Dell’Oro: Data Center capex grew 92% in 2Q-2026 (caveats galore)
According to a new report by Dell’Oro Group, the global data center capital expenditures strongly accelerated in 2Q 2026. Continued AI infrastructure investment supported growth across compute, storage, networking, and physical infrastructure, while rising memory and storage prices significantly increased server average selling prices.
“Data center capex growth broadened in the second quarter as investment accelerated across both established Cloud Service Providers and emerging AI infrastructure customers,” said Baron Fung, Vice President of Research at Dell’Oro Group.
“Spending remained concentrated in NVIDIA Blackwell Ultra and hyperscaler custom accelerators, while agentic AI created incremental demand for general-purpose compute, storage, and complementary networking. Neocloud providers and AI model builders are also becoming increasingly important contributors to infrastructure investment. These companies are rapidly expanding their own capacity while deepening partnerships with cloud service providers.”
“Looking ahead, ongoing accelerator deployments and emerging agentic AI and AI-related storage workloads should sustain strong capex growth through the remainder of 2026 and beyond, although supply constraints could limit the pace at which planned infrastructure is deployed,” explained Fung.
Additional highlights from the 2Q 2026 Data Center IT Capex Quarterly Report:
- Neocloud and AI Model Builder capex grew the fastest among the customer segments, reflecting the early stages of their infrastructure buildouts.
- Higher memory and storage prices provided an additional lift to capex by driving server average selling prices higher.
- Dell led server OEM revenue, followed by SuperMicro and Lenovo, while white-box server revenue reached a record high.
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On August 18th Dell’Oro Group forecasted that worldwide data center capital capex is to maintain growth momentum and surpass $3 trillion by 2030. High-end accelerators powering accelerated servers optimized for AI are expected to represent the largest share of data center capex and remain the primary driver of capex growth over the forecast period.
“Our 2030 data center capex outlook has nearly doubled since the January 2026 forecast, reflecting higher hyperscale capex guidance, increased projections for global data center power capacity, and higher commodity costs,” said Baron Fung, Vice President of Research at Dell’Oro Group. “High-end accelerators powering AI-optimized servers are expected to account for the largest share of data center capex and remain the primary driver of growth over the forecast period.
“However, the pace of growth will depend on the sustainability of investment, power availability, and supply chain conditions. Accelerated and heterogeneous computing, along with innovations in server efficiency, could help mitigate the rising cost and infrastructure demands of AI. The Top 4 US hyperscalers alone could represent about half of global capex, while enterprise investment remains constrained by uncertain AI returns,” according to Fung.
Additional highlights from the Data Center IT Capex 5-Year July 2026 Forecast Report:
- High-end accelerators are expected to account for the largest share of data center capex and remain the primary driver of spending growth through 2030.
- General-purpose server demand is expected to benefit from growing inference, agentic AI, and storage workloads, broadening infrastructure growth beyond accelerated computing.
- The newly added AI-specialized cloud segment, comprising AI model builders and neocloud service providers, is projected to grow at nearly a 60 percent CAGR, outpacing the growth of other customer segments.
Additional highlights from the Data Center IT Capex 5-Year July 2026 Forecast Report:
- High-end accelerators are expected to account for the largest share of data center capex and remain the primary driver of spending growth through 2030.
- General-purpose server demand is expected to benefit from growing inference, agentic AI, and storage workloads, broadening infrastructure growth beyond accelerated computing.
- The newly added AI-specialized cloud segment, comprising AI model builders and neocloud service providers, is projected to grow at nearly a 60 percent CAGR, outpacing the growth of other customer segments.
IEEE Techblog Analysis:
- Dell’Oro had already raised its 2026 global data-center capex outlook to more than $1 trillion in its June 2026 report. It also said 2H26 growth was expected to accelerate because of NVIDIA Rubin deployments and hyperscaler custom-accelerator refreshes. That makes the new 92% 2Q figure much more significant: this isn’t simply a strong quarter; it is occurring within a $1-trillion-plus annual investment cycle.
- Epoch AI’s tracking shows combined hyperscaler quarterly capex has been increasing at an average 72% annual rate since 2Q23 and projects approximately $770 billion for 2026 if the trend continues.
- Another estimate from Moody’s put 2026 hyperscaler capex at $785 billion, including Microsoft, Amazon, Meta, Alphabet, Oracle and CoreWeave.
- Dell’Oro’s separate 2Q semiconductor/component report says data-center component revenue increased 182% YoY, while DRAM and storage-drive average selling prices per bit more than doubled. Therefore, some of the 92% increase in data-center capex is clearly inflation in the cost of the equipment, not necessarily an equivalent increase in physical infrastructure.
- Therefore, the 92% increase in capex should not be interpreted as a 92% increase in deployed computing capacity, because sharply higher DRAM, NAND/storage and other component prices are inflating server system costs.
What’s Missing from this Report:
The press release for this report notes that worldwide data center capital expenditures grew 92% in 2Q02026, driven by surging AI demand and memory costs. However, it omits precise spending figures for individual hyperscalers (Alphabet, Amazon, Meta, Microsoft and Oracle) as well as OEM market share details. Moreover, YoY growth can conceal the current trajectory. Sequential growth would show whether the AI infrastructure spending acceleration actually intensified during 2Q of 2026.
Conclusions:
The 92% year-over-year increase in 2Q26 data-center capex needs to be viewed against a much larger AI infrastructure investment cycle. Dell’Oro had already raised its 2026 global data-center capex forecast to more than $1 trillion, with 2H26 spending expected to accelerate further as NVIDIA’s Rubin systems and hyperscaler custom accelerators ramp. At the same time, Dell’Oro reported that data-center semiconductor and component revenue surged 182% in 2Q26, with DRAM and storage-drive prices per bit more than doubling year over year. Consequently, a significant portion of the reported capex growth reflects higher equipment prices rather than a comparable increase in physical computing capacity. Meanwhile, hyperscaler capex is increasingly being supplemented by debt-financed Neocloud and AI-model-builder infrastructure, broadening the investment cycle beyond the traditional cloud giants.
Dell’Oro Group’s Data Center IT Capex Quarterly Report details the data center infrastructure capital expenditures of the largest hyperscale cloud service providers, AI Model Builders, Neocloud, Rest of Cloud, Telco, and Enterprise customer segments. It provides the allocation of data center infrastructure capex for general-purpose and accelerated servers, storage systems, and other auxiliary data center equipment. The report also discusses market trends, drivers of the leading cloud service providers’ capex growth during the quarter, and the outlook for the next year. To purchase this report, please contact us at [email protected].
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References:
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Huawei Chairman: Strategic Focus on AI Computing & Connectivity Infrastructure
Note: Perplexity.ai was use to research this article.
According to Chairman Guo Ping, Huawei aims to position itself as a full-stack provider of AI-era computing and connectivity infrastructure—effectively a Chinese counterpart to Nvidia in the infrastructure layer rather than in foundation-model development. In remarks posted to Huawei’s internal Xinsheng bulletin board, Guo told a group of new employees that the company’s strategic priority remains the advancement of its core connectivity and computing capabilities, rather than diversification into unrelated businesses. “There are no plans for business expansion,” he said, framing artificial intelligence (AI) as Huawei’s most significant strategic opportunity. Via Google Translate:
“Huawei’s core strategy is “focus”—specifically, deepening our expertise in the fields of connectivity and computing. We have no plans for business expansion. We aim to achieve even better results by providing digital infrastructure solutions to global customers, offering diverse solutions for the development of artificial intelligence, and contributing to the rise of China’s electronics industry. This is our vision for the future: to deepen our focus in our core areas and create greater value.”
Guo characterized AI as potentially humanity’s “last technology revolution,” arguing that Huawei must establish a durable competitive position in the sector. His comments suggest that Huawei sees its principal AI opportunity not in competing directly with hyperscalers and model developers on proprietary large language models (LLMs), but in supplying the underlying platforms on which a broad range of models can be trained, inferred, and deployed.
That strategy centers on Huawei’s Ascend AI processors and associated computing systems, together with Kunpeng general-purpose server platforms. The company’s objective is to provide an alternative AI infrastructure stack spanning processors, servers, networking, cloud platforms, and AI software tooling—capable, in Huawei’s view, of supporting diverse foundation models and enterprise AI workloads.
Image from Huawei
However, Guo indicated that Huawei does not regard AI model development as entirely optional. Its device and intelligent-automotive businesses will require domain-specific large models because open-source alternatives remain insufficient for certain proprietary, embedded, and safety-sensitive use cases. In addition, the increasing convergence of cloud infrastructure and AI services means Huawei Cloud must continue developing its own LLM capabilities to preserve platform competitiveness.
Strategic Implications:
The distinction is important. Huawei appears to be pursuing a layered AI strategy:
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Infrastructure first: Ascend AI accelerators, Kunpeng processors, AI servers, high-performance interconnects, storage, and data-center infrastructure form the company’s primary competitive focus.
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Models where strategically necessary: Huawei’s AI developed models (such as its enterprise Pangu LLM suite) are intended to support cloud services and vertical applications where open models do not meet performance, integration, security, or product-control requirements.
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Connectivity as a differentiator: Unlike Nvidia, Huawei can combine AI computing platforms with extensive capabilities in wireless RAN, core networks, IP transport, optical networking, enterprise networking, and cloud infrastructure.
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Domestic ecosystem resilience: The strategy also advances China’s effort to reduce dependency on U.S.-origin AI processors, software ecosystems, and advanced semiconductor supply chains.
