Dell’Oro: 6G RAN Capex to reach $500 billion by 2034 + Counterpoint
According to a recent Dell’Oro Group report, global telecom operators will spend $500 billion on 6G infrastructure over the next decade. During that same period, the overall network equipment market is projected to grow at just a 1% CAGR. Telco revenues are expected to grow at 3% over the next decade. The report’s base-case scenario envisions 6G as an evolutionary technology that builds on Massive MIMO, the existing site grid, and wider channel bandwidths to deliver step-change improvements in RAN economics.
“While the G decoupling movement is gaining momentum for all the right reasons, the most likely scenario is still that 6G will be another G, with 6G RAN capex expected to accelerate toward the end of the decade,” said Stefan Pongratz, Vice President of RAN and Telecom Capex Research at Dell’Oro Group. “At the same time, operators are in a much stronger position today from a network capacity perspective than they were during the transition from 4G to 5G. As a result, cumulative 6G RAN revenue during the first six years of the cycle is projected to be 10 to 20 percent lower than during the comparable period of the 5G cycle.”
Additional highlights from the June 2026 6G Advanced Research Report include:
- 6G RAN is expected to scale rapidly, with cumulative RAN revenue and wireless capex during the first six years projected to exceed $100 B and $500 B, respectively.
- 6G is not expected to expand the overall RAN market. Instead, the baseline scenario projects the broader RAN market to grow at a 1 percent CAGR between 2030 and 2034.
- Both Sub-7 GHz and cmWave spectrum bands are expected to play important roles in 6G deployments, although momentum behind spectrum above 7 GHz continues to build.
- Cumulative 6G RAN investments between 2029 and 2034 are projected to account for approximately half of total RAN capex during the same forecast period.
Dell’Oro Group’s 6G Advanced Research Report offers an overview of the RAN market, including tables showing total RAN revenue by technology (2G-6G) from 2000 to 2034. 6G RAN is analyzed by spectrum (Sub-7 GHz, cmWave, mmWave), by Massive MIMO, by RF Power (Macro, Micro, Pico), and by region (North America, Europe, Middle East and Africa, China, Asia Pacific Excl. China, and CALA). To purchase this report, please contact us by email at [email protected].
Editor’s Counterpoint:
We don’t agree with Stefan’s statement that “6G RAN is expected to scale rapidly,” based on the standards and specification delays and failures of ITU-R’s IMT 2020 (5G) and 3GPPs 5G SA core network specifications. A phased ramp is more likely, as early commercial launches begin around 2031, then slower scaling through the early 2030s as spectrum, devices, infrastructure, and business cases mature. At this point in time we have no idea what the IMT 2030 RIT/SRITs will be or 3GPPs 6G Core network functionality.
ITU WP5D’s IMT-2030 work is still setting requirements and evaluation criteria, and candidate RIT submissions are only expected in the 2027–2029 window; that points to a standards process that finishes just as first deployments begin, not one that guarantees immediate mass rollout. The ITU also says the technical performance requirements are minimum levels for consideration and do not guarantee real-world deployment performance. In other words, approval creates the foundation, not instant scale.
What’s needed by 2030: globally harmonized work, spectrum studies across low, mid, mmWave, and sub-THz bands, and network operator/vendor roadmaps. Once those crystalize they could support fast 6G uptake in premium pockets such as dense urban zones, enterprise campuses, and fixed wireless/edge-centric use cases where the economics are strongest. That means “rapid” is plausible in targeted launches, not across entire national footprints.
6G will likely face the same structural constraints that slowed 5G: spectrum availability, device ecosystems, deployment costs, and the need to integrate with 5G-Advanced during the transition time period. Higher-band operation, especially above 100 GHz, is technically feasible but still requires mature propagation, hardware, and deployment architectures before it can scale widely. The result is usually a long coexistence period where 5G remains the coverage layer while 6G expands selectively.
The likely pattern is “launch first, scale later,” with meaningful expansion depending more on spectrum policy, device availability, and operator ROI than on the standard approval itself.
Deployment timeline
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2026–2027: Standards and requirements work intensifies, with 3GPP/ITU alignment still being shaped and operators pushing for realistic deployment timelines.
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2028–2029: Pre-commercial and early pilot networks appear, especially in dense urban, enterprise, and testbed environments.
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2030: First commercial 6G launches are widely expected, but these will be selective rather than universal.
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2031–2033: Main capex ramp and larger-scale rollout window, as more of the macro grid, transport, and edge layers are upgraded.
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2034 and beyond: Broader geographic expansion and more mature multi-band coverage, with 6G taking on a larger share of traffic and enterprise use cases.
Capex outlook
Dell’Oro’s view that the 6G capex ramp starts around 2030 (we think it will be 2031), while cumulative 6G RAN investment in 2029–2034 could account for 55% to 60% of total RAN capex in that period. A separate data-driven forecast argues global 6G capex could land somewhere in the sub-$1 trillion to about $1.5 trillion range over a decade, depending on traffic growth and spec assumptions. That is a wide band, but it is consistent with a technology transition that reuses much of the existing macro grid rather than replacing everything at once.
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Sebastian Barros wrote:
6G will deliver necessary operational improvements. It will utilize new 7GHz spectrum, improve radio performance, and lower the cost per bit for operators still recovering from the 5G capex hangover. However, consumers and enterprises do not pay a premium for faster pipes. Connectivity is now a hyper-commoditized utility.
If Telcos want to capture value in this new economy, they must stop defining their core business as “connectivity.” Telecom is a distribution business.
Networks serve as the last-mile delivery system for the global economy. In the past, the industry distributed voice, SMS, and 8K video. Today, the asset being distributed is intelligence. Hyperscalers are building massive, centralized AI data centers, but that compute power requires a physical delivery mechanism to reach users.
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References:
Current 6G Trajectory is Evolutionary, According to Dell’Oro Group
Ookla: AI platform reliability decreases as outages surge
So you thought “AI Hallucinations” were the only big problem with AI performance? Think again! In a new Ookla reliability report, data from its Downdetector reveals that AI platform outages surged from 6 high-disruption days in Q1 2025 to 51 in Q1 2026 , as AI tools transitioned from novelties to critical business infrastructure. These disruptions stem from rapid scale-up volatility, cloud provider failures, and complex, agentic workflows. Analysing 471 days of US Downdetector data from 1 January 2025 to 16 April 2026 across ChatGPT, Claude, Gemini, Microsoft Copilot, AWS and Microsoft Azure, Ookla recorded 3.7 million user-reported problems.
High-signal disruption days, defined as when a service recorded more than 10 times its own median daily report volume, rose from six across four major AI apps in Q1 2025 to 51 in Q1 2026, according to the report by Ookla analyst Luke Kehoe.
