ciena access to technology that supports a wider range of data center use cases. It is is expected to close during Ciena’s fiscal 4th quarter.
Nubis’ solutions complement Ciena’s existing high-speed interconnects portfolio and will enable new capabilities to support growing AI workloads by significantly increasing scale up and scale out capacity and density inside the data center. The Nubis portfolio includes two key technologies:
- Co-Packaged Optics (CPO) / Near Packaged Optics (NPO): Nubis’ compact, high-density optical modules deliver ultra-fast data transfer using light instead of traditional electrical signals. Supporting up to 6.4 Tb/s full-duplex bandwidth, these modules are optimized for low-latency, low-power operation – making them ideal for scaling AI systems. Combined with Ciena’s high-speed SerDes, Nubis’ optical engines enable differentiated CPO solutions to address high-performance connectivity needs inside and between racks.
- Electrical ACC: Nubis’ advanced analog electronics enable Active Copper Cables (ACC) to support high-speed data transmission, allowing data to travel up to 4 meters at speeds of 200 Gb/s per lane. This low-power, low-latency solution helps customers connect more AI accelerators across racks without the limitations of traditional copper or DSP-based solutions
Nubis has developed two products to increase bandwidth and reduce latency within and between data center racks:
- XT Optical Engines is a series of optical modules that support up to 6.4 Tbps of full-duplex bandwidth while using light instead of traditional electrical signals.
- Nitro Linear Redriver aims to improve the performance of all the copper cables that are wired into the data center. Bloomberg has predicted copper usage in North American data centers could increase by 1.1-2.4 million tons by 2030 as “AI demands mount.”
“The acquisition of Nubis represents a significant step forward in Ciena’s strategy to address the rapidly growing demand for scalable, high-performance connectivity inside the data center, driven by the explosive growth of AI-related traffic,” said David Rothenstein, Chief Strategy Officer at Ciena. “With ownership of these key technologies for a wider range of use cases inside the data center, we are expanding our competitive advantage by advancing development of differentiated solutions, reducing development costs, and driving long-term efficiency and profitability. Nitro also supports up to 4m of reach for 200G per lane active copper cables, far beyond the limits of passive copper and legacy analog solutions. This is a game-changer for AI infrastructure, where short-reach, high-bandwidth copper is preferred for cost and latency reasons,” Rothenstein added.
“The Nubis team is thrilled to join Ciena and enhance its industry-leading portfolio with our breakthrough interconnect technologies,” said Dan Harding, CEO of Nubis. “Together, we will advance Ciena’s data center strategy by delivering reliable, high-quality, and high-performance interconnect solutions to support the next generation of AI workloads.”
Dell’Oro VP Jimmy Yu said Nubis is probably “one of [Ciena’s] most forward-looking” acquisitions, since the company is assembling the pieces it thinks are necessary to support future data center networking. “This acquisition aligns well with Ciena’s overall strategy to expand into the data center market, and it likely played a role in their decision to exit future investments in broadband PON,” Yu said.
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Ciena Cutting Back on Residential Broadband Access investments to focus on AI and Coherent Optics:
The Nubis takeover comes shortly after Ciena announced it will reduce investment in residential broadband access (e.g. 25G PON) to focus more on AI applications and its coherent optics business. Ciena CEO Gary Smith said on the company’s Q3 2025 earnings call:
“Folks are more concentrated on 10-gig and driving that out, and there’s a good market for that. As we looked at our overall portfolio and our investments in [25-gig], we see so much opportunity in these different AI workloads that we want to continue to really make sure we’re heavily invested in that….To be clear, we will continue to sell and support our existing broadband access products. However, we will be limiting our forward investments only to strategic areas such as DCOM [1.].”
Note 1. DCOM refers to Ciena’s data center out-of-band management solution, which involves replacing bulky legacy hardware like copper cabling and console servers with passive optical network (PON) technology.
Dell’Oro Group’s Jimmy Yu thinks Ciena’s move to re-allocate R&D dollars makes sense so that the company is not spread too thin and [misses] out the biggest opportunity sitting in front of them. “My guess is that to address the future of AI workloads and AI data center interconnect, Ciena will need to not only maintain their cadence on launching new high performance coherent optics like the WaveLogic 6e for long distance 1.6 Tbps connections, but also optical devices for shorter distances like 800 ZR/ZR+ plugs and even shorter distances that take them inside the data center,” Yu explained.
Ciena considers the WaveLogic series its bread-and-butter for coherent optics. The company in Q3 gained 11 new customers for its WaveLogic 6 Extreme product, bringing its total customer tally to 60. Companies deploying WaveLogic 6 include operators such as Arelion, Lumen and Telstra, which are upgrading their networks to support demand from cloud customers.