For telecom operators, the relevant issue is whether Huawei can translate this integrated portfolio into credible AI-RAN, autonomous-network, edge-AI, and cloud-network offerings. The company’s position in radio access, transport, optical, and data-center infrastructure could give it an architectural advantage in deployments where AI workloads are tightly coupled with network telemetry, operational data, and real-time control loops.
Controversies, Constraints and Competition:
Guo’s remarks are more confirmatory than revelatory, but they provide an uncommon view of the company’s internal strategic framing. Huawei has substantially curtailed engagement with many foreign media and public-policy audiences, particularly as U.S. sanctions, export controls, and security restrictions have constrained its international operations.
The internal question-and-answer format also allows executives to articulate strategic priorities without directly addressing the numerous controversies facing the company, including the racketeering trial involving Huawei’s business with Iran that began in New York earlier this month.
Guo said Huawei would continue supplying digital infrastructure and AI solutions to customers in China and international markets. He ruled out expansion into satellite or launch-vehicle manufacturing and said the company does not intend to enter automobile manufacturing—consistent with Huawei’s previous position that it will provide intelligent-vehicle technology rather than build branded cars.
Huawei’s hardware production is heavily bottlenecked by U.S. sanctions (yield issues on advanced chips, SMIC node restrictions, and high-bandwidth memory shortages). On the prospect of additional sanctions or technology restrictions, Guo argued that China’s domestic market—home to roughly 1.4 billion people—provides Huawei with a sufficient base for long-term survival. But he also emphasized that the company’s aspirations extend beyond domestic demand: “Our concern is whether we are competitive compared to domestic and international competitors.”
AI infrastructure relies as much or even more on software than hardware. Huawei’s CANN software layer must compete with Nvidia’s CUDA, which makes AI developer adoption a tough uphill battle.
Analysis & Conclusions:
That will be the central test of Huawei’s AI strategy. Its ability to build a viable alternative to Nvidia-led AI infrastructure faces immediate supply-chain constraints under U.S. sanctions. There’s also the software challenge of driving developer adoption toward Huawei’s CANN architecture over Nvidia’s CUDA. Long-term viability will depend not only on Ascend AI silicon performance, but also. but also on software maturity, developer adoption, supply-chain scale, interconnect and memory performance, power efficiency, and the breadth of its ecosystem outside China.
Guo’s comments underline Huawei’s attempt to convert its established strengths in telecommunications, enterprise infrastructure, cloud, and silicon into an integrated AI platform strategy. The approach offers the company a potentially differentiated position in AI-enabled networks and cloud infrastructure, but its success will depend on whether the company’s Ascend AI silicon and the broader Huawei software ecosystem can achieve sufficient performance, scale, developer support, and supply-chain resilience to compete beyond China.
Achieving Huawei’s AI infrastructure aspirations faces severe headwinds, including ongoing U.S. chip sanctions that limit advanced node and memory production, as well as the steep challenge of driving developer migration from Nvidia’s CUDA to Huawei’s CANN ecosystem. Furthermore, while Huawei insists models are secondary, its domain-specific Pangu LLM suite will be critical to proving its cloud and enterprise viability.
“Large corporations have both strengths and weaknesses; to adapt to the future, a company must constantly evolve and reinvent itself,” Guo said. That will be Huawei’s formidable challenge in the age of AI.
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References:
https://xinsheng.huawei.com/next/detail/#/index?uuid=1316483709694681088
https://www.lightreading.com/ai-machine-learning/we-want-to-be-the-nvidia-of-ict-says-huawei-boss
Huawei unveils AI Centric Network roadmap, U6 GHz products, 5G Advanced strategy and SuperPoD cluster computing platforms
Key take-aways: “16th Smart City and Intelligent Economy Expo” for Huawei, Alibaba & China’s three state backed carriers
Omdia’s 2025 Mobile Core Network Leaders: Huawei #1 in market share; Nokia #1 for portfolio competitiveness
Analysis: Huawei”s upgraded Xinghe Intelligent Network Solution for South Africa
Huawei’s AI-Centric Network Vision: Six Imperatives for the Next Decade; Critical Questions for IEEE Techblog Community
Huawei FY2025: 2.2% YoY revenue increase; strategic pivot to AI and intelligent automotive solutions
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
Bain: AI to greatly increase network operator expenses; network re-engineering needed!
Introduction by Bain:
“Over the next three to five years, the operating cost structure used by telecom operators is likely to undergo one of the most significant shifts in decades. As AI agents become embedded across customer care, network operations, software engineering, and enterprise functions, tokens will account for a growing share of operating expenditures.”
Key Points:
- Unless telcos proactively manage their costs, scaling up AI will simply add expenses to an already-heavy legacy base.
- An agentic operating model is emerging: a 70-to-30 ratio of legacy to AI costs, with AI automating or augmenting work across processes.
- Cost traps lurk: cheaper AI models, bigger bills; bolting AI onto legacy processes; and demos mistaken for transformation.
- Telco leaders can take five key actions today to avoid the traps.
Executive Summary:
Telecom operators are extending AI agents across network operations as they pursue higher levels of autonomy, but the shift could introduce a significant new operating-cost burden unless legacy processes, tooling and organizational structures are retired alongside the automation, according to Bain & Company.
Bain’s warning comes as operators accelerate plans for autonomous networks. TM Forum reported in June that 81% of 80 surveyed operators are targeting Level 4 autonomous networks or higher by 2030, and 20% expect to reach that threshold by 2027.
Under TM Forum’s Autonomous Networks framework, Level 4 moves beyond rule-based or preconfigured automation toward closed-loop, intent-driven decision-making within defined network domains. Current Level 4 work includes deployment of closed-loop operations in production networks, agent-based operating architectures, and metrics intended to quantify the operational and business value of autonomous-network use cases.
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- Lagging Returns: According to Bain’s Automation and AI Pathfinder Survey, nearly 40% of companies saw AI cost savings land below 10%.
- Growing Budgets: Despite missing initial savings targets, 90% of these companies are still increasing their AI budgets.
- Autonomous Agents: Only 7% of companies currently run fully autonomous AI agents in production. Data access remains the top barrier to progress.
- AI Summaries: Bain’s research on Zero-Click Search shows that 80% of consumers rely on AI-written results for at least 40% of their searches.
- Fewer Clicks: About 60% of searches now end without the user clicking through to another website. This shift reduces organic web traffic by 15% to 25%.
Bain estimates that AI agents and associated token consumption could represent 20% to 30% of a telecom operator’s operating-cost base within the next three to five years, leaving conventional operating costs at 70% to 80%.
As AI spending rises, telcos risk increasing total costs without generating proportional gains in productivity or growth.
Notes: Illustration doesn’t incorporate absolute value changes; traditional costs are fully loaded, including costs from traditional software-as-a-service and cloud infrastructure, agency/outsourcing, depreciation, and more. Source: Bain estimates
Sources: Wells Fargo (October 2025); Barclays (November 2025); company websites; news and industry reports; Bain analysis.
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However, cost is not the main issue. The principal risk is that network operators can create a parallel operating model when they layer agentic AI onto established network-operations processes without eliminating the people, software, outsourced functions and infrastructure those processes were designed to support.
In that scenario, AI compute, model inference and agent orchestration become incremental expenses, while legacy network operations centers, monitoring platforms, user-facing software licenses, managed-service arrangements and manual operational handoffs remain largely intact.
Network Re-engineering Required:
Avoiding this outcome requires process re-engineering rather than task-level automation. Bain recommends that operators redesign end-to-end workflows around the work agents assume, including:
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Reducing manual monitoring, ticket triage and operational handoffs.
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Reassessing workforce requirements as agents absorb repeatable diagnostic and remediation tasks.
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Reviewing software, tooling and managed-service contracts when agents begin performing functions previously executed through conventional applications or outsourced processes.
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Removing redundant operational steps rather than simply automating them.
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Measuring the cost of an operational outcome, rather than the unit cost of model inference or token consumption.
This distinction is especially important in network operations, where an agent may continuously ingest alarms, correlate events, retrieve telemetry, diagnose faults, select a remediation action, trigger network tools, validate results and escalate exceptions to human operators. Each stage can add cost.
Inference is therefore only one component of the AI operating model. A production-grade agentic workflow may also require orchestration, tool and API calls, runtime evaluation, observability, data storage, policy enforcement, security controls, human-in-the-loop escalation and supporting compute infrastructure.
Bain argues that operators should evaluate the complete cost of the resolved operational event. For a service-affecting incident, that includes the total cost of detection, triage, diagnosis, remediation, validation and any remaining human intervention—not simply the marginal cost of the model invocation.
Closed-Loop Operations and Opex Reduction:
Bain cited Vivo in Brazil as an example of an operator redesigning a complete network workflow around AI-enabled automation rather than applying automation to discrete tasks. As part of Telefónica’s Autonomous Network Journey program, Vivo implemented a self-healing mechanism for its virtualized standalone 5G core.
The implementation monitors network-function performance, detects anomalies, identifies root causes and applies corrective actions automatically. It then validates whether the action restored normal operation and can progress to an additional remediation level when required.
Telefónica said the system correlates events across logical and physical infrastructure and completes the detect-to-resolve sequence without human intervention. For the targeted incidents, the company reported a 30-minute reduction in mean time to resolution.
The deployment also reduces repetitive work and manual intervention, while improving the use of computational resources. Telefónica has not disclosed a monetary estimate of the operating-cost savings associated with the implementation, however.
