AI/ML

Autonomous customer experience required for AI-Native 6G and distributed intelligence at the network edge

Posted on by Alan Weissberger

Executive Sumary:

Communications service providers (CSPs) have historically competed on network-centric KPIs—coverage, capacity, reliability, and price—anchored in 3GPP performance and management frameworks (e.g., TS 28-series, TS 23.501 QoS models). However, these metrics alone are no longer sufficient to sustain differentiation in increasingly saturated and capital-intensive markets, according to Chantel Cary, Product Marketing Senior Manager at Oracle Communications [1],

“The battleground has shifted,” Cary told Capacity Global. “Today, customer experience is becoming the clearest point of differentiation, and in many cases, the most important driver of growth.”

This shift is unfolding alongside structural constraints: flat ARPU, rising capex associated with 5G standalone, fiber access (FTTx), and edge cloud expansion, and increasing customer acquisition and retention costs. At the same time, customer expectations—benchmarked against hyperscaler-grade digital platforms—are becoming uniformly high across mobile and fixed broadband services.

“They do not compare a telecom provider only to other providers,” she said. “They compare every experience to the best experience they have anywhere.”

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Note 1. Oracle Communications is a dedicated global business unit and product portfolio fully owned and operated by Oracle. It provides enterprise software and infrastructure designed specifically for telecommunications service providers (like AT&T or Verizon) and large enterprises. Their solutions manage everything from network routing, security, and signaling (including 5G) to back-office billing, revenue management, and customer experience operations,

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Analysys Mason reports that 97% of operators view AI-powered automation as essential for survival and growth, reinforcing alignment with TM Forum’s Autonomous Networks framework and the broader industry transition toward AI-native system design.

AI-Native Customer Experience Architecture:

Cary’s concept of “autonomous customer experience” maps directly to the emerging paradigm of AI-native networks, where intelligence is embedded across both network and service layers rather than applied as an overlay.  “It is not about removing the human element from engagement,” she explained. “It is about using AI to continuously orchestrate the customer lifecycle in ways that humans alone cannot manage at scale.”

In wireless networks, this evolution is reflected in 3GPP-defined enablers such as the Network Data Analytics Function (NWDAF, TS 23.288), which provides real-time analytics to optimize policy control, mobility, and QoS. In parallel, O-RAN Alliance architectures introduce the near-real-time and non-real-time RAN Intelligent Controllers (near-RT RIC, non-RT RIC), enabling AI-driven control loops for radio resource management and service optimization.

Image Credit: Aisera

In wireline and converged networks, similar principles are emerging through SDN-based control planes, broadband network gateways (BNG) with telemetry streaming, and ITU-T frameworks (e.g., Y.3172 for machine learning in future networks), enabling closed-loop optimization across access, aggregation, and core domains.

However, Cary notes that most OSS/BSS environments remain fragmented, limiting the ability to operationalize these capabilities at the customer experience layer. Data silos, batch-oriented processing, and loosely coupled workflows constrain real-time, cross-domain orchestration.

AI Across Commercial and Network Domains:

Cary identifies three primary domains of impact, increasingly converging with network intelligence:

  • Marketing: AI-driven personalization is evolving toward real-time, context-aware engagement informed by both customer behavior and network conditions (e.g., location, QoS state, congestion). This aligns with event-driven architectures and customer data platforms integrated with network analytics (e.g., NWDAF exposure via APIs). “Personalisation shifts from broad audiences to the individual,” she said, adding that relevance is now “a prerequisite for attention.”

  • Sales: AI enables next-best-action and dynamic offer generation, incorporating network-aware insights such as service availability, slice characteristics (in 5G SA), and fiber capacity constraints. Integration with policy control (3GPP TS 23.203) and service orchestration frameworks supports closed-loop order capture and fulfillment. “That combination of higher conversion and lower friction is valuable,” she said.

  • Service: AI-driven assurance is transitioning from reactive fault management to predictive and intent-based service assurance across both wireless and wireline domains. Telemetry from RAN, transport, and fixed access networks feeds AI models that anticipate degradation and trigger remediation before customer impact. “Human agents still play a central role,” Cary said, “but they can be augmented with real-time recommendations, contextual history and autonomous processes that improve both speed and consistency.”

Scaling Challenges in AI-Native Transformation:

Despite progress in AI models and domain-specific analytics, Cary highlights a systemic gap in operationalization.

“What it lacks, in many cases, is the ability to turn fragmented customer data into real-time decisions that can actually be executed across marketing, sales and service,” she said.

Analysys Mason data indicates that only 6% of operators achieve ROI above 25% from AI initiatives, while 60% advance just 20% of proofs of concept into production. This reflects challenges in integrating heterogeneous data sources across OSS, BSS, and network domains, as well as limitations in MLOps and real-time orchestration frameworks.

Fragmentation is compounded in converged networks, where wireless (3GPP-based) and wireline (e.g., Broadband Forum TR-369/USP, TR-383 for disaggregated BNG) ecosystems often evolve independently. Additionally, 93% of operators cite multi-vendor complexity as increasing total cost of ownership, underscoring the need for interoperable, standards-based integration across AI, network, and IT domains.

“That creates an unfortunate pattern across the industry,” she said, “promising AI initiatives that demonstrate value in isolation but fail to scale because they are not connected to the data, systems and processes where real work happens.”

Toward Fully AI-Native Operations:

Cary emphasizes that the target state is not incremental automation but fully AI-native operations, where intelligence is embedded into both network control loops and customer engagement workflows.

“This is why the future of customer experience in communications is not about layering AI onto the edge of the enterprise,” Cary said. “It is about making AI operational at the core of engagement.”

This vision aligns with emerging 6G research directions, where AI is treated as a native design primitive across RAN, core, and service layers, as well as with TM Forum’s Open Digital Architecture (ODA), which promotes composable, API-driven integration between OSS, BSS, and AI components.

Oracle’s approach reflects this convergence by unifying customer data, embedding AI into engagement and orchestration layers, and integrating these capabilities with telecom operational systems across both wireless and wireline domains.

Implementation Suggestions:

Cary said that network providers do not need to transform everything at once. She recommends starting by unifying customer data across touchpoints to establish a trusted, real-time view, before activating high-value AI use cases across marketing, sales and service. From there, providers can embed AI into workflows so insight translates into action rather than sitting in dashboards, eventually connecting those capabilities into end-to-end orchestration.

Indeed, Cary advocates a phased approach consistent with AI-native transformation:

  • Establish a unified, real-time data fabric spanning customer, service, and network domains.

  • Deploy high-value AI use cases (e.g., next-best-action, churn prediction, predictive assurance) leveraging both IT and network telemetry.

  • Embed AI into execution workflows to enable closed-loop, intent-driven orchestration across the customer lifecycle.

This progression reflects the broader industry trajectory toward converged, AI-native networks, where customer experience is no longer an overlay on connectivity, but a direct outcome of tightly coupled intelligence across wireless and wireline infrastructures.

“The communications providers that lead in the years ahead will not be the ones that simply adopt more AI tools. They will be the ones that use AI to rethink how customer engagement works across the enterprise. AI is not just enhancing customer experience,” she added. “It is redefining how customer experience is delivered, and in communications, that shift is likely to separate the providers that keep pace from the ones that set the pace.”

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Editorial Analysis:

Cary’s vision aligns with IMT 2030/6G’s shift from AI as an overlay to AI as an architectural primitive, spanning the air interface, semantic service handling, and distributed edge intelligence across wireless and wireline domains.  The cleanest way to map her suggestions into 6G is to treat “autonomous customer experience” as the service-layer expression of an AI-native network stack: AI decisions would no longer sit only in OSS/BSS, but would be distributed across RAN, transport, core, and edge applications, with closed-loop control spanning wireless and wireline domains. That maps well to current AI-native 6G proposals that emphasize model interdependencies, distributed intelligence, and AI embedded directly in the architecture rather than layered on top.

For the AI native air interface, the link is to AI-assisted radio control, where the network uses learned models to optimize scheduling, mobility, beam management, and QoS-aware policy decisions in real time. In a 6G framing, that extends beyond today’s AI for RAN optimization and toward an AI-native air interface in which the radio stack itself is designed for machine-driven adaptation, including distributed control loops between UE, RAN, and core. For your article, this supports language that customer experience is increasingly shaped by network intelligence at the point of access, not just by back-office engagement systems.ieeexplore.ieee+2

Semantic communications maps to the idea that the network should optimize for meaning or task relevance, not simply bit delivery. In practice, that means a 6G service layer could prioritize the semantic value of an interaction—such as whether a customer is trying to resolve an outage, confirm a move order, or change a plan—and allocate resources accordingly across wireless and wireline paths. The relevance to Cary’s argument is that customer experience becomes more contextual and intent-aware when the network itself can distinguish between low-value traffic and high-importance service interactions.