Supplemental Materials:
In conjunction with this announcement, Ciena has posted to the Events and Presentations page of the Investor Relations section of its website a recorded transaction overview presentation and accompanying transcript.
About Ciena:
Ciena is the global leader in high-speed connectivity. We build the world’s most adaptive networks to support exponential growth in bandwidth demand. By harnessing the power of our networking systems, components, automation software, and services, Ciena revolutionizes data transmission and network management. With unparalleled expertise and innovation, we empower our customers, partners, and communities to thrive in the AI era. For updates on Ciena, follow us on LinkedIn and X, or visit the Ciena Insights webpage and Ciena website.
About Nubis Communications:
Nubis says they innovate across photonics, electronics, packaging and manufacturing to create optics significantly more dense, scalable and lower power than existing solutions, breaking the I/O wall in data centers and enabling more advanced compute, AI and machine learning. The startup has raised over $50 million in funding with the help of investors such as Ericsson and Marvell Technology co-founders Weili Dai and Sehat Sutardja.
Nubis has just over 50 employees including a seasoned executive team. Founder Peter Winzer previously led fiber optic transmission research at Nokia’s Bell Labs, while CEO Dan Harding spent over 15 years at Broadcom.
References:
https://www.nubis-inc.com/about-us/
https://www.nubis-inc.com/products/
https://www.fierce-network.com/broadband/ciena-ramps-data-center-focus-new-270m-deal
https://www.fierce-network.com/broadband/ciena-pulls-back-broadband-focus-more-ai
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AI Data Center Boom Carries Huge Default and Demand Risks
“How does the digital economy exist?” asked John Medina, a senior vice president at Moody’s, who specializes in assessing infrastructure investments. “It exists on data centers.”
New investments in data centers to power Artificial Intelligence (AI) are projected to reach $3 trillion to $4 trillion by 2030, according to Nvidia. Other estimates suggest the investment needed to keep pace with AI demand could be as high as $7 trillion by 2030, according to McKinsey. This massive spending is already having a significant economic impact, with some analysis indicating that AI data center expenditure has surpassed the total impact from US consumer spending on GDP growth in 2025.
U.S. data center demand, driven largely by A.I., could triple by 2030, according to McKinsey. That would require data centers to make nearly $7 trillion in investment to keep up. OpenAI, SoftBank and Oracle recently announced a pact to invest $500 billion in A.I. infrastructure through 2029. Meta and Alphabet are also investing billions. Merely saying “please” and “thank you” to a chatbot eats up tens of millions of dollars in processing power, according to OpenAI’s chief executive, Sam Altman.
- OpenAI, SoftBank, and Oracle pledging to invest $500 billion in AI infrastructure through 2029.
- Nvidia and Intel collaborating to develop AI infrastructure, with Nvidia investing $5 billion in Intel stock.
- Microsoft spending $4 billion on a second data center in Wisconsin.
- Amazon planning to invest $20 billion in Pennsylvania for AI infrastructure.
Compute and Storage Servers within an AI Data Center. Photo credit: iStock quantic69
The spending frenzy comes with a big default risk. According to Moody’s, structured finance has become a popular way to pay for new data center projects, with more than $9 billion of issuance in the commercial mortgage-backed security and asset-backed security markets during the first four months of 2025. Meta, for example, tapped the bond manager Pimco to issue $26 billion in bonds to finance its data center expansion plans.
As more debt enters these data center build-out transactions, analysts and lenders are putting more emphasis on lease terms for third-party developers. “Does the debt get paid off in that lease term, or does the tenant’s lease need to be renewed?” Medina of Moody’s said. “What we’re seeing often is there is lease renewal risk, because who knows what the markets or what the world will even be like from a technology perspective at that time.”
Even if A.I. proliferates, demand for processing power may not. Chinese technology company DeepSeek has demonstrated that A.I. models can produce reliable outputs with less computing power. As A.I. companies make their models more efficient, data center demand could drop, making it much harder to turn investments in A.I. infrastructure into profit. After Microsoft backed out of a $1 billion data center investment in March, UBS wrote that the company, which has lease obligations of roughly $175 billion, most likely overcommitted.
Some worry costs will always be too high for profits. In a blog post on his company’s website, Harris Kupperman, a self-described boomer investor and the founder of the hedge fund Praetorian Capital, laid out his bearish case on A.I. infrastructure. Because the building needs upkeep and the chips and other technology will continually evolve, he argued that data centers will depreciate faster than they can generate revenue.