Anthropic’s Claude model accounted for 39 of those 51 disruption days. Gemini accounted for seven, Copilot three and ChatGPT two. Here’s a summary:
- Claude: Anthropic’s platform was the clearest example of scale-up volatility, accounting for 39 of the 51 high-signal disruption days in early 2026 due to rapid adoption and scaling.
- ChatGPT: While it generated some of the largest raw disruption spikes—often linked to model updates or demand surges—its median daily report trend improved compared to the prior year.
- Microsoft Copilot: Outage reports heavily clustered on weekdays, reflecting its core integration into enterprise business workflows rather than consumer use.
- Gemini: Incidents rose to seven alongside expanding user adoption.
- Cloud Infrastructure: A significant portion of AI downtime wasn’t the AI model itself, but outages at the cloud level that caused cascading failures. AWS’s 20 October 2025 DynamoDB DNS event generated more than 315,000 US disruption reports, while Microsoft’s Azure Front Door incident on 29 October produced nearly 96,000, illustrating how failures in cloud control planes can cascade into AI platform disruptions.
Claude’s growth over the past 12 months was accompanied by significant disruption. Ookla describes it as “the clearest example of scale-up volatility,” with disruptions to its offering starting to move the needle in July last year as adoption rose. There’s a hint that the upward trajectory will continue – Ookla notes that at 2,830 daily reports on average, Claude’s report volume in March was three times that it recorded in February.
AI reliability now spans multiple failure layers:
AI platforms are not single systems from the user’s point of view, even when they present a single interface. A ChatGPT, Claude, Gemini, or Copilot failure can sit in the product layer, the provider orchestration layer, the hyperscaler layer, or the edge and access layer. The product layer is what users actually see. The provider orchestration layer includes login, routing, model selection, rate limits, feature flags, inference scheduling, retry behavior, and capacity allocation. The hyperscaler layer includes compute, databases, storage, networking, and regional control planes. The edge and access layer includes DNS, web gateways, bot protection, content delivery, and authentication flows.
Ookla’s Kehoe wrote, “As AI systems move from short chat sessions into longer-running agentic tasks, a failed prompt, login loop, stalled code task, unavailable file, or broken connector can interrupt work that now sits inside real business processes.” This is a very serious concern!
Those layers are not always owned by different companies, and they are not the full physical internet stack. Network operators, subsea cables, data centers, and user access networks still matter. The focus here is narrower: the service and dependency layers that are most visible in Downdetector data and public incident records.
This distinction is important because the same user-facing symptom can have different operational meanings. A failed prompt, login loop, missing chat history, rate-limit error, unavailable file, or stalled agent task may not share the same root cause. For enterprise buyers and risk teams, resilience is about understanding more than whether an AI platform was simply available. They need to know where the issue occurred, which workflows were affected, and whether it reflected a problem with a single provider or a broader dependency across the AI stack.
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References:
https://www.ookla.com/articles/ai-platform-reliability
https://www.mobileworldlive.com/ai-cloud/ookla-finds-ai-platform-outages-surge-as-adoption-grows
https://www.telecoms.com/ai/ai-app-disruption-is-on-the-up
Will 2026 be the “Year of the AI Ontology” for telecoms?
Non-Terrestrial Networks (NTN) Tutorial: Architecture, Spectrum, and Technical Foundations
by Paresh Panchal, Principal Engineer – Charter Communications
Abstract:
Several Non-Terrestrial Network (NTN) related articles have appeared on the IEEE ComSoc Techblog over the past year. They include: Alan J Weissberger’s market overview (December 2025), the Keysight/Samsung frequency band n252 demonstration (January 2026), the Telecoms.com survey summary (July 2025), and the enterprise IoT hybrid-network article (January 2026). These contributions provide useful market context and early deployment perspective, but they do not fully address the engineering considerations that determine how an NTN system is actually designed, dimensioned, and deployed.
Importantly, they do not examine the 3GPP Release 18 NTN architecture options (A1–A4), which define key implementation choices for operator and satellite network integration. They also do not analyze NTN band planning and its regulatory variability across CEPT and FCC jurisdictions, or the propagation-delay effects that must be accounted for in HARQ timing, scheduling, and other RAN procedures. These issues are central to practical deployment planning and to the selection of an appropriate NTN architecture for a given use case.
This article fills that gap by providing a practitioner-oriented technical reference that complements the existing market-level coverage with engineering detail, e.g. NTN deployment options, spectrum applicability, and protocol-level implications. It is intended to serve as a practical guide for engineers and network planners assessing NTN architecture, spectrum strategy, and protocol behavior in real deployment scenarios.
Read my article at https://wireless-vector.com/ntn-article
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About the Author:
Paresh is a wireless communications professional with deep expertise in RAN systems and architecture, network design, performance engineering, and network analytics. He’s been an active contributor to radio access network innovation with deep expertise in 5G/4G/CBRS RF design and optimization, specializing in cloud-native and O-RAN environments. Proven track record across multi-vendor, multi-country engagements covering greenfield and commercial networks. Core competencies span RF network modeling, performance analytics, and cross-functional program execution. Inventor with 25+ patent applications in radio network technologies.
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References:
Non-Terrestrial Networks (NTNs): market, specifications & standards in 3GPP and ITU-R
Keysight Technologies Demonstrates 3GPP Rel-19 NR-NTN Connectivity in Band n252 (using Samsung modem chip set)
Telecoms.com’s survey: 5G NTNs to highlight service reliability and network redundancy
ITU-R recommendation IMT-2020-SAT.SPECS from ITU-R WP 5B to be based on 3GPP 5G NR-NTN and IoT-NTN (from Release 17 & 18)
China ITU filing to put ~200K satellites in low earth orbit while FCC authorizes 7.5K additional Starlink LEO satellites
Samsung announces 5G NTN modem technology for Exynos chip set; Omnispace and Ligado Networks MoU
Analysis and Implications of Deutsche Telekom’s potential full acquisition of T-Mobile
Deutsche Telekom is Europe’s largest telecommunications carrier with 273 million mobile customers in 50 countries. The German telco is reportedly considering a move to take full ownership of T-Mobile US by raising its stake from roughly 54% to 100%, thereby converting the publicly traded U.S. carrier into a wholly owned subsidiary. This potential merger/acquisition would represent a significant strategic and corporate restructuring within the global telecommunications landscape and would mark the consolidation of one of the largest wireless operators under a single parent entity.
During his 12-year tenure as Deutsche Telekom’s CEO Tim Höttges has helped turn T-Mobile from a money-losing underdog to the world’s most valuable telecom brand by market capitalization. Höttges has invested billions in T-Mobile’s U.S. infrastructure to support its 5G fixed-wireless home-internet product. T-Mobile contributes nearly two-thirds of Deutsche Telekom’s revenue.
