AI Partnerships
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Introduction:
The narrative surrounding “AI-RAN” — a term thrust into the spotlight by Nvidia — may have left many believing that boatloads of GPUs are already powering baseband compute in RAN equipment across the world’s seven million mobile sites. In truth, the reality is far more nascent.
Among major RAN vendors, Nokia stands alone in adapting baseband software for GPU acceleration. Yet even Nokia does not anticipate commercial readiness until late 2026, as confirmed by its Chief Technology Officer, Pallavi Mahajan, during the company’s MWC press conference earlier this year. For now, no operator has announced a commercial deployment — despite the buzz around trials.
Early Movers, Limited Momentum:
Much of the current AI-RAN activity centers on two operators: T-Mobile US and Japan’s SoftBank. At MWC, T-Mobile’s Executive Vice President of Innovation and ex-CTO, John Saw, acknowledged the limited availability of deployable solutions, quipping that he hoped Nokia would deliver an AI-RAN product within the year. Nokia CEO Justin Hotard quickly assured him that such a milestone was indeed on track.
Still, the debut of a GPU-based RAN stack does not imply an imminent large-scale rollout. Without tangible network performance or cost advantages over existing virtualized or disaggregated RAN approaches, operators are unlikely to move past controlled trials.
SoftBank, while often positioned as an AI-RAN pioneer, remains cautious. As Ryuji Wakikawa, Vice President of its Advanced Technology Division, outlined last year, the operator aims to deploy only a handful of AI-RAN sites over the next fiscal cycle. Transitioning from testing to carrying live commercial traffic, he emphasized, demands a significant maturity leap in quality and feature completeness.
Beyond Hype: Limited Commercial Engagement:
Elsewhere, Indonesia’s Indosat Ooredoo Hutchison (IOH) was heralded in 2025 as the first operator in Southeast Asia pursuing AI-RAN. More than a year later, authoritative sources indicate IOH’s work remains confined to its research facility in Surabaya, with no near-term plans for GPU investment at cell sites until measurable value is demonstrated.
The challenge for Nokia — and for GPU-backed AI-RAN broadly — is convincing operators that general-purpose accelerators offer sufficient performance or efficiency gains for most RAN workloads. T-Mobile and SoftBank continue evaluating both Nokia and Ericsson, whose AI-RAN philosophies diverge sharply. Nokia is developing GPU-based baseband software, while Ericsson maintains its focus on custom silicon and CPU architectures.
Divergent Architectures and Use Cases:
Ericsson contends that no core RAN performance enhancements intrinsically require GPUs. Its ongoing collaboration with Nvidia leverages the latter’s Grace CPU technology rather than its GPU portfolio, reserving GPU acceleration only for compute-intensive functions like forward error correction (FEC).
If Ericsson’s premise holds, GPUs in the RAN become justifiable only when supporting AI inference workloads. Even then, inference at every radio site remains improbable. A more incremental strategy — deploying GPUs selectively at edge locations where AI workloads justify their cost — may prove more practical.
This modular approach aligns with existing virtual RAN deployments based on Intel CPUs, which already include native FEC acceleration. “It is an off-the-shelf card that you can slide right into an HPE or Dell or Supermicro server,” said Alok Shah, the vice president of network strategy for Samsung Networks. “That gets you the edge functionality you are looking for.”
Rethinking the Economic Case for AI RAN:
Initially, Nvidia positioned GPUs for AI-RAN as viable only if broadly utilized for AI inference across the RAN. Following its strategic alignment with Nokia, however, the company has softened its stance — now suggesting that appropriately sized, power-efficient GPUs could make sense even when dedicated solely to baseband computation.
For now, the global RAN landscape remains far from GPU-saturated. AI-RAN remains an exploratory frontier — one testing not only the technical feasibility of GPUs at the edge, but also the economic/business case rationale for re-architecting a trillion-dollar telecom infrastructure around them.
The AI models suitable for RAN environments must be compact and efficient, far slimmer than those that drive data center-scale AI. There’s no room for the massive, parameter-heavy neural networks that justify a GPU’s cost or energy appetite. In that light, a GPU looks less like a breakthrough and more like a mismatch — a chainsaw brought to a task better handled with a sharp pair of scissors.
Evaluating the Case for AI-RAN Acceleration:
The central question is whether GPUs can deliver meaningful benefits over custom silicon or conventional CPUs for RAN compute. Ericsson’s engineers argue that AI models deployed at the RAN must remain relatively lightweight, with far fewer parameters than those used in large-scale data centers. Excessive model complexity could introduce signaling delays unacceptable in real-time radio environments. In this context, deploying a GPU for such workloads might seem disproportionate — a high-powered tool for a low-demand task.
The most compelling defense of GPU-based RAN acceleration came from Ronnie Vasishta, Nvidia’s Senior Vice President for Telecom, who told Light Reading last summer, “The world is developing on Nvidia.” His point underscores a shift in semiconductor economics: the cost and risk of building dedicated silicon for a mature and shrinking RAN market make general-purpose processors — supported by large-volume ecosystems — increasingly attractive alternatives.
Intel’s difficulties further illustrate this dynamic. Despite $53 billion in revenue during 2025, the former microprocessor king barely broke even despite $53 billion in revenue, following a $19 billion loss the previous year. A major restructuring cut its headcount by nearly 24,000, and its planned spinoff of the Network and Edge division — serving telecom infrastructure customers — was ultimately abandoned in December. Nvidia, the world’s most valuable company, may be eager to step into that space — but the economic logic seems upside down. Wireless network operators are looking to reduce costs, not import data center economics into the RAN.
Ecosystem or Echo Chamber?
Nvidia’s Aerial platform and CUDA-based software ecosystem do present a compelling story: open infrastructure, modular APIs, and space for smaller developers to innovate alongside giants like Nokia. On paper, it’s an alluring image of democratized RAN software. In practice, it ties the industry even more tightly to a vertically integrated, proprietary ecosystem.
Nokia appears comfortable with that trade-off. Nokia CTO Pallavi Mahajan’s recent blog post framed AI-RAN as a vehicle for “software speed and innovation.” He added, “Nokia’s AI-RAN initiative begins with a simple observation: AI is changing not only how networks are operated, but also the nature of the traffic they carry. AI workloads have already reached massive scale, with mobile devices accounting for more than half of AI interactions. Large language model interactions introduce richer uplink flows and burstier patterns as devices continuously send context to models.”
Indeed, that me be true someday. But for now, most wireless network operators need stable, cost-efficient networks, not AI-driven complexity or GPU-level power draw.
Image Credit: Nokia
Conclusions:
The uncomfortable truth is that AI-RAN feels more like a vendor-driven experiment than an operator-driven demand. Until someone proves that GPUs in the RAN deliver a measurable payoff — in performance, cost, or operational simplicity — the whole concept risks joining the long list of telecom “game-changers” that never made it past the trial stage. The hype cycle is predictable; the economics are not. Unless that equation changes, the real intelligence may be knowing when not to deploy AI RAN.
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In a Substack post today, Sebastian Barros writes: What Does AI-RAN Even Mean?
Despite the crazy hype, there is no definition for AI-RAN. Today it is at best a vibe, a dangerous reality for an industry that moves on strict standards that are currently completely absent.
The AI RAN hype is crazy right now. But despite the endless talk and vendor announcements, there is no actual technical definition of what it even means. As wild as it sounds for an industry built on strict 3GPP and O-RAN standards (those are specs- not standards), AI RAN is currently just a vendor interpretation designed to move hardware. Moreover, telecom has been using AI in the RAN before it was even cool. In fact, we were among the first industries to use neural networks in signal processing back in the 80s.
The problem is that treating AI-RAN as a marketing narrative rather than a rigid standard actively stalls progress. When the definition of AI-RAN is as different as night and day depending on which OEM you ask, it becomes impossible for any Telco to accurately model TCO or make solid CAPEX decisions.
Editor Notes:
- ITU-R’s IMT-2030 framework (ITU-R Recommendation M.2160-0 for IMT-2030) calls for an AI-native new air interface and AI-enhanced radio networks, but does not mention Nokia’s AI RAN.
- 3GPP Release 18 and later have study/work items on AI/ML for RAN functions such as energy saving, load balancing, mobility optimization, and AI/ML on the RAN air interface, but again no specifics have been discussed let alone agreed upon.
- 3GPP Release 19 continues and expands this work, with reporting that it builds on Release 18’s normative work and adds new AI/ML-based use cases for NG-RAN. In other words, 3GPP does have AI-RAN-related specs in progress and some normative features, but they are distributed across multiple RAN work items rather than packaged as one standalone “AI RAN” specification.
- AI RAN Alliance “is dedicated to driving the enhancement of RAN performance and capability with AI.” However, they’ve not yet produced any implementable specifications for AI RAN. Yet there are only four carriers that are “executive members“: Vodafone, T-Mobile, and SK Telecom, and Softbank (which is a conglomerate).
