Dell’Oro: Data Center Physical Infrastructure revenue to grow at 22% CAGR from 2025-2025/forecast comparisons, analysis, risks
According to Dell’Oro Group, global Data Center Physical Infrastructure (DCPI) manufacturer revenue is projected to grow at a 22% compound annual growth rate (CAGR) from 2025 to 2030, reaching $120 billion by the end of the period. This growth is driven by net additions to installed IT capacity, which account for the large majority of the forecast. Additionally, the infrastructure content per megawatt will have a smaller effect as higher-density and liquid-cooled architectures redistribute spend across DCPI categories.
“The AI buildout has moved past the point where it can be treated as a surge. It is now the baseline against which the rest of the market is measured,” said Alex Cordovil, Research Director at Dell’Oro Group. “What has changed in this forecast is where the risk sits. Demand is no longer the open question—delivery is. Equipment lead times, construction labor, grid interconnection, and community consent all remain constrained, especially with the first statewide data center moratorium now in force.”
Additional highlights from the Data Center Physical Infrastructure 5-Year July 2026 forecast report:
- Capacity Additions Peak in 2026: Annual net capacity additions peak in year-over-year growth terms in 2026 and moderate steadily thereafter, remaining in double-digit growth territory through 2030. The market is still expanding quickly, but no longer accelerating. North America leads capacity additions over the period, followed by China.
- Thermal Management Leads Segment Growth: Thermal Management remains the fastest-growing DCPI segment, with liquid cooling the fastest-growing technology as rack densification moves the technology from an option to a precondition. Heat rejection coverage has been expanded in this edition, with water-cooled chillers expected to grow faster than air-cooled units on scalability rather than efficiency. Chillers remain a staple of data center specifications, even in warm-water designs, since free cooling loses effectiveness during the hottest days of the year.
- UPS Growth Concentrates in Larger Systems: Growth within the UPS segment concentrates in higher power rating three-phase systems, which are expected to expand faster than smaller units as the larger building blocks of AI clusters push deployments up the capacity curve. Medium-voltage designs are gaining ground, connecting UPS systems closer to the grid and attracting new entrants alongside established suppliers. Solid-state transformers are projected to weigh meaningfully on UPS demand beginning in 2029, initially focusing on large AI factories that have largely moved away from UPS-based architectures.
- Hyperscalers and Colocation Anchor Demand: Hyperscalers end the period as the largest single contributor to DCPI revenue, although their growth has slowed compared to the pace seen in 2025–26, as they lean more heavily on colocation partners to serve workloads, particularly outside the United States. Colocation remains central to the buildout, and the spread of powered shell development is shifting equipment procurement onto the tenant, moving revenue among customer segments without altering building occupancy. Newly separated in this forecast, AI-specialized Cloud—the neoclouds and AI model builders—becomes one of the fastest-growing lines in our coverage. Enterprise demand continues to grow, but more slowly than the rest of the market.
- Regional Diversification Builds: North America continues to lead regional growth, with China the next largest contributor. EMEA is the only region revised downward from the January forecast, reflecting slower power availability and a more difficult permitting environment. Community opposition has become a material constraint on siting, blocking or delaying a meaningful share of announced projects. Together with the expected repricing of U.S. natural gas, are expected to support faster growth in CALA and Asia Pacific excluding China.
Dell’Oro Group’s Data Center Physical Infrastructure 5-Year Forecast report provides a complete overview of the Data Center Physical Infrastructure market. This covers market sizes and forecasts for uninterruptible power supplies (UPS), thermal management, cabinet power distribution and busway, rack power distribution, IT racks and containment, and software and services. Allocation of manufacturer revenues by hyperscaler, other cloud, colocation, telco, and enterprise customer segments is also provided, alongside a forecast of data center capacity additions by region. For more information about the report, please contact us at [email protected].
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Forecast Comparisons:
Dell’Oro’s $120 billion DCPI forecast through 2030 is at the high end of published physical-infrastructure manufacturer-revenue estimates, but it is directionally consistent with other firms’ forecasts for adjacent power, cooling, and mechanical/electrical (M&E) categories. The differences largely reflect market definition: DCPI is not interchangeable with total data-center capex, construction, IT equipment, or facilities real estate.
The 22% Dell’Oro CAGR should not be read as a consensus CAGR for every DCPI component. Power equipment forecasts around 7.5% and broader support-infrastructure forecasts around 8.2% suggest more moderate growth in legacy categories, while AI-linked liquid cooling is projected to grow in the mid- to high-teens.
The key forecasting judgment is therefore AI infrastructure content per MW: if GPU density keeps climbing and liquid cooling, high-voltage distribution, energy storage, and modular power systems become standard rather than niche, DCPI revenue can grow substantially faster than data-center floor space or even installed MW. Conversely, grid constraints, AI-demand normalization, and lower equipment dollars per watt from scale and engineering improvements could constrain manufacturer revenue growth even as deployed capacity continues to expand.