Analysts say T-Mobile is currently constrained on large deals: Its high leverage makes borrowing expensive, and it can’t issue stock without diluting its German parent’s stake. By combining Deutsche Telekom and T-Mobile into a single $300 billion behemoth, the company would likely be able to raise debt at a lower cost, among other benefits. T-Mobile has been buying fiber-internet operators in the U.S. to compete with AT&T and Verizon and offer bundled wireless and home internet to more customers.
Deutsche Telekom CEO Höttges addressing shareholders at the firm’s annual meeting in April. Oliver Berg/
From a corporate strategy perspective, full ownership would provide Deutsche Telekom with complete control over T-Mobile US’s capital allocation, operational priorities, and long-term network investment strategy. Currently, while Deutsche Telekom exercises effective control through its majority stake, a 100% acquisition would eliminate minority shareholders, simplify governance structures, and allow the parent company to internalize the full economic value generated by T-Mobile US’s operations. This could be particularly relevant as the U.S. market continues to drive substantial cash flow and growth relative to Deutsche Telekom’s European operations.
The transaction would also have implications for corporate structure and financial reporting. Full ownership would enable Deutsche Telekom to restructure T-Mobile US within its corporate hierarchy, potentially integrating it more closely with other group entities or aligning its financial reporting more directly with parent-company objectives. Such consolidation could improve transparency for investors and reduce the complexity associated with managing a majority-owned public subsidiary.
However, executing a deal of this magnitude would present substantial challenges. The transaction would likely require extensive regulatory review in both the United States and Europe, including scrutiny from the Federal Communications Commission, the Department of Justice, and European competition authorities. Valuation would be a critical consideration, given T-Mobile US’s market position as the second-largest wireless carrier in the United States and its ongoing investments in 5G infrastructure, network modernization, and enterprise services. Financing the acquisition would also require careful consideration of debt levels, capital structure, and the impact on Deutsche Telekom’s balance sheet.
From a market perspective, the potential merger could be viewed as a consolidation move that reflects the increasing importance of the U.S. wireless market in global telecommunications strategy. T-Mobile US has emerged as a competitive leader in recent years, with strong performance in 5G deployment, spectrum efficiency, and customer acquisition. Full ownership would enable Deutsche Telekom to align these strengths more closely with its broader global strategy, potentially accelerating technology transfer, network architecture harmonization, and cross-border service integration.
Höttges has also put billions into expanding the German network, where fiber-internet subscribers have nearly tripled since 2023. He champions a cause popular with European regulators: tech sovereignty, or reducing reliance on American and Chinese technology. In February Deutsche Telekom opened Germany’s first AI gigafactory, a massive data center. The gigafactory uses AI GPU chips from Nvidia, which is of course an American company, based in Santa Clara, CA.
The CEO plans to retire at the end of 2028 and wants the right successor to be found first, said people familiar with the matter. He said on the German “OMR” podcast that his successor will need a different skill set. Artificial intelligence (AI) is overhauling the workforce and automating next-generation networks, transforming the industry at an astonishing pace. “Back then, a sober numbers guy was the right choice,” he said. “Today, I believe we need a visionary who understands the future architecture of modern infrastructure.”
In summary, Deutsche Telekom’s reported interest in acquiring full ownership of T-Mobile US represents a significant strategic consideration that would consolidate corporate control, simplify governance, and potentially enhance the parent company’s ability to capture the full financial benefits of its U.S. operations. While the strategic rationale is compelling, the transaction would entail substantial regulatory, financing, and valuation complexities that would need to be carefully addressed before any definitive agreement could be reached.
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References:
https://www.telekom.com/en/company/companyprofile/company-profile-625808
https://www.wsj.com/business/telecom/t-mobile-deutsche-telekom-merger-4fdc8eba
https://finance.yahoo.com/markets/stocks/articles/deutsche-telekom-wants-whole-t-143743459.html
https://www.telekom.com/en/company/companyprofile/company-profile-625808
Analysis: OpenAI and Deutsche Telekom launch multi-year AI collaboration
Deutsche Telekom: successful completion of the 6G-TakeOff project with “3D networks”
Deutsche Telekom selects Iridium for NB-IoT direct-to-device (D2D) connectivity
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Deutsche Telekom offers 5G mmWave for industrial customers in Germany on 5G SA network
Deutsche Telekom migrates IP-based voice telephony platform to the cloud
Deutsche Telekom Network Day: Fiber, Mobile Network, Open RAN and 5G SA Launch in 2024
SK Telecom and Deutsche Telekom to Jointly Develop Telco-specific Large Language Models (LLMs)
Deutsche Telekom with AWS and VMware demonstrate a global enterprise network for seamless connectivity across geographically distributed data centers
T-Mobile expands FTTH footprint via 50-50 JVs with Oak Hill Capital and Wren House
T-Mobile US announces new broadband wireless and fiber targets, 5G-A with agentic AI and live voice call translation
Highlights of 2025 Broadband Nation Expo: Comcast, T-Mobile keynotes + selected quotes
AT&T deploys nationwide 5G SA while Verizon lags and T-Mobile leads
T-Mobile’s new CEO Srini Gopalan faces fierce competition from AT&T, Verizon and MVNOs
T-Mobile’s growth trajectory increases: 5G FWA, Metronet acquisition and MVNO deals with Charter & Comcast
Evercore: T-Mobile’s fiber business to boost revenue and achieve 40% penetration rate after 2 years
WSJ: T-Mobile hacked by cyber-espionage group linked to Chinese Intelligence agency
T-Mobile posts impressive wireless growth stats in 2Q-2024; fiber optic network acquisition binge to complement its FWA business
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Dell’Oro Group has raised its worldwide data center capex outlook for 2026 as hyperscale AI deployments accelerated, complemented by continued investments in general-purpose infrastructure and rising component costs.
“Rising memory and storage pricing substantially increased overall server system costs in the quarter and will likely remain a major capex growth factor this year,” said Baron Fung, Senior Research Director at Dell’Oro Group. “At the same time, AI infrastructure deployments continue to accelerate rapidly, while hyperscalers also expanded general-purpose infrastructure to support public cloud growth, agentic AI workloads, and rising AI-related storage requirements.
“Despite exceptionally strong spending growth in 1H2026, capex growth is expected to accelerate further in 2H26, driven by the ramp of NVIDIA Rubin systems and refresh cycles for hyperscaler custom accelerator platforms. Beyond hyperscalers, select enterprise verticals and sovereign cloud providers are increasing AI infrastructure adoption, though growth remains constrained by uncertain returns and infrastructure readiness. While near-term demand remains healthy, some spending may have been pulled forward ahead of expected price increases later this year,” explained Fung.
Additional highlights from the 1Q 2026 Data Center IT Capex Quarterly Report:
- The global data center capex outlook was raised to more than $1 trillion for 2026.