In Japan, NTT Docomo holds the largest cellular market share, with KDDI second, followed by SoftBank and the rapidly expanding Rakuten Mobile.
References:
https://www.lightreading.com/5g/ai-ran-lots-of-talk-little-action-no-guarantees
https://www.nokia.com/blog/ai-ran-bringing-software-speed-innovation-into-the-radio-network/
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Amazon Web Services (AWS) announced it plans to integrate AI processors from Cerebras Systems [1.] into its data centers, signaling growing confidence in the AI-focused semiconductor startup. Under a new multiyear partnership announced Friday, AWS will deploy Cerebras’s Wafer-Scale Engine (WSE) to accelerate inference workloads—the stage of AI operations where models generate responses to user queries. Financial details of the agreement were not disclosed.
Note 1. Founded in 2015 and headquartered in Sunnyvale, CA, Cerebras claims to have the world’s fastest AI inference and training platform.
The collaboration reflects a significant realignment in compute infrastructure strategies across the AI ecosystem. While initial industry focus centered on model training, the rapid expansion of deployed AI services is driving demand for optimized inference performance. Traditional GPUs, though unmatched for training, can be suboptimal for inference scenarios that require ultra-low latency and high throughput. Cloud and AI platform providers are therefore diversifying their silicon portfolios to better match workload profiles and to scale capacity efficiently.
AWS, the world’s largest cloud infrastructure provider, has traditionally relied on its in-house semiconductor division, Annapurna Labs, for custom chip design. Annapurna’s Trainium processors compete with GPUs from major suppliers such as Nvidia and AMD, offering cost and performance advantages for AI training workloads. The new partnership introduces Cerebras technology into AWS infrastructure, where it will work alongside Trainium to enhance large-scale inference capabilities.
Cerebras, best known for its wafer-scale architecture, markets its WSE processors as a high-speed inference platform capable of executing the decode phase of generative AI processing—where text, images, or other outputs are generated—at up to 25 times the speed of conventional GPU solutions. The company, valued at approximately $23 billion following a $1 billion funding round in February, has attracted backing from Fidelity, Benchmark, Tiger Global, Atreides, and Coatue.
The Cerebras deal underscores a major shift in the market for computing power. Image Credit: rebecca lewington/cerebras syste/Reuters
The AWS collaboration follows Cerebras’s major compute partnership with OpenAI, which reportedly involves deploying up to 750 MW of computing capacity powered by its chips. AWS and Cerebras will position their joint offering as a premium cloud inference solution, targeting enterprise AI developers requiring high-performance and scalable compute.
“The scale of AI demand is shifting from model creation to global deployment,” said Andrew Feldman, CEO of Cerebras. “Working with AWS aligns our technology with the industry’s largest cloud, giving us reach to a broad enterprise and developer base. If you want slow inference, there will be cheaper ways to go,” Feldman said. “But if you want fast tokens, if speed matters to you, if you’re doing coding or agentic work, not only are we the absolute fastest, but we intend to set the bar. We’re in this to win it.”
AWS and Cerebras will support both aggregated and disaggregated configurations. Disaggregated is ideal when you have large, stable workloads. Most customers run a mix of workloads with different prefill/decode ratios, where the traditional aggregated approach is still ideal. The start-up expects most customers will want access to both and the ability to route workloads to whichever configuration serves them best.
The move intensifies competition in the inference silicon segment, where Nvidia faces growing pressure from purpose-built processor architectures such as Cerebras’s WSE and other emerging alternatives. Nvidia, which recently announced a $20 billion licensing deal with Groq and plans to unveil a new inference-optimized platform, remains the dominant supplier but now contends with an accelerating wave of specialization across the AI compute stack.
AWS vice president and Annapurna Labs co-founder Nafea Bshara emphasized the company’s goal of offering flexible performance tiers. “Our job is to push the speed and lower the price,” he said, noting that AWS will continue to offer cost-optimized Trainium-only options alongside high-performance Cerebras-Trainium configurations.
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Amazon’s Internally Designed AI Silicon:
Amazon has built a fairly broad internal AI-oriented silicon portfolio through Annapurna Labs, primarily for AWS:
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Inferentia (Inferentia, Inferentia2) – Custom machine learning accelerators designed for high-throughput, low-cost inference at cloud scale. These power many AWS inference instances and are positioned as an alternative to Nvidia GPUs for production model serving.
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Trainium (Trainium, Trainium2, Trainium3) – AI training accelerators optimized for large-scale model training (including frontier and foundation models), with Trainium2 and Trainium3 as newer generations offering materially higher performance and better $/compute than the first generation. These are central to projects such as the Rainier supercomputer for Anthropic.
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Graviton (Graviton, Graviton2/3/4) – Arm-based general-purpose CPUs used heavily across EC2, increasingly in AI-adjacent roles (pre/post-processing, orchestration, model-serving microservices) and as part of cost-optimized AI stacks, even though they are not dedicated accelerators.
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Nitro system – While not an AI accelerator per se, the Nitro family (offload cards and system) is an internally developed data-plane and virtualization offload architecture that underpins EC2 and works in tandem with Graviton, Inferentia, and Trainium to free CPU cycles and improve I/O for AI/ML workloads.
All of these are designed and iterated internally by Annapurna Labs for exclusive use in AWS data centers, then exposed to customers via AWS services rather than as standalone merchant silicon.
Amazon’s Annapurna Labs is an internal chip design group that has become a core strategic asset for AWS, especially for custom data center and AI silicon.
Origins and acquisition:
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Annapurna Labs is an Israeli chip design startup founded in 2011 by semiconductor veterans of Intel and Broadcom, including Avigdor Willenz and Nafea Bshara.
- “When we talked with market sources and consulted with experts in the fields of data and servers, at that time only Amazon had a holistic vision and the ability to execute on a large scale,” recalls Bshara about the start of the romance with Amazon. “We were prepared to build the technology and at the same time were open to working with startups. From there we began a journey together with many meetings and shared thinking, among others with James Hamilton (Microsoft’s former data-base product architect and to AWS SVP), and from there within six months we found ourselves inside Amazon.”
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Amazon began working with the company around 2013 and acquired it in 2015 for an estimated $350–$400 million.
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Before the deal, Annapurna was in stealth, focusing on low‑power networking and server chips to improve data center efficiency.
Role inside Amazon and AWS:
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Post‑acquisition, Annapurna was folded into AWS as a specialist microelectronics and custom silicon group, designing chips to reduce cost and power per unit of compute.
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The group underpins several key AWS technologies: the Nitro system for offloading virtualization and I/O, Arm‑based Graviton CPUs for general compute, and Trainium and Inferentia accelerators for AI training and inference.
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These chips let AWS optimize performance per watt and per dollar versus x86 servers and third‑party accelerators, improving margins and competitive pricing.
Key products and architectures:
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Nitro: A combination of custom hardware and software that offloads storage, networking, and security functions from the host CPU, increasing tenant isolation and freeing CPU cycles for workloads.
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Graviton: A family of Arm‑based server CPUs; by 2018 Graviton was widely adopted on AWS and is now used by most AWS customers for general cloud infrastructure workloads due to better price‑performance and energy efficiency.
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Inferentia and Trainium: Custom accelerators designed by Annapurna for machine learning inference (Inferentia) and training (Trainium), intended to reduce AWS’s dependence on high‑priced Nvidia GPUs for AI workloads.
Strategic importance and AI focus:
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Annapurna’s work is central to Amazon’s strategy of vertical integration in the cloud: owning the silicon stack as much as the software and services.
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The group designs chips that power Amazon’s AI infrastructure, including systems used both by internal teams and external customers such as Anthropic, for which AWS is the primary cloud and silicon provider.
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Amazon and Anthropic are collaborating on “Project Rainier,” a massive supercomputer built around hundreds of thousands of Annapurna‑designed Trainium2 chips, targeting more than five times the compute used to train current frontier models.
Organization, footprint, and industry impact:
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Annapurna Labs maintains a significant presence in Israel, employing hundreds of engineers focused on advanced AI and networking processors for AWS.
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It also operates major engineering hubs such as an Austin, Texas lab where advanced semiconductors and AI systems are designed and tested.
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Analysts often describe the acquisition as one of Amazon’s most successful, arguing that Annapurna’s custom silicon is a “secret sauce” that helps AWS compete with Microsoft, Google, and others on performance, cost, and energy efficiency.
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References:
https://www.cerebras.ai/company
https://www.cerebras.ai/blog/cerebras-is-coming-to-aws
https://www.wsj.com/tech/amazon-announces-inference-chips-deal-with-cerebras-109ecd31
https://en.globes.co.il/en/article-nafea-bshara-the-israeli-behind-amazons-graviton-chip-1001420744
Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips
Custom AI Chips: Powering the next wave of Intelligent Computing
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Will “AI at the Edge” transform telecom or be yet another telco monetization failure?