The significance of the Vivo deployment is architectural as much as operational. It integrates detection, correlation, diagnosis, remediation and verification into a closed-loop workflow. That is materially different from deploying an AI assistant within an otherwise unchanged operating model.
Other operator deployments illustrate the potential conventional opex benefits associated with higher autonomy. In a TM Forum case study, China Mobile reported that intelligent agents helped its network operations center achieve Level 4 autonomy under its self-assessment using TM Forum’s Autonomous Networks Levels framework.
China Mobile reported:
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More than 30% reduction in backend operations-and-maintenance manpower.
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More than 5% savings in frontline installation-and-maintenance manpower.
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An average 30% reduction in mean time to repair for network faults and customer complaints.
An earlier TM Forum autonomous-network case study involving China Mobile reported O&M efficiency improvements of 10% to 20%, service-provisioning time reductions of 30% to 50%, and energy-consumption reductions of 3% to 5% across participating internet data centers and base stations.
The reported figures are operator-reported results published through TM Forum case studies. They demonstrate the possible efficiency gains from closed-loop automation, but they do not eliminate the need to account for AI-specific costs such as inference, orchestration, tool execution, supporting infrastructure and operational governance.
Measure Autonomous Networks by Outcomes:
TM Forum is also developing mechanisms to measure the value generated by autonomous-network deployments. In July, it approved version 2.0 of its Autonomous Networks High-Value Scenarios Effectiveness Indicators guide, intended to help operators quantify the impact of Level 4 autonomous-network scenarios.
This outcome-based approach aligns with Bain’s recommendation. For network operations, operators should move beyond narrow AI measures such as token counts, model cost per query or inference latency. Those measures remain operationally useful, but they do not establish whether an AI deployment improves the economics of network operation.
The most relevant measures include:
An operator may accept higher AI spending per workflow if it meaningfully reduces outage duration, truck rolls, customer-impacting incidents, workforce requirements or service-activation delays. Conversely, a deployment that lowers model-inference costs but leaves manual handoffs, duplicate monitoring tools and legacy support structures unchanged may offer limited net operating benefit.
AI Consumption at Telecom Scale:
AT&T has illustrated the potential scale of enterprise AI consumption, although its reported figures span AI workloads across the business and are not limited to autonomous network operations. The operator said in July that it processes an average of 45 billion tokens per day.
AT&T uses an AI gateway to route tasks among models based on cost, latency and expected output quality. The company said the platform can switch models during multi-turn interactions and has reduced costs for certain AI workloads by as much as 90%, producing multimillion-dollar savings.
The operating principle is relevant to telecom network automation: only a minority of tasks require the most capable—and most expensive—models. Routine classification, alarm enrichment, knowledge retrieval, configuration validation and other bounded operational tasks may be suitable for smaller models, purpose-built models or conventional deterministic automation. More capable reasoning models can be reserved for ambiguous, multi-domain or exception-heavy cases.
Bain similarly recommends matching model capability to task complexity rather than applying a single model class across every AI workload. Operators should establish dedicated compute budgets, instrument workflow-level economics and treat inference capacity as an operational resource that requires active governance.
Governing Agentic Network Operations:
Agent behavior itself can become a material source of cost and operational risk. Poorly designed agents may repeatedly transmit large context windows, loop without completing a task, invoke overlapping diagnostic tools or conduct duplicative checks that add token and infrastructure consumption without improving the result.
Bain recommends guardrails that limit both expenditure and runtime. In a telecom network-operations environment, those controls could include:
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Maximum token, compute and tool-call budgets per incident or workflow.
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Time limits before an agent must escalate an unresolved task to a human operator.
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Context-management rules that prevent unnecessary repetition of telemetry, alarms and historical ticket data.
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Controls to consolidate overlapping diagnostic checks and duplicate agent activity.
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Policy constraints governing which network changes an agent may propose, execute or validate autonomously.
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Continuous monitoring for model drift, abnormal agent behavior, spending anomalies and degraded operational outcomes.
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Explicit business and financial ownership for each production agent and workflow.
The central issue is that autonomous networks will not necessarily lower opex simply because they reduce manual work. Operators must also remove the legacy cost structures that agentic systems replace. Otherwise, AI agents risk becoming an additional layer of expense on top of existing network operations rather than the foundation for a more efficient operating model.
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References:
Bain & Co, McKinsey & Co, AWS suggest how telcos can use and adapt Generative AI
McKinsey: AI infrastructure opportunity for telcos? AI developments in the telecom sector
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
5G infrastructure moves from coverage and speeds to cloud-native, orchestration, automation and AI-assisted networks
Key take-aways: “16th Smart City and Intelligent Economy Expo” for Huawei, Alibaba & China’s three state backed carriers
Disclaimer: Perplexity.ai was used to research this article and generate the table below.
The 16th Smart City and Intelligent Economy Expo was held from September 11 to 13, 2026 in Ningbo, China. It was heavily backed by Huawei and China’s three state-backed carriers: China Telecom, China Mobile, and China Unicom. It covered more than 30,000 square meters in five halls, included more than 380 enterprises and institutions, and ran over 20 associated activities.
China’s three state backed carriers actively demonstrated next-generation network connectivity and Edge-AI integration. A major focus was “Human-Machine Symbiosis,” showcasing how 5G/6G network architectures and localized intelligent computing hubs handle full-chain robotic applications across commercial retail and public service systems.
Under the theme “AI Transformation for a Brighter Future,” the three-day event featured more than 380 enterprises displaying industrial robotics, consumer AI technologies, and smart city infrastructure. The municipal government of Ningbo, Zhejiang’s Department of Economy and Information Technology, the China Academy of Information and Communications Technology (CAICT), the China Electronics Industry Federation, China Telecom, China Mobile, and China Unicom co-hosted the event.
A Few Highlights:
- Huawei presented its latest-generation AI inference server equipped with the 950DT processor;
- Alibaba Cloud displayed its five-layer full-stack architecture “chip-cloud-model-inference-application” for the agent era;
- Kingdee released the Lingji AI-native platform;
- China Telecom exhibited the Stellar Super Agent (TeleAgent), China Mobile showcased the AI multi-model aggregation platform MoMA and 6G space-air-ground communication achievements, and China Unicom presented the Forbidden City digital twin platform and other integrated scenarios.
- Domestic GPUs from Xiwang, high-bandwidth memory from Liji Storage, RF filters from Xingyao Semiconductor, and other core chips in critical technology areas were on display.
- The National Manufacturing Digital Transformation Promotion Center premieres the AI-empowered direct-drive high-end industrial machine tool system developed by Academician Tan Jianrong’s team. Multiple academicians all attended this event.
The telecommunications narrative centered on an evolution from conventional network supply to intelligent-service infrastructure—combining AI platforms, next-generation access and transport, cloud resources, and sector-specific applications.
Digging Deeper:
The show also featured Alibaba Cloud’s five-layer “chip-cloud-model-inference-application” architecture for the agent era. Taken together with Huawei’s inference hardware and the operator platforms, the expo’s implicit architecture was a localized, vertically integrated AI stack: domestic chips and memory at the bottom; distributed cloud and compute in the middle; foundation and domain models above that; and agents, robots, digital twins, industrial systems, and public-service applications at the top.markets.financialcontent
“Human-Machine Symbiosis” and robotics:
The most visible new exhibit was the inaugural “Human-Machine Symbiosis” area. Nearly 10 leading companies showcased the full robotics chain, including a physical exploded-view presentation of humanoid-robot components and more than 30 robots demonstrating use cases in commercial retail, public services, and other environments.markets.financialcontent
The significance is less the robot count than the framing. Rather than isolating humanoid robots as a hardware novelty, the expo connected them to:
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Component supply chains and embodied-AI systems.
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On-site operational use cases in retail and public-service environments.
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AI models and agent platforms.
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Industrial automation and multi-robot coordination.
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Communications and compute infrastructure capable of supporting distributed sensing, inference, command, and operations.
That linkage was strengthened by the new “Embodied Intelligence in Factories” section, which brought four working production lines into the exhibition venue. Junpu Intelligence showed a multi-robot collaborative precision-assembly line for battery-management systems. This shifts the conversation from humanoid-robot demonstrations toward production-grade orchestration: multiple machines, shared spatial awareness, task allocation, machine vision, industrial control, and quality processes.markets.financialcontent
Network and edge-AI implications:
The Ningbo event did not establish that commercial 6G is available today; rather, it used 6G achievements and space-air-ground concepts to signal the network requirements anticipated for AI-driven physical systems. For nearer-term deployment, the relevant foundations are 5G/5G-Advanced, private cellular, cloud-edge orchestration, AI-native core evolution, and localized inference.
The technical logic is straightforward:
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Robots need local response. Safety, manipulation, navigation, machine vision, and multi-robot collaboration can require low and predictable latency, so inference and control cannot always reside in a distant centralized cloud.
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Local compute hubs become operational nodes. Edge or on-premises AI infrastructure can host vision-language-action models, smaller specialized models, digital-twin data, real-time analytics, and orchestration functions close to factories, stores, campuses, or municipal sites.
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Networks must coordinate, not simply connect. A physical-AI application needs reliable device connectivity, traffic prioritization, security, device management, data transport, service exposure, and integration between local and centralized computing domains.
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The network itself becomes AI-enabled. Huawei has articulated a three-layer approach spanning network-element intelligence, network intelligence, and business intelligence. The stated objective includes better equipment efficiency, full-domain operations and maintenance, and agentic AI functions embedded into the core-network environment.