- The Top 4 U.S. cloud providers—Amazon, Google, Meta, and Microsoft—increased data center capex by 78%.
- Dell led server OEM revenue in the quarter, followed by SuperMicro and Lenovo, while white-box vendors serving the hyperscale market accounted for the majority of server revenue. Nearly all server vendors benefited from higher memory-driven system pricing.
Image Credit: Futurum Group
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Dell’Oro Group’s Data Center IT Capex Quarterly Report details the data center infrastructure capital expenditures of each of the ten largest Cloud service providers, as well as the Rest-of-Cloud, Telco, and Enterprise customer segments. Allocation of the data center infrastructure capex for general-purpose and accelerated servers, storage systems, and other auxiliary data center equipment is provided. 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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Perplexity.ai generated- June 2026 forecast:
Approximately 75% of hyperscaler capex in 2026 is for AI infrastructure (~$450 billion).
Key Drivers of the Forecast Increase:
Additional Secondary Factors:
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NVIDIA Rubin system ramp: Capex growth expected to accelerate further in 2H26 driven by Rubin system ramp
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Hyperscaler custom accelerator refresh cycles: Refresh cycles for custom accelerator platforms will drive 2H26 growth
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Enterprise verticals & sovereign cloud adoption: Select enterprise verticals and sovereign cloud providers increasing AI infrastructure adoption
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Pulled-forward spending: Some spending pulled forward ahead of expected price increases later in 2026
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References:
https://futurumgroup.com/insights/ai-capex-2026-the-690b-infrastructure-sprint/
https://know.creditsights.com/insights/technology-hyperscaler-capex-2026-estimates/
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Alphabet’s 2026 capex forecast soars; Gemini 3 AI model is a huge success
Hyperscaler capex > $600 bn in 2026 a 36% increase over 2025 while global spending on cloud infrastructure services skyrockets
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
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?
ABI Research: 6G Radio Installed Base by Region from 2029 to 2034
According to ABI Research, fewer than 0.35 million 6G-supported radios are expected to be deployed worldwide at the end of 2029. That’s understandable, because those “6G radios” will be pre-standard deployments. The IMT 2030 Radio Interface Technology (RIT) standards won’t be completed till the end of 2030!
By 2034, around 7.2 million 6G radios will have been deployed, as per the market research firm’s forecast. The Asia-Pacific region (4 million deployments by 2034) will likely see the first major global deployment of this new generation of radio equipment. North America (1.2 million deployments by 2034) and Europe (940K deployments by 2034) will also see significant deployments once telecom operators complete their own transition from 5G densification to 6G expansion.
6G deployments are forecast to start low, due to continuing ITU-R standards/3GPP specifications work and early network operator caution, with rapid growth after 2031 to 2032 as mobile operators complete their overall first phase of 6G deployments and more operators gain confidence for scaled rollouts.
ABI is treating 6G as a multi-layer infrastructure market: not just spectrum and radios, but also core evolution, advanced antenna systems, and sensing/AI convergence. In practice, that means the report should help answer questions like where to target product planning, how regional adoption may diverge, and which infrastructure subsegments could capture early 6G spend.
ABI’s broader 5G/6G research service also emphasizes spectrum and infrastructure planning, advanced antenna systems, and Open RAN-adjacent market intelligence, which fits the framing of this 6G report as part of a wider network infrastructure portfolio.
References:
https://www.abiresearch.com/news-resources/chart-data/6g-radios-installed-base-forecast
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)
Roles of 3GPP and ITU-R WP 5D in the IMT 2030/6G standards process
NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU
AI wireless and fiber optic network technologies; IMT 2030 “native AI” concept
Analysis: Nvidia’s rumored new 6G AI-RAN – likely features/functions and industry impact
Executive Summary:
According to Light Reading, Nvidia is working on a GPU combo chip that would sit directly in the 6G radio unit [1.], extending its AI-RAN push from baseband/server into the radio itself. It’s reported to be a more hardware-integrated, sub-100W embedded design rather than just GPU acceleration in centralized RAN compute.
Note 1. 6G/IMT 2030 Radio Interface Technologies (RITs) have yet to be defined, let alone specified by 3GPP or ITU-R WP5D. They won’t be solidified until the end of 2030 so any specific silicon design won’t be completed until then or 2031!
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Light Reading’s headline frames it as a “radical new AI-RAN plan and they wrote that “the move was confirmed by knowledgeable sources, with Nvidia saying GPUs in more advanced radios will become “essential” in future. It marks a dramatic new development in the GPU giant’s “AI-RAN” strategy.”
If accurate, this would be a notable shift for Nvidia, because it would let them influence the whole RAN stack, not just centralized compute. That could matter for performance, power efficiency, and AI-native functions such as sensing, spectrum optimization, and real-time signal processing. Nvidia’s broader 6G messaging already emphasizes AI-native wireless, integrated sensing and communications, and spectrum agility as core themes.
The unconfirmed report fits Nvidia’s existing telecom roadmap rather than appearing out of nowhere. Nvidia has already announced an AI-native wireless stack for 6G with partners including Cisco, MITRE, Booz Allen, ODC, and T-Mobile, and it has promoted AI-RAN as a way to combine connectivity, computing, and sensing on one platform. It also aligns with the company’s recent partnership with Nokia, where Nvidia introduced the ARC-Pro 6G-ready accelerated computing platform and described it as a software-upgradable path from 5G-Advanced to 6G. That makes the rumored radio-chip move look like a vertical extension of the same strategy.
For wireless network operators, a radio-unit chip from Nvidia would be significant only if it improves cost, power, or flexibility versus incumbent RU silicon. The practical test will be whether it can deliver enough RF, baseband, and AI function integration to justify another architecture layer at the edge. It would also intensify competition in the radio-access supply chain and reinforce the trend toward AI-native, software-defined RANs. It also suggests Nvidia wants to shape not only the compute layer but the physical radio layer of 6G networks.
Possible AI Silicon Features and Functions:
Nvidia would most likely add AI-for-RAN features into radio silicon first, because those map directly to signal processing and link adaptation rather than to generic “AI at the edge.” Nvidia’s own AI-RAN materials emphasize embedding AI/ML into the radio signal-processing layer to improve spectral efficiency, coverage, capacity, and performance. Here are a few likely AI features/functions for the rumored 6G AI Nvidia super chip:
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Neural channel estimation and equalization, to infer cleaner channel state from noisy RF observations and improve link reliability. Nvidia’s open-source Aerial release specifically calls out advanced neural models for channel estimation.
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Real-time beam management, including beam selection, beam tracking, and beam refinement for massive MIMO and mmWave/upper-midband deployments. These are natural AI-RAN use cases because they depend on fast adaptation to changing propagation conditions.
