AI in Networks Market
Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic
According to Nokia, AI-generated traffic in most mobile networks is at an early stage, with application maturity and adoption by consumers and enterprises only at the start of a broader AI super cycle. The Finland based company analyzed more than 50 AI applications and came to three conclusions: higher uplink traffic, overall data growth and increasing sensitivity to delay in conversational services such as chat and voice. Also, the mobile network industry is moving toward “AI-RAN” or “6G-native” structures that embed AI into the network, transforming radio sites into “robotic” nodes capable of edge inference and handling these new demands.
–>Do those findings require a structural change in Radio Access Network (RAN) design? Let’s take a fresh look…..
Mobile networks traditionally support a heterogeneous mix of traffic, ranging from high-throughput video streaming to low-bandwidth, delay-tolerant messaging. Network operators typically address escalating capacity demands through infrastructure expansion and overprovisioning, relying on best-effort delivery—a model that has proven remarkably resilient. However, capacity alone is insufficient for new use cases.
The transition from circuit-switched voice to packet-switched (voice/video/data) IP traffic requires a redesign to accommodate variable packet sizes instead of predictable, continuous voice patterns. The proliferation of Internet of Things (IoT) devices introduced requirements for massive machine-type communications (mMTC), driving the development of LTE-M and NB-IoT to optimize for deep indoor penetration and power efficiency. Conversely, consumer web-based services and video streaming scale seamlessly by adding RAN and core capacity. Existing AI applications, such as generative AI chatbots, follow this model, making current RAN architectures adequate for the present load.
A paradigm shift is emerging with Physical AI [1.], which enables machines like autonomous vehicles and robots to interact with the environment in real time. Unlike traditional video streaming, these applications cannot leverage buffering to absorb network jitter. In Physical AI, high-definition video frames and sensor data must arrive within stringent time-to-live (TTL) constraints to remain actionable. This shifts the focus from average throughput to consistent low latency. Maintaining this strict QoS, particularly in the uplink, requires abandoning best-effort, overprovisioned models in favor of guaranteed scheduling, which necessitates substantial reserved capacity or specialized AI-RAN functionalities.
Note 1. Physical AI combines sensors, perception, decision-making, and actuators so machines can understand their environment and take physical (real world) action. Physical AI is used by robots, vehicles, drones, industrial machines, and smart infrastructure that generate and consume real-time sensor, video, and control traffic. These systems need tight coupling between low latency, high reliability, and continuous feedback loops because decisions in software immediately affect physical motion or control. Physical AI is different from typical generative AI because the output is not text or images; it is real-world action. That makes network performance critical, especially for uplink-heavy, latency-sensitive traffic where delays can affect safety, control accuracy, and operational efficiency.
“Physical AI introduces the possibility that large-volume uplink video with strict latency requirements. It will become a meaningful part of mobile traffic, creating both a design challenge and a monetization opportunity,” says Harish Viswanathan, Head of the Radio Systems Research Group at Nokia.
Image Credit: Techslang
Delivering uplink video with sub‑20 ms end-to-end latency can require provisioning three to four times the average uplink capacity. While this level of redundancy is manageable for low-bandwidth services such as voice or control signaling, it becomes prohibitively expensive when supporting high-throughput video streams.
As device densities increase, the required headroom for reserved capacity grows disproportionately, significantly constraining network scalability and driving up cost per bit. This makes Physical AI traffic—characterized by real-time sensor and video inputs for machine analysis—fundamentally different from conventional services, and unsuited to existing best‑effort transport models. From a Nokia blog post:
“Physical AI will rely on low latency videos to enable real-time control. While the machines or robots will perform most functions locally, there will be situations where they need to rely on more powerful models or human operators to provide remote control via the network. For example, driverless taxis may require remote assistance in unexpected scenarios; service robots may need guidance in complex environments; drones may depend on real‑time video analysis at the point of delivery; and field workers using AR may require timely visual instructions. In all these cases, the network must deliver fresh video information with low and predictable latency.”
To address these challenges, telecom operators are expected to adopt a multi‑layer approach encompassing network architecture, traffic management, and service monetization.
At the Application layer, not all traffic requires identical latency treatment. When video or sensor data is processed by AI rather than consumed by humans, only semantically relevant information may need immediate uplink transmission. This emerging paradigm, known as semantic communication, allows for significant data reduction while preserving information integrity within latency‑critical loops.
Within the network domain, established mechanisms such as Quality of Service (QoS) and network slicing remain essential. QoS enables prioritization of specific traffic classes, while slicing supports logically isolated virtual networks with guaranteed service-level attributes—latency, jitter, bandwidth, and reliability.
At the service and business model level, supporting low-latency, bandwidth-intensive applications reshapes network economics. Operators must evolve beyond best‑effort pricing structures toward differentiated service tiers or performance-based charging models aligned with enterprise and industrial use cases.
For the RAN, Physical AI underscores the need for greater programmability and elasticity. Future RAN designs will depend on dynamic resource allocation, real-time traffic classification, and AI-driven orchestration to balance throughput, latency, and reliability at scale.
As Physical AI deployments expand—from autonomous mobility to precision manufacturing and tele‑robotics—managing high‑volume, low‑latency uplink traffic will become a defining capability for next‑generation network strategy and differentiation. Unlike conventional mobile data, Physical AI cannot rely on buffering to manage traffic spikes. The requirement for continuous video and sensor data to arrive within strict time limits to inform real-time actions makes traditional “best-effort” network approaches inefficient and costly.
- Uplink-Centric Demand: Physical AI shifts the network requirement from downlink-heavy (human consumption) to uplink-heavy (machine-generated) traffic.
- Strict Latency & Throughput: Maintaining consistent low latency (e.g., around 20 milliseconds) for high-volume video uploads can require 3x to 4x more capacity than average, making overprovisioning unsustainable.
- Need for Programmable Architectures: To support this, RAN must move toward more flexible, AI-native architectures that prioritize critical data and provide deterministic, rather than best-effort, performance.
- Semantic Communication: To reduce data volume while maintaining performance, the RAN will need to adopt semantic communication—transmitting only the essential data needed for the AI to make decisions.
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References:
https://www.nokia.com/asset/215147/
https://www.nokia.com/blog/physical-ai-redefining-ran-and-telco-monetization/
https://telcomagazine.com/news/nokia-report-points-to-ai-driven-shift-in-mobile-traffic
Arm Holdings unveils “Physical AI” business unit to focus on robotics and automotive
Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?
The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core
Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026
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)
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?
How Far is the Far Edge?
As major telcos size up distributed edge sites for a possible AI inferencing model, they’re trying to determine how far out the right place is in their networks to invest in AI computing capacity. According to Light Reading, the “far edge” is a divisive option for inferencing. According to Omdia, owned by Informa, the Far edge includes: radio access network (RAN) cell sites, aggregation hubs, exchange offices, optical line terminal (OLT) nodes, and Tier 2 metro hubs.