Comparable Forecasts:
A useful interpretation is that Dell’Oro’s $120 billion is plausible only if the market increasingly captures high-value AI-ready electrical and thermal systems—not merely traditional UPS, air-conditioning, and rack revenue. Adding standalone power and cooling forecasts cannot produce a clean “DCPI total,” because analysts differ in whether they include services, software/DCIM, integration, installation, generators, switchgear, rack infrastructure, and edge facilities.
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Image Generated by Perplexity.ai
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Analysis – main spending drivers:
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AI accelerator density. GPU/accelerator clusters raise rack power from conventional enterprise levels to much higher levels, increasing demand for power distribution, UPS capacity, switchgear, busways, backup generation, and energy storage. ABI Research expects AI-dedicated active data-center capacity to rise from 11.5 GW in 2026 to 43.6 GW in 2031, and projects that AI will represent more than half of total data-center capacity in the early 2030s.
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Shift from air cooling to liquid cooling. Higher-density AI systems cannot be served economically—or sometimes technically—by conventional room-level air cooling alone. Direct-to-chip cold plates, coolant-distribution units, rear-door heat exchangers, liquid loops, heat-rejection equipment, and controls raise cooling-system content per MW. Cooling equipment is therefore forecast to grow faster than the more mature broad power-equipment category.
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Rapid capacity additions by hyperscalers and colocation operators. JLL expects roughly 97 GW of data-center capacity to be added globally from 2025 to 2030, approximately doubling the sector to about 200 GW. Every new MW requires a physical plant, even where the IT stack is sourced separately.
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Power availability is becoming the binding constraint. Global data-center electricity consumption is expected to roughly double to 945 TWh by 2030 in the IEA base case. This puts a premium on grid interconnection equipment, substations, medium-voltage distribution, on-site generation, batteries, and energy-management systems—and can cause operators to overbuild or deploy infrastructure earlier than their server installations.
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Resilience requirements and time-to-power. AI facilities require high availability alongside enormous load ramps. Operators are spending on redundant electrical paths, backup generation, battery systems, microgrids, and modular/skid-based electrical infrastructure to shorten construction schedules and reduce exposure to grid-connection delays.
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Retrofitting the installed base. Demand is not solely greenfield. Existing hyperscale, colocation, and enterprise sites must upgrade electrical distribution and thermal plants to host AI pods, often retaining conventional infrastructure for legacy workloads while adding liquid-cooling islands.
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Efficiency, water, and carbon constraints. Higher energy costs, grid constraints, water availability, and sustainability targets push investment toward more efficient thermal architectures, heat reuse where feasible, advanced controls, and power-management systems. These are often capital-intensive even when they lower lifetime PUE, water use, or operating cost.
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Huge Risks to the forecast:
The central downside to Dell’Oro’s forecast is an AI-demand and funding reset: if OpenAI, Anthropic, or other frontier-model providers fail to turn extraordinary usage growth into durable, high-margin cash flows, capacity commitments could be deferred, resized, or cancelled. Because the DCPI forecast assumes nearly 200 GW of added data-center capacity through 2030, even a partial reduction in the AI build plan would materially affect power, cooling, and electrical-equipment orders.
The OpenAI/Anthropic risk:
The potential issue is not that either company vanishes overnight. It is that they—and the hyperscalers and GPU-cloud firms supporting them—may discover that the revenue and gross-margin trajectory does not justify the scale of previously contracted compute.
The risk chain is: AI monetization miss→lower compute utilization / pricing→capex deferrals→fewer energized MW→lower DCPI revenue.
The exposure is unusually concentrated. Advanced AI demand is dominated by a small number of hyperscalers and frontier-model providers; McKinsey estimates that 60–65% of AI workloads in the United States and Europe will be hosted on hyperscaler infrastructure by 2030. Thus, a retrenchment by a few large buyers can have an outsized effect on the physical-infrastructure supply chain.
Why OpenAI is a focal point:
OpenAI’s downside case would be a mismatch between compute obligations and customer monetization:
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Consumer AI usage may remain high, but paid conversion, enterprise seat expansion, API volume, or willingness to pay for frontier-model performance may disappoint.
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Inference costs may not decline fast enough relative to prices, leaving growth without attractive contribution margins.
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New models may yield diminishing commercial differentiation, shortening product cycles and weakening customers’ willingness to pay premium prices.
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Its financing requirements could become harder to meet if capital markets reassess terminal valuations, the cost of debt rises, or strategic partners limit exposure.
Some reporting and commentary point to very large continuing compute costs and funding needs relative to reported revenue, but the precise economics are opaque because OpenAI remains private and uses non-GAAP and run-rate measures inconsistently across reports. That opacity is itself a risk: DCPI vendors can see announced projects and committed capacity, but cannot fully observe the ultimate cash-flow support for the tenant’s demand.