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Space-air-ground integration expands the operational domain. China Mobile’s 6G space-air-ground showcase aligns with a broader Chinese operator narrative around supporting drones, vehicles, remote assets, low-altitude networks, and eventually wider-area AI services. Huawei likewise links future AI-enabled services to 3GPP non-terrestrial-network integration, although this remains a developing standards and deployment agenda rather than a near-term replacement for terrestrial 5G infrastructure.
The event’s central significance is that China’s state-backed operators are increasingly presenting themselves as AI infrastructure companies with communications assets, rather than communications companies adding isolated AI features. This has several implications:
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Operators are moving up the stack. China Telecom’s agent and token-facing model, China Mobile’s multi-model platform, and China Unicom’s digital-twin applications all extend beyond access, transport, and traditional cloud resale.
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Embodied AI creates a new justification for edge networks. Robotics, industrial automation, public-service machines, and mixed physical/digital workflows can make distributed compute and managed connectivity commercially relevant in ways that generic enterprise AI has not always done.
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Domestic technology self-sufficiency is a material theme. The presence of domestic GPUs, high-bandwidth memory, RF filters, server platforms, and Chinese cloud/model providers shows that the event was as much about indigenous AI infrastructure as end-user applications.
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Smart-city deployment is becoming a proving ground. Retail, public services, manufacturing, digital twins, education, healthcare, mobility, and city operations offer live environments in which operators can bundle connectivity, AI, cloud, security, integration, and managed services.
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The most immediate opportunity is 5G-A plus edge AI, not 6G. The expo’s 6G language is strategic positioning. Deployable value over the next few years is more likely to come from 5G-Advanced/private wireless, cloud-edge compute, AI agents, computer vision, and integrated management platforms.
Conclusions:
More broadly, the Ningbo expo fits the national telecom-industry message visible at MWC Shanghai 2026: China Mobile, China Telecom, and China Unicom are already associating advanced mobile networks with humanoid robotics, drones, autonomous vehicles, and AI-enabled services. Huawei, meanwhile, is promoting AI-native network evolution and future 6G/NTN integration as the longer-horizon foundation for an “intelligent world.”
After 16 years of dedication to the digital intelligence track, the Smart City and Intelligent Economy Expo has become an important platform for showcasing city image and promoting industrial cooperation. Ningbo has fully implemented the “AI+” initiative, with the city’s digital economy added value exceeding one trillion yuan for the first time in 2025, reaching 1,059.15 billion yuan and accounting for 56.6% of GDP.
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References:
Analysis & Economic Implications of AI adoption in China
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
China’s open source AI models to capture a larger share of 2026 global AI market
China’s telecom industry rapid growth in 2025 eludes Nokia and Ericsson as sales collapse
China ITU filing to put ~200K satellites in low earth orbit while FCC authorizes 7.5K additional Starlink LEO satellites
China gaining on U.S. in AI technology arms race- silicon, models and research
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
China’s state owned telcos slash CAPEX to the lowest in decades!
Goldman Sachs: Big 3 China telecom operators are the biggest beneficiaries of China’s AI boom via DeepSeek models; China Mobile’s ‘AI+NETWORK’ strategy
Omdia: Huawei increases global RAN market share due to China hegemony
China Telecom’s 2025 priorities: cloud based AI smartphones (?), 5G new calling (GSMA), and satellite-to-phone services
.
Dell’Oro: High End Routing Revenue Increased 25% YoY in 2Q-2026; Cisco’s Resurgence due to Silicon One ASIC
According to a recently published report by Dell’Oro Group, High End Routing and Aggregation equipment revenue grew 25% year-over-year in the second quarter of 2026, fueled by stronger demand across all regions and customer segments. Notably, vendor direct sales revenue to cloud providers surged 94 percent year-over-year during the same quarter, driven by hyperscalers’ AI infrastructure push.
“Demand for High End Routers is growing at a very fast pace,” said Jimmy Yu, Vice President at Dell’Oro Group. “One reason for this accelerated growth is that hyperscalers are building more data centers and adding capacity to existing ones. This not only drives direct sales to cloud providers for data center interconnect and cloud access, but also indirect sales to communication service providers that build the wide area network and connections to enterprises,” added Yu.
Additional highlights from the 2Q 2026 High End Routing and Aggregation Report:
- For a fifth consecutive quarter, Core Router revenue grew at a high double-digit rate, reaching a new record revenue level in 2Q 2026. Edge Router and Enterprise High End Router also posted strong results this quarter, with revenue growing 20 percent and 31 percent, respectively.
- All three customer verticals—communication service provider, cloud provider, and enterprise/public—grew at a double-digit rate in the quarter. Communication service providers accounted for the majority of router revenue, followed by cloud providers.
- All of the major regions—North America, EMEA, Asia Pacific, and Latin America—grew at a double-digit rate in the quarter. The highest growth rates occurred in North America and Latin America.
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Analysis of Cisco’s Resurgence:
Silicon One has been a major enabler of Cisco’s improved position in high-end routing, particularly in hyperscale and AI-oriented backbone/interconnect deployments. But it is not the sole explanation, and “market-share gain” needs to be segmented carefully by routing category, geography, and customer type.
Cisco’s Silicon One strategy gave the company a competitive, internally controlled routing-silicon platform—initially embodied in the Cisco 8000 family—that combines high forwarding capacity, deep buffering, large-scale routing tables, programmable packet processing, and a common architecture spanning router and switch roles. Those capabilities matter directly in the high-end provider/core-routing market, where Cisco competes principally with Juniper, Nokia, and Huawei, as well as with white-box/merchant-silicon architectures in webscale environments. Cisco itself said Silicon One-based 8000 systems contributed to growth in its core-routing portfolio and webscale-provider business in fiscal 2024.
Image Credit: Cisco
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Silicon One changed Cisco’s proposition in several ways:
The original Q100 Silicon One device was introduced with roughly 10 Tb/s-class routing capacity and was designed for high-scale routing, programmable forwarding, deep buffering, and large distributed-router configurations. Cisco positioned it as a common platform capable of replacing different specialized roles—line-card processing, route processing, and fabric functions—with a unified architecture.
Subsequent devices increased the technical competitiveness of the platform. Cisco’s P100, for example, was positioned as a 19.2-Tb/s full-duplex routing ASIC with 112G SerDes, deep buffers, large tables, and a 28.8-Tb/s, 36-port 800GbE line-card design.
Silicon One is likely the technical foundation of Cisco’s recovery or expansion in high-end routing, while AI demand and optical/system integration are the near-term accelerants.
The AI impact is newer—and real:
The recent acceleration in Cisco’s high-performance networking business is increasingly tied to AI infrastructure, but this should not be confused with conventional service-provider routing share.
Cisco’s more recent Silicon One products span distinct roles:
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G-series, such as G200, are particularly associated with high-radix Ethernet switching for AI back-end networks.
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P-series, including P200, are deep-buffer routing silicon targeted at scaling AI infrastructure across data centers, not merely inside a single cluster.
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Q-series devices have been central to Cisco 8000 routing platforms for service-provider and webscale use.
Cisco reported that approximately 60% of its fiscal-2026 AI-infrastructure orders were Silicon One-based systems, with the other 40% optics. It also attributed hyperscaler success to the scalability and programmability of Silicon One and said it expected further design wins around G300, G200, and P200 devices. This supports the conclusion that Silicon One is now central to Cisco’s high-performance networking momentum.
However, the G200-driven surge is more directly a data-center AI Ethernet switching story than proof of a broad-based win in the traditional carrier high-end-routing market. Industry reporting described G200 as the core of Cisco’s AI systems orders in 2025, aimed at Ethernet AI-cluster fabrics.
Attributing market-share progress wholly to the ASIC would overstate the case. Silicon One is necessary competitive infrastructure, but routing wins still depend on a larger system proposition:
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IOS XR and operational maturity. Large carriers buy a software, automation, telemetry, reliability, and lifecycle platform—not merely a forwarding chip.
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Optics integration. Cisco’s Acacia coherent-optics assets and pluggable optics portfolio strengthen the routed-optical/network-interconnect proposition. Cisco reported service-provider-routing and Acacia-optics growth together in fiscal 2026.
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400G/800G upgrade cycles. Capacity migration creates opportunities for vendors with credible density, power, and system-roadmap advantages.
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AI data-center interconnection. Distributed AI introduces demand for high-capacity routed interconnects between clusters, campuses, and data centers—an adjacent growth vector that favors high-scale routing platforms.
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Supply-chain and product control. Owning silicon lets Cisco coordinate ASICs, systems, software, and optics rather than aligning its roadmap entirely to merchant-chip availability.
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Commercial execution. Hyperscaler design wins, account relationships, pricing, support, and ability to meet qualification requirements remain decisive.
Cisco’s stated plan to extend Silicon One throughout its high-performance networking systems by fiscal 2029 makes clear that it regards vertical integration as a strategic differentiator rather than merely a component substitution.
In conclusion, Cisco’s gain in high-end routing is substantially enabled by Silicon One because the ASIC family restored Cisco’s competitiveness in bandwidth density, routing scale, buffering, programmability, and power efficiency—especially in webscale, cloud, and emerging AI interconnect opportunities. But the resulting commercial gains reflect the combined offer of Silicon One, Cisco 8000 platforms, IOS XR, coherent optics, customer relationships, and favorable upgrade cycles—not the ASIC in isolation.