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
OpenAI and Broadcom in $10B deal to make custom AI chips
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
2026 Consumer Electronics Show Preview: smartphones, AI in devices/appliances and advanced semiconductor chips
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Google announces Gemini: it’s most powerful AI model, powered by TPU chips
Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026
Ericsson and Intel at MWC 2026:
Building on milestones in Cloud RAN, 5G Core, and open network innovation, Ericsson and Intel are showcasing joint technology advancements at the Mobile World Congress (MWC) 2026 in Barcelona this week. Demonstrations can be experienced at the Ericsson Pavilion (Hall 2), Intel Booth (Hall 3, Stand 3E31), and across partner event spaces, highlighting the companies’ shared progress in enabling the next era of AI-driven networks.
The two companies are strengthening their long-standing technology partnership to accelerate ecosystem readiness for AI-native 6G networks and use cases. The expanded collaboration spans next-generation mobile connectivity, cloud infrastructure, and compute acceleration — with a focus on AI-driven RAN and packet core evolution, platform-level security, and scalable cloud-native architectures designed to shorten time-to-market for advanced network solutions.
“6G is not merely an iteration of mobile technology; it will serve as the foundational infrastructure distributing AI across devices, the edge, and the cloud,” said Börje Ekholm, President and CEO of Ericsson. “With our deep history in network innovation and global-scale operator deployments, Ericsson is uniquely positioned to drive practical 6G integration from research to commercialization.”
Lip-Bu Tan, CEO of Intel, added: “Intel’s vision is to lead the industry in unifying RAN, Core, and edge AI to enable seamless deployment of AI-native 6G environments. Together with Ericsson, we are proving that next-generation connectivity can be open, energy-efficient, secure, and intelligent. With future Ericsson Silicon built on Intel’s most advanced process technologies, coupled with Intel Xeon-powered AI-RAN ready Cloud RAN and collaborative multi-year research efforts, we are delivering the performance, efficiency, and supply assurance demanded by leading operators worldwide.”
As 6G transitions from research to commercialization, the industry must align around a mature, standards-based ecosystem. The Ericsson–Intel collaboration aims to accelerate development of high-performance, energy-efficient compute architectures optimized for both AI for Networks and Networks for AI.
AI-native 6G will fuse intelligent, programmable network functions with distributed compute and real-time sensing, bringing processing power closer to the network edge and enabling ultra-responsive, adaptive services. This convergence will enhance network efficiency, agility, and service intelligence across future deployments.
About Ericsson:
Ericsson‘s high-performing networks provide connectivity for billions of people every day. For 150 years, we’ve been pioneers in creating technology for communication. We offer mobile communication and connectivity solutions for service providers and enterprises. Together with our customers and partners, we make the digital world of tomorrow a reality.
About Intel:
Intel is an industry leader, creating world-changing technology that enables global progress and enriches lives. Inspired by Moore’s Law, we continuously work to advance the design and manufacturing of semiconductors to help address our customers’ greatest challenges. By embedding intelligence in the cloud, network, edge and every kind of computing device, we unleash the potential of data to transform business and society for the better.
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Related AI-Native 6G Announcements at MWC 2026:
In addition to the Ericsson-Intel collaboration, several vendors and operators announced AI-native 6G advancements or related demos at MWC Barcelona 2026. These initiatives emphasize AI-RAN integration, software-defined architectures, and ecosystem partnerships to bridge 5G-A to 6G.
NVIDIA Multi-Partner Commitment: NVIDIA rallied operators and vendors including Booz Allen, BT Group, Cisco, Deutsche Telekom, Ericsson, Nokia, SK Telecom, SoftBank, and T-Mobile to build open, secure AI-native 6G platforms. The focus is on software-defined wireless with AI embedded in RAN, edge, and core for integrated sensing, communications, and interoperability.
Nokia AI-RAN: Nokia highlighted new partnerships with Dell, Quanta, Red Hat, SuperMicro, NVIDIA, and operators like T-Mobile, Indosat Ooredoo Hutchison, BT, Elisa, NTT DOCOMO, and Vodafone for AI-RAN trials paving the way to cognitive 6G networks. Live demos at Nokia’s Hall 3 Booth 3B20 included Southeast Asia’s first AI-RAN Layer 3 5G call on shared GPU infrastructure and vision AI for immersive services.
T-Mobile & Deutsche Telekom Hub: T-Mobile US and (major shareholder) Deutsche Telekom launched a joint 6G Innovation Hub targeting AI-native autonomous networks, secure sensing/positioning, and connectivity-compute convergence for Physical AI. It builds on agentic AI proofs like network-integrated translation, emphasizing “kinetic tokens” for real-time physical world control.
ZTE GigaMIMO 6G Prototype: ZTE unveiled the world’s first 6G prototype with 2000+ U6G-band antenna elements (GigaMIMO), powered by AI algorithms for 10x capacity over 5G-A, 30% spectral efficiency gains, and AI-driven immersive services. Booth 3F30 demos integrate AI across connectivity, computing, and devices for “AI serves AI” networks.
Qualcomm Agentic AI RAN: Qualcomm announced AI-native RAN management services in its Dragonwing suite for autonomous 6G-grade networks, plus new Open RAN AI features for performance optimization. CEO Cristiano Amon’s keynote focused on “Architecting 6G for the AI Era,” with device-to-data-center transformations.
Huawei U6GHz for 6G Path:
Huawei released all-scenario U6GHz products (macro/micro sites, microwave) with AI-centric solutions for 5G-A capacity (100 Gbps downlink) and low-latency AI apps, enabling smooth 6G evolution. Emphasizes hyper-resolution MU-MIMO and multi-band coordination for indoor/outdoor AI experiences.
Summary Chart:
| Vendor/Operator | Key Focus | Partners/Demos | Booth/Location |
|---|---|---|---|
| NVIDIA | Open AI-native platforms | Multiple operators/vendors | MWC general |
| Nokia | AI-RAN trials & cognitive networks | NVIDIA, T-Mobile, IOH et al. | Hall 3, 3B20 |
| T-Mobile/DT | Physical AI hub | Joint R&D | Announced pre-MWC |
| ZTE | GigaMIMO 6G prototype | China Mobile, Qualcomm | Hall 3, 3F30 |
| Qualcomm | Agentic RAN automation | Open RAN ecosystem | Keynote & demos |
| Huawei | U6GHz AI-centric evolution | Carrier-focused | MWC showcase |
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References:
NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU
Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)
SKT 6G ATHENA White Paper: a mid-to-long term network evolution strategy for the AI era
Dell’Oro: RAN Market Stabilized in 2025 with 1% CAG forecast over next 5 years; Opinion on AI RAN, 5G Advanced, 6G RAN/Core risks
Nokia and Rohde & Schwarz collaborate on AI-powered 6G receiver years before IMT 2030 RIT submissions to ITU-R WP5D
SK Telecom, DOCOMO, NTT and Nokia develop 6G AI-native air interface
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips
Intel and AI chip startup SambaNova have entered into a multi-year strategic collaboration to deploy high-performance, cost-efficient AI inference solutions [1.] tailored for AI-native firms, enterprises, and government sectors. This global initiative leverages Intel® Xeon® infrastructure, with Intel Capital further signaling commitment through participation in SambaNova’s $350M Series E financing round. The collaboration will give customers a powerful alternative to GPU‑centric solutions, offering optimized performance for leading open‑source models with predictable throughput and total cost of ownership. Founded in 2017, the Palo Alto, CA company specializes in AI chips and software. SambaNova’s Chairman is Lip-Bu Tan, who is also the CEO of Intel!
Note 1. AI inferencing is the process of using a trained AI model to make real-time predictions, decisions, or generate content from new, previously unseen data. It transforms inputs (a query, image, sensor reading) into useful results (a sentence, classification, alert). Unlike training and language models, inference is about prompt execution, often requiring low-latency (speed) and high efficiency. AI Inference chips have attracted intense investor interest following a wave of deal making around rivals to Nvidia, as AI companies seek faster and more efficient hardware. See References below for more information.
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For high-scale AI workloads, the integration of Intel CPUs with SambaNova’s specialized AI platform was said to offer a high-efficiency rack-level inference alternative. This partnership serves as a strategic bridge as Intel scales its independent GPU-based offerings. Intel remains fully committed to its internal GPU roadmap, continuing aggressive investment across architecture, software, and systems. This collaboration enhances Intel’s edge-to-cloud strategy without altering its competitive trajectory in the GPU market. By combining Xeon processors, Intel networking, and SambaNova systems, the two companies are positioned to capture a significant share of the multi-billion-dollar inference market through heterogeneous data center architectures.