“Even worse, since losing the A.I. race is potentially existential, all future cash flow, for years into the future, may also have to be funneled into data centers with fabulously negative returns on capital,” he added. “However, lighting hundreds of billions on fire may seem preferable than losing out to a competitor, despite not even knowing what the prize ultimately is.”
It’s not just Silicon Valley with skin in the game. State budgets are being upended by tax incentives given to developers of A.I. data centers. According to Good Jobs First, a nonprofit that promotes corporate and government accountability in economic development, at least 10 states so far have lost more than $100 million per year in tax revenue to data centers. But the true monetary impact may never be truly known: Over one-third of states that offer tax incentives for data centers do not disclose aggregate revenue loss.
Local governments are also heralding the expansion of energy infrastructure to support the surge of data centers. Phoenix, for example, is expected to grow its data center power capacity by over 500 percent in the coming years — enough power to support over 4.3 million households. Virginia, which has more than 50 new data centers in the works, has contracted the state’s largest utility company, Dominion, to build 40 gigawatts of additional capacity to meet demand — triple the size of the current grid.
The stakes extend beyond finance. The big bump in data center activity has been linked to distorted residential power readings across the country. And according to the International Energy Agency, a 100-megawatt data center, which uses water to cool servers, consumes roughly two million liters of water per day, equivalent to 6,500 households. This puts strain on water supply for nearby residential communities, a majority of which, according to Bloomberg News, are already facing high levels of water stress.
“I think we’re in that era right now with A.I. models where it’s just who can make the bigger and better one,” said Vijay Gadepally, a senior scientist at the Lincoln Laboratory Supercomputing Center at the Massachusetts Institute of Technology. “But we haven’t actually stopped to think about, Well, OK, is this actually worth it?”
References:
What Wall Street Sees in the Data Center Boom – The New York Times
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
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Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
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Ericsson integrates agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
Ericsson is integrating agentic AI into its NetCloud platform to create self-healing and autonomous 5G private (enterprise) networks. This initiative upgrades the existing NetCloud Assistant (ANA), a generative AI tool, into a strategic partner capable of managing complex workflows and orchestrating multiple AI agents. The agentic AI agent aims to simplify private 5G adoption by reducing deployment complexity and the need for specialized administration. This new agentic architecture allows the new Ericsson system to interpret high-level instructions and autonomously assign tasks to a team of specialized AI agents.
Key AI features include:
- Agentic organizational hierarchy: ANA will be supported by multiple orchestrator and functional AI agents capable of planning and executing (with administrator direction). Orchestrator agents will be deployed in phases, starting with a troubleshooting agent planned in Q4 2025, followed by configuration, deployment, and policy agents planned in 2026. These orchestrators will connect with task, process, knowledge, and decision agents within an integrated agentic framework.
- Automated troubleshooting: ANA’s troubleshooting orchestrator will include automated workflows that address the top issues identified by Ericsson support teams, partners, and customers, such as offline devices and poor signal quality. Planned to launch in Q4 2025, this feature is expected to reduce downtime and customer support cases by over 20 percent.
- Multi-modal content generation: ANA can now generate dynamic graphs to visually represent trends and complex query results involving multiple data points.
- Explainable AI: ANA displays real-time process feedback, revealing steps taken by AI agents in order to enhance transparency and trust.
- Expanded AIOps insights: NetCloud AIOps will be expanded to provide isolation and correlation of fault, performance, configuration, and accounting anomalies for Wireless WAN and NetCloud SASE. For Ericsson Private 5G, NetCloud is expected to provide service health analytics including KPI monitoring and user equipment connectivity diagnostics. Planned availability Q4 2025.
Manish Tiwari, Head of Enterprise 5G, Ericsson Enterprise Wireless Solutions, adds: “With the integration of Ericsson Private 5G into the NetCloud platform, we’re taking a major step forward in making enterprise connectivity smarter, simpler, and adaptive. By building on powerful AI foundations, seamless lifecycle management, and the ability to scale securely across sites, we are providing flexibility to further accelerate digital transformation across industries. This is about more than connectivity: it is about giving enterprises the business-critical foundation they need to run IT and OT systems with confidence and unlock the next wave of innovation for their businesses.”
Pankaj Malhotra, Head of WWAN & Security, Ericsson Enterprise Wireless Solutions, says: “By introducing agentic AI into NetCloud, we’re enabling enterprises to simplify deployment and operations while also improving reliability, performance, and user experience. More importantly, it lays the foundation for our vision of fully autonomous, self-optimizing 5G enterprise networks, that can power the next generation of enterprise innovation.”
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Highlights of Nokia’s Smart Factory in Oulu, Finland for 5G and 6G innovation
Nokia has opened a Smart Factory in Oulu, Finland, for 5G/6G design, manufacturing, and testing, integrating AI technologies and Industry 4.0 applications. It brings ~3,000 staff under one roof and is positioned as Europe’s flagship site for radio access (RAN) innovation.