Distributed intelligence at the network edge is the most direct bridge between CSP operations and 6G design. In a converged wireless-wireline environment, edge AI can fuse RAN telemetry, fixed access metrics, subscriber context, and service history to trigger local decisions such as proactive care, dynamic QoS adjustment, or preemptive fault mitigation. That makes the experience layer more autonomous because the decision point moves closer to where the event occurs, reducing dependence on centralized, slower, batch-oriented processing.

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References:

https://capacityglobal.com/news/why-customer-experience-is-becoming-telecoms-clearest-differentiator/

What is AI Native?

https://www.nokia.com/6g/unlocking-the-full-potential-of-ai-native-6g-through-standards/

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Huawei’s AI-Centric Network Vision: Six Imperatives for the Next Decade; Critical Questions for IEEE Techblog Community

Posted on by Alan Weissberger

The Case for AI-Native Networks:

At MWC Shanghai 2026  [1.], David Wang, Deputy Chairman of the Board and Rotating Chairman of Huawei, outlined a strategic roadmap for AI-native mobile networks, positioning artificial intelligence as the cornerstone of industry growth over the next decade.

Note 1. MWC Shanghai 2026 was held June 24–26, 2026 at the Shanghai New International Expo Center (SNIEC), with Huawei showcasing products and solutions in Hall N1.

Over the past 40 years, innovation in mobile technology from each generation to the next has been key to the industry’s success. “With each generation, we have pushed the limits of spectral efficiency and performance,” said Wang. “Network architecture has gradually flattened, with new application scenarios and services emerging left and right. This has consistently expanded the boundaries of communications, helping carriers translate network capabilities into commercial value,” he added.

Huawei argues that traditional telecom infrastructure built around data traffic is no longer sufficient. As the global digital ecosystem transitions toward real-time interactions with AI applications and intelligent agents, mobile and transport networks must be completely redesigned to support both communication and computing. According to Huawei, an AI-native architecture transforms networks from simple communication utilities into revenue-generating engines while helping operators transition to Level-4 and Level-5 network autonomy

Huawei’s Six Strategic Imperatives:

Wang identified six imperatives to guide the industry through the age of intelligence:

  1. Developing new services and capabilities for future mobile communications systems

  2. Integrating AI with mobile communications to build three distinct layers of intelligence

  3. Building network architecture for integrated satellite-ground communications

  4. Advocating for sustainable and future-oriented spectrum planning and allocation

  5. Clearly defining the specifications of AI-native core networks

  6. Exploring new business models and application scenarios for mobile services

Photo Credit: Huawei

Innovations Unveiled: Byte and Token Monetization:

Huawei released a portfolio of innovations targeting both services and infrastructure. On the services side, in collaboration with China’s three major carriers, the company announced advances in 5G-Advanced (5G-A) high-uplink and experience monetization, AI-powered business upgrades, and token monetization.

For infrastructure, Huawei launched the AI-centric target network, designed to enhance carrier competitiveness in byte and token monetization. This architecture comprises three layers:

  • Basic Communications Network: A shift from traffic-centric to real-time interaction networking, offering guaranteed connectivity with high uplink and downlink capabilities alongside advanced QoS mechanisms.

  • Computing Network: A transition from traffic transport to network-wide compute scheduling and supply, where “connecting to the network is equivalent to accessing compute.”

  • AI Computing Infrastructure: High-performance, efficient compute with support for open-source and open ecosystems.

5G-Advanced: 100 Million Users and Beyond:

The global 5G-A (based on 3GPP Release 18) user base has surpassed 100 million. Huawei is now working with network operators worldwide to advance 5G-A experience monetization and integrate it into installed base operations, targeting mid-range and high-end user retention, ARPU growth, and sustainable revenue expansion.

High Uplink Speed: The New Frontier for AI Applications:

High uplink capacity is critical for token monetization. Emerging AI applications—such as multimodal AI glasses for real-time translation and augmented exhibitions—demand uplink speeds of 20 Mbps or higher. In 2026, leading carriers globally are piloting commercial high-uplink services with guaranteed peak speeds, latency, and universal uplink performance.

Upper-6 GHz: The Next Golden Band:

The proliferation of AI agents is expected to drive rapid growth in token services, requiring ultra-broadband networks with high uplink, high reliability, and low latency. Upper-6 GHz (U6 GHz) is positioned as the next-generation golden frequency band for this purpose.

  • More than 20 countries and regions have designated U6 GHz for IMT, covering nearly 80% of the global population.

  • 2026 marks the commercial debut of U6 GHz, with the Middle East expected to deploy the world’s first commercial 5G-A network on U6 GHz.

  • Select carriers in Hong Kong and Macao will also initiate commercial U6 GHz deployment.

AI-Native B2C and B2B Services:

Huawei plans to collaborate with carriers in Guangdong, Shanghai, Hebei, and other regions in 2026 to reengineer B2C and B2H services with AI, targeting consumer applications such as smart home assistants, personal communication assistants, and integrated consumer-home services. In the B2B segment, the focus is on AI computing services centered on compute-network integration, unlocking new business growth avenues.

Path to Level-4 Autonomous Networks:

Huawei is advancing AI-native technologies toward Level-4 autonomous networks by developing domain-specific intelligence. In 2026, the company will work with carriers to deploy domain-specific intelligence across wireless and transmission network domains in key regions. This will enable cross-domain synergy in maintenance, optimization, energy efficiency, and user experience, enhancing network quality and enabling differentiated products for high-speed rail, event venues, and campuses.

Critical Questions:

Huawei’s AI-centric network vision positions AI not as an incremental improvement to mobile networks but as a foundational network architecture. That vision raises several critical questions for the IEEE community and IEEE Techblog readers:

  • Interoperability: How does Huawei’s AI-centric target network align—or conflict—with AI-RAN Alliance initiatives and O-RAN specifications?

  • Vendor Comparison: How does Huawei’s AI-centric target network compare with Ericsson’s cloud RAN strategy and Nokia’s Altiplano/Corteca agentic AI platforms in terms of technical architecture and commercial viability?

  • Specifications and Standards: What role will 3GPP and ITU-R play in standardizing AI-native core network specifications, particularly for token monetization and compute-network integration?
  • Autonomous Networks: How do Huawei’s domain-specific intelligence approaches compare with vendor-neutral SMO/rApp ecosystems, and what are the implications for multi-vendor interoperability?

  • Are carriers adequately prepared for the operational and cultural shifts required to transition from traffic monetization to token monetization?

  • How will U.S./European regulatory frameworks (e.g. spectrum policy, AI governance, data sovereignty) shape the deployment of AI-native networks compared to China’s more centralized approach?

  • Spectrum Policy: With Upper 6 GHz emerging as a key enabler for AI-driven token services, what are the regulatory and coexistence challenges, particularly in regions yet to designate Upper 6 GHz for IMT 2030?   What will WRC 2027 decide?

Conclusions:

Huawei’s roadmap underscores the ICT industry’s rapid shift towards AI token monetization, positioning 5G-Advanced high-uplink, AI-native networks, and Upper 6 GHz spectrum as the foundational pillars for the next decade of growth.  The success of this vision depends not only on technological feasibility but also on standards alignment, regulatory support, and carrier willingness to reinvent business models—a complex challenge that warrants close scrutiny from the IEEE technical community.

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References:

https://www.huawei.com/en/news/2026/6/mwcs-ai-byte-token

https://carrier.huawei.com/minisite/mwcs2026/en/

https://www.huawei.com/en/news/2026/6/mwcs-gsma-asac-5g-advanced

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Bloomberg: Meta to sell AI compute in a new cloud services offering

Posted on by Alan Weissberger

Disclaimer: Perplexity.ai was used for research resulting in this article.

Executive Summary:

According to Bloomberg, Meta Platforms is advancing plans to commercialize its internal AI infrastructure through a new cloud services offering, signaling a strategic expansion beyond its traditional hyperscale consumer platforms into the competitive AI infrastructure market. This initiative would position Meta alongside established cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud, while also overlapping with emerging GPU-centric “neocloud” providers.  Meta’s move represents a significant evolution in the AI infrastructure landscape, with potential ripple effects across data center architecture, optical transport networks, and the broader telecom ecosystem.