As part of the collaboration, Intel plans to make a strategic investment in SambaNova to accelerate the rollout of an Intel‑powered AI cloud. The collaboration is expected to span three key areas:
- AI Cloud Expansion – Scaling SambaNova’s vertically integrated AI cloud, built on Intel Xeon‑based infrastructure and optimized for large language and multimodal models. The platform will deliver low‑latency, high‑throughput AI services, supported by reference architectures, deployment blueprints, and partnerships with system integrators and software vendors.
- Integrated AI Infrastructure – Combining SambaNova’s systems with Intel’s CPUs, accelerators, and networking technologies to power scalable, production‑ready inference for reasoning, code generation, multimodal applications, and agentic workflows.
- Go‑to‑Market Execution – Joint co‑selling and co‑marketing through Intel’s global enterprise, cloud, and partner channels to accelerate adoption across the AI ecosystem.
Together, SambaNova and Intel aim to shape the next generation of heterogeneous AI data centers — integrating Intel Xeon processors, Intel GPUs, Intel networking and storage, and SambaNova systems — to unlock a multi‑billion‑dollar inference market opportunity.
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SambaNova also announced its SN50 AI chip, which boasts a max speed that’s 5X faster than competitive chips, according to the company.
Image Credit: SambaNova
Positioned as the most efficient chip for agentic AI, the SN50 chip offers enterprises a 3X lower total cost of ownership – a powerful foundation to scale fast inference and bring autonomous AI agents into full production. The SN50 will be shipping to customers later this year. To quickly scale and distribute SN50, SambaNova is collaborating with Intel, and has obtained $350 million in strategic Series E financing to expand manufacturing and cloud capacity.
“AI is no longer a contest to build the biggest model,” said Rodrigo Liang, co‑founder and CEO of SambaNova. “With the SN50 and our deep collaboration with Intel, the real race is about who can light up entire data centers with AI agents that answer instantly, never stall, and do it at a cost that turns AI from an experiment into the most profitable engine in the cloud.”
“Customers are asking for more choice and more efficient ways to scale AI,” said Kevork Kechichian, EVP, General Manager, Data Center Group, Intel. “By combining Intel’s leadership in compute, networking, and memory with SambaNova’s full-stack AI systems and inference cloud platform, we are delivering a compelling option for organizations looking for GPU alternatives to deploy advanced AI at scale.”
The SN50 delivers five times more compute per accelerator and four times more network bandwidth than the previous generation. It links up to 256 accelerators over a multi‑terabyte‑per‑second interconnect, cutting time‑to‑first‑token and supporting larger batch sizes. The result: Enterprises can deploy bigger, longer‑context AI models with higher throughput and responsiveness — while keeping performance high and costs and latency under control.
“AI is moving from a software story to an infrastructure story,” said Landon Downs, co-founder and managing partner at Cambium Capital. “SN50 is engineered for the real-world latency and economic requirements that will determine who successfully deploys agentic AI at scale.”
Built on SambaNova’s Reconfigurable Data Unit (RDU) architecture, SN50 delivers:
- Instant AI Experiences – Ultra‑low latency delivers real‑time responsiveness for next‑gen enterprise apps like voice assistants.
- Unmatched Scale and Concurrency – Power thousands of simultaneous AI sessions with consistent high performance.
- Breakthrough Model Capacity – Three‑tier memory architecture unlocks 10T+ parameter models and 10M+ context lengths for deeper reasoning and richer outputs.
- Maximum Efficiency at Scale – Higher hardware utilization lowers cost‑per‑token, driving greater performance and ROI.
- Smarter Memory, Smarter Efficiency – Resident multi‑model memory and agentic caching optimize the three‑tier architecture, cutting infrastructure costs for enterprise‑scale AI deployments.
“The new SambaNova SN50 RDU changes the tokenomics of AI inference at scale. By delivering both high performance and high throughput with a chip that uses existing power and is air cooled, SambaNova is changing the game,” said Peter Rutten, Research Vice-President Performance Intensive Computing at analyst firm IDC.
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SoftBank Corp. will be the first customer to deploy SN50 within its next‑generation AI data centers in Japan. The deployment will power low‑latency inference services for sovereign and enterprise customers across Asia‑Pacific, supporting both open‑source and proprietary frontier models with aggressive latency and throughput requirements.
“With SN50, we are building an AI inference fabric for Japan that can serve our customers and partners with the speed, resiliency and sovereignty they expect from SoftBank,” said Hironobu Tamba, Vice President and Head of the Data Platform Strategy Division of the Technology Unit at SoftBank Corp. “By standardizing on SN50, we gain the ability to deliver world‑class AI services on our own terms — with the performance of the best GPU clusters, but with far better economics and control.”
The SN50 deployment deepens SambaNova’s existing relationship with SoftBank Corp., which already hosts SambaCloud to provide ultra‑fast inference for developers in the region. By anchoring its newest clusters on SN50, SoftBank positions SambaNova as the inference backbone for its sovereign AI initiatives and future large‑scale agentic services.
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References:
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
CES 2025: Intel announces edge compute processors with AI inferencing capabilities
Groq and Nvidia in non-exclusive AI Inference technology licensing agreement; top Groq execs joining Nvidia
Analysis: Edge AI and Qualcomm’s AI Program for Innovators 2026 – APAC for startups to lead in AI innovation
Custom AI Chips: Powering the next wave of Intelligent Computing
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
OpenAI and Broadcom in $10B deal to make custom AI chips
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
Nscale pitches “Sovereign AI” to telecom operators to provide AI-as-a-service (AIaaS)
Nscale [1.], headquartered in London, UK, is suggesting that telecom networks host “Sovereign AI” infrastructure, to ensure that data remains within regional borders while driving efficiency and automation. The company is collaborating with Nokia to accelerate global AI infrastructure deployment and is showcasing these solutions at MWC 2026. The company is partnering with telecom operators to transform their existing national fiber and edge sites into high-performance AI data centers. They aim to leverage telco assets to deliver GPU-powered AI-as-a-Service (AIaaS), optimize their 5G networks, and support AI-driven analytics.
Note 1. Nscale is building the advanced infrastructure, systems and solutions that enables practitioners, enterprises, and governments across the globe to create, deploy, and scale their most transformative AI systems. Nscale’s AI Compute offering provides on-demand access to high-performance GPUs, enabling businesses and developers to execute complex computational tasks like AI model training and data analysis without the need for upfront investment in expensive hardware. Nscale is building its own high-density data centers with direct liquid cooling to support these initiatives.
Nscale says they are “empowering telecommunications providers to deliver a range of AI services and solutions which help support network optimization and network performance monitoring, alongside improving customer experience with AI-powered automation tools. With our scalable GPU infrastructure and AI expertise, our telco customers can provide industry-leading AI-as-a-service (AIaaS), scale for 5G and benefit from artificial intelligence.”
Last week at the UK Telecoms Innovation Network (UKTIN)’s AI & Advanced Connectivity: State of AI panel, Nscale’s Simon Rowell spoke about the importance of building infrastructure that is resilient and able to adapt over time. Technologies evolve, but what matters is whether the underlying infrastructure can accommodate that change. Across telco networks and digital services, the fundamentals remain consistent: efficiency, automation, productivity, and resilience. Nscale is focused on building flexible AI infrastructure that can support real services as requirements change.
UK Telecoms Innovation Network Panel Session State of AI in UK Telecoms. Photo Credit: Nscale
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Nokia and Nscale are collaborating to accelerate the development of AI-ready data center infrastructure across Europe and globally. As part of this partnership, Nokia serves as the preferred networking partner for Nscale, providing IP, optical networking, and data center switching technology to support high-performance AI clusters. Key aspects of the collaboration include:
- Infrastructure Build-out: Nokia is supplying its 7220 IXR and 7750 SR platforms to support Nscale’s AI-ready data centers, including a key project in Stavanger, Norway, and a 50 MW AI Campus in Loughton, U.K..
- Strategic Investment: Nokia is an investor in Nscale’s Series B funding round, supporting the company’s expansion and the deployment of up to 300,000 GPUs.
- Technology & Innovation: The partnership focuses on co-developing networking stacks for AI clusters, utilizing Nokia’s Ethernet-based data center fabric for low-latency, high-performance computing. Sustainability
- Focus: The collaboration emphasizes energy-efficient cooling and 100% renewable energy for data center operations. Nokia Nokia +4
David Power CTO at Nscale said, “Our mission is to redefine the boundaries of AI and High-Performance Computing through innovative, sustainable solutions. Nokia’s data center fabric enables us to scale our GPU clusters while maintaining the reliability and performance needed to serve our customers with cutting-edge AI services. The flexibility of Nokia’s solution ensures we can bring advanced AI capabilities to market faster.”