The Oulu campus will initially focus on 5G, including: Standardization, System-on Chips as well as 5G radio hardware and software and patents. Oulu Factory, part of the new campus, will target New Production Introduction for Nokia’s 5G radio and baseband products. The new campus strengthens Oulu’s ecosystem as a global testbed for resilient and secure networks for both civilian and defense applications.
At Oulu “Home of Radio” campus, Nokia’s research and innovation underpins high quality, tested world class products readymade for customers across markets. Nokia’s experts will continue to foster innovation, from Massive MIMO radios like Osprey and Habrok to next-generation 6G solutions, creating secure, high-performance, future-proof connectivity.
Sustainability is integral to the facility. Renewable energy is used throughout the site, with additional energy used to heat 20,000 households in Oulu. The on-site energy station is one of the world’s largest CO2-based district heating and cooling plants.
Active 6G proof-of-concept trials will be tested using ~7 GHz and challenging propagation scenarios.
“Our teams in Oulu are shaping the future of 5G and 6G developing our most advanced radio networks. Oulu has a unique ecosystem that integrates Nokia’s R&D and smart manufacturing with an ecosystem of partners – including universities, start-ups and NATO’s DIANA test center. Oulu embodies our culture of innovation and the new campus will be essential to advancing connectivity necessary to power the AI supercycle,” said Justin Hotard, President and CEO of Nokia
Nokia Oulu Facts:
- Around 3,000 employees and 40 nationalities working on the campus.
- Oulu campus covers the entire product lifecycle of a product, from R&D to manufacturing and testing of the products.
- Footprint of the building is overall 55,000 square metres, including manufacturing, R&D and office space.
- Green campus with all energy purchased green and all surplus energy generated fed back into the district heating system and used to heat 20,000 local households.
- The campus boasts 100% waste utilization rate and 99% avoidance in CO2 emissions.
- Construction started in the second half of 2022, with the first employees moving into the facility in the first half of this year.
- YIT constructed the site and Arkkitehtitoimisto ALA were the architects.
References:
https://www.sdxcentral.com/analysis/behind-the-scenes-at-nokias-new-home-of-radio/
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OpenAI and Broadcom in $10B deal to make custom AI chips
Overview:
Late last October, IEEE Techblog reported that “OpenAI the maker of ChatGPT, was working with Broadcom to develop a new artificial intelligence (AI) chip focused on running AI models after they’ve been trained.” On Friday, the WSJ and FT (on-line subscriptions required) separately confirmed that OpenAI is working with Broadcom to develop custom AI chips, a move that could help alleviate the shortage of powerful processors needed to quickly train and release new versions of ChatGPT. OpenAI plans to use the new AI chip internally, according to one person close to the project, rather than make them available to external customers.
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Broadcom:
During its earnings call on Thursday, Broadcom’s CEO Hock Tan said that it had signed up an undisclosed fourth major AI developer as a custom AI chip customer, and that this new customer had committed to $10bn in orders. While Broadcom did not disclose the names of the new customer, people familiar with the matter confirmed OpenAI was the new client. Broadcom and OpenAI declined to comment, according to the FT. Tan said the deal had lifted the company’s growth prospects by bringing “immediate and fairly substantial demand,” shipping chips for that customer “pretty strongly” starting next year. “The addition of a fourth customer with immediate and fairly substantial demand really changes our thinking of what 2026 would be starting to look like,” Tan added.
Image credit: © Dado Ruvic/Reuters
HSBC analysts have recently noted that they expect to see a much higher growth rate from Broadcom’s custom chip business compared with Nvidia’s chip business in 2026. Nvidia continues to dominate the AI silicon market, with “hyperscalers” still representing the largest share of its customer base. While Nvidia doesn’t disclose specific customer names, recent filings show that a significant portion of their revenue comes from a small number of unidentified direct customers, which likely are large cloud providers like Microsoft, Amazon, Alphabet (Google), and Meta Platforms.
In August, Broadcom launched its Jericho networking chip, which is designed to help speed up AI computing by connecting data centers as far as 60 miles apart. By August, Broadcom’s market value had surpassed that of oil giant Saudi Aramco, making the chip firm the world’s seventh-largest publicly listed company.
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Open AI:
OpenAI CEO Sam Altman has been saying for months that a shortage of graphics processing units, or GPUs, has been slowing his company’s progress in releasing new versions of its flagship chatbot. In February, Altman wrote on X that ChatGPT-4.5, its then-newest large language model, was the closest the company had come to designing an AI model that behaved like a “thoughtful person,” but there were very high costs that came with developing it. “We will add tens of thousands of GPUs next week and roll it out to the plus tier then. (hundreds of thousands coming soon, and i’m pretty sure y’all will use every one we can rack up.)”