At the core of this strategy is the monetization of Meta’s rapidly expanding AI compute footprint. The company has aggressively invested in large-scale data center infrastructure—reportedly including multi-hundred-billion-dollar campus developments—to support training and inference for its proprietary large language models (LLMs) and recommendation systems. As these deployments scale, Meta appears to be seeking to externalize surplus capacity, transforming a cost center into a revenue-generating platform.

The proposed service portfolio is expected to span two primary layers. First, Meta may expose access to hosted AI models via APIs, analogous to AWS Bedrock or Azure AI Services, enabling enterprises to integrate generative AI and foundation model capabilities without managing underlying infrastructure. Second, Meta is exploring the provision of raw compute capacity—primarily GPU-accelerated workloads—mirroring the infrastructure-as-a-service (IaaS) model offered by neocloud providers such as CoreWeave. This dual-layer approach would allow Meta to compete both in higher-margin AI platform services and in lower-level compute provisioning.

Telecom & Networking Implications:

From a telecom and network infrastructure perspective, this development has several implications. Hyperscale AI workloads are increasingly bandwidth-intensive, requiring high-capacity, low-latency interconnects within and between data centers. Meta’s investments are therefore likely to drive demand for advanced optical networking technologies, including coherent pluggable optics (e.g., 400ZR/800ZR), data center interconnect (DCI) architectures, and AI-optimized fabric designs leveraging Ethernet-based scale-out topologies. In addition, the geographic placement of these data centers—often in power-abundant, rural locations—introduces new requirements for long-haul fiber connectivity and edge aggregation.

The initiative, internally referred to as “Meta Compute,” reflects a broader industry shift toward vertically integrated AI infrastructure stacks, where hyperscalers tightly couple compute, networking, and software frameworks. For telecom operators and infrastructure vendors, this trend underscores the growing convergence between cloud, AI, and network domains, particularly as AI-driven workloads begin to influence traffic patterns, peering strategies, and edge deployment models.

Strategically, Meta’s entry into the AI cloud market raises competitive pressure across multiple fronts. Unlike traditional cloud providers, Meta brings extensive experience in hyperscale distributed systems and open-source AI frameworks (e.g., PyTorch), but lacks a mature enterprise cloud ecosystem. Its success will likely depend on its ability to translate internal infrastructure efficiencies into externally consumable services, while addressing enterprise requirements for reliability, security, and service-level agreements.

Meta’s cloud push is best viewed as a network-and-infrastructure strategy as much as a software business, because monetizing AI capacity depends on how well it can expose compute, move data, and preserve performance at hyperscale. The telecom significance is that Meta is turning internal AI infrastructure into a market-facing platform, which increases the importance of optical transport, data-center interconnect, and low-latency backbone engineering.

From a telecom perspective, the key issue is not simply that Meta may sell AI models or GPU capacity; it is that the company is building a service layer on top of a very large, power- and bandwidth-intensive distributed system. Reuters reported that Meta is considering both hosted model access and raw compute sales, with the former resembling an AI platform service and the latter looking more like neocloud infrastructure.That means the network becomes part of Meta’s product offering. Large AI inference and training environments require high-bisection fabrics inside the data center, plus dense east-west traffic handling, which pushes demand for faster Ethernet switching, advanced optical modules, and carefully engineered rack-to-rack and site-to-site interconnects.  Meta’s AI cloud ambitions reinforce a broader shift: hyperscalers are no longer treating networking as a background utility, but as a primary constraint on scale.

Network World’s coverage of Meta Compute notes that Meta has unified data center and network oversight and is planning multi-gigawatt AI buildouts, underscoring how tightly power, fiber, switching, and facility design are now linked.

For network operators and vendors, that translates into stronger demand for long-haul fiber, DCI platforms, low-latency transport, and high-radix switching. It also raises the strategic value of metro and regional interconnect corridors that can support AI clusters, especially when capacity must be spread across multiple sites for power, land, or resiliency reasons.

Meta’s potential move into raw compute sales is especially relevant to telecom because it resembles the economics of infrastructure-heavy cloud and colocation models. In practice, the service quality will depend on how efficiently Meta can provision GPU clusters, maintain deterministic performance, and avoid congestion across the transport layer connecting those clusters.  That implies growing importance for:

  • Coherent optical transport and scalable DCI.

  • High-capacity Ethernet fabrics for AI clusters.

  • Open-rack and disaggregated infrastructure designs.

  • Network automation that can track workload placement and traffic hotspots.

These are not just cloud concerns; they are telecom-grade capacity-planning problems. As AI clusters become larger and more distributed, network planning starts to look more like core network engineering than conventional enterprise hosting.

Image Credits: Gabby Jones/Bloomberg / Getty Images

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Conclusions:

Meta’s entry would not only compete with AWS, Azure, and Google Cloud, but could also pressure specialized neocloud providers more directly. Reuters noted that Meta’s spare capacity could matter more to neo-cloud vendors than to the largest hyperscalers, because those providers rely on access to external GPU supply and managed infrastructure growth.  For telecom analysts, that suggests the competitive battleground is shifting from “who has the best model” to “who can deliver the most resilient compute-network-power stack.” The winners will likely be those that can couple AI accelerators with fiber-rich sites, robust interconnect, and energy-secure data center footprints.

Meta’s move reflects the convergence of cloud, AI, and transport networks. The story is less about Meta becoming a generic cloud vendor and more about hyperscale AI infrastructure evolving into a new class of network-dependent utility.  Indeed, Meta’s cloud initiative highlights a broader industry reality — in the AI era, compute is valuable, but connectivity, optical scale, and power-aware architecture increasingly determine whether compute can be monetized at all.

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References:

https://www.bloomberg.com/news/articles/2026-07-01/meta-is-building-a-cloud-business-to-sell-excess-ai-compute?embedded-checkout=true  (PAYWALL)

https://www.reuters.com/business/meta-sell-excess-ai-computing-capacity-via-cloud-business-bloomberg-news-reports-2026-07-01/

https://www.networkworld.com/article/4115975/meta-establishes-meta-compute-to-lead-ai-infrastructure-buildout.html

Meta, like SpaceX, looks to turn excess AI compute into cash

https://www.cnbc.com/2026/05/27/mark-zuckerberg-says-meta-starting-cloud-business-on-the-table.html

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Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact

Posted on by Alan Weissberger

Executive Summary:

According to Light Reading, Nvidia is working on a GPU combo chip that would sit directly in the 6G radio unit [1.], extending its AI-RAN push from baseband/server  into the radio itself.  It’s reported to be a more hardware-integrated, sub-100W embedded design rather than just GPU acceleration in centralized RAN compute.

Note 1.  6G/IMT 2030 Radio Interface Technologies (RITs) have yet to be defined, let alone specified by 3GPP or ITU-R WP5D.  They won’t be solidified until the end of 2030 so any specific silicon design won’t be completed until then or 2031!

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Light Reading’s headline frames it as a “radical new AI-RAN plan and they wrote that “the move was confirmed by knowledgeable sources, with Nvidia saying GPUs in more advanced radios will become “essential” in future. It marks a dramatic new development in the GPU giant’s “AI-RAN” strategy.”

If accurate, this would be a notable shift for Nvidia, because it would let them influence the whole RAN stack, not just centralized compute. That could matter for performance, power efficiency, and AI-native functions such as sensing, spectrum optimization, and real-time signal processing. Nvidia’s broader 6G messaging already emphasizes AI-native wireless, integrated sensing and communications, and spectrum agility as core themes.

The unconfirmed report fits Nvidia’s existing telecom roadmap rather than appearing out of nowhere. Nvidia has already announced an AI-native wireless stack for 6G with partners including Cisco, MITRE, Booz Allen, ODC, and T-Mobile, and it has promoted AI-RAN as a way to combine connectivity, computing, and sensing on one platform.  It also aligns with the company’s recent partnership with Nokia, where Nvidia introduced the ARC-Pro 6G-ready accelerated computing platform and described it as a software-upgradable path from 5G-Advanced to 6G. That makes the rumored radio-chip move look like a vertical extension of the same strategy.