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References:
Sovereign AI infrastructure for telecom companies: implementation and challenges
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Nokia selects Intel’s Justin Hotard as new CEO to increase growth in IP networking and data center connections
Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)
Private 5G networks move to include automation, autonomous systems, edge computing & AI operations
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Rakuten Mobile and Intel partnership to embed AI directly into vRAN
Today, Rakuten Mobile and Intel announced a partnership to embed Artificial Intelligence (AI) directly into the virtualized Radio Access Network (vRAN) stack. While vRAN currently represents a small percentage of the total RAN market (Dell’Oro Group recently forecasts vRAN to account for 5% to 10% of the total RAN market by 2026), this partnership could boost increase that percentage as it addresses key adoption hurdles—performance, power, and AI integration. Key areas of innovation include:
- Enhanced Wireless Spectral Efficiency: Optimizing spectrum utilization for superior network performance and capacity.
- Automated RAN Operations: Streamlining network management and reducing operational complexities through intelligent automation.
- Optimized Resource Allocation: Dynamically allocating network resources for maximum efficiency and subscriber experience.
- Increased Energy Efficiency: Significantly reducing power consumption in the RAN, contributing to sustainable network operations.
The partnership essentially aims to make vRAN superior in performance and TCO (Total Cost of Ownership) compared to traditional, proprietary, purpose built RAN hardware.
“We are incredibly excited to expand our collaboration with Intel to pioneer truly AI-native RAN architectures,” said Sharad Sriwastawa, co-CEO and CTO, Rakuten Mobile. “Together, we are validating transformative AI-driven innovations that will not only shape but define the future of mobile networks. This partnership showcases how intelligent RAN can be achieved through the seamless and efficient integration of AI workloads directly within existing vRAN software stacks, delivering unparalleled performance and efficiency.”
Rakuten Mobile and Intel are engaged in rigorous testing and validation of cutting-edge RAN AI use cases across Layer 1, Layer 2, and comprehensive RAN operation and network platform management. A core objective is the seamless integration of AI directly into the RAN stack, meticulously addressing integration challenges while upholding carrier-grade reliability and stringent latency requirements.
Utilizing Intel FlexRAN reference software, the Intel vRAN AI Development Kit, and a robust suite of AI tools and libraries, Rakuten Mobile is collaboratively training, optimizing, and deploying sophisticated AI models specifically tailored for demanding RAN workloads. This collaborative effort is designed to realize ultra-low, real-time AI latency on Intel Xeon 6 SoC, capitalizing on their built-in AI acceleration capabilities, including AVX512/VNNI and AMX.
“AI is transforming how networks are built and operated,” said Kevork Kechichian, Executive Vice President and General Manager of the Data Center Group, Intel Corporation. “Together with Rakuten, we are demonstrating how AI benefits can be achieved in vRAN. Intel Xeon processors power the majority of commercial vRAN deployments worldwide, and this transformation momentum continues to accelerate. Intel is providing AI-ready Xeon platforms that allow operators like Rakuten to design AI-ready infrastructure from the ground up, with built-in acceleration capabilities.”
Rakuten says they are “poised to unlock new levels of RAN performance, efficiency, and automation by embedding AI directly into the RAN software stack, this AI-native evolution represents the future of cloud-native, AI-powered RAN – inherently software-upgradable and built on open, general-purpose computing platforms. Additionally, the extended collaboration between Rakuten Mobile and Intel marks a significant step toward realizing the vision of autonomous, self-optimizing networks and powerfully reinforces both companies’ commitment to open, programmable, and intelligent RAN infrastructure worldwide.”
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- AI-Native Efficiency & Performance: The collaboration focuses on integrating AI to improve network performance and energy efficiency, which is a major pain point for operators. By embedding AI directly into the vRAN stack, they are enhancing wireless spectral efficiency, reducing power consumption, and automating RAN operations.
- Leveraging High-Performance Hardware: The initiative utilizes Intel® Xeon® 6 processors with built-in vRAN Boost. This eliminates the need for external, power-hungry accelerator cards, offering up to 2.4x more capacity and 70% better performance-per-watt.
- Validation of Large-Scale Commercial Viability: Rakuten Mobile operates the world’s first fully virtualized, cloud-native network. Its continued collaboration with Intel to make the vRAN AI-native provides a proven blueprint for other operators, reducing the perceived risk of adopting vRAN, particularly in brownfield (existing) networks.
- Acceleration of Open RAN Ecosystem: The collaboration supports the broader push towards Open RAN, which is expected to see a significant rise in market share, doubling between 2022 and 2026.
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- Market Share Shift: Omdia forecasts that vRAN’s share of the RAN baseband subsector will reach 20% by 2028. That’s a significant jump from its current low single-digit percentage.
- Explosive CAGR: The global vRAN market is projected to grow from approximately $16.6 billion in 2024 to nearly $80 billion by 2033, representing a 19.5% CAGR.
- Small Cell Dominance: By the end of 2026, it is estimated that 77% of all vRAN implementations will be on small cell architectures, a key area where Rakuten and Intel have demonstrated success.
References:
https://corp.mobile.rakuten.co.jp/english/news/press/2026/0210_01/
Virtual RAN gets a boost from Samsung demo using Intel’s Grand Rapids/Xeon Series 6 SoC
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
vRAN market disappoints – just like OpenRAN and mobile 5G
LightCounting: Open RAN/vRAN market is pausing and regrouping
Dell’Oro: Private 5G ecosystem is evolving; vRAN gaining momentum; skepticism increasing
https://www.mordorintelligence.com/industry-reports/virtualized-ran-vran-market
https://www.grandviewresearch.com/industry-analysis/virtualized-radio-access-network-market-report
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
Corning Incorporated and Meta Platforms, Inc. (previously known as Facebook) have entered a multiyear agreement valued at up to $6 billion. This strategic collaboration aims to accelerate the deployment of cutting-edge data center infrastructure within the U.S. to bolster Meta’s advanced applications, technologies, and ambitious artificial intelligence initiatives. The agreement specifies that Corning will furnish Meta with its latest advancements in optical fiber, cable, and comprehensive connectivity solutions. As part of this commitment, Corning plans to significantly scale its manufacturing capabilities across its North Carolina facilities.
A key element of this expansion is a substantial capacity increase at its fiber optic cable manufacturing plant in Hickory NC, for which Meta will serve as the foundational anchor customer. The construction and operation of these data centers — critical infrastructure that supports our technologies and moves us toward personalized superintelligence — necessitate robust server and hardware systems designed to facilitate information transfer and connectivity with minimal latency. Fiber optic cabling is a cornerstone component for enabling this high-speed, near real-time connectivity, powering applications from sophisticated wearable technology like the Ray-Ban Meta AI glasses to the global connectivity services utilized by billions of individuals and enterprises.
“This long-term partnership with Meta reflects Corning’s commitment to develop, innovate, and manufacture the critical technologies that power next-generation data centers here in the U.S.,” said Wendell P. Weeks, Chairman and Chief Executive Officer, Corning Incorporated. “The investment will expand our manufacturing footprint in North Carolina, support an increase in Corning’s employment levels in the state by 15 to 20 percent, and help sustain a highly skilled workforce of more than 5,000 — including the scientists, engineers, and production teams at two of the world’s largest optical fiber and cable manufacturing facilities. Together with Meta, we’re strengthening domestic supply chains and helping ensure that advanced data centers are built using U.S. innovation and advanced manufacturing.”
Meta is expanding its commitment to build industry-leading data centers in the U.S. and to source advanced technology made domestically. Here are two quotes from them:
- “Building the most advanced data centers in the U.S. requires world-class partners and American manufacturing,” said Joel Kaplan, Chief Global Affairs Officer at Meta. “We’re proud to partner with Corning – a company with deep expertise in optical connectivity and commitment to domestic manufacturing – for the high-performance fiber optic cables our AI infrastructure needs. This collaboration will help create good-paying, skilled U.S. jobs, strengthen local economies, and help secure the U.S. lead in the global AI race.”
- “As digital tools and generative AI continue to transform our economy — in fields like healthcare, finance, agriculture, and more — the demand for fiber connectivity will continue to grow. By supporting American companies like Corning and building and operating data centers in America, we’re helping ensure that our nation maintains its competitive edge in the digital economy and the global race for AI leadership.”
Key elements of the agreement:
- Multiyear, up to $6 billion commitment.
- Corning to supply latest generation optical fiber, cable and connectivity products designed to meet the density and scale demands of advanced AI data centers.
- New optical cable manufacturing facility in Hickory, North Carolina, in addition to expanded production capacity across Corning’s North Carolina operations.
- Agreement supports Corning’s projected employment growth in North Carolina by 15 to 20 percent, sustaining a skilled workforce of more than 5,000 employees in the state, including thousands of jobs tied to two of the world’s largest optical fiber and cable manufacturing facilities.