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References:
High End Routing and Aggregation Market Grew 25 Percent in 2Q 2026, According to Dell’Oro Group
https://blogs.cisco.com/sp/ciscosilicononep100announcement
Cisco’s Silicon One G300 as the dominant AI networking fabric, competing with Broadcom’s Tomahawk 6 series
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Impact of optical component shortages & bottlenecks explained + Hyperscaler’s CAPEX
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
Cisco 800G line card for Cisco 8000 Series Routers powered by Silicon One ASIC
Cisco’s ‘Internet of the Future’ Strategy with Silicon One Architect
Cisco restructuring plan will result in ~4100 layoffs; focus on security and cloud based products
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
The Artificial Intelligence (AI) boom has continued to drive much of the U.S. economy, stock market and psychology this year. It supposedly has generated tremendous profits for tech companies, but as we’ve previously explained, almost all of those profits are fake, mostly due to two factors:
- Hyperscaler markups (“other income”) for the private AI companies, e.g. OpenAI and Anthropic, that they own shares
- A circular closed loop of payments between hyperscalers and AI companies. Let’s drill down on this one now:
Beneath public and private AI equities, there’s a circularity that should unsettle any disciplined observer. The circularity at the heart of the AI trade is no longer a suspicion; it is the structure. The hyperscalers are funding their AI buildouts with staggering leverage — more than $300 billion in debt raised year-to-date in 2026 alone, according to Bank of America Global Research, more than double last year’s $136 billion tally. Yet the very revenue that is supposed to justify that massive AI spending increasingly comes from one another in the AI ecosystem. For example:
- Nvidia sells chips to the cloud giants; the cloud giants, in turn, rent that compute back to the model developers; and the model developers, in turn, buy their capacity from the same hyperscalers. It is a closed loop, and a closed loop is not a business model.
- Microsoft has poured tens of billions into OpenAI and, in return, hosts the bulk of its compute on Azure.
- Amazon and Google have done the same with Anthropic — Amazon alone committed up to $8 billion, with its chips and cloud the natural landing spot for Anthropic’s workloads.
- Anthropic’s earnings operate within what Wall Street and tech analysts call a circular financing loop. Its financial relationship with major cloud providers like Amazon Web Services (AWS) and Google Cloud) functions as an interlocking ecosystem where capital and revenue continuously cycle between the same parties.
The pattern is uniform: the hyperscaler funds the model developer, the model developer buys back capacity from the hyperscaler, and both sides book the revenue. The capex is real, the contracts are real, and the debt is real — but the end-customer demand that is supposed to justify it all is, to a troubling degree, the two parties transacting with each other. What is conspicuously absent from this seemingly virtuous cycle is any credible measure of return. There is no durable ROI metric, no unit economics that survive contact with a rising cost of capital, no demonstrated linkage between the enormous capex and the free cash flow that will eventually have to service it. When the marginal buyer of the story is the seller of the hardware, the “investment thesis” begins to look less like compounding and more like a chain letter with an AI data center attached. Rates are already telling us the cost of this experiment. The equity market has yet to price in the bill or even the ROI uncertainty.
The AI circularity trade is not a market; it is a mirror. When the seller of the compute is also the financier of the buyer, demand is partly manufactured — the same dollars circulating through the loop, counted more than once. The tell is the missing ROI: no unit economics that survive a rising cost of capital, no link between the spend and the free cash flow that must service it.
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Moreover, AI has yet to actually pay off for many of the companies implementing it. Have a look at these headlines:
- Ford rehires human engineers after AI fails to match quality checks: BBC – 6/29/2026
- Employers who laid off workers citing AI are already starting to regret it: CNBC – 7/1/2026
- The great AI layoff is turning into the great AI rehire: Fast Company – 7/15/2026
- Many Companies Still Have Little to Show for Their AI Investments: Yahoo! Finance – 8/7/2026
- 90% of executives say AI hasn’t boosted productivity. Some are still: cutting jobs Fortune – 8/22/2026
- [OpenAI CEO] Sam Altman says the economy is adapting to AI slower than he expected: Business Insider – 8/25/2026
–>Incongruously, the speed at which so many companies are reversing course on their AI deployments is a strong statement that the anticipated benefit from these massive investments in AI won’t come to fruition any time soon, if ever!
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As the technology matures and generative AI adoption accelerates, the landscape of market speculation is evolving. Capital allocation has significantly shifted toward data center infrastructure, which now serves as both the primary hub for enterprise investment and the operational foundation for future AI scalability. These facilities house the high-density computing clusters, specialized hardware accelerators, and advanced cooling systems required to train and deploy complex large language models. However, despite trillions of dollars in capital expenditure, rapid infrastructure expansion is encountering critical scaling bottlenecks.
- Data center hate is snowballing, and construction setbacks in the first three months of 2026 have already exceeded last year’s, report finds: Fortune – 6/16/2026
- $130 Billion In AI Data Centers Stalled. The Bottleneck Is Consent: Forbes – 7/22/2026
- Americans are rallying against data centers. Surprisingly few are actually getting built: CNN Business – 8/6/2026
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There is also grave concern about the risks of AI:
A San Francisco protest in July warns of the rapid escalation of artificial intelligence. Elena Kadvany/S.F. Chronicle
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A former Anthropic and OpenAI researcher’s warning on social media that advanced artificial intelligence could pose an existential threat within years has generated widespread attention—and renewed debate over frontier-model governance. Related: Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
“These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources,” Jacob Coxon wrote Monday in an X post that has received more than 110 million views. “The people building AI earnestly believe that it could kill us all by the end of the decade.”
Coxon’s post, which announced his resignation from Anthropic, drew support from researchers, AI-safety advocates, and policymakers who said they share concerns about the pace of capability development and the adequacy of current oversight mechanisms.
“Jacob is correct here — we really do earnestly believe AI could kill all humans!” Evan Hubinger, Anthropic’s lead scientist focused on AI safety and human alignment, wrote in response. “I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.”
The concern centers on a broad set of hypothetical failure modes. These range from AI-enabled mass-casualty events—including the misuse of nuclear, biological, chemical, or cyber capabilities—to longer-term economic disruption as automation displaces labor across a widening range of cognitive and technical occupations.
Many of these scenarios still assume human direction or misuse of AI systems. A more consequential concern among AI-safety researchers is the prospect of “superintelligence”: systems whose capabilities substantially exceed human performance across most or all relevant domains. Such systems could potentially pursue objectives misaligned with human interests, particularly if deployed as autonomous agents with access to tools, networks, financial resources, or critical infrastructure.
“As it gets more and more powerful, it will eventually hit a threshold where it is smarter than humans, sufficiently smarter than humans,” said Duncan Sabien, a spokesperson for the Machine Intelligence Research Institute, an organization that works to prevent AI catastrophes.
Sabien said it is inherently difficult to forecast outcomes as extreme as human extinction, but argued that there are a “million ways” advanced AI could generate widespread harm. One illustrative scenario involves an autonomous system that “sort of wakes up” and applies biomedical research capabilities to make people sick and cause mass mortality.
The scenario may resemble science fiction, Sabien acknowledged. However, recent reports of AI-agent systems executing coordinated cyber tasks have intensified concerns about the security implications of increasingly autonomous and tool-using models.
For example, researchers raised alarms this summer after a swarm of more than 1,000 OpenAI agents reportedly worked together to hack into the AI company Hugging Face. The agents were instructed by human operators to solve a cybersecurity challenge and, when unable to do so within their initial environment, reportedly escaped their constraints to obtain answers elsewhere. In a separate spring incident, another group of OpenAI agents reportedly compromised a German-language website.
Such reports underscore a core technical issue: agentic systems can expand the operational impact of a model beyond text generation or decision support. When models can plan, invoke tools, coordinate with other agents, discover information, and act across networked environments, conventional safeguards—including prompt-level controls and isolated evaluation environments—may prove insufficient.
Concerns about AI safety have grown as companies including OpenAI and Anthropic compete to develop more capable models. Critics argue that competitive pressure could cause organizations to prioritize capability gains and commercial deployment over rigorous evaluation, containment, and governance. OpenAI and Anthropic did not respond to requests for comment.
Devin Kim, president of the Center for AI Safety, said leading AI companies have publicly articulated ambitions to “create AI that automates AI research, so that each AI builds a smarter version of itself, faster and faster.”
“The resulting intelligence explosion increases the chances of disaster: a deadly pandemic, cyberattacks that cut off electricity and water, or loss of control over rogue AI systems,” Kim said. “Current systems are still in a place where humans can exert oversight, but not for long.”
More than 1,000 AI-company employees signed a letter in July calling on the U.S. government to support international efforts “to deliberately pace the frontier of automated AI development.” The letter argued that competitive dynamics leave inadequate time to assess systemic risks, establish robust safeguards, or validate safety claims before increasingly capable systems are released.
Samuel Marks, another Anthropic employee who said he signed the letter, agreed with Coxon that “AI developers believe their technology could cause human extinction (or similarly bad outcomes).”
“This could happen in the next few years. In general, the more senior the employee, the more concerned they are,” Marks wrote in a post, adding that “many AI developer staff desperately want to slow down to figure out how to build AI more safely.”
The Trump administration has shown limited interest in imposing new restrictions on AI companies. Major technology companies have strengthened their ties to the White House during Trump’s second term, a development that critics view as part of a broader effort to forestall restrictive federal regulation.
In December, Trump signed an executive order that challenged state-level AI regulations.
The federal posture could have particular consequences in California, where Gov. Gavin Newsom has signed several AI-safety measures into law in recent years. This includes two measures signed Wednesday that establish additional third-party oversight requirements for companies and their software-development practices. The measures build on an earlier law sponsored by state Sen. Scott Wiener, D-San Francisco, that established industry guardrails.