For wireless network operators, a radio-unit chip from Nvidia would be significant only if it improves cost, power, or flexibility versus incumbent RU silicon. The practical test will be whether it can deliver enough RF, baseband, and AI function integration to justify another architecture layer at the edge. It would also intensify competition in the radio-access supply chain and reinforce the trend toward AI-native, software-defined RANs. It also suggests Nvidia wants to shape not only the compute layer but the physical radio layer of 6G networks.

Possible AI Silicon Features and Functions:

Nvidia would most likely add AI-for-RAN features into radio silicon first, because those map directly to signal processing and link adaptation rather than to generic “AI at the edge.” Nvidia’s own AI-RAN materials emphasize embedding AI/ML into the radio signal-processing layer to improve spectral efficiency, coverage, capacity, and performance.  Here are a few likely AI features/functions for the rumored 6G AI Nvidia super chip:

  • Neural channel estimation and equalization, to infer cleaner channel state from noisy RF observations and improve link reliability. Nvidia’s open-source Aerial release specifically calls out advanced neural models for channel estimation.

  • Real-time beam management, including beam selection, beam tracking, and beam refinement for massive MIMO and mmWave/upper-midband deployments. These are natural AI-RAN use cases because they depend on fast adaptation to changing propagation conditions.

  • Spectrum agility and interference mitigation, such as identifying jammed or congested resource blocks and dynamically avoiding them. NVIDIA and partners have already described spectrum agility applications that freeze only affected frequencies while keeping the rest of the system online.

  • Dynamic resource scheduling, using learned traffic and channel patterns to allocate PRBs, power, and compute more efficiently in real time. Nvidia describes AI-RAN as improving spectral efficiency and dynamic traffic handling through AI.

  • Integrated sensing and communications support, where the radio helps detect objects, motion, or environmental context in parallel with communication. Nvidia has already highlighted ISAC-style applications with camera/RF fusion and object tracking.

  • Edge inference hooks, letting the RU expose real-time PHY data to AI applications or a dApp-style framework. Nvidia’s open-source Aerial stack says third-party apps can access physical-layer data through secure APIs and modify RAN behavior in real time.

  • Self-optimization and closed-loop control, where the radio silicon learns local conditions and continuously retunes thresholds, coding, MCS selection, and precoding policies. That fits Nvidia’s broader framing of AI-native networks as software-defined and continuously adaptable.

The most plausible first wave is not a fully autonomous “AI radio,” but a hybrid RU chip that accelerates selected PHY functions and exposes telemetry/data paths to the rest of the AI-RAN stack. Nvidia’s current messaging emphasizes software-defined infrastructure, deterministic performance, and layered AI-RAN capabilities rather than replacing the entire RAN with a black-box model.

The real differentiator would be whether Nvidia can combine RF signal processing with its GPU/CUDA ecosystem, so the same platform handles channel learning, inference, and orchestration across RU/DU/CU tiers. That would let operators optimize for spectral efficiency and OPEX while still keeping a software-upgrade path to 6G.  Radio electronics is constrained by power, latency, determinism, and certification, so Nvidia would need to prove these AI features help without destabilizing PHY timing. That is why the likely starting point is assistive AI inside the signal chain, not a fully learned end-to-end radio.

Image Credit: Nvidia

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Competitive Analysis:

Nvidia’s reported move into a 6G radio-unit chip is most threatening to Marvell and Qualcomm at the silicon layer, while it is more of a strategic architecture challenge to Nokia and Ericsson at the system level. The immediate effect is less about a single chip and more about Nvidia trying to pull compute, connectivity, and AI deeper into the RAN value chain

Qualcomm is the closest direct competitor if Nvidia is trying to put silicon into the radio or near-radio layer. Qualcomm already has a Layer 1 strategy that combines silicon and software in SmartNIC/server-adjacent form factors, so Nvidia would be moving into a space where Qualcomm has both telecom credibility and established IP.

The risk for Qualcomm is that Nvidia can use its AI brand, CUDA ecosystem, and hyperscale relationships to redefine what “performance” means in RAN silicon, especially if AI-native functions become a buying criterion. The counterpoint is that Qualcomm still has a strong edge in wireless-specific silicon integration and standards heritage, which matters if the 6G radio path remains RF- and modem-centric.

Nokia looks less exposed in the short term because it is already partnering with Nvidia rather than treating it as a pure adversary. Nvidia and Nokia have publicly framed their relationship as an AI-native 5G-Advanced/6G platform effort, and Nokia says it will add NVIDIA-powered commercial AI-RAN products to its RAN portfolio.

Nonetheless, a Nvidia radio-chip push could still compress Nokia’s differentiation over time if more of the RAN stack becomes software-defined and GPU-centric. The strategic question is whether Nokia remains the integrator and operator-facing systems vendor, or whether Nvidia gradually becomes the architectural center of gravity.

Ericsson is the most structurally interesting case because it sits at the high end of global RAN share and has been more cautious about Nvidia as a Layer 1 option. Light Reading notes Ericsson is currently dismissive of Nvidia as a Layer 1 choice, even while the broader ecosystem explores AI-RAN collaboration.

For Ericsson, the threat is not immediate revenue loss from a single chip; it is erosion of the traditional assumption that RAN leadership comes from proprietary radio and baseband stacks. If Nvidia can make AI-native RAN a default design paradigm, Ericsson may be forced to defend its software and systems value rather than simply its box-selling model.

Samsung Electronics contacted Light Reading after their story was published to point out that it also works with AMD as a chip partner. “Samsung supports full Layer 1 (L1) processing using Intel’s telco CPUs (e.g., Xeon 6 Granite Rapids) and lookaside accelerator approach and in addition has successfully demonstrated full L1 processing on AMD’s CPUs without relying on dedicated L1 accelerators,”  a Samsung spokesperson said via email.

Marvell is the most exposed chip supplier in this story because its telecom position is more concentrated in custom Layer 1 silicon. Light Reading specifically points out that Marvell is a critical supplier to Nokia in Layer 1, which makes a Nvidia radio-chip effort a direct substitution threat in portions of the stack.

If Nvidia succeeds, Marvell faces a two-sided squeeze: loss of design wins in telecom silicon and a narrative shift toward AI-native programmable platforms that favor Nvidia’s broader ecosystem. Marvell’s defense is that telecom operators still care about power, latency, and deterministic functionality, areas where custom silicon can remain more efficient than a generalized AI-compute approach.

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Summary Table:

Company Impact level Why
Qualcomm High Direct silicon adjacency and overlapping Layer 1 ambitions.
Marvell High Telecom custom-silicon exposure, especially Layer 1.
Ericsson Medium Strategic and architectural threat more than immediate chip displacement.
Nokia Medium to low near term Partnered with Nvidia, so risk is more about future dependence and stack control.

Source: Perplexity.ai

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Conclusions:

It’s unknown whether Nvidia’s rumored radio chip becomes a product, a reference design, or just an extension of its AI-RAN platform. If it ships, watch for operator trials, power-envelope disclosures, and whether it targets RU integration, DU acceleration, or a hybrid AI-RAN endpoint. If it stays at the partnership/reference-design level, the market impact will be more narrative than revenue-relevant.

Another unanswered question is whether Nokia and Ericsson keep treating Nvidia as a collaborator while preserving their own Physical layer control, or whether they start to see Nvidia as a platform owner in the making. That boundary will determine whether this is a tactical ecosystem play or the beginning of a deeper industry reset.

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References:

https://www.lightreading.com/6g/nvidia-has-a-radical-new-ai-ran-plan-a-6g-radio-unit-chip

https://www.lightreading.com/6g/analyst-insight-6g-coming-into-focus

https://www.nvidia.com/en-us/industries/telecommunications/ai-ran/

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

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

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

Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN

Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions

Inside Nokia’s new AI Networking Innovation Lab

Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy

Marvell shrinking share of the RAN custom silicon market & acquisition of XConn Technologies for AI data center connectivity

 

 

Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035

Posted on by Alan Weissberger

Executive Summary:

Consumer-driven AI traffic [1.] currently represents a marginal share of aggregate Internet traffic. However, accelerating adoption of agentic AI is expected to materially reshape traffic composition over the next decade. In its “AI Impact on Wide Area Networks” report, Cisco projects that AI will emerge as the dominant driver of network traffic growth. As consumer AI adoption approaches “near-universal usage,” AI and agentic AI are forecast to increase consumer-driven network traffic by approximately 6.6× by the mid-2030s (see chart below).