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Comment and Analysis:
Corning’s “up to $6 billion” Meta agreement is essentially a long‑term, anchor‑tenant bet that AI‑era data centers will be fundamentally more fiber‑intensive than legacy cloud resident data centers, with Corning positioning itself as the default U.S. optical plant for Meta’s buildout through ~2030. In practice, this deal is a long‑term take‑or‑pay style capacity lock that de‑risks Corning’s capex while giving Meta priority access to scarce, high‑performance data‑center‑grade fiber and cabling.
AI data centers are becoming the new FTTH in the sense that hyperscale AI buildouts are now the primary structural driver of incremental fiber demand, design innovation, and capex prioritization—but with far higher fiber intensity per site and far tighter performance constraints than residential access ever imposed.
Why “AI Data Centers are the new FTTH” for fiber optic vendors:
For fiber‑optic vendors, AI data centers now play the role that FTTH did in the 2005–2015 cycle: the anchor use case that justifies new glass, cable, and connectivity capacity.
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AI‑optimized data centers need 2–4× more fiber cabling than traditional hyperscalers, and in some designs more than 10×, driven by massively parallel GPU fabrics and east–west traffic.
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U.S. hyperscale capacity is expected to triple by 2029, forcing roughly a 2× increase in fiber route miles and a 2.3× increase in total fiber miles, a demand shock comparable to or larger than the early FTTH boom but concentrated in fewer, much larger customers.
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This is already reshaping product roadmaps toward ultra‑high‑fiber‑count (UHFC) cable, bend‑insensitive fiber, and very‑small‑form‑factor connectors to handle hundreds to thousands of fibers per rack and per duct.
In other words, where FTTH once dictated volume and economies of scale, AI data centers now dictate density, performance, and margin mix.
Carrier‑infrastructure: from access to fabric:
From a carrier perspective, the “new FTTH” analogy is about what drives long‑haul and metro planning: instead of last‑mile penetration, it’s AI fabric connectivity and east–west inter‑DC routes.
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Each new hyperscale/AI data center is modeled to require on the order of 135 new fiber route miles just to reach three core network interconnection points, plus additional miles for new long‑haul routes and capacity upgrades.
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An FBA‑commissioned study projects U.S. data centers alone will need on the order of 214 million additional fiber miles by 2029, nearly doubling the installed base from ~160M to ~373M fiber miles; that is the new “build everywhere” narrative operators once used for FTTH.
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Carriers now plan backbone routes, ILAs, and regional rings around dense clusters of AI campuses, treating them as primary traffic gravity wells rather than as just a handful of peering sites at the edge of a consumer broadband network.
The strategic shift: FTTH made the access network fiber‑rich; AI makes the entire cloud and transport fabric fiber‑hungry.
Strategic implications:
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AI is now the dominant incremental fiber use case: residential fiber adds subscribers; AI adds orders of magnitude more fibers per site and per route.
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Network economics are moving from passing more homes to feeding more GPUs: route miles, fiber counts, and connector density are being dimensioned to training clusters and inference fabrics, not household penetration curves.
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Policy and investment narratives should treat AI inter‑DC and campus fiber as “national infrastructure” on par with last‑mile FTTH, given the scale of projected doubling in route miles and more than doubling in fiber miles by 2029.
In summary, the next decade of fiber innovation and capex will be written less in curb‑side PON and more in ultra‑dense, AI‑centric data centers with internal fiber optical fabrics and interconnects.
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References:
Meta Announces Up to $6 Billion Agreement With Corning to Support US Manufacturing
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
Lumen Technologies to connect Prometheus Hyperscale’s energy efficient AI data centers
Proposed solutions to high energy consumption of Generative AI LLMs: optimized hardware, new algorithms, green data centers
Hyper Scale Mega Data Centers: Time is NOW for Fiber Optics to the Compute Server
Analysis: OpenAI and Deutsche Telekom launch multi-year AI collaboration
Deutsche Telekom (DT) has formalized a strategic, multi-year collaboration with OpenAI to integrate advanced artificial intelligence (AI) solutions across its internal operations and customer engagement platforms. The partnership aims to co-develop “simple, personal, and multi-lingual AI experiences” focused on enhancing communication and productivity. Initial pilot programs are slated for deployment in Q1 2026. AI will also play a larger role in customer care, internal copilots, and network operations as the Group advances toward more autonomous, self-healing networks.DT plans a company-wide rollout of ChatGPT Enterprise, leveraging AI to streamline core functions including:
- Customer Care: Deploying sophisticated virtual assistants to manage billing inquiries, service outages, plan modifications, roaming support, and device troubleshooting [1].
- Internal Operations: Utilizing AI copilots to increase internal efficiency.
- Network Management: Optimizing core network provisioning and operations.
- Sovereign Cloud (2021): DT’s T-Systems division partnered with Google Cloud to offer sovereign cloud services.
- T Cloud Suite (Early 2025): The launch of a comprehensive suite providing sovereign public, private, and AI cloud options leveraging hybrid infrastructure.
- Industrial AI Cloud (Early 2025): A collaboration with Nvidia to build a dedicated industrial AI data center in Munich, scheduled for Q1 2026 operations.
- Edge AI compute services for enterprises.
- Vertical AI solutions tailored for healthcare, retail, and manufacturing sectors.
- Integrated private 5G and AI bundles for industrial logistical hubs.
“Telcos – if they execute – will have a big play in the edge inferencing space as well as providing hosting and colo services that can host domain specific SLMs that need to be run closer to the user data,” he said. “Furthermore, telcos will play a role in connectivity services across Neocloud providers such as CoreWeave, Lambda Labs, Digital Ocean, Vast.AI etc. OpenAI does not want to lose the opportunity to partner with telcos so they are striking early,” Nag added.
Other Voices:
- Roger Entner notes the model is highly applicable to European incumbents (e.g., Orange, Telefonica) due to the relative scarcity of existing AI data centers in the region, allowing operators to fill a critical infrastructure gap. Conversely, the model is less viable for U.S. operators, where hyperscalers already dominate the extensive data center market.
- AvidThink Founder and colleague Roy Chua cautions that while DT presents a robust “reference blueprint,” replicating this strategy requires significant scale, substantial financial investment, and regulatory alignment—factors not easily accessible to all network operators.
- Futurum Group VP and Practice Lead Nick Patience told Fierce Network, “This deal elevates DT from being a user of AI to being a co-developer, which is pretty significant. DT is one of the few operators building a full-stack AI story. This is an example of OpenAI treating telcos as high-scale distribution and data channels – customer care, billing, network telemetry, national reach and government relationships. This suggests OpenAI is deliberately building an operator channel in key regions (U.S., Korea, EU) but still in partnership with existing cloud and infra providers rather than displacing them.”
OpenAI has established significant partnerships with several telecom network providers and related technology companies to integrate AI into network operations, enhance customer experience, and develop new AI-native platforms. Those deals and collaborations include:
- T-Mobile: T-Mobile has a multi-year agreement with OpenAI and is actively testing the integration of AI (specifically IntentCX) into its business operations for customer service improvements. T-Mobile is also collaborating with Nokia and Nvidia on AI-RAN (Radio Access Network) technologies for 6G innovation.
- SK Telecom (SKT): SK Telecom has an in-house AI company and collaborates with OpenAI and other AI leaders like Anthropic to enhance its AI capabilities, build sovereign AI infrastructure, and explore new services for its customers in South Korea and globally. They are also reportedly integrating Perplexity into their offerings.
- Deutsche Telekom (DT): DT is partnering with OpenAI to offer ChatGPT Enterprise across its business to help teams work more effectively, improve customer service, and automate network operations.
- Circles: This global telco technology company and OpenAI announced a strategic global collaboration to build a fully AI-native telco SaaS platform, which will first launch in Singapore. The platform aims to revolutionize the consumer experience and drive operational efficiencies for telcos worldwide.
- Rakuten: Rakuten and OpenAI launched a strategic partnership to develop AI tools and a platform aimed at leveraging Rakuten’s Open RAN expertise to revolutionize the use of AI in telecommunications.
- Orange: Orange is working with OpenAI to drive new use cases for enterprise needs, manage networks, and enable innovative customer care solutions, including those that support African regional languages.
- Indian Telecoms (Reliance Jio, Airtel): Telecom providers in India are integrating AI tools from companies like Google and Perplexity into their mobile subscriptions, providing millions of users access to advanced intelligence resources.
- Nokia & Nvidia: In a broader industry collaboration, Nvidia invested $1 billion in Nokia to add Nvidia-powered AI-RAN products to Nokia’s portfolio, enabling telecom service providers to launch AI-native 5G-Advanced and 6G networks. This partnership also includes T-Mobile US for testing.