Many telcos are struggling to define how far is the edge from customer premises and how to serve various use cases with compute and intelligence? It seems that 5G SA core with network slicing would be mandatory to support multiple unique use cases, each with different QoS requirements.
According to Omdia’s Telco Edge Computing Survey last year, just 15% of telcos ranked network far edge as the top location for where most AI inferencing will take place, while even less (11%) said the network near edge would be the main spot (which includes central offices, headend sites and large telco data centers). The results showed AI inferencing is expected to be handled mostly on the end devices themselves and at the enterprise edge (e.g., offices, campus or manufacturing sites).
Kerem Arsal, Omdia senior principal analyst for telco enterprise and whoIe sale, predicted in a research note that this year will see telcos split into camps of “believers” and “doubters” of the far edge.
Image Credit: Sphere
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AT&T VP Yigal Elbaz, speaking at the recent New Street Research and BCG Global Connectivity Leaders Conference, expressed a cautious view on AI compute at the “far edge,” questioning how far the edge truly needs to extend to serve specific use cases effectively. He said the following (Source: Light Reading)
“The proliferation of compute and high-performing compute across the nation, in all metros is just happening, with a software layer on top of this [and] with the tools that developers need. So, I am not sure that there’s much value in extending that compute all the way to the far edge just to save another millisecond or two milliseconds of latency.”
“AT&T’s fiber and wireless networks can provide the “deterministic experience” needed between any new use cases and help them to “intelligently connect to the right model that they use, the context or the infrastructure that they need because that’s going to be heavily distributed across the US.”
“There’s no doubt that that AI is going to be embedded into wireless networks, and we’re going to call it AI-native and combine the physical space with the intelligence of the network. This is all true,” said Elbaz.
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Distributed AI Grids:
- Ethernet with RDMA (RoCE): The foundation is built on Nvidia Spectrum-X Ethernet, which utilizes RDMA over Converged Ethernet (RoCE). This allows for direct memory access between edge GPUs (e.g., Nvidia RTX PRO 6000 Blackwell Server Edition) and the network core, bypassing CPU overhead to achieve near-line-rate performance.
- Scale-Across Networking: Using Nvidia Spectrum-XGS, the architecture extends standard RoCE to scale across geographically distributed sites. This creates a unified “AI Factory Grid” where remote edge nodes function as a single, programmable compute substrate.
- Silicon One Routing: Cisco’s Silicon One-based routing is utilized for AI-optimized traffic management, providing the high-speed, high-density throughput required for token-intensive inference workloads.
- Zero Trust & Secure Pathways: The interconnect includes a Zero Trust security layer embedded directly into the fabric. It utilizes localized traffic breakout and policy-enforced pathways to ensure that sensitive IoT and video data (such as public safety feeds) remain within the customer’s secure domain at the network edge.
- Orchestration Control Plane: A workload-aware control plane manages these protocols to intelligently route tasks based on real-time KPIs (latency, cost-per-token, and data sovereignty), ensuring that “mission-critical” inference happens at the optimal node.
- Proprietary Software Lock-in: Integrating network functions into a proprietary ecosystem (like Nvidia’s CUDA or AI Aerial) can create a “subscription trap,” where software is inseparable from specific hardware, making it nearly impossible to swap vendors without a total architectural overhaul.
- Data Fragmentation: Deploying AI across a distributed grid often leads to fragmented data sets across legacy and new multi-vendor platforms, which can result in inaccurate AI models and increased operational complexity.
- Standardization Lag: While industry bodies like the GSMA are pushing for Open Telco AI standards, the rapid deployment of proprietary AI systems often outpaces these frameworks, leading to entrenched, incompatible systems that require significantly more resources to reconcile later.
- Integration with Legacy Systems: Modern “agentic AI” and AI-native stacks often struggle to orchestrate processes across siloed legacy infrastructure, creating rigid operational environments that prevent the seamless flow of data needed for automated network troubleshooting.
Bottom Line: While the AI Grid may offer a more viable roadmap than AI-RAN, there is insufficient industry discourse regarding the strategic risks of a global, geographically distributed computing platform—as Nvidia defines it—reliant on a single-vendor hardware stack. Although Nvidia currently maintains undisputed market dominance, historical precedents such as Intel serve as a cautionary tale; long-term dominance is never guaranteed, and even market leaders face potential obsolescence. Furthermore, Nvidia’s practice of providing capital injections to entities that subsequently re-invest those funds back into Nvidia’s own ecosystem raises significant concerns regarding market sustainability and long-term financial health.
References:
https://www.lightreading.com/ai-machine-learning/at-t-cto-casts-doubt-on-ai-compute-at-the-far-edge
https://www.lightreading.com/5g/nvidia-lines-up-ai-grid-as-orange-cto-echoes-the-ai-ran-doubts
Edge AI Computing Explained: Key Concepts and Industry Use Cases
Will “AI at the Edge” transform telecom or be yet another telco monetization failure?
Analysis: Edge AI and Qualcomm’s AI Program for Innovators 2026 – APAC for startups to lead in AI innovation
Private 5G networks move to include automation, autonomous systems, edge computing & AI operations
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
Nvidia’s networking solutions give it an edge over competitive AI chip makers
IDC Survey of Networking Leaders: Enterprise AI progress stalls despite ambitious goals
New IDC research released in April 2026 highlights a growing disconnect between ambitious enterprise AI goals and the reality of their technical execution. The 2026 IDC AI in Networking Special Report (LinkedIn Video hyperlink) [1.] found that organizations expecting to move from early and selective AI use for business and IT initiatives to more advanced deployments largely haven’t. The result is a widening gap between intent and execution that is becoming harder to ignore. This widening gap in AI execution is driven by a mismatch between ambitious goals and the realities of legacy infrastructure, which cannot handle the data demands for production-grade models.
Despite high expectations, many organizations have seen their AI progress stall over the last 18 months, with “select use” adopters failing to advance to more “substantial” deployments. A critical shortage of specialized AI experienced personnel, combined with lagging security and governance controls, has caused widespread “pilot paralysis” across most enterprises. To overcome this, organizations are shifting toward “AI factories” to create a repeatable, governed pipeline for deploying AI.
Note 1. IDC’s 2026 AI in Networking Special Report is a report driven by a worldwide survey of 500+ enterprise network executives and experts. The report covers both the impact and plans for supporting AI workloads across the network and using AI-powered networking solutions. The focus of this research is comprehensive, covering datacenters, cloud services, multi-cloud environments, network core and edge, and network management.
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Mark Leary, IDC research director, Network Observability and Automation:
“Many solution suppliers are prioritizing a platform approach to the challenges associated with moving AI workloads into production. This survey of networking leaders highlights the shift in preference from platforms to best-in-class solutions when supporting AI workloads across their networks. As certain functional requirements intensify, as IT staff experience and expertise build, and as platforms fall short in delivering expected advantages, IT organizations are more willing to take on the added responsibilities associated with assembling their own mix of best-in-class solutions. For the supplier, the challenge is to avoid developing and delivering a platform that is classified as a jack-of-all-trades and master of none.”