Cisco estimates that this AI expansion will account for roughly 63% of incremental traffic growth relative to non-AI scenarios. The study focuses specifically on WAN implications, rather than data center or GPU infrastructure, and provides guidance on network design and capacity planning. Methodologically, the report integrates real-world traffic observations (via Cisco Crosswork Assurance User Experience), third-party industry datasets, and controlled laboratory evaluations of AI agents to characterize how AI-generated traffic diverges from conventional web traffic patterns.

Token-consumption data shows nearly 10x year-over-year growth, while in some service provider measurements Cisco is seeing ~4x growth in just eight months. Sustained growth at these rates means AI traffic will become a meaningful component of overall network traffic by 2035.

Note 1. Consumer AI traffic has a few defining technical traits: it is still dominated by short text-based exchanges, but it is becoming more stateful, more upstream-heavy, and more latency-sensitive as users move from simple prompts to agentic workflows and multimodal interactions.  Today’s consumer AI traffic is still overwhelmingly text-oriented, which is one reason the aggregate bandwidth impact remains modest despite rapid adoption. Comcast’s network observation is a useful real-world proxy: 97.1% of AI traffic was text-based, while images accounted for 2.6% and video only 0.3%. The key technical implication is that current traffic volumes are often limited more by conversation frequency and session behavior than by very large payloads, though that changes quickly as users adopt image, audio, and video generation.

Although AI inference traffic is currently “negligible” relative to dominant categories such as video streaming, Cisco projects it will comprise approximately 25% of total network traffic by 2035 (see chart below). At that point, AI traffic is expected to represent a “meaningful component” of overall network load. Importantly, AI-generated traffic exhibits distinct characteristics: inference flows are approximately twice the duration of typical web transactions, demonstrate higher upstream bandwidth demand, and operate at “software speed” rather than human interaction rates.

The emergence of AI agents as “power users” further amplifies these dynamics. Cisco notes that agent-executed tasks can generate up to 450% more traffic per task compared to human-driven interactions. This shift is expected to drive operator adoption of “flow-aware network and security systems” as traffic patterns become increasingly machine-driven and less predictable.

Cisco’s broader framing is that AI traffic “isn’t just adding traffic,” but is changing the shape of traffic, with inference flows running about twice as long as typical web transactions and, in some cases, generating up to 450% more traffic per task when an agent executes the workload.  AI inference sessions tend to hold resources longer, create more sustained flows, and push operators to think in terms of flow-aware behavior rather than only peak-throughput sizing. Cisco also notes that about 9% of AI inference flows carry more upstream than downstream traffic, versus about 0.5% for typical web traffic, which is a meaningful shift for access and broadband networks.  Cisco reports that approximately 9% of AI inference flows are upstream-dominant, compared to roughly 0.5% for traditional web traffic, with this divergence expected to widen alongside increased agentic AI utilization. In parallel, latency sensitivity is anticipated to become a more critical performance parameter for AI-driven applications.

Latency and symmetry:

AI traffic is also more sensitive to latency than many ordinary consumer web transactions because the user experience is often conversational and interactive, with the expectation of near-immediate turn-taking. Cisco describes AI inference as operating at “software speed” rather than human speed, which means small delays can be more noticeable and operationally important. At the same time, upstream demand becomes more significant because prompts, context, attachments, and agent-generated actions can increase return-path traffic, especially as multimodal inputs and agentic tool use expand.

Multimodal growth:

The biggest step-up in technical impact comes when consumer AI shifts from text-only prompting to multimodal generation and agent-driven workflows. In those cases, each task can involve multiple model calls, retrieval steps, tool invocations, and richer media payloads, which expands both flow count and bytes per session. Cisco’s study suggests that this is why AI traffic will increasingly require “flow-aware network and security systems,” because the traffic profile is not just larger, but structurally different from conventional browsing.

 

Infrastructure Implications:

Telecom infrastructure is becoming “increasingly intertwined with hyperscale infrastructure, not because operators are leading AI investment, but because they are becoming part of the ecosystem that supports it,” analyst firm MTN Consulting said in an April 27th research note.  “Demand for optical transport, data-center interconnect, and edge infrastructure is rising as telecom networks carry growing volumes of cloud and AI-driven traffic,” the firm said.

“AI network traffic is already reshaping infrastructure needs. What we are seeing is clear: AI isn’t just adding traffic. It’s changing the shape of traffic,” Javier Antich, principal product management engineer in the CTO office of Cisco’s provider connectivity group, and Gurudatt Shenoy, SVP, product management, provider connectivity, explained in this blog post.

These shifts are beginning to influence access network evolution. Fiber networks already provide relatively symmetric throughput and low latency, while cable operators are advancing similar capabilities through DOCSIS upgrades. Mid-split and high-split architectures increase upstream spectrum allocation, enabling more balanced capacity profiles. Concurrently, Tier 1 operators such as Comcast and Charter Communications are introducing low-latency enhancements within DOCSIS networks.

Operational data reflects early-stage impacts. Comcast Chief Network Officer Elad Nafshi noted at the Cable Next-Gen event in March that approximately 97.1% of AI traffic on Comcast’s network remains text-based, with images accounting for 2.6% and video just 0.3%, indicating that bandwidth-intensive multimodal AI traffic has yet to scale materially.

Network design impact:

For broadband and access networks, the immediate engineering issues are upstream traffic capacity, queue behavior, and latency consistency rather than raw total throughput alone. Symmetry upgrades (such as DOCSIS mid-split and high-split for MSOs), along with low-latency capabilities, are relevant because consumer AI creates more return-path pressure and more time-sensitive sessions. In other words, the challenge is not simply to carry more bytes; it is to carry more interactive sessions with predictable performance, especially as multimodal and agentic usage scales.

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References:

https://www.cisco.com/c/dam/en/us/solutions/collateral/artificial-intelligence/mass-scale-infrastructure/ai-network-traffic-report.pdf

https://www.lightreading.com/ai-machine-learning/ai-emerging-as-top-driver-of-overall-internet-traffic-growth-study

https://www.cisco.com/site/us/en/products/networking/software/provider-connectivity-assurance/user-experience/index.html

Petabits per rack: How AI traffic is reshaping networks

Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?

Telecom operators investing in Agentic AI while Self Organizing Network AI market set for rapid growth

Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers

Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Ericsson integrates Agentic AI into its NetCloud platform for self healing and autonomous 5G private networks

STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

Agentic AI and the Future of Communications for Autonomous Vehicles (V2X)

Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements

Is the “far edge” a bridge to far to cross for AI inferencing? What about “Distributed AI Grids”?

T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation

Intel and AI chip startup SambaNova partner; SN50 AI inferencing chip max speed said to be 5X faster than competitive AI chips

CES 2025: Intel announces edge compute processors with AI inferencing capabilities

Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements

Posted on by Alan Weissberger
Recent analysis by SiliconANGLE and Morgan Stanley highlights that the bottleneck for generative AI in telecom has shifted from software capabilities to physical hardware availability. While telecom network operators have successfully designed AI models for network optimization, predictive maintenance, and autonomous traffic routing, they lack the raw compute power to run them at scale. Traditional telecom data centers were built for central office workloads and basic virtualization, not the massive parallel processing required by modern Large Language Models (LLMs) and real-time AI inference. As a result, carriers are trapped in a compute-constrained environment, forced to queue workloads or ration processing power.
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The growth slope of generative AI in networking through 2026–2027 is now entirely bound to the physical deployment speed of raw, rack-scale data center infrastructure. This dependency is driven by three main factors:
  • Gigawatt-Scale Power and Liquid Cooling: Next-generation AI clusters require unprecedented power density, often exceeding 40kW to 100kW per rack. Telcos cannot simply drop these into existing facilities; they require entirely new or heavily retrofitted data centers featuring advanced liquid cooling architectures to prevent thermal throttling.
  • The Fragmented Edge vs. Centralized Fortresses: Operators are realizing that centralized hyperscale data centers (like AWS or Azure clusters in Virginia) cannot support latency-sensitive “Physical AI” or real-time agentic workflows. To make AI-native networking work, carriers must deploy high-density compute racks directly at the network edge, a highly complex and capital-intensive roll-out.
  • Neutral Interconnection Hubs: Multi-cloud setups and distributed training workloads are putting immense pressure on backbones. The expansion rate of neutral interconnect hubs (like Equinix and Digital Realty) is directly gating how fast enterprises and telcos can orchestrate data between fragmented training clusters and edge inference nodes.
  • Rack-scale architecture is rapidly emerging as the primary deployment unit as enterprises transition from discrete servers to fully integrated systems capable of supporting the power density, thermal constraints, and interconnect requirements of production-scale AI workloads.