Conclusions:
With more than 261 million mobile customers globally, Deutsche Telekom provides a strong foundation to bring AI into everyday use at scale. The new collaboration marks the next step in Deutsche Telekom’s AI journey – moving from early pilots to large-scale products that make AI useful for everyone
References:
https://www.telekom.com/en/media/media-information/archive/openai-and-telekom-collaborate-1100164
https://www.telekom.com/en/company/companyprofile/company-profile-625808
Deutsche Telekom: successful completion of the 6G-TakeOff project with “3D networks”
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Deutsche Telekom offers 5G mmWave for industrial customers in Germany on 5G SA network
Deutsche Telekom migrates IP-based voice telephony platform to the cloud
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
OpenAI and Broadcom in $10B deal to make custom AI chips
Custom AI Chips: Powering the next wave of Intelligent Computing
OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
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
Reuters & Bloomberg: OpenAI to design “inference AI” chip with Broadcom and TSMC
AI infrastructure spending boom: a path towards AGI or speculative bubble?
by Rahul Sharma, Indxx with Alan J Weissberger, IEEE Techblog
Introduction:
The ongoing wave of artificial intelligence (AI) infrastructure investment by U.S. mega-cap tech firms marks one of the largest corporate spending cycles in history. Aggregate annual AI investments, mostly for cloud resident mega-data centers, are expected to exceed $400 billion in 2025, potentially surpassing $500 billion by 2026 — the scale of this buildout rivals that of past industrial revolutions — from railroads to the internet era.[1]
At its core, this spending surge represents a strategic arms race for computational dominance. Meta, Alphabet, Amazon and Microsoft are racing to secure leadership in artificial intelligence capabilities — a contest where access to data, energy, and compute capacity are the new determinants of market power.
AI Spending & Debt Financing:
Leading technology firms are racing to secure dominance in compute capacity — the new cornerstone of digital power:
- Meta plans to spend $72 billion on AI infrastructure in 2025.
- Alphabet (Google) has expanded its capex guidance to $91–93 billion.[3]
- Microsoft and Amazon are doubling data center capacity, while AWS will drive most of Amazon’s $125 billion 2026 investment.[4]
- Even Apple, typically conservative in R&D, has accelerated AI infrastructure spending.
Their capex is shown in the chart below:
Analysts estimate that AI could add up to 0.5% to U.S. GDP annually over the next several years. Reflecting this optimism, Morgan Stanley forecasts $2.9 trillion in AI-related investments between 2025 and 2028. The scale of commitment from Big Tech is reshaping expectations across financial markets, enterprise strategies, and public policy, marking one of the most intense capital spending cycles in corporate history.[2]
Meanwhile, OpenAI’s trillion-dollar partnerships with Nvidia, Oracle, and Broadcom have redefined the scale of ambition, turning compute infrastructure into a strategic asset comparable to energy independence or semiconductor sovereignty.[5]
Growth Engine or Speculative Bubble?
As Big Tech pours hundreds of billions of dollars into AI infrastructure, analysts and investors remain divided — some view it as a rational, long-term investment cycle, while others warn of a potential speculative bubble. Yet uncertainty remains — especially around Meta’s long-term monetization of AGI-related efforts.[8]
Some analysts view this huge AI spending as a necessary step towards achieving Artificial General Intelligence (AGI) – an unrealized type of AI that possesses human-level cognitive abilities, allowing it to understand, learn, and adapt to any intellectual task a human can. Unlike narrow AI, which is designed for specific functions like playing chess or image recognition, AGI could apply its knowledge to a wide range of different situations and problems without needing to be explicitly programmed for each one.
Other analysts believe this is a speculative bubble, fueled by debt that can never be repaid. Tech sector valuations have soared to dot-com era levels – and, based on price-to-sales ratios, are well beyond them. Some of AI’s biggest proponents acknowledge the fact that valuations are overinflated, including OpenAI chairman Bret Taylor: “AI will transform the economy… and create huge amounts of economic value in the future,” Taylor told The Verge. “I think we’re also in a bubble, and a lot of people will lose a lot of money,” he added.
Here are a few AI bubble points and charts:
- AI-related capex is projected to consume up to 94% of operating cash flows by 2026, according to Bank of America.[6]
- Over $75 billion in AI-linked corporate bonds have been issued in just two months — a signal of mounting leverage. Still, strong revenue growth from AI services (particularly cloud and enterprise AI) keeps optimism alive.[7]
- Meta, Google, Microsoft, Amazon and xAI (Elon Musk’s company) are all using off-balance-sheet debt vehicles, including special-purpose vehicles (SPVs) to fund part of their AI investments. A slowdown in AI demand could render the debt tied to these SPVs worthless, potentially triggering another financial crisis.
- Alphabet’s (Google’s parent company) CEO Sundar Pichai sees “elements of irrationality” in the current scale of AI investing which is much more than excessive investments during the dot-com/fiber optic built-out boom of the late 1990s. If the AI bubble bursts, Pichai said that no company will be immune, including Alphabet, despite its breakthrough technology, Gemini, fueling gains in the company’s stock price.
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From Infrastructure to Intelligence:
Executives justify the massive spend by citing acute compute shortages and exponential demand growth:
- Microsoft’s CFO Amy Hood admitted, “We’ve been short on capacity for many quarters” and confirmed that the company will increase its spending on GPUs and CPUs in 2026 to meet surging demand.
- Amazon’s Andy Jassy noted that “every new tranche of capacity is immediately monetized”, underscoring strong and sustained demand for AI and cloud services.
- Google reported billions in quarterly AI revenue, offering early evidence of commercial payoff.
Macro Ripple Effects – Industrializing Intelligence:
AI data centers have become the factories of the digital age, fueling demand for:
- Semiconductors, especially GPUs (Nvidia, AMD, Broadcom)
- Cloud and networking infrastructure (Oracle, Cisco)
- Energy and advanced cooling systems for AI data centers (Vertiv, Schneider Electric, Johnson Controls, and other specialists such as Liquid Stack and Green Revolution Cooling).
| Company Name | Core Expertise | Key Solutions for AI Data Centers |
|---|---|---|
| Vertiv | Critical infrastructure (power & cooling) | Offers full-stack solutions with air and liquid cooling, power distribution units (PDUs), and monitoring systems, including the AI-ready Vertiv 360AI portfolio. |
| Schneider Electric | Energy management & automation | Provides integrated power and thermal management via its EcoStruxure platform, specializing in modular and liquid cooling solutions for HPC and AI applications. |
| Johnson Controls | HVAC & building solutions | Offers integrated, energy-efficient solutions from design to maintenance, including Silent-Aire cooling and YORK chillers, with a focus on large-scale operations. |
| Eaton | Power management | Specializes in electrical distribution systems, uninterruptible power supplies (UPS), and switchgear, which are crucial for reliable energy delivery to high-density AI racks. |
- LiquidStack: A leader in two-phase and modular immersion cooling and direct-to-chip systems, trusted by large cloud and hardware providers.
- Green Revolution Cooling (GRC): Pioneers in single-phase immersion cooling solutions that help simplify thermal management and improve energy efficiency.
- Iceotope: Focuses on chassis-level precision liquid cooling, delivering dielectric fluid directly to components for maximum efficiency and reduced operational costs.
- Asetek: Specializes in direct-to-chip (D2C) liquid cooling solutions and rack-level Coolant Distribution Units (CDUs) for high-performance computing.
- CoolIT Systems: Known for its custom direct liquid cooling technologies, working closely with server OEMs (Original Equipment Manufacturers) to integrate cold plates and CDUs for AI and HPC workloads.
–>This new AI ecosystem is reshaping global supply chains — but also straining local energy and water resources. For example, Meta’s massive data center in Georgia has already triggered environmental concerns over energy and water usage.
Global Spending Outlook:
- According to UBS, global AI capex will reach $423 billion in 2025, $571 billion by 2026 and $1.3 trillion by 2030, growing at a 25% CAGR during the period 2025-2030.
Compute demand is outpacing expectations, with Google’s Gemini saw 130 times rise in AI token usage over the past 18 months, highlighting soaring compute and Meta’s infrastructure needs expanding sharply.[9]
Conclusions:
The AI infrastructure boom reflects a bold, forward-looking strategy by Big Tech, built on the belief that compute capacity will define the next decade’s leaders. If Artificial General Intelligence (AGI) or large-scale AI monetization unfolds as expected, today’s investments will be seen as visionary and transformative. Either way, the AI era is well underway — and the race for computational excellence is reshaping the future of global markets and innovation.
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Footnotes:
[1] https://www.investing.com/news/stock-market-news/ai-capex-to-exceed-half-a-trillion-in-2026-ubs-4343520?utm_medium=feed&utm_source=yahoo&utm_campaign=yahoo-www
[2] https://www.venturepulsemag.com/2025/08/01/big-techs-400-billion-ai-bet-the-race-thats-reshaping-global-technology/#:~:text=Big%20Tech’s%20$400%20Billion%20AI%20Bet:%20The%20Race%20That’s%20Reshaping%20Global%20Technology,-3%20months%20ago&text=The%20world’s%20largest%20technology%20companies,enterprise%20strategy%2C%20and%20public%20policy.