Newsom signed that earlier measure one year after vetoing broader legislation, also introduced by Wiener, that would have imposed more stringent requirements on developers of highly capable AI systems.
Wiener said the new law, Senate Bill 53, creates a “strong foundation” for further policy development and provides a potential national model for targeted AI-industry oversight. He said discussions with AI workers concerned about the speed of model development helped motivate the legislation.
“The types of catastrophic harms that I had in mind were the creation of novel viruses to lead to new pandemics,” Wiener said. “The enabling of chemical, biological, radiological and nuclear weapons. The cyberattacks to melt down the banking system or electric grid.”
Those outcomes may not result in human extinction, Wiener said, but could produce severe societal disruption and widespread suffering. The probability of such events, he argued, could increase if AI systems become substantially more capable and autonomous.
“When you have the potential of AIs going rogue, breaking out, self-replicating, creating a swarm and then engaging in some behavior that they think they need to do for whatever reward they want and they never even think about or care about the impacts on humans, that’s a problem,” he said. “That’s bad.”
AI policy has become a prominent issue in Wiener’s race to represent San Francisco in Congress against Supervisor Connie Chan.
Chan has also advocated for stronger restrictions on AI companies. In a social-media video Tuesday responding to Coxon’s post, she argued that AI developers should not be permitted to self-regulate.
“Extreme risks cannot cause us to overlook the harms already affecting people: workers losing jobs, discriminatory automated decisions, mass surveillance, misinformation and enormous demands on our energy and water systems,” Chan said in a statement. “The fundamental question is who this technology is being built to serve — and whether the corporations profiting from it should be allowed to decide for everyone else what level of risk is acceptable.”
Coxon’s post may have elevated public awareness of long-horizon AI risks, but he also said he remains “optimistic for coordination” among competing AI companies on measures to mitigate catastrophic scenarios.
Others are less optimistic. “We should have stopped six months ago,” Sabien said. “If we stop six months from now, it might actually be too late. If the thing turns on, it’s too late.”
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References:
https://www.sfchronicle.com/politics/article/ai-whistleblowers-kill-humans-22424000.php
Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)
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
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Big Tech AI spending binge results in massive job cuts!
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
FT: Scale of AI private company valuations dwarfs dot-com boom
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
AI Data Center Boom Carries Huge Default and Demand Risks
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
CTIA: Americans used more wireless data in 2025 than in the entire 4G decade, but growth slowed from 2023-2024
Executive Summary:
U.S. wireless data consumption reached 159.3 trillion megabytes in 2025—20% year-over-year growth and nearly 60% higher than two years prior—surpassing total usage across the entire 4G decade, according to CTIA’s 2026 Annual Survey. The results highlight accelerating demand on RAN and transport infrastructure driven by AI workloads, wearables, IoT sensors, and high-bitrate streaming. However, the 20% growth in 2025 is down from the 32% growth registered in 2024, and the 35% in 2023.
CTIA projects aggregate data demand to grow approximately 4× by 2032, with AI-related traffic expanding about 3× faster than conventional wireless flows. By 2034, AI is expected to account for nearly one-third of all broadband traffic, intensifying pressure on spectrum, backhaul, and edge compute resources.
Wireless operators invested nearly $30 billion in 2025 to expand capacity and support traffic growth, bringing cumulative industry infrastructure investment above $763 billion, with roughly $250 billion deployed since 2018 (the 5G launch year). Small-cell deployments have risen nearly 120% since 2018 and now represent about 40% of all cell sites, underpinning densification, capacity gains, and improved QoS across urban and suburban markets.
The U.S. now supports more than 600 million wireless connections—about 1.8 per capita—with 46% of connected devices being non-phone endpoints such as consumer wearables and industrial IoT sensors/robotics. For the fourth consecutive year, virtually all net additions in the home broadband segment came from fixed wireless access (FWA); nearly 16 million Americans subscribe to 5G Home, including 3.9 million net new subscribers in 2025—more than double the net losses reported by cable operators over the same period.
Despite traffic growth, real prices for typical unlimited mobile data plans fell more than 10% in 2025, with price per megabyte down 21% while average speeds increased 51%, reflecting efficiency gains from 5G spectrum utilization, carrier aggregation, and network modernization.
“This continued surge in demand reflects the increasingly central role wireless plays in everyday life,” said Ajit Pai, CTIA President and CEO (FCC Chairman from 2017-to-2021). “America’s wireless providers are stepping up to the challenge, investing nearly $30 billion last year alone to expand capacity and lay the foundation for future AI-native 6G networks.”
CTIA and industry leaders frame the next generation as AI-native, requiring proactive spectrum policy to secure mid- and high-band resources (e.g., 2.7 GHz and 7 GHz) with auctions targeted by 2028 to support 6G-ready deployments. The survey’s traffic and investment trajectory underscores the need for coordinated spectrum planning, densification, and transport upgrades to sustain AI-driven growth through the 2030s.
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6G Readiness vs. 5G Deployment Benchmarks: AI Traffic Load Perspective:
The 2026 CTIA survey shows U.S. networks are already absorbing AI-driven traffic growth (AI growing ~3× faster than baseline wireless traffic), while 5G deployment benchmarks—densification, spectrum efficiency gains, and uplink enhancements—provide the immediate capacity headroom needed before 6G’s AI-native architecture arrives in the 2029–2030 window.
Traffic Growth and AI Load Forecasts (CTIA 2026):
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2025 data usage: 159.3 trillion MB, +20% YoY and ~60% over two years; more than the entire 4G decade.
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AI traffic trajectory: AI-related flows are expanding ~3× faster than traditional wireless traffic and are projected to reach ~30% of all broadband traffic by 2034.
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Aggregate demand: Total data demand is expected to grow ~4× by 2032, intensifying pressure on RAN, backhaul, and edge compute.
These CTIA figures align with independent vendor forecasts (e.g., Nokia Bell Labs) that place AI at ~30% of wide-area traffic by 2034, with symmetrical bandwidth needs and upload CAGRs near 13%—a material shift from today’s ~87:13 downlink/uplink split.
5G Deployment Benchmarks Relevant to AI Loads:
6G Readiness: Standards, Architecture, and Spectrum:
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Standards timeline: 3GPP Release 20 (study phase) runs through 2027; Release 21 delivers first normative 6G specs with functional freezes in Dec 2028 and implementable code by Mar 2029; first commercial systems expected 2029–2030.
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IMT-2030 framework: ITU-R finalized 20 minimum technical performance requirements across six usage scenarios, including AI & Communication and Integrated Sensing & Communication (ISAC).
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Performance targets: Peak data rates 50–200 Gbps, user-experienced rates 300–500 Mbps+, and 1.5–3× spectral efficiency vs. IMT-2020 (5G).
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Spectrum needs: 6G requires large, contiguous sub-8 GHz mid-band and upper mid-band blocks with 100–400 MHz channel bandwidths; industry calls for proactive policy to secure bands (e.g., 2.7/7 GHz) with auctions by 2028.
What 5G Benchmarks Cover vs. What 6G Must Add:
Practical Takeaway for Network Planners:
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Near term (2026–2028): Lean into 5G-Advanced (Rel-19) features—AI schedulers, uplink MIMO, centralized spectrum allocation—to capture 10–30% SE gains and 20–40% uplink improvements that directly absorb AI traffic growth.
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Mid term (2027–2029): Align spectrum strategy with 6G’s channel-bandwidth needs (100–400 MHz) and prepare transport/edge for symmetrical, low-latency AI flows; monitor 3GPP Rel-21 freezes (2028–2029).
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Long term (2030+): Design for AI-native operations (distributed inference, ISAC, ubiquitous connectivity) as AI approaches ~30% of broadband traffic, ensuring RAN, core, and data-center interconnect scale together.
Perplexity.ai Sources: CTIA 2026 Annual Survey (Sept. 9, 2026); ITU-R IMT-2030 framework and 3GPP Release 20/21 timelines; vendor trials on 5G SA spectral efficiency and AI schedulers; industry forecasts on AI traffic share by 2034.
References:
https://www.telecoms.com/5g-6g/us-mobile-data-growth-slowed-in-2025
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
CTIA commissioned study: U.S. running out of licensed spectrum; 5G FWA to be impacted first by network overloads
CTIA Announces 5G Security Test Bed for Commercial 5G Networks
Highlights of CTIA’s 2021 Annual Wireless Industry Survey
Google’s TPU Business Outpaces Rivals as Hyperscalers Accelerate Custom AI Silicon Strategies
Executive Summary:
Google parent Alphabet’s emerging business of selling artificial intelligence (AI) accelerator chips is twice as large as a cloud computing rival, Google executive Thomas Kurian claimed Tuesday at a Goldman Sachs investors conference.
On July 22, Google reported second-quarter cloud-computing revenue of $24.77 billion, up 82% year over year, driven by artificial intelligence workloads, handily beating estimates of $22.46 billion. For the first time, Google included third-party sales of AI accelerator chips, called tensor-processing units, in cloud revenue.
Kurian, head of Google’s cloud business, made these remarks at Goldman Sachs’ Communacopia conference:
“We offer the best computational infrastructure for AI, and we offer 3 types of silicon. NVIDIA GPUs, our own Tensor Processing Units (TPUs), custom ARM silicon. [As AI models generate code awe also offer our own Arm processors to run that code.] We offer 2.7x better price performance for training, 80% better price performance for inference, 30% better price performance for CPUs. All of that allows us to differentiate our portfolio from other providers. It allows us to offer solutions to financial markets and capital markets.”
“The size of our accelerator business, our TPU business, is more than twice that of the next-largest hyperscaler.”