In recent years, OpenAI has relied heavily on so-called “off the shelf” GPUs produced by Nvidia, the biggest player in the chip-design space. But as demand from large AI firms looking to train increasingly sophisticated models has surged, chip makers and data-center operators have struggled to keep up. The company was one of the earliest customers for Nvidia’s AI chips and has since proven to be a voracious consumer of its AI silicon.
“If we’re talking about hyperscalers and gigantic AI factories, it’s very hard to get access to a high number of GPUs,” said Nikolay Filichkin, co-founder of Compute Labs, a startup that buys GPUs and offers investors a share in the rental income they produce. “It requires months of lead time and planning with the manufacturers.”
To solve this problem, OpenAI has been working with Broadcom for over a year to develop a custom chip for use in model training. Broadcom specializes in what it calls XPUs, a type of semiconductor that is designed with a particular application—such as training ChatGPT—in mind.
Last month, Altman said the company was prioritizing compute “in light of the increased demand from [OpenAI’s latest model] GPT-5” and planned to double its compute fleet “over the next 5 months.” OpenAI also recently struck a data-center deal with Oracle that calls for OpenAI to pay more than $30 billion a year to the cloud giant, and signed a smaller contract with Google earlier this year to alleviate computing shortages. It is also embarking on its own data-center construction project, Stargate, though that has gotten off to a slow start.
OpenAI’s move follows the strategy of tech giants such as Google, Amazon and Meta, which have designed their own specialized custom chips to run AI workloads. The industry has seen huge demand for the computing power to train and run AI models.
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References:
https://www.ft.com/content/e8cc6d99-d06e-4e9b-a54f-29317fa68d6f
https://www.wsj.com/tech/ai/openai-broadcom-deal-ai-chips-5c7201d2
Reuters & Bloomberg: OpenAI to design “inference AI” chip with Broadcom and TSMC
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
OpenAI announces new open weight, open source GPT models which Orange will deploy
OpenAI partners with G42 to build giant data center for Stargate UAE project
Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Network operators are bracing themselves for a wave of AI traffic, partially based on a RtBrick survey, as well as forecasts by Cisco and Nokia, but that hasn’t happened yet. The heavy AI traffic today is East to West (or vice-versa) within cloud resident AI data centers and for AI data center interconnects.
1. Cisco believes that AI Inference agents will soon engage “continuously” with end-users, keeping traffic levels consistently high. has stated that AI will greatly increase network traffic, citing a shift toward new, more demanding traffic patterns driven by “agentic AI” and other applications. This perspective is a core part of Cisco’s business strategy, which is focused on selling the modernized infrastructure needed to handle the coming surge. Cisco identified three stages of AI-driven traffic growth, each with different network demands:
- Today’s generative AI models produce “spikey” traffic which spikes up when a user submits a query, but then returns to a low baseline. Current networks are largely handling this traffic without issues.
- Persistent “agentic” AI traffic: The next phase will involve AI agents that constantly interact with end-users and other agents. Cisco CEO Chuck Robbins has stated that this will drive traffic “beyond the peaks of current chatbot interaction” and keep network levels “consistently high”.
- Edge-based AI: A third wave of “physical AI” will require more computing and networking at the edge of the network to accommodate specialized use cases like industrial IoT.
“As we move towards agentic AI and the demand for inferencing expands to the enterprise and end user networking environments, traffic on the network will reach unprecedented levels,” Cisco CEO Chuck Robbins said on the company’s recent earnings call. “Network traffic will not only increase beyond the peaks of current chatbot interaction, but will remain consistently high with agents in constant interaction.”
2. Nokia recently predicted that both direct and indirect AI traffic on mobile networks will grow at a faster pace than regular, non-AI traffic.
- Direct AI traffic: This is generated by users or systems directly interacting with AI services and applications. Consumer examples: Using generative AI tools, interacting with AI-powered gaming, or experiencing extended reality (XR) environments. Enterprise examples: Employing predictive maintenance, autonomous operations, video and image analytics, or enhanced customer interactions.
- Indirect AI traffic: This occurs when AI algorithms are used to influence user engagement with existing services, thereby increasing overall traffic. Examples: AI-driven personalized recommendations for video content on social media, streaming platforms, and online marketplaces, which can lead to longer user sessions and higher bandwidth consumption.