Image Credit:  AMD

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AI data centers supporting telecom networks require fundamentally different power and cooling infrastructure compared to legacy enterprise facilities. The transition to generative AI and real-time edge processing has pushed power density per rack from an average of 5–10 kW up to 40–100+ kW.

Dell Technologies Inc. has been strategically aligning its portfolio to this shift, and at Dell Technologies World 2026, the company introduced an expanded PowerRack portfolio that integrates compute, networking, and storage within a unified rack-scale platform. This evolution underscores a broader transition in system design priorities—from server-centric architectures to tightly coupled, rack-level systems—driven by the escalating demands of AI infrastructure. As Arun Narayanan, senior vice president of compute and networking product management at Dell, indicated, increasing power density and system complexity are making rack-level architectural optimization not just advantageous, but essential.

“Go back two years ago, the largest, most powerful rack was 80 kilowatts,” Narayanan said. “Come to Vera Rubin, you’re going to get racks of 235 kilowatts, and then get to the next generation of Rubin Ultra and Kyber, you’re going to very quickly get to one megawatt racks. You have to fundamentally redesign everything from power distribution to cooling.”

Power Requirements and Delivery:
To prevent massive line losses and voltage drops at high densities, data centers must completely overhaul their internal alternating current (AC) distribution.
    • Medium-Voltage Power Distribution: Traditional facilities step utility power down to 480V AC far from the rack. High-density AI data centers run medium-voltage  or  power directly down to the row or container level before stepping down. This minimizes conduction losses through the heavy copper busbars.
    • The Move to 48V DC Busbars: Within the server chassis, power shelf architectures are shifting from traditional 12V DC distribution to DC busbars. A  delivery architecture reduces the current  required to deliver the same wattage  by a factor of four. Resistive power loss occurs when electrical energy is converted into heat due to the inherent opposition to current flow in a conductor. The formula (P{loss} = I^2 R dictates that this power dissipation is highly sensitive to current changes.  Therefore, cutting the current to one-fourth reduces internal rack heat and conduction power losses by 93.75%
    • Grid Interconnection and Substation Constraints: A single rack-scale AI cluster (such as a cluster of 32 or 64 interconnected nodes) can easily pull 2 to 3 Megawatts (MW). Operators are bypassing traditional local distribution grids entirely. They are building dedicated on-site substations tied directly to transmission-level lines to guarantee upstream capacity.

Cooling Requirements and Technologies:
Air cooling hits a hard physical performance ceiling at roughly 30–35 kW per rack. Beyond this threshold, the volume of air required to pass through the server chassis creates unacceptable fan power consumption and audible noise. AI data centers deploy liquid-based thermodynamics to dissipate the thermal energy.
       [ Liquid Cooling Architectures for AI Racks ]
       
 ┌───────────────────────────┐      ┌───────────────────────────┐
 │       Direct-to-Chip      │      │     Immersion Cooling     │
 ├───────────────────────────┤      ├───────────────────────────┤
 │ Closed loop micro-channels│      │ Entire server submerged   │
 │ bolted directly onto GPUs │      │ in dielectric fluid tank  │
 │                           │      │                           │
 │     [ GPU ] ──► [ Liquid] │      │    ┌───┐ ┌───┐ ┌───┐      │
 │   Cold Plate   Coolant    │      │    │GPU│ │CPU│ │RAM│      │
 │    Circuit     Circuit    │      │    └───┴─┴───┴─┴───┘      │
 └───────────────────────────┘      └───────────────────────────┘

    • Direct-to-Chip (Cold Plate) Cooling: This is the primary architecture for 2026 deployments. A closed-loop copper block with micro-channels is bolted directly onto high-thermal-flux components like the GPU or CPU. A specialized dielectric or water-glycol fluid circulates through the block. This absorbs heat directly from the silicon via conduction and pumps it away to a secondary heat exchanger.
    • Immersion Cooling (Single-Phase and Two-Phase):
        • Single-Phase: The entire server blade is submerged in a bath of non-conductive, hydrocarbon- or synthetic-based dielectric fluid. The fluid circulates through the chassis via natural convection or pumps to remove heat.
        • Two-Phase: The dielectric fluid has a low boiling point (\(50^{\circ }\text{C}\)). The heat from the chips boils the fluid into a vapor. The vapor rises to a condenser coil at the top of the sealed tank, condenses back into liquid, and falls back into the pool. This utilizes the latent heat of vaporization, making it highly efficient.

    • Cooling Distribution Units (CDUs): High-density loops rely on CDUs to act as the barrier between the internal facility water loops (which can be lower quality) and the ultra-pure, treated water circuit flowing directly through the server cold plates.

Strategic Market Outlook (2026–2027):
Because hardware deployment cannot be short-circuited by software updates, a clear divide is emerging in the telecom sector. Operators who secured early private capital, locked in GPU supply chains, and invested in dark fiber infrastructure are positioned to scale their AI capabilities rapidly. Conversely, carriers relying on incremental, legacy virtualization upgrades will face a hard performance ceiling. Through 2027, the market winners will be determined not by who has the best AI algorithms, but by who can build and power physical rack space the fastest.
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References:

https://www.nae.edu/349862/The-Evolving-Power-Architecture-of-AI-Data-Centers-?layoutChange=Normal

Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies

Posted on by Alan Weissberger

1.  Hyperscalers’ earnings growth this quarter was boosted by an unusually large contribution from “other income,” which was actually mark-ups of their equity stakes in private Gen AI companies.  For example:

  • Nearly half of Alphabet’s (Google) record $62.6 billion profit—about $28.7 billion—did not come from search ads, cloud services or any of its products at all. It came from Alphabet updating the value of the equity it owns in private AI companies, primarily Anthropic.  Alphabet holds a 14% stake before the announcement of an additional $40 billion commitment last week.
  • Amazon’s earnings release stated that first-quarter net income “includes pre-tax gains of $16.8 billion included in non-operating income from our investments in Anthropic”—more than half of Amazon’s pre-tax income (or profit) for the quarter.
  • Alphabet and Amazon generated “other income” totaling $53 billion in Q1 2026, which accounted for nearly 60% of those two companies’ total net income in Q1 and 34% of the total $155 billion in income this quarter. Of this $53 billion in “other income,” $49 billion was explicitly due to equity stakes in private AI companies.
  • Microsoft reported “only” $942mn of other income in the first three months of the year, but this line item has now made $7.2bn over the past nine months.
  • Under U.S. accounting rules, publicly traded firms must adjust and report the assessed value of their private equity holdings every quarter. Because private AI start-ups like Anthropic experienced meteoric valuation updates (e.g., Anthropic climbing to an estimated $380 billion), both Alphabet and Amazon were required to record those massive “on-paper” gains directly to their bottom-line net income.
  • When the AI bubble finally bursts (and it will) the private AI companies assessed market value will collapse, resulting in “impairment write-downs” and huge earnings declines for the hyperscalers, e.g. Amazon, Google/Alphabet, Microsoft, FB/Meta, and Oracle.

2. Now here’s the merry-go-round/ dog chasing its tail relationship:

Not only have private investments and increasingly engorged funding rounds become a meaningful driver of the hyperscalers’ aggregate earnings, but the money the hyperscalers have pumped into the likes of Anthropic and OpenAI has allowed those private AI companies to sign huge computing deals with Alphabet’s Google Cloud, Microsoft’s Azure and Amazon Web Services (AWS).  OpenAI and Anthropic now make up about half of the entire cloud computing order books at Oracle, Alphabet, Amazon and Microsoft! 

Indeed, AI startups have loaded up hyperscalers with unprecedented long-term financial commitments.

–>OpenAI and Anthropic make up over $1 trillion of the estimated $2 trillion cumulative revenue backlog currently held by major cloud service providers!

  • OpenAI to Microsoft Azure: Internal documents show OpenAI’s massive server rentals have generated more than $23 billion in direct cloud spending for Microsoft.
  • Anthropic to Google Cloud: Anthropic signed a contract committing to spend $200 billion over five years on Google’s cloud infrastructure and TPU chips.
  • Anthropic to AWS: In tandem with a fresh $5 billion investment from Amazon, Anthropic committed to spend over $100 billion over the next decade on AWS technologies.