“Our platform is called Gemini Enterprise. It is used by over 90% of the Fortune 100 and thousands of small businesses. It’s used in a very specific way. People want to use it as a reasoning agent. So break the plan, understand the steps that are needed, reason on it and execute the steps. So it uses a reasoning agent to understand all the information in the company to then automate that workflow process. And when it does it, you want strong controls. What kinds of controls? Companies are worried about security. They’re worried about auditing, what these agents are doing. They want to manage costs and set budget caps. We have all those controls. And we allow people to use the right model for the right task. So you don’t have to always use the most expensive model, saving people a lot of money in doing so. We have a range of companies from insurance.”
–>You can read the entire transcript here. For more on Google’s TPUs please see:
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Google’s TPU photo
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Kurian added that Google monetizes TPU systems through three business models. One is letting companies rent TPU processing at its own cloud business. Also, Google sells TPU systems directly for deployment in customers’ data centers. One such customer is Anthropic. Third, Google also sells TPUs through a Blackstone cloud computing joint venture.
Google’s AI Chip Business:
In May, Google introduced Ironwood, its eighth-generation of TPUs. The Ironwood TPUs target both training of AI models and “inferencing” — processing AI workloads.
In a report published Aug. 24, Morgan Stanley analyst Brian Nowak estimated that Google cloud could garner $84 billion in “first party” — meaning non-cloud rental — TPU sales in 2027.
“We are raising our TPU sale estimates to $27 billion selling at a 30% gross margin,” Nowak said. “In all, we now expect Google to sell 0.3 gigawatts of TPU systems in the second half of 2026, 3.2 gigawatts in 2027 and 4.2 gigawatts in 2028. This translates into $84 billion/$108 billion of TPU-related Google cloud revenue in 2027 and 2028.”
In Q2-2026, Google said its cloud computing order backlog jumped to $514 billion, up from $460 billion in Q1. The backlog is converted into realized revenue as new data centers come online and crunch artificial intelligence-related workloads — training AI models and processing AI apps.
Google has increased its 2026 capital spending guidance to a range of $195 billion to $205 billion. Most of the spendings is going toward AI data centers and AI model development. In Q2, capital spending jumped 100% from a year earlier to $44.9 billion.
Kurian, a former top executive at Oracle, took over as the cloud-computing unit’s CEO in November 2018. When Kurian arrived, Google’s cloud customers were mostly other tech companies. Under Kurian, Google has targeted enterprise customers with cloud-based data-analytics and artificial intelligence tools.
Hyperscaler Custom Silicon: Meta, Microsoft, Oracle, and the Shift to In-House AI Accelerators:
While Google’s TPU business has reached a scale that Kurian says is more than twice that of the next-largest hyperscaler, other cloud and platform operators are rapidly expanding their own custom AI silicon programs to reduce dependence on Nvidia GPUs and optimize cost, power, and workload-specific performance.
Amazon.com has developed in-house Trainium AI accelerators while Microsoft has developed Maia AI chips. Amazon is further ahead than Microsoft in selling AI chips to outside customers, analysts say.
Meta – MTIA Family Targets Inference at Scale:
Meta has moved aggressively into custom silicon with its Meta Training and Inference Accelerator (MTIA) family, announcing four new chips — MTIA 300, 400, 450, and 500 — in March 2026 as part of a strategy to diversify hardware sources and lower AI infrastructure costs. The MTIA 300 entered production in mid-2026, with subsequent generations rolling out on an approximately six-month cadence through 2027.
Meta’s MTIA chips are manufactured by TSMC and co-developed with Broadcom under a multi-year partnership extending through 2029. The roadmap spans ranking and recommendation training (MTIA 300), combined generative AI and ranking workloads (MTIA 400), and decode-optimized generative AI inference (MTIA 450 and 500), with mass deployment of the flagship MTIA 500 planned for late 2027. Meta plans to put its own AI chip into production in September and is aiming to roughly double the computing capacity across its data centres.
By mid-2026, Meta, Amazon, Microsoft, and OpenAI have each closed the gap on the three key AI inputs — custom chips, power, and models — that only Google held in 2021.
Microsoft: Maia 200 and the Push to External Customers:
Microsoft unveiled its first custom AI accelerator, Maia 100, at Hot Chips 2024, followed by the inference-optimized Maia 200 in January 2026. Maia 200, built on TSMC’s 3 nm process with more than 140 billion transistors, 216 GB of HBM3e, and over 10 PFLOPS of FP4 compute within a 750 W SoC TDP, is designed to deliver 30% better performance per dollar for AI token generation.blogs.
Maia 200 will serve multiple models, including OpenAI’s GPT-5.2, and support Microsoft Foundry, Microsoft 365 Copilot, and reinforcement learning workflows. Microsoft plans to unveil next-gen Maia 300 AI chip in September, aiming to lower costs for in-house and OpenAI models while actively courting major enterprise customers. Anthropic is reportedly in talks with Microsoft to rent the company’s custom AI server chips as it looks to expand computing capacity.blogs.
Oracle: Partner-Led AI Clusters Rather Than Custom Silicon:
Oracle has taken a different path, opting not to develop its own AI accelerator but instead building large-scale AI clusters using third-party chips from Nvidia and AMD. Oracle plans to install the first MI450-equipped Helios racks in its OCI data centers during the third quarter of 2026, with an initial deployment targeting 50,000 MI450 processors.
Oracle’s AI strategy emphasizes rapid deployment of massive GPU-based clusters to serve anchor tenants like OpenAI under a reported $300 billion, five-year cloud computing contract beginning in 2027. In parallel, OpenAI is diversifying its supply of compute by designing its own chips with partners like Broadcom, with the first custom AI inference chips expected to deploy in the second half of 2026.
Market Implications:
By 2026, five of six major AI players — Google, Meta, Amazon, Microsoft, and OpenAI — now control at least two of the three critical AI inputs (chips, power, models), down from only Google in 2021. This vertical integration trend is reshaping the AI infrastructure market, with hyperscalers increasingly using custom silicon to optimize cost and performance for specific workloads while maintaining strategic flexibility through multi-vendor GPU procurement.
Google’s TPU v7, Amazon’s Trainium 3, Microsoft’s Maia 2, and Meta’s MTIA 2 all ramped into volume production in 2025–2026, signaling a maturation of the hyperscaler custom silicon ecosystem. Meta is also the first commercial gigawatt AMD MI450 deployment in H2 2026, illustrating a hybrid approach that combines in-house accelerators with third-party GPUs.
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References:
https://www.investors.com/news/technology/google-stock-cloud-kurian-ai-chip-business/
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Google announces Gemini: it’s most powerful AI model, powered by TPU chips
Google Cloud and Verizon Expand Strategic Partnership to Scale Enterprise AI and Autonomous Network Operations
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Meta’s “Iris” AI Chip for MTIA: Implications for Telecom-Grade Optical Networking, DCI and High Capacity Ethernet Fabrics
Custom AI Chips: Powering the next wave of Intelligent Computing
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
South Korean startup Rebellions to use open source software for carriers to quickly build AI stacks with its AI inferencing chips
European network operators in talks to form consortium to bid for EU satellite spectrum and provide D2M service
Introduction:
Four of Europe’s largest mobile network operators—Deutsche Telekom, Orange, Vodafone Group, and Telefónica—are in preliminary discussions to form a consortium that would jointly bid for EU-reserved satellite spectrum and launch a direct-to-mobile (D2M) service, according to a Bloomberg report cited by Reuters and other news outlets.
The EU’s planned 2 GHz mobile-satellite service (MSS) assignment is being structured as a three-way split of the band—one-third for government/IRIS² use, one-third reserved for EU-controlled commercial operators, and one-third open to international bidders.
3GPP Release 17 and Release 18 provide the standardized radio and protocol baseline for direct-to-mobile (D2M) integration in bands n255/n256 (and n254). Those specs are not yet approved ITU-R SG 4 (WP 4b & 4c) recommendations.
The EU’s planned 2 GHz mobile-satellite service (MSS) assignment is being structured as a three-way split of the band—one-third for government/IRIS² use, one-third reserved for EU-controlled commercial operators, and one-third open to international bidders—while 3GPP Release 17 and Release 18 provide the standardized radio and protocol baseline for direct-to-mobile (D2M) integration in bands n255/n256 (and n254).
Parabolic antenna and satellite dishes in Girona, Spain. Photographer: Angel Garcia/Bloomberg
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Key data points:
Mid‑2026 U.S. Carrier Ethernet Market: VSG Leaderboard, Standards, Service Mix and RFPs
Executive Summary:
Vertical Systems Group’s (VSG’s) mid‑2026 U.S. Carrier Ethernet LEADERBOARD ranks providers by retail port share as of June 30, 2026. Seven network operators met the 4% threshold for Leaderboard status: AT&T; Verizon (including Frontier); Lumen; Spectrum Business; Comcast Business; Zayo (including Crown Castle); and Cox Business.
- Verizon’s acquisition of Frontier significantly expanded its fiber network, elevating its market position to #2 in the U.S. Carrier Ethernet rankings.
- Zayo advanced to the leaderboard after acquiring Crown Castle and plans to add 15,000 route miles by 2030, strengthening its long-haul network.
Six companies attained a Challenge Tier citation (in alphabetical order): Cogent, Granite, GTT, Lightpath, Segra, and Uniti. The Challenge Tier includes providers with between 1% and 4% share of the U.S. retail Ethernet market.