The Finland based network equipment vendor warned that the AI wave could bring “a potential surge in uplink data traffic that could overwhelm our current network infrastructure if we’re not prepared,” noting that the rise of hybrid on-device and cloud tools will require much more than the 5-15 Mbps uplink available on today’s networks. Nokia’s Global Network Traffic 2030 report forecasts that overall traffic could grow by 5 to 9 times current levels by 2033. All told, Nokia said AI traffic is expected to hit 1088 exabytes (EB) per month by 2033. That means overall traffic will grow 5x in a best case scenario and 9x in a worse case.
To manage this anticipated traffic surge, Nokia advocates for radical changes to existing network infrastructure.
- Cognitive networks: The company states that networks must become “cognitive,” leveraging AI and machine learning (ML) to handle the growing data demand.
- Network-as-Code: As part of its Technology Strategy 2030, Nokia promotes a framework for more flexible and scalable networks that leverage AI and APIs.
- 6G preparation: Nokia Bell Labs is already conducting research and field tests to prepare for 6G networks around 2030, with a focus on delivering the capacity needed for AI and other emerging technologies.
- Optimizing the broadband edge: The company also highlights the need to empower the broadband network edge to handle the demands of AI applications, which require low latency and high reliability.
Nokia’s Global Network Traffic 2030 report didn’t mention agentic AI, which are artificial intelligence systems designed to autonomously perceive, reason, and act in their environment to achieve complex goals with less human oversight. Unlike generative AI, which focuses on creating content, agentic AI specializes in workflow automation and independent problem-solving by making decisions, adapting plans, and executing tasks over extended periods to meet long-term objectives.
3. Ericsson did point to traffic increases stemming from the use of AI-based assistants in its 2024 Mobility Report. In particular, it predicted the majority of traffic would be related to the use of consumer video AI assistants, rather than text-based applications and – outside the consumer realm – forecast increased traffic from “AI agents interacting with drones and droids. Accelerated consumer uptake of GenAI will cause a steady increase of traffic in addition to the baseline increase,” Ericsson said of its traffic growth scenario.
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Dissenting Views:
1. UK Disruptive Analysis Founder Dean Bubley isn’t a proponent of huge AI traffic growth. “Many in the telecom industry and vendor community are trying to talk up AI as driving future access network traffic and therefore demand for investment, spectrum etc., but there is no evidence of this at present,” he told Fierce Network.
Bubley argues that AI agents won’t really create much traffic on access networks to homes or businesses. Instead, he said, they will drive traffic “inside corporate networks, and inside and between data centers on backbone networks and inside the cloud. “There might be a bit more uplink traffic if video/images are sent to the cloud for AI purposes, but again that’s hypothetical,” he said.
2. In a LinkedIn post, Ookla analyst Mike Dano said he was a bit suspicious about “Cisco predicting a big jump in network traffic due to AI agents constantly wandering around the Internet and doing things.” While almost all of the comments agreed with Dano, it still is an open question whether the AI traffic Armageddon will actually materialize.
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References:
RtBrick survey: Telco leaders warn AI and streaming traffic to “crack networks” by 2030
https://www.fierce-network.com/cloud/will-ai-agents-really-raise-network-traffic-baseline
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Both telecom and enterprise networks are being reshaped by AI bandwidth and latency demands of AI. Network operators that fail to modernize architectures risk falling behind. Why? AI workloads are network killers — they demand massive east-west traffic, ultra-low latency, and predictable throughput.
- Real-time observability is becoming non-negotiable, as enterprises need to detect and fix issues before they impact AI model training or inference.
- Self-driving networks are moving from concept to reality, with AI not just monitoring but actively remediating problems.
- The competitive race is now about who can integrate AI into networking most seamlessly — and HPE/Juniper’s Mist AI, Cisco’s assurance stack, and Nvidia’s AI fabrics are three different but converging approaches.
Cisco, HPE/Juniper, and Nvidia are designing AI-optimized networking equipment, with a focus on real-time observability, lower latency and increased data center performance for AI workloads. Here’s a capsule summary:
Cisco: AI-Ready Infrastructure:
- Cisco is embedding AI telemetry and analytics into its Silicon One chips, Nexus 9000 switches, and Catalyst campus gear.
- The focus is on real-time observability via its ThousandEyes platform and AI-driven assurance in DNA Center, aiming to optimize both enterprise and AI/ML workloads.
- Cisco is also pushing AI-native data center fabrics to handle GPU-heavy clusters for training and inference.
- Cisco claims “exceptional momentum” and leadership in AI: >$800M in AI infrastructure orders taken from web-scale customers in Q4, bringing the FY25 total to over $2B.