Image Generated by Chat GPT

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3. Because hyperscalers report their overall cloud results as broad aggregates, the exact percentage of current quarter revenue generated purely by AI startups varies by provider. However, recent financial disclosures and analyst tracking pinpoint the enormous impact of these startups on current revenues and future order books:
-Google Cloud:
    • Backlog Percentage: Over 40%. Anthropic‘s $200 billion Multi-Year Commitment accounts for nearly half of Google Cloud’s total disclosed $240 billion revenue backlog.
    • Current Revenue Share: Estimated 12% to 15% of its current $20 billion quarterly revenue run-rate is driven directly by AI infrastructure consumption from startups (both frontier labs and over 40 mid-tier AI companies built on Google Cloud Vertex AI).

-Microsoft Azure:
    • Current Revenue Share: Estimated 15% to 18%. Microsoft’s annualized AI revenue run-rate hit $37 billion. A massive chunk of Azure’s overall 40% growth rate is anchored directly by OpenAI’s compute demands and the commercialization of OpenAI-tied products.

-Amazon Web Services (AWS):
  • Current Revenue Share: Estimated 6% to 8%. While AWS has the largest overall cloud scale ($150 billion annual run rate), its revenue is traditionally diversified across enterprise SaaS and retail. However, Anthropic’s new $100 billion infrastructure commitment means AWS’s revenue mix is aggressively shifting toward AI startups. [1, 2, 3, 4]

–>This is another sign of just how incestuously codependent the big tech industry is to astronomically valued private AI start-ups.

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4. Another example of this codependency is Oracle and OpenAI’s massive, debt-fueled financial loop. In September 2025, the two companies signed a staggering five-year, $300 billion cloud-computing contract. This single deal radically transformed both companies’ financial profiles, binding their survival together as inextricably tied.

The deal functioned as an aggressive narrative magnifier for both companies:
    • For Oracle: The $300 billion contract instantly added to Oracle’s Remaining Performance Obligations (RPO), which skyrocketed 359% to $455 billion. This accounting metric allowed Oracle to position itself as a dominant “hyperscaler,” pushing its market cap upward.
    • For OpenAI: The contract allowed OpenAI to claim it had secured the long-term compute capacity needed to achieve Artificial General Intelligence (AGI). This backed up its massive valuations, enabling OpenAI to close a historic $122 billion funding round in March 2026 at an $852 billion valuation.  

The financial codependency between the two entities is asymmetrical and high-risk:  
  • Oracle is a Financial Proxy for OpenAI: If OpenAI faces a “credit event” or cash crunch, Oracle’s stock directly plummets. Critics note that Oracle signed a contract with a startup that historically burns far more cash than it takes in, making OpenAI’s ability to actually pay the $300 billion highly volatile.
  • The Debt Spiral: To physically fulfill OpenAI’s compute demands, Oracle has gone on a massive, debt-fueled construction spree. Oracle raised $18 billion in bonds in late 2025 and an additional $30 billion in early 2026. Its capital expenditures have eclipsed operating cash flows, leading to deeply negative free cash flow and over $134 billion in total corporate debt.
The scale of this relationship has triggered systematic friction on Wall Street:
    • Project Finance Bottlenecks: Major commercial banks have struggled to syndicate the massive multi-billion-dollar construction loans Oracle needs to build out the required data centers (such as its 4.5-gigawatt capacity goals).
    • Bank Limits: The sheer volume of debt concentrated around this single enterprise relationship has pushed several Wall Street institutions against their regulatory exposure limits for a single corporate partnership.

Ultimately, critics view the partnership as a circular loop: Oracle borrows tens of billions of dollars to build data centers for OpenAI, hoping OpenAI can continuously raise venture capital from the market to pay Oracle back, while Oracle uses OpenAI’s paper contracts to justify its skyrocketing stock value to its own investor

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References:

https://www.ft.com/content/be97df0a-76b1-4cb0-9ba4-d1117d8d1450
https://fortune.com/2026/04/30/google-amazon-ai-profits-anthropic-stake-bubble-earnings-2026/
https://finance.yahoo.com/sectors/technology/articles/google-amazon-biggest-profit-driver-170449859.html

AI infrastructure spending boom: a path towards AGI or speculative bubble?

Expose: AI is more than a bubble; it’s a data center debt bomb

Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”

Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble

China’s open source AI models to capture a larger share of 2026 global AI market

OpenAI and Broadcom in $10B deal to make custom AI chips

Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight

 

 

 

STL Partners webinar: Agentic AI needed for RAN autonomy & efficiency

Posted on by Alan Weissberger

Yesterday, a STL Partners webinar titled “Turning autonomy into margin: Agentic AI and the autonomous RAN,” suggested agentic AI is the missing layer that can turn RAN autonomy from a technical goal into a direct profit margin booster. It argues that operators should prioritize autonomy use cases by business impact, not just by how much automation coverage they add, and that the right roadmap can move autonomy from an engineering KPI to a commercial advantage.

The central message was that autonomy only matters if it improves economics (see poll results below). The webinar revealed that network operators need a dual-axis framework that combines the usual autonomous-network maturity view with a value-creation lens, so they can focus on the capabilities that scale into measurable business outcomes.

Agentic AI is presented as the practical enabler for moving beyond human-in-the-loop operations. In this framing, agents help orchestrate tasks, make decisions, and coordinate network actions in ways that support more closed-loop automation than traditional workflows can deliver.

The results of an “actuality” poll relating to RAN autonomy revealed that controlling costs and reliability were most important, with the enablement of new revenue growth through APIs and sensing only scoring 10.87% of respondents.  Similarly, results for an “aspirations” poll for RAN autonomy were also fairly evenly spread between reducing costs and optimizing the customer experience, with just 13.21% citing new revenue growth.

Source: STL Partners

Terje Jensen, SVP, global business security officer and head of network and cloud technology strategy at Telenor, said that he had expected to see network operators’ aspirations shift more clearly towards improving customer experience and even revenue generation, not just efficiency.

Darwin Janz, strategic technology planner at SaskTel, also thought network operators’ ambitions would be higher, but he noted that they still struggle to identify concrete, monetizable use cases. Without that, there’s a real risk of building technical solutions in search of a problem, rather than starting from clear enterprise needs and value, Darwin noted. “We really need to see those use cases and enterprise customer needs,” he added.

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The webinar was built around four practical questions:

  1. Which use cases create real commercial impact?
  2. How to shift from autonomy as an engineering metric to a margin driver?
  3. Where agentic does AI add value today?
  4. What data, orchestration, and organizational foundations are needed to scale beyond pilots.

For network operators, the implication is that autonomous RAN strategy should be tied to P&L outcomes such as lower operating cost, better resource utilization, and faster optimization cycles. The webinar’s message is that autonomy becomes strategically important only when it is deployed in a way that compounds across the network and business.

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References:

https://www.lightreading.com/network-automation/telcos-showing-limited-aspiration-for-ran-autonomy-benefit

The Financial Trap of Autonomous Networks: Scaling Agentic AI in the Telecom Core

Nokia to showcase agentic AI network slicing; Ericsson partners with Ookla to measure 5G network slicing performance

 

 

South Korea’s top 3 telcos reinvent themselves as “AI Companies;” growth strategies revealed

Posted on by Alan Weissberger

Overview:

South Korea’s telecommunications industry is rapidly shifting its center of gravity to AI, with SK Telecom, KT and LG Uplus all declaring their transformation into AI companies. Industry officials describe this as a restructuring process.

  • SK Telecom is pushing a full-stack AI strategy spanning infrastructure, models and services.
  • KT is accelerating a B2B-focused push to become an “AX” platform company.
  • LG Uplus is positioning itself as an AI software company through its ixi-O agent, stressing safety and security. Industry officials say the next test is profitability.
Photo credit: Shutterstock
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Here’s a summary of the AI strategies of the three South Korean telcos:
1. SK Telecom – Pioneering Sovereign AI and Full-Stack Infrastructure:

Ryu Jong-heon, SKT’s CEO, wrote in a letter sent to shareholders ahead of last month’s annual general meeting, “If our AI business so far was about incubating various areas, we will now focus more on businesses where SKT can be competitive and secure sustainability in AI competition that is expanding without limit.”