“Agentic AI is to have a profound effect on the network infrastructure and on networking staff. Two years ago, AI assistants were labeled leading edge when they offered natural language processing for operator interactions and network management guidance driven by technical manual content. How things have changed! Agentic AI is no longer just a passive informer and instructor but an active intelligent virtual network engineer. Agents gather and process comprehensive network data, develop deep and precise insights, and determine and, increasingly, execute needed network management actions. Whether fixing a network problem, activating a network service, optimizing a network configuration, or responding to a developing network condition, agentic AI solutions are proving more and more useful across the entire network and the entire set of tasks required to engineer and operate the network.”
While this IDC Survey Spotlight offers only an overview of responses relating to agentic AI, detailed results are available by geographic region, select country, company size, major vertical industries, respondent role, and the AI maturity level of the respondent’s organization.
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Organizations are pursuing AI in networking across two categories:
1.] Supporting AI workloads across network infrastructure and
2.] Applying AI to network operations.
But in both cases, progress is constrained by persistent challenges. “2026 is when organizations find out if AI in networking delivers real operational impact—or remains stuck in pilot mode,” Leary said in the referenced LinkedIn Video.
Source: IDC
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Security remains the top concern among enterprises, both as a barrier to deployment and a primary use case for AI itself. “You have to fight AI with AI from a network security perspective,” said Brandon Butler, senior research manager at IDC. “There’s a realization that nefarious actors are leveraging AI themselves. The pressure is already on the network. The question now is whether organizations can keep up with what AI is demanding of their infrastructure,” he added.
Integration with existing systems and a shortage of skilled talent follow close behind. “Most folks don’t feel their staff can fully evaluate and select the right solutions,” Leary said. As a result, many organizations are turning outward for help:
- 81% say they are increasing spending on managed service providers (MSP) to support AI initiatives.
- 89% of data centers expect to increase bandwidth by at least 11% within the next year, driven by AI workloads.
- That demand extends beyond individual facilities, with 91% expecting similar growth in inter-data center connectivity, highlighting the strain on distributed architectures.
- Nearly half of respondents (46%) prefer AI systems that can both determine and execute network actions autonomously.
- Another 41% favor a guided approach, while 13% prefer no AI involvement.
Cloud environments are seeing sharper increases in AI use. Organizations anticipate an average 49% rise in bandwidth for cloud connectivity over the next year. “The cloud is almost always involved,” Leary says. “The biggest group mixes one cloud platform with one or more data centers.”
Beyond the data center and cloud, the network edge is emerging as the next major growth area. Today, 27% of organizations have deployed AI workloads at the edge, and 54% plan to do so within two years. Butler said: “Folks who are leveraging AI more extensively are already pushing workloads to the edge. We see this as a leading indicator of where the market is going.”
“Two years in a row, the largest group said they want AI to both determine and execute actions. It was honestly surprising,” he added.
Enterprise edge bandwidth is projected to grow by an average of 51% in the next year. As AI becomes more distributed, network teams will need to manage greater complexity across environments while maintaining performance and security.
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When assessing expected ROI from AI in networking, IDC survey respondents focused on elevating IT capabilities, with 31% prioritizing superior service levels and 30% focusing on operational efficiency. These outcomes ranked above worker productivity and revenue, suggesting that leaders are strategically utilizing AI to enhance foundational operational workflows. Notably, reducing operating costs ranked seventh, suggesting a focus on strategic value rather than immediate expense reduction.
Source: IDC
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IDC Research identified specific applications—from automated configuration validation to AI-enhanced threat response—as catalysts for measurable performance gains and the organizational trust essential for broader implementation. For network executives, this phased approach represents the most strategic methodology for achieving long-term operational objectives.
“It doesn’t have to be handing the keys of your kingdom to AI to really get some benefits from these AI tools,” Butler concluded.
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References:
https://www.networkworld.com/article/4152655/ai-for-it-stalls-as-network-complexity-rises.html
Using AI, DeepSig Advances Open, Intelligent Baseband RAN Architectures
Using advanced AI techniques, DeepSig has reportedly managed to eliminate a mobile network’s pilot signal, thereby removing signaling overhead without degrading overall performance. Founded in 2016, the U.S.-based startup occupies a leading position at the intersection of artificial intelligence (AI) and the radio access network (RAN), developing data-driven models that could supplant traditional, human-engineered signal processing algorithms.
This work has become especially relevant as the telecom industry moves toward open and software-defined RAN architectures. DeepSig is now a visible contributor to OCUDU (Open Centralized Unit Distributed Unit), an open-source initiative announced by the Linux Foundation in collaboration with the U.S. Department of Defense and its FutureG ecosystem partners to accelerate open CU/DU development for 5G and early 6G systems. OCUDU is intended to establish a carrier-grade reference platform for baseband software, with support for AI-based algorithms and solutions embedded in the RAN compute stack.
As AI becomes a central theme across the telecom ecosystem, DeepSig has rapidly moved from relative obscurity to prominence through collaborations with major industry and government stakeholders. Most recently, the company emerged as a key contributor to OCUDU—the Open Central Unit Distributed Unit initiative announced by the Linux Foundation and the U.S. Department of Defense (DoD) ahead of MWC Barcelona 2026. The program’s goal is to introduce open-source software elements into the RAN baseband domain, an area historically dominated by proprietary offerings from Ericsson, Nokia, and Samsung. By lowering barriers to entry, OCUDU aims to foster innovation and enable smaller players like DeepSig to participate more freely in the U.S. baseband ecosystem.
Image Credit: DeepSig
DeepSig was identified, alongside Ireland-based Software Radio Systems (SRS), as one of two startups selected to deliver OCUDU’s initial software stack. “The National Spectrum Consortium had an RFQ for developing an open-source stack,” explained Jim Shea, DeepSig’s CEO. “SRS already had a capable baseline, but it needed to be elevated to carrier-grade—adding new features and strengthening reliability,” he added.
Meanwhile, major vendors Ericsson and Nokia were named “premier members” of the new OCUDU Ecosystem Foundation. While both could, in principle, leverage the platform to integrate third-party components into their baseband systems, industry observers remain skeptical that these incumbents will fully embrace open-source alternatives over their established proprietary stacks. In comments at MWC, Nokia CEO Justin Hotard characterized OCUDU as a welcome ecosystem evolution to accelerate innovation but clarified that “not everything necessarily needs to be open source.”
Driven in part by DoD interests, OCUDU reflects broader U.S. government ambitions to ensure that 5G and future 6G networks remain open to domestic innovation, particularly for defense and mission-critical use cases. For vendors like Ericsson and Nokia—who view defense markets as increasingly strategic—this alignment could bring both opportunity and complexity.
DeepSig’s trajectory extends beyond OCUDU. The company’s technology originated from research by Tim O’Shea, now CTO, during his tenure at Virginia Tech, where he explored deep learning’s application to wireless signal processing. “You can apply deep learning to enhance the way communication systems operate by replacing many of the traditional algorithms,” said Jim Shea. While these methods do not circumvent theoretical limits such as Shannon’s Law, small efficiency gains can yield substantial operational and economic benefits for cost-sensitive mobile operators.