- Cisco Nexus switches now fully and seamlessly integrated with NVIDIA’s Spectrum-X architecture to deliver high speed networking for AI clusters
HPE + Juniper: AI-Native Networking Push:
- Following its $13.4B acquisition of Juniper Networks, HPE has merged Juniper’s Mist AI platform with its own Aruba portfolio to create AI-native, “self-driving” networks.
- Key upgrades include:
-Agentic AI troubleshooting that uses generative AI workflows to pinpoint and fix issues across wired, wireless, WAN, and data center domains.
-Marvis AI Assistant with enhanced conversational capabilities — IT teams can now ask open-ended questions like “Why is the Orlando site slow?” and get contextual, actionable answers.
-Large Experience Model (LEM) with Marvis Minis — digital twins that simulate user experiences to predict and prevent performance issues before they occur.
-Apstra integration for data center automation, enabling autonomous service provisioning and cross-domain observability
Nvidia: AI Networking at Compute Scale
- Nvidia’s Spectrum-X Ethernet platform and Quantum-2 InfiniBand (both from Mellanox acquisition) are designed for AI supercomputing fabrics, delivering ultra-low latency and congestion control for GPU clusters.
- In partnership with HPE, Nvidia is integrating NVIDIA AI Enterprise and Blackwell architecture GPUs into HPE Private Cloud AI, enabling enterprises to deploy AI workloads with optimized networking and compute together.
- Nvidia’s BlueField DPUs offload networking, storage, and security tasks from CPUs, freeing resources for AI processing.
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Here’s a side-by-side comparison of how Cisco, HPE/Juniper, and Nvidia are approaching AI‑optimized enterprise networking — so you can see where they align and where they differentiate:
| Feature / Focus Area | Cisco | HPE / Juniper | Nvidia |
|---|---|---|---|
| Core AI Networking Vision | AI‑ready infrastructure with embedded analytics and assurance for enterprise + AI workloads | AI‑native, “self‑driving” networks across campus, WAN, and data center | High‑performance fabrics purpose‑built for AI supercomputing |
| Key Platforms | Silicon One chips, Nexus 9000 switches, Catalyst campus gear, ThousandEyes, DNA Center | Mist AI platform, Marvis AI Assistant, Marvis Minis, Apstra automation | Spectrum‑X Ethernet, Quantum‑2 InfiniBand, BlueField DPUs |
| AI Integration | AI‑driven assurance, predictive analytics, real‑time telemetry | Generative AI for troubleshooting, conversational AI for IT ops, digital twin simulations | AI‑optimized networking stack tightly coupled with GPU compute |
| Observability | End‑to‑end visibility via ThousandEyes + DNA Center | Cross‑domain observability (wired, wireless, WAN, DC) with proactive issue detection | Telemetry and congestion control for GPU clusters |
| Automation | Policy‑driven automation in campus and data center fabrics | Autonomous provisioning, AI‑driven remediation, intent‑based networking | Offloading networking/storage/security tasks to DPUs for automation |
| Target Workloads | Enterprise IT, hybrid cloud, AI/ML inference & training | Enterprise IT, edge, hybrid cloud, AI/ML workloads | AI training & inference at hyperscale, HPC, large‑scale data centers |
| Differentiator | Strong enterprise install base + integrated assurance stack | Deep AI‑native operations with user experience simulation | Ultra‑low latency, high‑throughput fabrics for GPU‑dense environments |
Key Takeaways:
- Cisco is strongest in enterprise observability and broad infrastructure integration.
- HPE/Juniper is leaning into AI‑native operations with a heavy focus on automation and user experience simulation.
- Nvidia is laser‑focused on AI supercomputing performance, building the networking layer to match its GPU dominance.
- Cisco leverages its market leadership, customer base and strategic partnerships to integrate AI with existing enterprise networks.
- HPE/Juniper challenges rivals with an AI-native, experience-first network management platform.
- Nvidia aims to dominate the full-stack AI infrastructure, including networking.
SoftBank’s Transformer AI model boosts 5G AI-RAN uplink throughput by 30%, compared to a baseline model without AI
Softbank has developed its own Transformer-based AI model that can be used for wireless signal processing. SoftBank used its Transformer model to improve uplink channel interpolation which is a signal processing technique where the network essentially makes an educated guess as to the characteristics and current state of a signal’s channel. Enabling this type of intelligence in a network contributes to faster, more stable communication, according to SoftBank. The Japanese wireless network operator successfully increased uplink throughput by approximately 20% compared to a conventional signal processing method (the baseline method). In the latest demonstration, the new Transformer-based architecture was run on GPUs and tested in a live Over-the-Air (OTA) wireless environment. In addition to confirming real-time operation, the results showed further throughput gains and achieved ultra-low latency.