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Spectrum agility and interference mitigation, such as identifying jammed or congested resource blocks and dynamically avoiding them. NVIDIA and partners have already described spectrum agility applications that freeze only affected frequencies while keeping the rest of the system online.
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Dynamic resource scheduling, using learned traffic and channel patterns to allocate PRBs, power, and compute more efficiently in real time. Nvidia describes AI-RAN as improving spectral efficiency and dynamic traffic handling through AI.
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Integrated sensing and communications support, where the radio helps detect objects, motion, or environmental context in parallel with communication. Nvidia has already highlighted ISAC-style applications with camera/RF fusion and object tracking.
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Edge inference hooks, letting the RU expose real-time PHY data to AI applications or a dApp-style framework. Nvidia’s open-source Aerial stack says third-party apps can access physical-layer data through secure APIs and modify RAN behavior in real time.
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Self-optimization and closed-loop control, where the radio silicon learns local conditions and continuously retunes thresholds, coding, MCS selection, and precoding policies. That fits Nvidia’s broader framing of AI-native networks as software-defined and continuously adaptable.
The most plausible first wave is not a fully autonomous “AI radio,” but a hybrid RU chip that accelerates selected PHY functions and exposes telemetry/data paths to the rest of the AI-RAN stack. Nvidia’s current messaging emphasizes software-defined infrastructure, deterministic performance, and layered AI-RAN capabilities rather than replacing the entire RAN with a black-box model.
The real differentiator would be whether Nvidia can combine RF signal processing with its GPU/CUDA ecosystem, so the same platform handles channel learning, inference, and orchestration across RU/DU/CU tiers. That would let operators optimize for spectral efficiency and OPEX while still keeping a software-upgrade path to 6G. Radio electronics is constrained by power, latency, determinism, and certification, so Nvidia would need to prove these AI features help without destabilizing PHY timing. That is why the likely starting point is assistive AI inside the signal chain, not a fully learned end-to-end radio.
Image Credit: Nvidia
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Competitive Analysis:
Nvidia’s reported move into a 6G radio-unit chip is most threatening to Marvell and Qualcomm at the silicon layer, while it is more of a strategic architecture challenge to Nokia and Ericsson at the system level. The immediate effect is less about a single chip and more about Nvidia trying to pull compute, connectivity, and AI deeper into the RAN value chain
Qualcomm is the closest direct competitor if Nvidia is trying to put silicon into the radio or near-radio layer. Qualcomm already has a Layer 1 strategy that combines silicon and software in SmartNIC/server-adjacent form factors, so Nvidia would be moving into a space where Qualcomm has both telecom credibility and established IP.
The risk for Qualcomm is that Nvidia can use its AI brand, CUDA ecosystem, and hyperscale relationships to redefine what “performance” means in RAN silicon, especially if AI-native functions become a buying criterion. The counterpoint is that Qualcomm still has a strong edge in wireless-specific silicon integration and standards heritage, which matters if the 6G radio path remains RF- and modem-centric.
Nokia looks less exposed in the short term because it is already partnering with Nvidia rather than treating it as a pure adversary. Nvidia and Nokia have publicly framed their relationship as an AI-native 5G-Advanced/6G platform effort, and Nokia says it will add NVIDIA-powered commercial AI-RAN products to its RAN portfolio.
Nonetheless, a Nvidia radio-chip push could still compress Nokia’s differentiation over time if more of the RAN stack becomes software-defined and GPU-centric. The strategic question is whether Nokia remains the integrator and operator-facing systems vendor, or whether Nvidia gradually becomes the architectural center of gravity.
Ericsson is the most structurally interesting case because it sits at the high end of global RAN share and has been more cautious about Nvidia as a Layer 1 option. Light Reading notes Ericsson is currently dismissive of Nvidia as a Layer 1 choice, even while the broader ecosystem explores AI-RAN collaboration.
For Ericsson, the threat is not immediate revenue loss from a single chip; it is erosion of the traditional assumption that RAN leadership comes from proprietary radio and baseband stacks. If Nvidia can make AI-native RAN a default design paradigm, Ericsson may be forced to defend its software and systems value rather than simply its box-selling model.
Samsung Electronics contacted Light Reading after their story was published to point out that it also works with AMD as a chip partner. “Samsung supports full Layer 1 (L1) processing using Intel’s telco CPUs (e.g., Xeon 6 Granite Rapids) and lookaside accelerator approach and in addition has successfully demonstrated full L1 processing on AMD’s CPUs without relying on dedicated L1 accelerators,” a Samsung spokesperson said via email.
Marvell is the most exposed chip supplier in this story because its telecom position is more concentrated in custom Layer 1 silicon. Light Reading specifically points out that Marvell is a critical supplier to Nokia in Layer 1, which makes a Nvidia radio-chip effort a direct substitution threat in portions of the stack.
If Nvidia succeeds, Marvell faces a two-sided squeeze: loss of design wins in telecom silicon and a narrative shift toward AI-native programmable platforms that favor Nvidia’s broader ecosystem. Marvell’s defense is that telecom operators still care about power, latency, and deterministic functionality, areas where custom silicon can remain more efficient than a generalized AI-compute approach.
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Summary Table:
| Company | Impact level | Why |
|---|---|---|
| Qualcomm | High | Direct silicon adjacency and overlapping Layer 1 ambitions. |
| Marvell | High | Telecom custom-silicon exposure, especially Layer 1. |
| Ericsson | Medium | Strategic and architectural threat more than immediate chip displacement. |
| Nokia | Medium to low near term | Partnered with Nvidia, so risk is more about future dependence and stack control. |
Source: Perplexity.ai
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Conclusions:
It’s unknown whether Nvidia’s rumored radio chip becomes a product, a reference design, or just an extension of its AI-RAN platform. If it ships, watch for operator trials, power-envelope disclosures, and whether it targets RU integration, DU acceleration, or a hybrid AI-RAN endpoint. If it stays at the partnership/reference-design level, the market impact will be more narrative than revenue-relevant.
Another unanswered question is whether Nokia and Ericsson keep treating Nvidia as a collaborator while preserving their own Physical layer control, or whether they start to see Nvidia as a platform owner in the making. That boundary will determine whether this is a tactical ecosystem play or the beginning of a deeper industry reset.
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References:
https://www.lightreading.com/6g/nvidia-has-a-radical-new-ai-ran-plan-a-6g-radio-unit-chip
https://www.lightreading.com/6g/analyst-insight-6g-coming-into-focus
https://www.nvidia.com/en-us/industries/telecommunications/ai-ran/
RAN Silicon Rethink- Part II; vRAN and General-Purpose Compute
Orange, Nokia, Nvidia, and Intel debate: ASICs vs. GPUs vs. General-Purpose CPUs for RAN Baseband Processing
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
Inside Nokia’s new AI Networking Innovation Lab
Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy
Marvell shrinking share of the RAN custom silicon market & acquisition of XConn Technologies for AI data center connectivity
Oriole Networks photonic networking platform to be integrated with AMD GPUs/CPUs for next-gen AI data center fabrics
London, England based Oriole Networks today announced continued progress in its collaboration with AMD in support of the UK’s Advanced Research & Invention Agency (ARIA) Scaling Inference Lab. The initiative integrates Oriole’s photonic interconnect architecture with AMD Instinct GPUs and AMD EPYC CPUs to evaluate next-generation data center fabrics capable of addressing the performance, latency, and energy constraints inherent in large-scale AI workloads.