[3] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[4] https://www.investing.com/analysis/meta-plunged-12-amazon-jumped-11–same-ai-race-different-economics-200669410
[5] https://www.cnbc.com/2025/10/15/a-guide-to-1-trillion-worth-of-ai-deals-between-openai-nvidia.html
[6] https://finance.yahoo.com/news/bank-america-just-issued-stark-152422714.html
[7] https://news.futunn.com/en/post/64706046/from-cash-rich-to-collective-debt-how-does-wall-street?level=1&data_ticket=1763038546393561
[8] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[9] https://finance.yahoo.com/news/ai-capex-exceed-half-trillion-093015889.html
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About the Author:
Rahul Sharma is President & Co-Chief Executive Officer at Indxx – a provider of end-to-end indexing services, data and technology products. He has been instrumental in leading the firm’s growth since 2011. Raul manages Indxx’s Sales, Client Engagement, Marketing and Branding teams while also helping to set the firm’s overall strategic objectives and vision.
Rahul holds a BS from Boston College and an MBA with Beta Gamma Sigma honors from Georgetown University’s McDonough School of Business.
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References:
Curmudgeon/Sperandeo: New AI Era Thinking and Circular Financing Deals
Expose: AI is more than a bubble; it’s a data center debt bomb
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
FT: Scale of AI private company valuations dwarfs dot-com boom
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
AI Data Center Boom Carries Huge Default and Demand Risks
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Reuters: US Department of Energy forms $1 billion AI supercomputer partnership with AMD
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Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
According to Dell’Oro VP Stefan Pongratz, Nokia has outlined a clear plan to arrest its declining RAN revenue share (see chart below), with NVIDIA now a central pillar of that strategy. The partnership is designed to deliver AI RAN [1.] while meeting wireless network operators’ near-term constraints and concerns on performance, power, and TCO (Total Cost of Ownership). IEEE Techblog has noted in many past blog posts that telcos have huge doubts about AI RAN which implies they won’t buy into that new RAN architecture.
This is especially relevant considering the monumental failure of multi-vendor Open RAN which was promoted as a game changer, but has dismally failed to attain that vision.
Note 1. AI RAN is a mobile RAN architecture where AI and machine learning are embedded into the RAN software and underlying compute platform to optimize how the network is planned, configured, and operated. It is being pushed by NVIDIA to get its GPUs into 5G, 5G Advanced and 6G base stations and other wireless network equipment in the RAN.
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Nokia aims to use collaboration with NVIDIA (which invested $1B in the Finland based company) to stabilize its RAN market share in the near term and create a platform for long-term growth in AI-native 5G-Advanced and 6G networks. The timing—following a dense cadence of disclosures at NVIDIA’s GPU Technology Conference and Nokia’s Capital Markets Day—makes this an ideal time to reassess the scope of the joint announcements, the RAN implications, and Nokia’s broader competitive posture in an increasingly concentrated market.
Both companies share a belief that telecom networks will evolve from best-effort connectivity into a distributed compute fabric underpinning autonomous machines, self-driving vehicles, humanoids, and industrial digital twins. From that perspective, the RAN becomes an “AI grid” that executes and orchestrates AI workloads at the edge, enabling massive numbers of latency-sensitive, bandwidth-intensive AI use cases.
Unlike prior attempts to penetrate the RAN market with its GPUs, NVIDIA is now taking a more pragmatic approach, explicitly targeting parity with incumbent, purpose-built RAN equipment based on performance, power, and TCO rather than leading with speculative multi-tenant or new-revenue narratives. Nokia, acutely aware of wireless telco risk tolerance, is positioning the solution so that the ROI must be justifiable on a pure RAN basis, with additional AI and edge-compute upside treated as optional rather than foundational.
A quick recap of NVIDIA’s entry into RAN: Based on the announcement and subsequent discussions, our understanding is that NVIDIA will invest $1 B in Nokia and that NVIDIA-powered AI-RAN products will be incorporated into Nokia’s RAN portfolio starting in 2027 (with trials beginning in 2026). While RAN compute—which represents less than half of the $30B+ RAN market—is immaterial relative to NVIDIA’s $4+ T market cap, the potential upside becomes more meaningful when viewed in the context of NVIDIA’s broader telecom ambitions and its $165 B in trailing-twelve-month revenue.
With a deployed base of more than 1 million BTS, Nokia is prioritizing three migration vectors to GPU/AI-RAN, in order of expected impact:
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Purpose-built D-RAN [2.], by inserting a new card into existing AirScale slots.
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D-RAN vRAN [3.], using COTS servers at the cell site.
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Cloud RAN [4.] or vRAN, using centralized COTS infrastructure.
This approach aligns with wireless network operators’ desire to sweat existing AirScale assets while minimizing operational disruption.
Note 2. Purpose-built D-RAN is a distributed RAN architecture where the baseband processing runs on dedicated, vendor-specific hardware at or very close to the cell site, rather than on generic COTS servers. It is “purpose-built” because the silicon, boards, and software stack are tightly integrated and optimized for RAN performance, power efficiency, and footprint, not general-purpose compute.
Note 3. vRAN or virtual RAN is a technology that virtualizes the functions of a cellular network’s radio access network, moving them from dedicated hardware to software running on general-purpose servers. This approach makes mobile networks more flexible, scalable, and cost-efficient by replacing proprietary hardware with software on common-off-the-shelf (COTS) hardware.
Note 4. Cloud RAN (C-RAN) is a centralized cellular network architecture that uses cloud computing to virtualize and process radio access network (RAN) functions. This architecture centralizes baseband units in a “BBU hotel,” allowing for more flexible and scalable network management, efficient resource allocation, and improved network performance. It allows operators to pool resources, adjust capacity based on demand, and support new services, which is a key enabler for 5G networks.
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In this model, the Distributed Unit, and often the higher-layer functions, are physically collocated with the radio unit at the site, making each site a largely self-contained RAN node. This contrasts with Cloud RAN or vRAN, where baseband functions are centralized or virtualized on shared cloud infrastructure, and with cloud/AI-RAN approaches that rely on GPUs or other general-purpose accelerators instead of custom RAN hardware.
The macro-RAN market (baseband plus radio) is roughly a $30 billion annual opportunity, with on the order of 1–2 million macro sites shipped per year. In that context, operators have limited appetite to pay more than $10,000 for a GPU per sector, even if software-led benefits accumulate over time, which is why NVIDIA is signaling GPU pricing in line with ARC-Compact, but at roughly double the capacity and Nokia is targeting 48–50% gross margins in Mobile Infrastructure by 2028, slightly above the current run-rate.
If the TCO and performance-per-watt gap versus custom silicon continues to narrow, the partnership could materially influence AI-RAN and Cloud-RAN trajectories while also supporting Nokia’s margin expansion goals. AI-RAN was already expected to scale to roughly one-third of the RAN market by 2029; Nokia’s decision to lean harder into GPUs amplifies this structural shift without fundamentally changing the long-term 6G direction.
In the near term, GPU-enabled D-RAN using empty AirScale slots is expected to dominate deployments, reflecting operators’ preference for incremental, site-level upgrades. At the same time, the Nokia-NVIDIA partnership is not expected to meaningfully alter the overall Cloud RAN vs. D-RAN mix, Open RAN adoption (slow or non-existent) , or the trajectory of multi-tenant RAN, which remain more dependent on network operator architecture and commercial decisions than on a single vendor–silicon alignment.
Nokia plans to remain disciplined and focus on areas where it can differentiate and unlock value—particularly through software/faster innovation cycles via its recently announced partnership with NVIDIA. The company sees meaningful opportunities to capture incremental share in North America, Europe, India, and select APAC markets. And it is already off to a solid start— we estimate that Nokia’s 1Q25–3Q25 RAN revenue share outside North America improved slightly relative to 2024. Following this stabilization phase, Nokia is betting that its investments will pay off and that it will be well-positioned to lead with AI-native networks and 6G.
Nokia’s objective is clear: stabilize RAN in the short term, then grow by leading in AI-native networks and 6G over the longer horizon. Success now hinges on Nokia’s ability to operationalize the GPU-based RAN roadmap at scale and on NVIDIA’s ability to deliver carrier-grade economics and performance—turning the AI-RAN narrative into production-grade, repeatable deployments.
Nokia sees meaningful opportunities to capture incremental RAN market share in North America, Europe, India, and select APAC markets. And it is already off to a solid start— we estimate that Nokia’s 1Q25–3Q25 RAN revenue share outside North America improved slightly relative to 2024. Following this stabilization phase, Nokia is betting that its investments will pay off and that it will be well-positioned to lead with AI-native networks and 6G.
References:
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Indosat Ooredoo Hutchison, Nokia and Nvidia AI-RAN research center in Indonesia amongst telco skepticism
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
AI RAN Alliance selects Alex Choi as Chairman
Expose: AI is more than a bubble; it’s a data center debt bomb