As DeepSig and peers continue to redefine how intelligence is integrated into the RAN, their work signals a shift toward AI-native architectures—where machine learning, rather than handcrafted algorithms, becomes the foundation for next-generation network optimization.
References:
https://www.lightreading.com/5g/small-deepsig-is-at-heart-of-ai-ran-challenge-to-ericsson-nokia
Accelerating 5G vRAN, AI-RAN, and 6G on OCUDU, “the Linux of RAN”
AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?
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
InterDigital led consortium to advance wireless spectrum coexistence & sharing
Telecom sessions at Nvidia’s 2025 AI developers GTC: March 17–21 in San Jose, CA
Sources: AI is Getting Smarter, but Hallucinations Are Getting Worse
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
Ericsson and Forschungszentrum Jülich MoU for neuromorphic computing use in 5G and 6G
Ericsson and major European research center Forschungszentrum Jülich are collaborating to develop technologies for the continued evolution of 5G and for the future introduction of 6G (IMT 2030) networks. The organizations signed a Memorandum of Understanding (MoU) on March 24, 2026.The project aims to leverage JUPITER, Europe’s first “exascale” supercomputer, to design and test new artificial intelligence solutions for the complex demands of 6G. The partnership will explore AI models and methods to enhance Ericsson’s core network, network management, and Radio Access Network (RAN).
Important objectives include exploring ultra-efficient, “brain-inspired” computing approaches like neuromorphic computing [1.] to handle intense network tasks and strengthen Europe’s digital infrastructure. Modern mobile networks rely heavily on Massive MIMO, a technology where many devices communicate simultaneously via numerous antennas. By exploring novel system architecture approaches like neuromorphic computing, researchers aim to speed up optimization and reduce energy use versus classical methods.
Note 1. Neuromorphic computing is a brain-inspired engineering approach that mimics biological neural networks using analog or digital electronic circuits. It combines memory and processing in one place—similar to neurons and synapses—to achieve extreme energy efficiency, speed, and learning capabilities, moving beyond the limitations of traditional computing architecture. Unlike traditional AI that uses continuous data, neuromorphic systems use “spikes”—discrete events in time—to mimic how neurons communicate. Such systems only consume significant power when processing data (“spiking”), making them ideal for ultra-low-power edge computing, unlike traditional computers that are always on. They can process complex, real-world data (like vision or touch) much faster and with far less power than traditional computers.
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The alliance will study operational strategies like heat recovery to boost energy efficiency in HPC and cloud deployments. The collaboration involves systematic benchmarking of AI methods – including the application of neuromorphic AI – across Ericsson products to assess execution speed, scalability to large datasets, information retention, and storage efficiency. In addition, the partnership will provide insights into the feasibility of cloud strategies based on concepts from the EuroHPC ecosystem, which is establishing a world-class supercomputing infrastructure.
Professor Laurens Kuipers, a member of the Executive Board of Forschungszentrum Jülich, said: “This collaboration has the potential to make a significant contribution to a more sustainable digital future. By combining our excellence in high-performance computing and our research into novel, neuro-inspired computing approaches with Ericsson’s expertise in telecommunications, we aim to develop more energy-efficient network solutions and strengthen a sovereign European digital infrastructure.”
Image Credit: Image: Forschungszentrum Jülich / Kurt Steinhausen
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Nicole Dinion, Head of Architecture and Technology, Cloud Software and Services, Ericsson said: “The future of mobile networks is deeply intertwined with AI and the need for unparalleled energy efficiency. Our collaboration with Forschungszentrum Jülich, for years a global leader in supercomputing and applied physics, combines their research and computing power with our expertise in all domains of telecoms technology. We will explore architectures that define the next generation of telecommunication.”
The collaboration covers several areas of research:
- AI methods for Ericsson products across the full portfolio: systematic benchmarking of approaches to assess execution speed, scalability to large datasets, information retention, and storage efficiency. Where security and commercial conditions permit, the teams may also use JUPITER for large-scale model training, leveraging its compute resources.
- Energy-efficient computing for AI inference at the radio and edge: developing and prototyping highly efficient solutions for tasks such as radio channel estimation and Massive MIMO – a key technology in modern mobile networks, in which many devices communicate simultaneously via numerous antennas. This includes exploring novel system architecture approaches like neuromorphic computing (e.g., memristors) to speed up optimization and reduce energy use versus classical methods.
- HPC and cloud architectures and operations for AI: researching and implementing Modular Supercomputing Architecture (MSA) concepts from exascale work at Forschungszentrum Jülich – in particular, at the Jülich Supercomputing Centre (JSC) – and studying operational strategies, such as heat recovery, to boost energy efficiency in HPC and cloud deployments.
The collaboration will provide insights into the feasibility of cloud strategies based on concepts from the EuroHPC ecosystem, which is establishing a world-class supercomputing infrastructure with leading European centers such as the JSC.
ABOUT FORSCHUNGSZENTRUM JÜLICH:
Shaping change: This is what drives us at Forschungszentrum Jülich. As a member of the Helmholtz Association with more than 7,000 employees, we conduct research into the possibilities of a digitized society, a climate-friendly energy system, and a resource-efficient economy. We combine natural, life, and engineering sciences in the fields of information, energy, and the bioeconomy with specialist expertise in simulation and data science. www.fz-juelich.de
References:
https://www.ericsson.com/en/blog/2026/1/ai-future-will-be-defined-by-the-intelligent-digital-fabric
https://www.ibm.com/think/topics/neuromorphic-computing
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
AI infrastructure spending boom: a path towards AGI or speculative bubble?
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Expose: AI is more than a bubble; it’s a data center debt bomb
Sovereign AI infrastructure for telecom companies: implementation and challenges
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
Custom AI Chips: Powering the next wave of Intelligent Computing
Groq and Nvidia in non-exclusive AI Inference technology licensing agreement; top Groq execs joining Nvidia
Will “AI at the Edge” transform telecom or be yet another telco monetization failure?
New Telco Opportunity – AI at the Edge:
At MWC 2026 last week, there were a flurry of claims that “AI at the Edge” would transform the telecom industry. One of many examples is an article titled, “The AI edge boom is giving telecom a new strategic role.” In that piece, Jeff Aaron, vice president of product and solutions marketing at Hewlett Packard Enterprise (HPE) spoke with theCUBE’s John Furrier at MWC Barcelona, during an exclusive broadcast on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed telecom edge AI and why networking is becoming a strategic foundation for data-centric services. Aaron said:
“A big reason for [reignited interest in routing] is AI workloads. They’re moving everywhere now. They have to move to the edge. For them to move to the edge, you’ve got to get them outside of the factory and to all the locations. We’re right in the core of that, and it’s super exciting.”