The multi-year collaboration is advancing toward deployment of what is positioned as the first production-scale, all-photonic AI network fabric. The system is designed to deliver ultra-low latency and deterministic transport characteristics at the system level, leveraging optical circuit switching to optimize east-west traffic flows across accelerator clusters. The primary objective is to demonstrate how optical interconnect technologies can support large-scale inference and distributed AI processing under stringent performance and energy constraints.
Oriole’s PRISM photonic networking platform [2.] replaces conventional electronic switching in the network core with nanosecond-scale optical circuit switching. In contrast to packet-switched electronic fabrics, this approach is intended to reduce forwarding overhead, lower core power consumption, and improve end-to-end transport efficiency for accelerator-dense workloads. AMD is contributing compute hardware and technical collaboration to support modeling and execution of large-scale network workloads relevant to frontier AI systems. However, PRISM is not built for any single chip vendor. It works across any accelerator platform, giving the wider industry a path to frontier-scale system-wide performance without the need for proprietary stacks.
Note 1. Oriole Networks is a photonic networking company, developing disruptive technologies for AI/ML and HPC networking that will revolutionize data centers. These technologies address AI’s biggest challenges – speed, latency, and sustainability. Our holistic approach replaces energy-hungry electrical switching with photonic switching. By using only light to move data in the network, our solution will increase the efficiency of LLM training and inference to unprecedented levels while dramatically reducing the energy consumption of data centers, currently putting a huge strain on energy grids. We can offer faster, more efficient, and more sustainable AI without sacrificing the planet.
Note 2. Oriole’s PRISM is a fully photonic network system designed to provide port-level, all-to-all connectivity, eliminating the need for electrical switches and dramatically reducing the number of optical transceivers needed in the network. This evolution greatly reduces power consumption and latency, increases bandwidth, and strengthens network resilience by eliminating single points of failure.
Image Credit: Oriole Networks
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The deployment also represents the first commercial implementation of Oriole’s technology following an R&D-to-production transition completed in approximately three years. The company states that its xPU-agnostic architecture is intended to support heterogeneous accelerator environments and broader industry rollout beginning in 2027.
Photonic networking architecture:
PRISM is designed to route data optically rather than electrically, using photonic circuit paths in place of conventional electronic switching elements. As AI training and inference workloads scale, data center interconnect requirements increasingly exceed the efficiency limits of traditional switch-based architectures, particularly in terms of power dissipation, thermal load, and communication latency.
By eliminating electronic switching in the fabric core, the PRISM architecture seeks to reduce core network power consumption and limit buffering- and queuing-related delay. The use of optical circuit switching is consistent with ongoing industry interest in photonic interconnects, co-packaged optics, and optical disaggregation as potential enablers of high-density AI clusters.
The company reports that the architecture can substantially reduce GPU idle time and improve system-level utilization by shortening data movement paths between compute nodes. It also indicates potential reductions in cooling demand and associated water usage due to lower network power dissipation.
Quotes:
James Regan, CEO of Oriole, said: “A year ago, we were proving the physics; today, we’re proving the business. Our collaboration with AMD has moved from concept to deployment to a system an order of magnitude larger, and the data proves this is already driving performance increases at pace. This is what it looks like when photonic networking stops being a research curiosity and starts being the foundation of how serious AI infrastructure gets built. There’s a big problem now with electrical switches, which are basically bottlenecking AI traffic, and it’s going to get worse. What we do is we replace all the electrical switches.”
“AMD is excited to collaborate with Oriole on the ARIA Scaling Inference Lab cluster,” said Madhu Rangarajan, corporate vice president, Compute and Enterprise AI business, AMD. “Oriole’s AI backend networking with nanosecond optical circuit switching represents a fundamentally different way to connect accelerators at scale. We are helping to validate how photonic fabrics can work alongside AMD compute to deliver the low-latency, high-bandwidth connectivity that AI Inference workloads demand.”
“Meeting the demands for modern AI requires rapidly identifying ways to improve the performance and cost-efficiency of large-scale AI clusters. ARIA is thrilled to collaborate with Oriole and AMD to demonstrate the benefits of this new technology and it’s exactly the type of collaboration, between innovative startups and industry leaders, that the Scaling Inference Lab was designed to foster,” said Suraj Bramhavar, Program Director at ARIA
Standards and interoperability context:
From a standards perspective, photonic AI fabrics remain an active area of industry development rather than a fully mature architectural class. Relevant technical domains include IEEE 802.3 optical Ethernet interfaces, ITU-T optical transport frameworks such as G.694 and G.709, and ecosystem work in optical interconnect and co-packaged optics initiatives.
A vendor-neutral, accelerator-agnostic photonic fabric may be of interest to standards and industry groups evaluating future data center interconnect models for AI and high-performance computing. The Oriole–AMD collaboration therefore provides an early reference point for assessing the operational characteristics, integration constraints, and interoperability implications of optical circuit-switched AI infrastructure.
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References:
Oriole to Deploy World’s First AI System with Pure Photonic Network to Supercharge Data Centers
https://www.fierce-network.com/cloud/oriole-networks-pushes-pure-photonic-networking-ai-data-centers
NTT’s IOWN is (finally) evolving to an All Photonics Network (APN); Physics based AI for enterprise OT
Goldman Sachs report: Optical Networking is the next mega trend in AI infrastructure
Hyperscaler design of networking equipment with ODM partners
Technavio: Silicon Photonics market estimated to grow at ~25% CAGR from 2024-2028
Amazon and Corning in Multi-Billion-Dollar Fiber Infrastructure Deal in North Carolina
Introduction:
The surge in optical fiber demand is intensifying as hyperscale cloud providers accelerate infrastructure buildouts to support AI-driven workloads and high-density data center interconnect (DCI). Corning [1.] today announced a multi‑billion‑dollar investment from Amazon to expand fiber manufacturing capacity in North Carolina—incremental to its previously announced $10 billion regional cloud infrastructure expansion—reflects a broader structural shift in how optical supply chains are being secured and scaled.