Editor’s note: A Transformer model is a type of neural network architecture that emerged in 2017. It excels at interpreting streams of sequential data associated with large language models (LLMs). Transformer models have also achieved elite performance in other fields of artificial intelligence (AI), including computer vision, speech recognition and time series forecasting. Transformer models are lightweight, efficient, and versatile – capable of natural language processing (NLP), image recognition and wireless signal processing as per this Softbank demo.
Significant throughput improvement:
- Uplink channel interpolation using the new architecture improved uplink throughput by approximately 8% compared to the conventional CNN model. Compared to the baseline method without AI, this represents an approximately 30% increase in throughput, proving that the continuous evolution of AI models leads to enhanced communication quality in real-world environments.
Higher AI performance with ultra-low latency:
- While real-time 5G communication requires processing in under 1 millisecond, this demonstration with the Transformer achieved an average processing time of approximately 338 microseconds, an ultra-low latency that is about 26% faster than the convolution neural network (CNN) [1.] based approach. Generally, AI model processing speeds decrease as performance increases. This achievement overcomes the technically difficult challenge of simultaneously achieving higher AI performance and lower latency. Editor’s note: Perhaps this can overcome the performance limitations in ITU-R M.2150 for URRLC in the RAN, which is based on an uncompleted 3GPP Release 16 specification.
Note 1. CNN-based approaches to achieving low latency focus on optimizing model architecture, computation, and hardware to accelerate inference, especially in real-time applications. Rather than relying on a single technique, the best results are often achieved through a combination of methods.
Using the new architecture, SoftBank conducted a simulation of “Sounding Reference Signal (SRS) prediction,” a process required for base stations to assign optimal radio waves (beams) to terminals. Previous research using a simpler Multilayer Perceptron (MLP) AI model for SRS prediction confirmed a maximum downlink throughput improvement of about 13% for a terminal moving at 80 km/h.*2
In the new simulation with the Transformer-based architecture, the downlink throughput for a terminal moving at 80 km/h improved by up to approximately 29%, and by up to approximately 31% for a terminal moving at 40 km/h. This confirms that enhancing the AI model more than doubled the throughput improvement rate (see Figure 1). This is a crucial achievement that will lead to a dramatic improvement in communication speeds, directly impacting the user experience.
The most significant technical challenge for the practical application of “AI for RAN” is to further improve communication quality using high-performance AI models while operating under the real-time processing constraint of less than one millisecond. SoftBank addressed this by developing a lightweight and highly efficient Transformer-based architecture that focuses only on essential processes, achieving both low latency and maximum AI performance. The important features are:
(1) Grasps overall wireless signal correlations
By leveraging the “Self-Attention” mechanism, a key feature of Transformers, the architecture can grasp wide-ranging correlations in wireless signals across frequency and time (e.g., complex signal patterns caused by radio wave reflection and interference). This allows it to maintain high AI performance while remaining lightweight. Convolution focuses on a part of the input, while Self-Attention captures the relationships of the entire input (see Figure 2).
(2) Preserves physical information of wireless signals
While it is common to normalize input data to stabilize learning in AI models, the architecture features a proprietary design that uses the raw amplitude of wireless signals without normalization. This ensures that crucial physical information indicating communication quality is not lost, significantly improving the performance of tasks like channel estimation.
(3) Versatility for various tasks
The architecture has a versatile, unified design. By making only minor changes to its output layer, it can be adapted to handle a variety of different tasks, including channel interpolation/estimation, SRS prediction, and signal demodulation. This reduces the time and cost associated with developing separate AI models for each task.
The demonstration results show that high-performance AI models like Transformer and the GPUs that run them are indispensable for achieving the high communication performance required in the 5G-Advanced and 6G eras. Furthermore, an AI-RAN that controls the RAN on GPUs allows for continuous performance upgrades through software updates as more advanced AI models emerge, even after the hardware has been deployed. This will enable telecommunication carriers to improve the efficiency of their capital expenditures and maximize value.
Moving forward, SoftBank will accelerate the commercialization of the technologies validated in this demonstration. By further improving communication quality and advancing networks with AI-RAN, SoftBank will contribute to innovation in future communication infrastructure. The Japan based conglomerate strongly endorsed AI RAN at MWC 2025.
References:
https://www.softbank.jp/en/corp/news/press/sbkk/2025/20250821_02/
https://www.telecoms.com/5g-6g/softbank-claims-its-ai-ran-tech-boosts-throughput-by-30-
https://www.telecoms.com/ai/softbank-makes-mwc-25-all-about-ai-ran
https://www.ibm.com/think/topics/transformer-model
https://www.itu.int/rec/R-REC-M.2150/en
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