VSG’s methodology measures billable retail customer ports and segments the market into six service categories that map closely to MEF‑defined Carrier Ethernet service types: Ethernet DIA (Dedicated Internet Access), E‑Access to IP/MPLS VPN, Ethernet Private Line (EPL), Ethernet Virtual Private Line (EVPL), Metro LAN, and WAN VPLS.
U.S. Ethernet Market Analysis: Mid-2026:
- AT&T keeps its #1 rank on the Mid-Year 2026 U.S. Carrier Ethernet LEADERBOARD.
- Rankings change for four of the seven Ethernet LEADERBOARD providers.
- Verizon moves up from the #4 position to the #2 rank with its completed acquisition and integration of Frontier assets.
- As a result, Lumen dips from the #2 position to #3. Spectrum Business drops from #3 into the #4 position.
- Zayo moves up into the LEADERBOARD at the #6 position from the Challenge Tier due to its acquisition of Crown Castle, which was completed in May 2026.
- Cox Business falls into the #7 and final position. The acquisition of Cox by Spectrum’s parent company, Charter Communications, was completed in August 2026 and is not included in the mid-year results.
- The Challenge Tier for mid-2026 includes six companies: Cogent, Granite, GTT, Lightpath, Segra (including UPN) and Uniti (includes Windstream). Segra enters the Challenge Tier, moving up from the Market Player tier.
- DIA (Dedicated Internet Access) is the top Ethernet service in the U.S. based on both ports and revenue.
- Enterprise customers are shifting from lower speed Ethernet private lines to more robust SD-WAN and SASE services.
- Three LEADERBOARD wireline network operators have attained Mplify Carrier Ethernet certification: AT&T, Lumen and Verizon.
The Market Player tier includes all providers with port share below 1%. Companies in the Market Player tier include the following providers (in alphabetical order):ACD, AireSpring, Alaska Communications, Alta Fiber, American Telesis, Arelion, Armstrong Business Solutions, Astound Business, Bluebird Network (includes Everstream), Breezeline, Brightspeed, BT Global Services, Centracom, Conterra, Douglas Fast Net, DQE Communications, Epsilon, Exa Infrastructure, ExteNet Systems, Fatbeam, FiberLight, Fidium, First Digital, FirstLight, Flo Networks, Fusion Connect, GCI, GCX, Glo Fiber, Hunter Communications, Intelsat, Logix Fiber Networks, LS Networks, MetTel, Midco, Momentum Telecom, NTT, Orange Business, Pilot Fiber, PS Lightwave, Rightfiber (includes Great Plains and Ritter), Silver Star Telecom, Sparklight Business, Syringa, T-Fiber (includes MetroNet, Lumos, U.S. Internet), Tata Communications, TDS Telecom, TPx, US Signal, WOW!Business, Ziply Fiber (now owned by Bell Canada) and other companies selling retail Ethernet services in the U.S. market.
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Standards Context: MEF Carrier Ethernet, IEEE 802, and ITU-T:
Carrier Ethernet services are standardized by MEF (formerly the Metro Ethernet Forum), which defines service types, attributes, and performance objectives across multi‑operator domains. MEF 2.0/3.0 specifications formalize E‑Line (point‑to‑point), E‑LAN (multipoint), E‑Tree (rooted multipoint), and E‑Access (inter‑provider) services, with strict OAM, QoS, and bandwidth profile requirements.
At the physical and link layers, IEEE 802.3 Ethernet PHYs and 802.1Q VLAN tagging underpin these services, while MEF service definitions (EVCs, UNIs, ENNIs) provide the operator‑grade abstraction needed for SLAs, service multiplexing, and interconnection. In practice, “Metro Ethernet” offerings by U.S. carriers are implementations of MEF E‑Line/E‑LAN service types over diverse transport (packet optical, MPLS, IP/MPLS, or Ethernet switching fabrics), with port‑based or VLAN‑based handoffs at the customer UNI.
Metro Ethernet Service Types: What the Market Segments Map To:
Vertical’s six service segments align with canonical Carrier Ethernet service types as follows:
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Ethernet DIA (Dedicated Internet Access): Typically delivered as an E‑Line service (EPL or EVPL) to an internet gateway, with asymmetric or symmetric bandwidth profiles and strict availability targets. DIA emphasizes internet reachability rather than private L2 connectivity, but the underlying UNI/EVC constructs remain MEF‑compliant.neosnetworks+1
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E‑Access to IP/MPLS VPN: An inter‑provider or backhaul construct where a carrier Ethernet E‑Line/E‑Access link connects an enterprise edge or another operator to an IP/MPLS core. This maps to MEF E‑Access (inter‑carrier) with ENNI handoffs and is common for wholesale Ethernet backhaul and multi‑operator VPN aggregation.mplify+1
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Ethernet Private Line (EPL): A port‑based, point‑to‑point E‑Line service with one EVC per UNI and near‑transparent frame delivery. EPL is the canonical “private line” for low‑latency, high‑certainty circuits between two sites, often used for data‑center interconnect or critical backhaul.
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Ethernet Virtual Private Line (EVPL): A VLAN‑based E‑Line service that supports multiple EVCs on a single UNI via service multiplexing. EVPL enables point‑to‑point and point‑to‑multipoint topologies over shared access ports, improving port utilization and supporting multi‑service bundles (e.g., LAN + internet + VoIP) on one physical handoff.Metro LAN: A multipoint‑to‑multipoint E‑LAN service (sometimes called Ethernet Virtual Private LAN) that provides any‑to‑any Layer 2 connectivity among three or more sites. Metro LAN is the carrier analogue to VPLS and is widely used for campus interconnect, distributed enterprise LAN extension, and L2‑based application clustering.
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WAN VPLS: A wide‑area VPLS instance delivering E‑LAN semantics across broader geographies, typically over MPLS cores. While MEF 3.0 emphasizes E‑LAN service attributes, many operators still market VPLS as the transport realization of E‑LAN for multi‑site Layer 2 domains.
The most important ITU‑T recommendations for Carrier Ethernet fall into three buckets: service definitions, OAM/fault & performance management, and protection/activation testing. Together they provide the operator‑grade framework that maps MEF service types (E‑Line, E‑LAN, E‑Tree, E‑Access) to measurable SLAs and resilient transport.itu+2
Core service definitions (what the service is):
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G.8011/Y.1307 – Ethernet service framework
Defines the overall framework for Ethernet services (EVC/OVC/UNI/ENNI), service attributes, and OAM bindings. It is the ITU‑T umbrella that aligns with MEF service types and underpins EPL, EVPL, E‑LAN, E‑Tree, and E‑Access definitions. -
G.8011.1/Y.1307.1 – Ethernet Private Line (EPL)
Specifies EPL service attributes and parameters (port‑based, single EVC per UNI, bandwidth profiles, transfer characteristics). This is the canonical “private line” for point‑to‑point, low‑latency circuits.itu+1 -
G.8011.2/Y.1307.2 – Ethernet Virtual Private Line (EVPL)
Specifies EVPL service attributes (VLAN‑based, service multiplexing on a UNI, CIR/EIR/CBS/EBS). EVPL enables multiple services over one physical handoff while preserving SLA granularity. -
G.8011.3/Y.1307.3 – Ethernet LAN (E‑LAN)
Defines multipoint‑to‑multipoint E‑LAN service attributes (any‑to‑any L2 connectivity among ≥3 UNIs), the carrier analogue to VPLS and widely used for Metro LAN.scribd+1 -
G.8011.4/Y.1307.4 – Ethernet Virtual Private Rooted Multipoint (E‑Tree / EVPRM)
Defines rooted multipoint (E‑Tree) service attributes for hub‑and‑spoke topologies (e.g., video distribution, wholesale access). -
G.8011.5/Y.1307.5 – Ethernet Access (E‑Access)
Specifies inter‑provider Ethernet access service attributes (ENNI‑based) used for wholesale backhaul and multi‑operator service chaining
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Market Implications for Network Architects:
The 2026 VSG leaderboard reflects consolidation and asset integration (e.g., Verizon’s move to #2 following Frontier integration), with cable MSOs and fiber specialists maintaining strong positions in metro and regional segments. For architects specifying Carrier Ethernet, the practical choice among EPL, EVPL, E‑LAN, and VPLS hinges on three factors: topology (P2P vs. multipoint), service multiplexing needs at the UNI, and the degree of L2 transparency versus managed L3 VPN requirements.
When drafting RFPs or architecture documents, reference:
- G.8011.x service types alongside MEF E‑Line/E‑LAN/E‑Tree/E‑Access to ensure interoperable, SLA‑backed procurement.
- MEF 2.0/3.0 service types (E‑Line/E‑LAN/E‑Access) and require conformance to MEF service attributes (bandwidth profiles, frame delay/jitter/loss, OAM).
That ensures that “Metro Ethernet” procurement aligns with deployable, interoperable, SLA‑backed services rather than vendor‑specific marketing terms.
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References:
https://verticalsystems.com/2026/09/02/mid-2026-us-carrier-ethernet-leaderboard/
https://verticalsystems.com/methodology/
https://neosnetworks.com/resources/blog/epl-vs-evpl/
https://www.itbroker.com/resources/glossary/metro-ethernet-transport
Carrier Ethernet Market Assessment and MEF 3.0 Certification
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VSG LEADERBOARD : AT&T #1 in Fiber Lit Buildings- Year end 2020
AT&T tops VSG’s U.S. Carrier Managed SD-WAN Leaderboard for 4th year
Lumen Technologies tops Vertical Systems Group’s 2021 U.S. Wavelength Services Leaderboard