Note 1. Corning’s fiber-optic infrastructure uses highly pure strands of optical glass thinner than a human hair to transmit massive amounts of data as pulses of light. These networks serve as the backbone for modern communications, connecting everything from rural broadband rollouts to hyperscale data centers driving generative AI. In hyperscale cloud and AI data centers, Corning provides high-density optical hardware and cables, such as their GlassWorks AI™ solutions. These large setups feature massive fiber-optic trunk cables containing hundreds to thousands of individual fibers bundled together to link powerful processors and servers. For outdoor networks running underground or on utility poles, you will see ruggedized cables protected by thick jackets and aramid yarn. These cables are designed to withstand weather, crushing, and extreme temperatures.
Corning’s structured cable solutions for internal data center connectivity. Image Credit: Corning
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This trend is not isolated. Hyperscalers including Meta, Microsoft, and wireline network operator Lumen are proactively entering long-term supply and co-investment agreements with fiber and cable manufacturers, effectively reshaping the upstream optical ecosystem.
Recent Fiber Supply Agreements with Corning:
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May 2026: NVIDIA committed $500 million to Corning to support construction of three new optical manufacturing facilities in North Carolina and Texas. This investment is expected to increase Corning’s U.S.-based optical connectivity manufacturing capacity by approximately 10× and expand domestic fiber production by over 50%, targeting AI cluster interconnect requirements characterized by high fiber count and low-latency links aligned with IEEE 802.3 Ethernet and emerging co-packaged optics ecosystems.
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January 2026: Meta finalized a $6 billion agreement with Corning to secure fiber supply for large-scale data center fabrics. These fabrics increasingly rely on high-fiber-density architectures consistent with leaf-spine topologies and standards such as IEEE 802.3bs/ck (400G/800G Ethernet), as well as parallel single-mode fiber (PSM) and wavelength-division multiplexing (WDM) approaches defined in ITU-T G.694.x.
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September 2025: Microsoft entered a manufacturing agreement with Corning and Heraeus focused on hollow-core fiber (HCF), a technology aligned with ITU-T G.650 characterization frameworks. HCF offers lower latency (reduced group index) and improved performance for latency-sensitive AI workloads and inter-data center transport.
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August 2024: Corning and Lumen established a supply agreement for next-generation fiber optic cable to support AI-driven traffic growth. This aligns with ITU-T G.652.D and G.657 fiber standards for bend-insensitive and high-capacity terrestrial deployments, as well as evolving requirements for high-count ribbon fiber cables in dense metro and campus environments.
Structural Implications for the Optical Supply Chain:
Hyperscalers are transitioning from passive consumers of optical components to active participants in manufacturing scale-up, including:
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Anchor tenancy models: As seen with Meta’s backing of Corning’s North Carolina facility, hyperscalers are underwriting capacity expansion, effectively securing preferential access to supply.
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Vertical influence: Direct investments and long-term offtake agreements allow hyperscalers to influence fiber specifications, manufacturing roadmaps, and deployment architectures (e.g., optimized fiber types for short-reach vs. long-haul DCI).
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Workforce development: Amazon and Corning’s collaboration with Catawba Valley Community College to expand fiber technician training reflects a strategic effort to address labor constraints in optical manufacturing and deployment, reinforcing domestic supply chain resilience.
Implications for Telecom Operators:
These developments introduce non-trivial risks and strategic considerations for telecom operators:
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Supply prioritization: Hyperscaler-backed agreements may shift allocation dynamics, potentially constraining availability for traditional telecom buyers during periods of tight supply.
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Pricing pressure: Long-term, high-volume contracts could influence pricing benchmarks, potentially disadvantaging operators without comparable scale or capital flexibility.
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BEAD timing mismatch: U.S. operators anticipating fiber expansion funded by BEAD (Broadband Equity, Access, and Deployment) may face supply bottlenecks if hyperscaler demand absorbs near-term manufacturing output.
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Architectural divergence: Hyperscaler-driven requirements—optimized for short-reach, ultra-high-capacity intra-data-center and DCI links—may skew innovation toward their use cases, potentially misaligning with traditional access network needs governed by ITU-T G.984 (GPON), G.9807 (XGS-PON), and emerging 25G/50G PON standards.
A useful analogy is the semiconductor industry, where hyperscaler influence has already reshaped foundry capacity allocation and advanced node prioritization. A similar dynamic is now emerging in optical fiber and connectivity, with hyperscalers effectively acting as quasi-industrial planners for next-generation optical infrastructure.
Quotes:
“Amazon’s investments in North Carolina have created more than 26,000 jobs across the state. This multibillion-dollar agreement with Corning continues that commitment, channeling investment into American manufacturing and creating 1,000 new jobs at their facilities near our data centers,” said Matt Garman, CEO of AWS. “We’re also partnering to train North Carolinians for highly skilled roles in fiber optics and fusion splicing. These long-term investments create long-term careers and real opportunity in the communities where we operate.”
“This agreement with Amazon represents a significant milestone for Corning and for American manufacturing,” said Wendell Weeks, chairman, CEO, and president of Corning. “For 175 years, Corning has pioneered the technologies that connect people and transform industries. Amazon’s investment will help us expand production, create 1,000 new advanced manufacturing jobs at our facilities, and lead the way toward building a resilient U.S. manufacturing base.”
Clearfield CEO Cheri Beranek told Fierce Network at Fiber Connect that supply chain issues are re-emerging, particularly around high-count fiber. “There’s absolutely a shortage of ribbon fiber,” she said, referring to a conversation with Hawaii Telecom, a Clearfield customer. “The high count for the ribbon fiber … everything over 432 is tough to get,” she said. “The fiber companies want to tell you that there’s enough American‑made fiber… but there can’t be.”
“In talking to fiber optic suppliers, they all say one thing, ‘It’s nice to finally be the cool kid on the block.’ Hyperscalers are finally realizing that they not only need compute, storage, chips, power, water and real estate, they also need fiber optic connectivity,” said Fierce Network’s Chief Analyst Linda Hardesty.
The net effect is a tightening coupling between AI infrastructure demand and optical supply chain strategy—one that telecom operators will need to actively manage through procurement strategy, vendor diversification, and potentially deeper participation in supply-side partnerships.
End Note:
Amazon’s long-term commitment to North Carolina goes beyond direct investments and jobs created in the state. Through workforce development, Career Choice, and upskilling programs, Amazon has already provided practical training for nearly 7,000 people in North Carolina, helping to open new pathways for higher-paying jobs and fulfilling careers.
In the last decade, Amazon has contributed more than $72 million to charities and organizations supporting local needs across North Carolina, with $10 million provided in 2025 alone to 26 local community partners. This includes contributions like $1.5 million to enhance public safety services for southeastern Hamlet and surrounding Richmond County communities by funding a new fire substation that is expected to lower emergency response times and homeowner insurance premiums.