As AI expands to the edge, data will need to move not only to local compute, but also between many distributed edge sites, making routing paramount. There are four ways AI infrastructure is scaling — inside data centers and across distributed edge locations, according to Aaron.
“There’s scale-out, scale-across, scale-up, and on-ramp. Two are within the data center — scale-out and scale-up — but scale-across and edge on-ramp basically mean you got to figure out how to connect to those areas, and those are just networking,” he added.
Scale-across refers to connecting distributed data centers and edge locations, while edge on-ramp brings remote sites such as factories or branch locations into the network to access AI services. Supporting those distributed environments creates an opportunity for HPE to bring networking and compute together into a more integrated infrastructure stack. At MWC 2026 Barcelona, those trends are clearly coming into focus, according to Aaron.
“Data is moving everywhere right now, and the network is back. The network isn’t just plumbing. The network is how you build a value-added service using an AI workload as a telco infrastructure,” he added.
Telecom carriers are now urgently trying to move from being “dumb data pipes” to becoming “AI performance platforms” by leveraging their geographically distributed infrastructure to host AI closer to the end user. They urgently want to pivot from selling just bandwidth and connectivity to selling outcomes and intelligence with a heavy focus on industrial and enterprise-specific edge deployments. They are considering the following services and business models:
- Infrastructure as a Service (IaaS) & GPUaaS: Offering raw computing power, specifically GPUs, from edge data centers to enterprises that need low-latency processing without building their own facilities.
- Sovereign AI Clouds: Providing AI services that guarantee data remains within national borders, appealing to government and highly regulated sectors like finance and healthcare.
- API Monetization: Exposing real-time network data (e.g., location intelligence, predictive network quality, fraud risk scoring) via APIs that enterprises pay to integrate into their own applications.
- Outcome-Based Pricing: Charging for specific business results, such as a “guaranteed video call quality” or “fraud loss reduction share,” rather than just data usage.
- AI-as-a-Service (AIaaS): Bundling pre-trained models or specialized AI agents (e.g., for customer service or industrial monitoring) with connectivity
Major Carrier AI Edge Deployment Plans:
- AT&T:
- Launched Connected AI for Manufacturing in March 2026, which unifies 5G, IoT, and generative AI to provide real-time fault detection (claiming a 70% reduction in waste).
- Deploying “Edge Zones” in major U.S. cities (Detroit, LA, Dallas) to allow developers to run low-latency, cloud-based software locally.
- Partnering with AWS to link fiber and 5G directly into AWS environments for distributed AI workloads.
- Verizon:
- Unveiled Verizon AI Connect, a suite of products designed to manage resource-intensive AI workloads for hyperscalers like Google Cloud and Meta.
- Trialing V2X (Vehicle-to-Everything) platforms to provide carmakers with standardized APIs for low-latency edge processing in autonomous driving.
- Collaborating with NVIDIA to integrate GPUs into private 5G networks for on-premise AI inferencing in robotics and AR.
- SK Telecom (SKT):
- Announced an “AI Native” strategy at MWC 2026, including a roadmap for AI-RAN (Radio Access Network) that uses GPUs to optimize network performance and host user AI apps simultaneously.
- Building a Manufacturing AI Cloud powered by over 2,000 NVIDIA RTX GPUs to support digital twin simulations and robotics.
- Expanding AI Data Centers (AIDC) across South Korea and Southeast Asia (Vietnam, Malaysia) using energy-optimized LNG-powered facilities.
- Orange & Deutsche Telekom:
- Deploying AI-powered planning tools to cut fiber rollout costs and optimize site power consumption by up to 33% using AI “Deep Sleep” modes.
- Focusing on Sovereign AI strategies to ensure data governance for European enterprise customers.
- Vodafone:
- Utilizing AI/ML applications for daily power reduction at 5G sites and testing autonomous network healing via AI agents
- BT:
- Offers 5G-connected VR for manufacturing design teams (e.g., Hyperbat) to collaborate on 3D models in real-time.
| Product Category | Primary Target | Key Value Proposition |
|---|---|---|
| AI-RAN | Industry 4.0 | Seamless, ultra-low latency for robotics and sensing. |
| Connected AI Platforms | Manufacturing | Real-time predictive maintenance and waste reduction. |
| AI-as-a-Service (AIaaS) | Developers/SMBs | Access to GPU power and pre-trained models via telco edge nodes. |
| Network Slicing APIs | App Developers | Programmatic control over bandwidth for AR/VR and gaming. |
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A Dissenting View of “AI at the Edge”:
The global market for AI within the global telecommunications sector is valued at $6.69 billion in 2026, growing at a compound annual rate (CAGR) of 41.9% from 2025. The broader edge AI market—including hardware, software, and services—is forecast to reach $29.98 billion in 2026, according to The Business Research Company. We think those estimates are way too high.
The market research firm states:
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Author’s Opinion:
Unless telcos change their corporate culture along with slowing the footprint growth of cloud service providers/hyperscalers, we think that AI at the Edge will be yet another telco monetization failure. Just like their failure to monetize: 4G LTE apps, the telco cloud, 5G, multi-access edge computing (MEC), OpenRAN, LPWANs and other telecom technologies that never lived up to their promise and potential.
That’s largely because telcos are very weak: developing IT platforms, compute services, killer applications, and rapid execution of new services (e.g. 5G services require a 5G SA core network which telcos were very slow to deploy). Telecom execs themselves cite cultural and speed‑of‑change issues: the industry is not organized like a software company, so it struggles to iterate products at AI/cloud pace. Also, telcos historically struggle with software. Managing distributed GPU clusters is vastly different from managing cell towers.
After spending billions on 5G with very little or no ROI, investors are skeptical of the increased capex required for AI-grade edge servers which must be maintained by telcos. Those servers will be expensive (especially if they contain clusters of Nvidia GPUs) and consume a lot of power, which is a critical issue at the edge of the carrier’s network.
Many network operators frame AI/edge as “network optimization” or “utilizing underused sites,” not as building monetizable AI platforms with APIs, SDKs, and ecosystems. This mirrors 5G, where huge RAN/core builds were not matched by a clear product and platform strategy, leaving value to OTTs and hyperscalers which are extending their control planes and protocol stacks to the network edge (local zones, operator co‑lo, on‑premises stacks).
Telcos risk becoming “dumb pipes” for AI traffic if they can’t provide a superior developer ecosystem. If they only sell space/power/connectivity, the cloud service providers will continue to own the developer and AI value chain. Analysts warn that edge is a “right to participate, not a right to win.” As such, value accrues to whoever owns the AI platform, tools, marketplace, and pricing power, not the entity that provides connectivity, PoP or cell towers.
Data fragmentation and weak “intelligence” layer:
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AI monetization depends on high‑quality, cross‑domain data, but telco data is fragmented across OSS, BSS, probes, and partner systems; without unification, it is hard to expose compelling network/edge intelligence services.
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Analysts emphasize that failure here reduces telcos to generic GPU landlords, while higher‑margin offers (real‑time quality, fraud, identity, mobility/context APIs) remain unrealized.