SK Telecom (SKT) is prioritizing a “Sovereign AI” strategy, designed to offer localized, secure AI infrastructure that mitigates reliance on external hyper-scalers. By integrating AI Data Centers (AIDC) with industry-specific applications and their proprietary A.X K1 model—a 500B parameter hyper-scale LLM—SKT aims to deliver an end-to-end “Sovereign AI Package.”
To fortify its AI full-stack, SKT is leveraging a robust partnership ecosystem:
  • Next-Gen Compute: Strategic collaboration with Arm and Rebellions for AI CPU/NPU innovation.
  • Infrastructure & Power: Agreements with Supermicro and Schneider Electric to optimize AIDC efficiency and server density.
  • Model Scaling: With A.X K1 outperforming benchmarks like DeepSeek V3.1, SKT plans to transition to multimodal capabilities and trillion-parameter scaling to secure market dominance across B2B and B2C segments.

2. KT Corporation – Transitioning to an AX Platform Operator:

Under the leadership of CEO Yun-young Park, KT is accelerating its AX (AI Transformation) strategy with a sharp focus on the B2B sector. Following a structural reorganization that established the AX Future Technology Institute and the AX Business Division, KT is positioning itself as a platform enabler rather than a mere solution provider. Despite perceived lags in proprietary model development (e.g., the mi-deum LLM), KT is pursuing a pragmatic “practical gains” strategy. By partnering with Microsoft, KT is adopting a “detour” approach to rapidly integrate global-standard AI capabilities into its existing corporate customer base. CEO Yun-young Park explained, “If AI services are actors on a theatre stage, we are an AX platform company that builds that stage.”

3. LG Uplus -Move to AI-Driven Software and Security:

LG Uplus, led by CEO Beom-sik Hong, is leveraging security and reliability as its primary competitive differentiators. The company is transitioning into an AI-centric software (SW) company, focusing on high-margin service architectures over raw infrastructure. The cornerstone of this strategy is ixi-O, a voice AI agent. The upcoming ixi-O Pro will feature advanced behavioral analytics, including tone and emotional state detection, to provide proactive customer engagement.  Hong stated, “We will become an AI-centred software (SW) company that leads solutions in telecommunications and AX technology,” signaling a two-track global expansion strategy involving both service exports and technology stack licensing.

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Market Outlook: The Race for Monetization:
As the “Three Firms, Three Strategies” AI era unfolds, the industry focus has shifted from experimental incubation to sustainable monetization. An industry official noted, “The key is how to graft telecommunications network technology built up so far onto AI services. All three telcos have finished setting specific roadmaps. Now is the time to prove it with results.” Many believe that the Korean network operator that successfully bridges the gap between massive CAPEX in AI infrastructure and scalable, profitable AI-native services will ultimately define the next generation of telecommunications.
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References:

https://www.digitaltoday.co.kr/en/view/49093/koreas-top-three-telecoms-bet-future-on-ai-shift-from-networks

SKT 6G ATHENA White Paper: a mid-to-long term network evolution strategy for the AI era

SK Group and AWS to build Korea’s largest AI data center in Ulsan

South Korea has 30 million 5G users, but did not meet expectations; KT and SKT AI initiatives

McKinsey: AI infrastructure opportunity for telcos? AI developments in the telecom sector

WSJ: 5G in South Korea has not lived up to expectations

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Anthropic’s Project Glasswing aims to reshape IT cybersecurity

Posted on by Alan Weissberger

Backgrounder:

Late last year, Anthropic said that state-sponsored Chinese hackers had used its artificial intelligence (AI) technology in an effort to infiltrate the computer systems of roughly 30 companies and government agencies around the world. The company said it was the first reported case of a cyberattack in which AI technologies had gathered sensitive information with limited help from human operators.

As Anthropic and its chief rival, OpenAI, prepare to release new and more powerful AI systems, cybersecurity experts are increasingly vocal in their warnings that AI is fundamentally changing cybersecurity.  AI technology could allow hackers to identify security holes in computer systems far faster than in the past, vastly raising the stakes in the decades-long fight between hackers and the security experts guarding computer networks.  As hackers deploy AI to break and steal, security experts are also leaning on AI to spot flaws in their systems — including some that had gone unnoticed for decades.

“This is the most change in the cyber environment, ever,” said Francis deSouza, the chief operating officer and president of security products at Google Cloud. “You have to fight A.I. “This is the most change in the cyber environment, ever,” said Francis deSouza, the chief operating officer and president of security products at Google Cloud. “You have to fight AI with AI.”

Hackers have used AI chatbots to draft phishing emails and ransom notes, cybersecurity experts said. Others have used AI to parse large quantities of stolen data and determine what information might be valuable. Without help from AI attackers could sometimes break into computer networks within minutes, Mr. deSouza said, but with the help of AI breaches can take just seconds.  Some hackers specialize in breaking into systems and then selling off their access to other attackers. Those handoffs used to take as much as eight hours, as hackers negotiated the sales and passed along the compromised entry points, deSouza added. Now that process has accelerated to about 20 seconds, he said, with hackers sometimes using A.I. agents to speed up the process.

Some experts argue that the guardrails added by companies like Anthropic and OpenAI can actually provide an advantage to malicious attackers. Guardrails could cause an AI chatbot to deny help to a user trying to defend a system from an attack, they argue, but persistent hackers could be more diligent about finding vulnerabilities — and keeping those tricks to themselves.

In February, Anthropic said it had used its A.I. technologies to find over 500 so-called zero-day vulnerabilities — security holes that were unknown to software makers — in various pieces of commonly used open source software. The next month, a researcher at Anthropic revealed that he had used A.I. to find a serious security vulnerability in the core of the Linux operating system, which is software that powers much of the internet and is used in computer servers, cloud computing services, Android phones and Teslas. The bug had existed, apparently undiscovered, since 2003.

Project Glasswing Overview:

Anthropic has announced Project Glasswing – a new initiative that brings together Amazon Web Services, Anthropic, Apple, Broadcom, Cisco, CrowdStrike, Google, JPMorganChase, the Linux Foundation, Microsoft, NVIDIA, and Palo Alto Networks – in an effort to secure the world’s most critical software.

The fast growing AI private company has found that AI models (like its own Claude) have reached a level of coding capability where they can surpass all but the most skilled humans at finding and exploiting software vulnerabilities. Their Mythos Preview language model has already found thousands of high-severity vulnerabilities, including some in every major operating system and web browser.

Given the rate of AI progress, it will not be long before such capabilities proliferate, potentially beyond actors who are committed to deploying them safely. The fallout—for economies, public safety, and national security—could be severe. Project Glasswing is an urgent attempt to put these capabilities to work for defensive purposes.

The Project Glasswig partners will use Mythos Preview as part of their defensive security work. Anthropic will share what they learn so the entire IT industry can benefit. They have also extended access to a group of over 40 additional organizations that build or maintain critical software infrastructure so they can use the model to scan and secure both first-party and open-source systems.

Anthropic is committing up to $100M in usage credits for Mythos Preview across these efforts, as well as $4M in direct donations to open-source security organizations.

Project Glasswing Core Objectives:
  • Give Defenders a Head Start: The initiative aims to use Mythos’s capabilities to find and fix zero-day vulnerabilities in critical codebases before they can be discovered by malicious actors.
  • Secure Critical Infrastructure: Partners use the model to scan first-party systems and open-source software that underpin global banking, energy, and logistics networks.
  • Modernize Defense Practices: Anthropic is collaborating with partners to evolve security workflows, such as patching and disclosure processes, to match the “machine speed” of AI-driven vulnerability discovery.
Claude Mythos Capabilities:
The Glasswing initiative was formed after Anthropic researchers observed that the Mythos model had reached a threshold where its reasoning and coding skills surpassed all but the most skilled human security researchers.
  • Zero-Day Discovery: In early testing, the model autonomously found thousands of high-severity vulnerabilities, including a 27-year-old bug in OpenBSD and a 16-year-old flaw in FFmpeg code that had been scanned by automated tools millions of times without detection.
  • Performance Benchmarks: Mythos Preview scored 83% on the CyberGym cybersecurity benchmark, significantly outperforming previous models like Claude Opus.

 

References:

https://www.anthropic.com/glasswing

https://www.nytimes.com/2026/04/06/technology/ai-cybersecurity-hackers.html

Anthropic Glasswing: AI Vulnerability Detection Has Crossed a Threshold

Anthropic Claude Users Reveal AI Hallucinations as their Top Concern

Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators

New Linux Foundation white paper: How to integrate AI applications with telecom networks using standardized CAMARA APIs and the Model Context Protocol (MCP)

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