References:
https://www.corning.com/data-center/au/en/home/applications/enterprise-private-data-center.html
https://www.aboutamazon.com/news/company-news/amazon-corning-fiber-optics-1000-jobs-north-carolina
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
Corning to Build New Fiber Optic Plant in Phoenix, AZ for AT&T Fiber Network Expansion
Calix and Corning Weigh In: When Will Broadband Wireline Spending Increase?
Verizon-Corning $1.05B fiber deal part of larger build-out or buy program
Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure
By Alan J Weissberger
Executive Summary:
The rapid rise of agentic artificial intelligence (AI) is expected to drive material changes across data centers, service provider networks, and the broader telecom ecosystem. As agentic AI moves from chat-oriented interactions to autonomous digital agents, Cisco says that those workloads will not only increase traffic volumes, but also alter traffic characteristics in ways that place new demands on latency, security, orchestration, and distributed compute placement.
“We are entering into a Network Supercycle,” Jeetu Patel, Cisco’s president and chief product officer, said during his opening keynote at Cisco Live in Las Vegas.
As a result, network operators will need more resilient transport, edge compute, and optical capacity to support new traffic patterns and security demands.
Cisco execs pictured (left to right): Jeetu Patel, president and chief product officer; Chuck Robbins, chairman and CEO; Liz Centoni, EVP and chief customer experience officer; and Steven Clayton, SVP and chief communications officer.
Source: Jeff Baumgartner/Light Reading
AI Traffic Impact on Transport Requirements:
From a transport perspective, agentic AI traffic is likely to be more persistent, more interactive, and more latency-sensitive than conventional application traffic. Cisco has said AI-related network traffic is expected to triple over the next three years, with inference flows emerging as a major driver of load growth. That shift could place pressure on transport architectures that were optimized primarily for human-driven web, video, and enterprise application traffic
The implication for service providers is that traffic engineering will need to evolve toward finer-grained path control, stronger telemetry, and improved handling of asymmetric flows. AI sessions that span multiple exchanges between users, applications, and digital agents may also require more sophisticated policy enforcement and security integration across WAN, metro, and access layers.
Edge Compute Needs Grow:
Cisco’s remarks also point to a growing role for edge compute in telecom and cable networks. Some operators are already repurposing legacy central offices and mini data centers to support AI workloads, reflecting a broader shift toward distributed inference close to the user or device.
That architecture matters because many agentic AI use cases will be latency constrained and will not perform efficiently if all processing is centralized in distant cloud regions. Comcast and Charter have both announced AI edge strategies, underscoring how access networks can become part of the compute fabric rather than acting solely as last-mile connectivity.
For network operators, this suggests a new operational model in which compute, storage, and network functions are increasingly coordinated across regional and edge sites. In practical terms, the network becomes part of the application execution environment, not just the transport layer beneath it.
Optical Network Implications:
Optical infrastructure will likely carry much of the burden created by distributed AI deployments. As inference workloads expand across regional hubs, edge sites, and centralized clouds, operators may need higher-capacity optical transport to sustain east-west traffic between distributed compute nodes.
That points to greater demand for dense 400G and 800G interconnects, more flexible wavelength management, and lower-latency optical paths between metro aggregation points and AI facilities. The challenge is not only to scale throughput, but also to preserve path diversity, minimize jitter, and maintain predictable performance for machine-to-machine workloads that are increasingly sensitive to delay.
As AI traffic becomes more dynamic and more operationally critical, optical networks may need to be engineered with the same level of service awareness traditionally associated with enterprise transport and carrier-grade voice or mobile backhaul.
Security is a Top Priority:
Cisco cited security as a serious concern for agentic AI traffic. CEO Chuck Robbins said AI agents designed to help enterprise customers can run roughshod without a proper defense that can quickly detect, intercept and possibly “kill” them before they get out of control. It becomes an even bigger issue when they are built to be nefarious.
“AI changes the speed of defense,” Robbins said. “It’s empowering adversaries at a pace that we haven’t seen in our careers … These [AI] models are as bad as they are ever going to be …They’re only going to get better.”
Anthropic’s new Claude Mythos model, which can auto-detect and possibly exploit software vulnerabilities at scale, is now a “CEO-level discussion,” he added.
“We’re living in a post-Mythos world where security has to be fused and baked into the network,” Patel said, holding that vulnerabilities can now being attacked as soon as they arise.
“We need to reimagine security” in the AI era, Patel said, noting that AI agents will not only handle tasks locally but will be heading outside to connect to third-party agents, servers and various tools.
“Every agentic action is a routing challenge, a trust decision and a telemetry event,” Patel said. The emergence of agentic AI, he said, is shifting the security and permission focus from “access control” (for us humans) to “action control” for agents that will need to be closely monitored, controlled and, if needed, quickly intercepted.
“People don’t trust these agents right now,” Patel said later during a separate discussion with press and analysts.
These concerns also extend to AI agent identity, which Cisco is addressing with its recent agreement to acquire Astrix Security.
This extends to other types of guardrails and observability metrics, too, including the notion of “tokenomics” – essentially keeping tabs on how many tokens an AI agent could consume. If the agent is found to be overspending on tokens, it could be intercepted and shut down.
Patel suggested that, without guardrails, what a company pays for AI tokens for a year could be consumed by an agent in a week. Assessing such AI agent behavior was a key driver of Cisco’s acquisition of Galileo Technologies.
Cisco’s AI Stack:
Cisco is focused on a vertically integrated platform – starting with its Silicon One platform for data centers and enterprise devices, optics, switches, routers and access points, apps and services, and wrapped by a new Cisco Cloud Control platform announced this week. Though Cisco Cloud Control is able to provide unified access to Cisco’s tools, apps and services, such as Meraki, Catalyst and Splunk, Patel stressed that it will also be able to integrate with third parties and support an open ecosystem. Cisco is starting out with support from 52 partners, including AWS, Google Cloud, NetBrain and ServiceNow.
Telecom Market Transition:
Robbins said Cisco used AI to scan 1.8 billion lines of code in 25 different programming languages over the past eight weeks. Without AI models, that would’ve taken eight years, he said.
Patel described the industry as being at a pivotal moment, moving from chat bots to more advanced agents that function as “digital coworkers.” He noted that “These agents are going to be everywhere.”
That transition suggests telecom networks will increasingly support autonomous machine interactions at scale, with implications that extend beyond bandwidth growth into security, policy control, and distributed systems design. For operators and vendors alike, the strategic question is no longer whether AI will affect the network, but how quickly the network architecture can adapt.
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References:
https://www.lightreading.com/ai-machine-learning/cisco-ai-driving-a-network-supercycle-
Cisco report: Agentic AI to reshape WAN traffic, AI inference will be ~25% of total traffic by 2035
Cisco’s Silicon One G300 as the dominant AI networking fabric, competing with Broadcom’s Tomahawk 6 series
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Cisco to join Stargate UAE consortium as a preferred tech partner
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