Narrow internal focus on cost savings:
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Many operators’ early AI focus is inward (Opex reduction in assurance, planning, customer care) rather than building external, revenue‑generating products, echoing how early 5G was justified mainly on cost/efficiency.
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Commentators warn that if AI/edge remains a “network efficiency” play, the commercial upside will go to cloud/AI natives that turn similar capabilities into products sold to enterprises.
What analysts say telcos must do differently:
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Build “Sovereign AI factories” and edge AI clouds: GPU‑enabled sites with cloud‑like developer experience (APIs, self‑service portals, metering, SLAs) and clear sovereign/regional guarantees.
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Combine differentiated connectivity with AI services (latency‑backed SLAs, AI‑on‑RAN, domain‑specific models for verticals) and use modern, flexible commercial models instead of just selling bandwidth or colocation.
Conclusions:
In summary, the main risk for telcos is to successfully transition from owning and maintaining network infrastructure to owning and operating AI platforms and products at software industry speed. AI at the edge is less of a new service or product and more an architectural upgrade. The two ways telcos can benefit are from:
- Internal cost reduction: If telcos use it to lower their own costs (fraud prevention, risk management, predictive maintenance, fault isolation, self-healing networks, etc.), it’s an automatic win but won’t increase the top line.
- Revenue from new AI -Edge services, e.g. Verizon uses edge-based video analytics in warehouses to improve inventory turnover by up to 40%. If they expect to charge a massive premium for “AI-enabled 5G,” they face the same monetization wall that has doomed them for the past 20 years!
References:
https://siliconangle.com/2026/03/04/telecom-edge-ai-makes-networking-strategic-mwc26/
https://www.nvidia.com/en-us/lp/ai/the-blueprint-for-ai-success-ebook/
AT&T and AWS to deliver last mile connectivity for AI workloads; AT&T Geo Modeler™ AI simulation tool
Analysis: Edge AI and Qualcomm’s AI Program for Innovators 2026 – APAC for startups to lead in AI innovation
Ericsson goes with custom silicon (rather than Nvidia GPUs) for AI RAN
Private 5G networks move to include automation, autonomous systems, edge computing & AI operations
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
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
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
CES 2025: Intel announces edge compute processors with AI inferencing capabilities
AT&T and AWS to deliver last mile connectivity for AI workloads; AT&T Geo Modeler™ AI simulation tool
AT&T is strategically re-architecting its infrastructure for the AI era through high-capacity network modernization and deep integration with hyperscale cloud providers.
In addition to its almost six year old deal to run its 5G SA core network in Microsoft Azure’s cloud, AT&T announced at MWC 2026 that it’s now woring with Amazon Web Services (AWS) to extend 5G and fiber connectivity from business customers and locations directly into AWS environments, creating secure, resilient and reliable premises‑to‑cloud architectures for AI workloads. The collaboration is designed to reduce network complexity and latency while supporting real‑time analytics, machine learning, and agentic AI use cases.
This collaboration continues a long-standing relationship between AT&T and AWS and follows recent news outlining broader efforts to modernize the nation’s connectivity infrastructure by providing high-capacity fiber to AWS data centers, migrate AT&T workloads to AWS cloud capabilities and explore emerging satellite technologies.
AWS Interconnect – last mile embeds AT&T‑delivered connectivity directly into AWS workflows, designed to enable customers to provision and manage last‑mile connectivity within the AWS environment and lays the foundation for the use of AI agents to monitor and manage the AI experience from the user to the cloud. This streamlined, self‑managed approach helps enterprises reduce network complexity while maintaining control of their extended enterprise network, allowing businesses to move faster as they scale AI.
High level illustration of the planned AWS Interconnect – last mile architecture, showing how resilient interconnections and AT&T Fiber and fixed wireless access are intended to simplify private connectivity from customer locations into AWS environments.
Diagram Source: AT&T
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“AI does not just need more compute; it needs flatter networks and faster connections,” said Shawn Hakl, SVP & Head of Product, AT&T Business. “By bringing high‑capacity connectivity closer to cloud platforms, integrating the management of the networks directly into the cloud provisioning process and engineering for resiliency at the metro level, AT&T is helping enterprises streamline their networks, improve performance, security, and scale AI with confidence.”
AT&T says they are building an AI‑ready network (?) designed to scale performance by continuing ongoing network investment, including the growth of capacities up to 1.6Tbps across key metro and long‑haul routes.
AT&T also announced it would work with Nvidia, Microsoft and MicroAI through its Connected AI platform for “smart manufacturing.”
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Finally, AT&T described AT&T Geo Modeler™ which is able to better predict connectivity for emerging technologies like autonomous vehicles, drones, and robotics.
The Geo Modeler is an AI-powered simulation tool that helps predict, in near real time, how a wireless network will perform in the real world. Inspired by the video games Kounev played with his family growing up, the virtual model and simulation is “essentially like a giant video game of the United States” that, infused with AI tools, gives engineers a clearer picture of where potential weak spots may appear. Then issues can be addressed earlier and fixes can roll out faster. In essence, it creates virtual models, similar to the way video games are designed and developed.
“The Geo Modeler helps us see how the real world will shape coverage before we build, so we can deliver connectivity that’s ready for what’s next,” said AT&T scientist Velin Kounev.
Matt Harden, VP of Connected Solutions at AT&T, agrees. “The Geo Modeler is a foundational capability for the connected mobility era,” he said. “By marrying advanced geospatial simulation with AI-driven network orchestration, we can deliver predictable, high-performance connectivity that adapts with the environment. Whether it’s a hurricane, a packed stadium, or a city corridor full of autonomous vehicles, we will be prepared.”
References:
https://about.att.com/story/2026/aws-collaboration-scalable-business-ai.html
https://about.att.com/blogs/2026/150-years-of-connection.html
https://about.att.com/blogs/2025/geo-modeler.html
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
AT&T deploys nationwide 5G SA while Verizon lags and T-Mobile leads
AT&T to buy spectrum licenses from EchoStar for $23 billion
AT&T’s convergence strategy is working as per its 3Q 2025 earnings report
Progress report: Moving AT&T’s 5G core network to Microsoft Azure Hybrid Cloud platform
AT&T 5G SA Core Network to run on Microsoft Azure cloud platform
Huawei unveils AI Centric Network roadmap, U6 GHz products, 5G Advanced strategy and SuperPoD cluster computing platforms
Missing from all the MWC 2026 6G AI alliance announcements, Huawei released a series of all-scenario U6 GHz products to help carriers unlock the full potential of 5G Advanced (5G-A) and set the stage for a seamless transition to 6G. Huawei also showcased its SuperPoD cluster for the first time outside China, which they have created to offer “a new option for the intelligent world.”
- The all-scenario U6 GHz products and solutions Huawei released today use innovative technologies to create a high-capacity, low-latency, optimal-experience backbone designed for mobile AI applications.
- There are already 70 million 5G-A users globally, and 5G-A is increasingly being adopted by carriers at scale. In China, Huawei has helped carriers deliver contiguous 5G-A coverage across 270 cities and launch 5G-A packages that monetize experience in over 30 provinces.
The company also launched enhanced AI-Centric Network solutions [1.] that will help carriers prepare for the agentic era by enabling intelligent services, networks, and network elements (NEs). The company’s plans to build more AI-centric networks and computing backbones that will help carriers and industry customers seize opportunities from the AI era.
Note 1. Huawei’s AI-Centric Network roadmap is designed to integrate intelligence directly into 5G-Advanced (5G-A) infrastructure and accelerate the transition toward Level-4 Autonomous Networks. The company plans to work with global carriers (where its not blacklisted) on the large-scale 5G-A deployment, use high uplink to address surging consumer and industry demand for mobile AI applications, and use the U6 GHz band to unlock the full value of spectrum and pave the way for smooth evolution to 6G.
Photo Credit: Huawei
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Three-Layer Intelligence in AI-Centric Networks: Accelerating the Agentic Era:
As mobile network operators transition toward AI-native 5G-Advanced and early 6G architectures, Huawei is positioning its AI-Centric Network portfolio as the blueprint for next-generation intelligent networks. By embedding intelligence across service, network, and network element (NE) layers, Huawei aims to establish the foundation for fully agentic, autonomously managed infrastructures.
- Service Layer: Focuses on multi-agent collaboration platforms to transform core carrier services—such as voice and home broadband—into intelligent service platforms.
- Network Layer: Aims to evolve from single-scenario automation to end-to-end single-domain network autonomy. Huawei officially launched AUTINOps, an AI-native intelligent operations solution designed to replace traditional manual O&M with predictive, preventive “digital employees”.
- Network Element (NE) Layer: Utilizes AI to optimize algorithms for RANs (Radio Access Networks) and core networks, improving spectral efficiency and service awareness.
At the Service layer, Huawei is enabling carriers to operationalize multi-agent collaboration frameworks that embed domain-specific intelligence into key service categories: voice, broadband, and digital experience monetization. These AI agents dynamically manage customer experience and lifecycle value, supporting the transformation of core connectivity services into intelligent, context-aware digital offerings.
At the Network layer, the company’s Autonomous Driving Network Level 4 (ADN L4) initiative focuses on single-scenario automation, delivering measurable improvements in O&M efficiency, service quality, and monetization agility. By the close of 2025, ADN single-scenario deployments were active across more than 130 commercial telecom networks. The next phase targets end-to-end, single-domain autonomy across transport, access, and core networks—an essential step toward zero-touch O&M and intent-driven orchestration in 5G-A and 6G environments.
At the Network Element layer, Huawei is jointly advancing AI-driven innovation across RAN, WAN, and core domains. This includes algorithmic optimization for intelligent RAN scheduling, service-aware traffic identification in WANs, and unified intent modeling across B2C and B2H use cases. Such capabilities enhance spectral and energy efficiency, enable predictive resilience, and provide fine-grained service awareness—all foundational for AI-native air interface and network control in 6G.
Computing Backbone with SuperPoD Clusters:
Supporting this vision, Huawei is introducing its next-generation SuperPoD and cluster computing platforms, designed as high-performance compute backbones for distributed AI model training and inference within telecom and enterprise domains. Featuring the proprietary UnifiedBus interconnect and system-level architecture innovations, the Atlas 950, TaiShan 950, and Atlas 850E SuperPoDs, along with the TaiShan 200–500 servers, deliver ultra-low latency and high throughput optimized for trillion-parameter AI models and real-time agentic operations.
Aligned with its open innovation strategy, Huawei continues to expand an open, collaborative computing ecosystem, supporting open-source frameworks and open-access platforms to accelerate the deployment of intelligent, AI-driven digital infrastructure worldwide.
Intelligent Transformation Across Industry Domains:
At MWC Barcelona 2026, Huawei is highlighting 115 end-to-end industrial intelligence showcases across verticals, underscoring its role in helping enterprises adopt AI-centric operational models. Through the SHAPE 2.0 Partner Framework, 22 co-developed AI and digital infrastructure solutions will demonstrate how vertical industries—from manufacturing and energy to transportation and healthcare—can harness 5G-A and AI integration to deliver measurable business outcomes.
Toward 5G-A Commercialization and 6G Evolution:
With large-scale 5G-Advanced rollouts accelerating, Huawei is collaborating with global carriers and ecosystem partners to realize level-4 autonomous networks and establish the architectural bridge to 6G. Central to this evolution is the convergence of AI, connectivity, and computing—enabling networks that can self-learn, self-optimize, and autonomously orchestrate service intent. These AI-Centric Network initiatives and SuperPoD-based computing backbones form the foundation for value-driven, intelligent networks built for the agentic era.
5G-Advanced and Infrastructure Innovations:
Huawei’s 5G-A strategy, branded as GigaUplink, focuses on delivering the high-uplink capacity and low latency required for mobile AI applications:
- U6 GHz Spectrum: Launched a comprehensive portfolio of all-scenario U6 GHz products to unlock 5G-A’s full potential and provide a smooth evolution path to 6G.
- Agentic Core: Introduced the Agentic Core solution, which integrates intelligence natively into the core network to support ubiquitous AI agent access across devices.
- All-Optical Target Network: Proposed an AI-centric optical roadmap featuring dual strategies: “AI for networks” (optimizing operations) and “networks for AI” (supporting AI workloads with ultra-low latency benchmarks of 1-5ms).
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References:
https://www.huawei.com/en/news/2026/3/mwc-ai-centric-network
https://carrier.huawei.com/en/minisite/events/mwc2026/
Huawei FY2025: 2.2% YoY revenue increase; strategic pivot to AI & Automotive
NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU
Omdia on resurgence of Huawei: #1 RAN vendor in 3 out of 5 regions; RAN market has bottomed
Huawei, Qualcomm, Samsung, and Ericsson Leading Patent Race in $15 Billion 5G Licensing Market
Huawei Cloud Review and Global Sales Partner Policies for 2026
Huawei’s Electric Vehicle Charging Technology & Top 10 Charging Trends
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
Huawei launches CloudMatrix 384 AI System to rival Nvidia’s most advanced AI system
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC
Overview:
AT&T and Ericsson have completed a milestone Cloud RAN test by successfully demonstrating Ericsson’s AI-native Link Adaptation [1.] on a Cloud RAN stack powered by Intel Xeon 6 SoC. The test showed how artificial intelligence (AI) can improve spectral efficiency and network responsiveness in real-world conditions. Conducted over AT&T’s licensed frequency bands, the experiment was the first to use portable Ericsson RAN software running on Intel’s new Xeon 6 system-on-chip (SoC) platform—an architecture designed for high-performance, cloud-native processing of RAN workloads. Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.
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Note 1. AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.
Other Notes:
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vRAN: A radio access network (RAN) in which the baseband processing functions run as software on general-purpose processors (mostly from Intel) instead of on dedicated hardware at the cell site. In vRAN, the functional split defines how baseband processing is divided between centralized processors and the radio unit at the site, and that split drives fronthaul bandwidth, latency, and cost.
- Cloud RAN: An evolution of vRAN where those same RAN functions are re-architected as cloud‑native microservices/containers with CI/CD (Continuous Integration and either Continuous Delivery or Continuous Deployment), automation, and orchestrators, optimized for elastic scaling across distributed cloud infrastructure.
- Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.
- According to Dell’Oro Group, Cloud RAN (often encompassing vRAN) accounted for approximately 5% to 10% of the total global Radio Access Network (RAN) market revenues in 2025. In early 2026, Dell’Oro revised Cloud RAN projections downward. While virtualization remains a “key pillar” for the long term, short-term adoption is being slowed by performance, power, and cost-parity challenges when compared to purpose-built hardware.
- The total RAN market stabilized in late 2025 after losing approximately 20% of its value between 2022 and 2024. Market concentration reached a 10-year high in 2025, with the top five vendors (Huawei, Ericsson, Nokia, ZTE, and Samsung) capturing 96% of the revenue.
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Image Credit: Ericsson
In this proof-of-concept setup, Ericsson’s disaggregated and containerized RAN software operated within AT&T’s target Cloud RAN configuration, built on open, commercial off-the-shelf hardware. The test advanced from basic call functionality to validation of feature-rich network behavior in a cloud computing environment. Ericsson’s AI-native Link Adaptation is a learning algorithm that continuously assesses channel state and interference to determine the optimal modulation and coding scheme for each transmission interval. By generating real-time predictions of link quality, the AI model dynamically adjusts data rates to maximize throughput and spectral efficiency.
Early results were promising. Throughput gains reached up to 20% compared with conventional rule-based link adaptation approaches, alongside measurable improvements in spectral efficiency. Ericsson and Intel also used the trial to benchmark various AI inference models, demonstrating performance scalability and energy efficiency on general-purpose compute nodes rather than proprietary hardware accelerators. This suggests a more pragmatic path for deploying AI workloads across distributed RAN architectures.
AI-native Link Adaptation dynamically adjusts to changes in signal quality and interference, boosting RAN performance on purpose-built and cloud-based infrastructure alike.
Ericsson Cloud RAN is a cloud native software solution that handles compute functionality in the RAN. It virtualizes RAN functions on Commercial Off The Shelf (COTS) hardware, decoupling software from hardware to enable more flexible, scalable, and efficient network deployments.
Engineered specifically for network and edge deployments, Intel Xeon 6 SoC delivers breakthrough AI RAN performance with built-in acceleration. Integrated Intel Advanced Vector Extensions (AVX) and Intel Advanced Matrix Extension (AMX) technologies eliminate the need for discrete accelerators while maximizing capacity, efficiency, and TCO optimization.
Beyond the immediate performance improvements, the trial illustrates how open RAN architectures can accelerate innovation. By decoupling RAN software from vendor-specific hardware, AT&T can integrate AI capabilities and update network functions more quickly, avoiding the constraints of lock-in. The portability demonstrated here—running production-grade Ericsson RAN software on Intel Xeon 6 silicon—marks an industry first.
For AT&T, the achievement represents more than a lab milestone. It provides a technical template for scaling AI-native RAN functions into its cloud infrastructure, pointing to a future where machine learning operates natively within radio environments to fine-tune performance in real time. As operators continue balancing cost, flexibility, and efficiency, AI-optimized Cloud RAN deployments could become the next competitive frontier in 5G—and eventually, 6G—network evolution.
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Quotes:
Rob Soni, Vice President, RAN Technology at AT&T, says: “AT&T is leading the charge toward an open, intelligent, and scalable network future by advancing Open RAN and Cloud RAN with AI-native capabilities at their core. This demo highlights how AI capabilities, powered by our next-generation Cloud RAN platform, can be deployed seamlessly to drive innovation and deliver superior customer experiences.”
Mårten Lerner, Head of Networks Strategy and Product Management, Business Area Networks at Ericsson, says: “Together with AT&T and Intel, Ericsson is demonstrating how our domain expertise combined with AI-native RAN software can drive transformative advancements in both Cloud RAN and purpose-built deployments. Our industry-leading AI-native Link Adaptation serves as the first proof point on this journey. With a hardware-agnostic RAN software stack, Ericsson is committed to offering maximum flexibility and enabling all our customers to benefit from future innovations – regardless of their chosen underlying hardware. This milestone underscores Ericsson’s commitment to helping operators advance their networks by deploying AI functionality across the RAN stack.”
Cristina Rodriguez, VP and GM, Network and Edge at Intel, says: “This successful collaboration with AT&T and Ericsson showcases the power of Intel Xeon 6 SoC to enable and accelerate AI workloads in Cloud RAN environments. Xeon 6 SoC is architected to handle the demanding compute requirements of AI-native network functions, delivering the performance and efficiency operators need to unlock the full potential of intelligent networks. By providing a flexible, standards-based platform, Intel Xeon 6 enables service providers like AT&T to deploy innovative AI capabilities while maintaining the openness and choice that drive industry innovation.”
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AI-Native Link Adaptation vs. Traditional Methods:
Traditional link adaptation in RAN relies on deterministic, rule-based algorithms that select the Modulation and Coding Scheme (MCS) from predefined lookup tables. These methods primarily use instantaneous Channel Quality Indicator (CQI) reports or estimated Signal-to-Interference-plus-Noise Ratio (SINR) thresholds, often adjusted via Outer Loop Link Adaptation (OLLA) based on ACK/NACK feedback from the UE. This reactive approach applies conservative margins to account for channel estimation errors, prediction lag, and varying interference, which can lead to suboptimal throughput—either underutilizing the link with low MCS or triggering excess HARQ retransmissions with overly aggressive selections.
AI-native Link Adaptation shifts to a predictive, model-driven paradigm using machine learning (typically lightweight neural networks or time-series models) trained on historical channel data. Rather than static thresholds, the AI processes sequences of CQI, beam metrics, mobility patterns, and interference traces to forecast the probable channel state for the next transmission time interval (TTI). This enables precise MCS selection that hugs the Shannon capacity limit more closely, minimizing BLER while maximizing spectral efficiency in dynamic scenarios like high-mobility NLOS or bursty interference.
Key differences include:
| Aspect | Traditional (Rule-Based) | AI-Native (ML-Based) |
|---|---|---|
| Decision Mechanism | Lookup tables, SINR thresholds, OLLA offsets | Real-time inference from ML models |
| Channel Handling | Reactive (past CQI/SINR) | Predictive (time-series forecasting) |
| Adaptation Speed | Step-wise, with feedback lag | Continuous, sub-TTI granularity |
| Performance Gains | Baseline (0% reference) | Up to 20% throughput, 10% spectral efficiency |
| Compute Needs | Low (fixed arithmetic) | Moderate (edge inference on COTS like Xeon 6) |
| Limitations | Struggles with non-stationary channels | Requires training data, retraining overhead |
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