Why Anthropic is not a complete hedge:
Anthropic’s enterprise orientation and reported revenue growth could diversify the sector’s demand base, but it does not eliminate systemic risk. It faces many of the same conditions:
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Revenue is substantially concentrated in a relatively early enterprise-AI adoption cycle.
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Enterprise customers can trial models broadly but consolidate suppliers quickly if performance differences narrow.
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Model-price competition could reduce revenue per token or per API call faster than cost-per-token declines.
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Large training runs are discretionary. A pause in the cadence of frontier-model releases would immediately reduce the urgency of new GPU clusters and associated electrical/cooling plant.
Recent reports describe unusually rapid revenue expansion and positive adjusted operating income for Anthropic, but the sustainability and definition of those measures are not independently transparent in the way public-company financial statements are. The relevant question for DCPI is not just whether Anthropic grows revenue, but whether its long-term contracted compute load and its own capital support remain sufficient to sustain multi-year facility commitments.
Other downside mechanisms:
An AI crash is the sharpest downside scenario, but Dell’Oro’s bullish outcome also depends on several more gradual assumptions.
Dell’Oro itself reportedly frames the immediate risk as delivery—equipment lead times, construction labor, grid interconnection, and community consent—rather than demand. Those bottlenecks can cut near-term revenue even if AI demand is real, because DCPI is recognized when facilities are physically delivered and commissioned, not when a GPU cluster or capacity plan is announced.
Efficiency is a double-edged sword:
Dell’Oro’s premise benefits from high rack density: AI systems require more substantial electrical architecture and move cooling from conventional air systems toward liquid cooling. McKinsey notes that direct-to-chip cooling can address roughly 60–120 kW racks, and that immersion can support still higher densities; those architectures increase DCPI content per rack and often per MW.
But efficiency can reverse the volume implication. Better accelerators, model distillation, mixture-of-experts approaches, lower-precision inference, and power-system improvements can reduce electricity and infrastructure required per unit of AI output. The IEA explicitly models a “High Efficiency” pathway in which technology and software efficiency gains materially restrain data-center electricity demand, while its “Headwinds” case assumes slower AI uptake and capacity growth that plateaus beyond 2030, with efficiency offsetting much of the effect of increased IT use.
The key analytical distinction is:
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Revenue per MW can rise because AI racks need liquid cooling, high-capacity UPS, switchgear, busways, and sophisticated controls.
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Total MW deployed can fall if model efficiency improves or commercial demand disappoints.
Dell’Oro’s $120 billion outcome requires both substantial net new MW and elevated DCPI content per MW. A positive outcome on only the second factor would not fully protect the forecast.
What would signal trouble:
For a forward-looking DCPI thesis, monitor leading indicators rather than announced headline capex:
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OpenAI and Anthropic: paid enterprise adoption, API demand, realized—not merely annualized—revenue, gross margin, cash burn, and financing terms.
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Hyperscalers: capex guidance, AI-service revenue, remaining performance obligations, capacity utilization, and disclosure of power or data-center commitments.
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GPU-clouds and colocation firms: customer concentration, lease pre-commitments, cancellations, financing costs, and the ratio of contracted versus speculative capacity.
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Physical deployment: utility interconnection queues, energized MW rather than planned MW, transformer/switchgear order cancellations, and data-center construction starts.
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Economics: inference price declines versus cost declines, GPU utilization, and evidence that enterprise AI deployments generate measurable productivity or revenue returns.
A particularly bearish signal would be simultaneous model-price deflation, falling GPU utilization, and delayed data-center energization. That combination would mean the sector is not merely supply constrained; it would imply that the financial rationale for capacity has weakened.
Bottom line:
A failure by OpenAI or Anthropic to meet expectations could trigger a classic capital-cycle correction: capacity was ordered on expectations of demand, but the cash flows needed to validate the investment arrive later, at lower margins, or not at all. In that scenario, DCPI’s most vulnerable segments are discretionary greenfield power and cooling deployments attached to single large AI tenants or thinly capitalized GPU-cloud providers.
However, a single lab’s disappointment would not necessarily collapse the entire market. DCPI demand also comes from hyperscaler internal workloads, enterprise AI, cloud migration, conventional data growth, colocation expansion, and infrastructure upgrades. The most likely downside is therefore a lower and lumpier growth path, with project delays and inventory/order corrections, rather than zero growth. The more severe Dell’Oro downside requires a broad AI-ROI failure that causes multiple frontier labs and hyperscalers to retrench at the same time.
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Omdia’s 2025 Mobile Core Network Leaders: Huawei #1 in market share; Nokia #1 for portfolio competitiveness
Highlights of Omdia’s “Market Landscape: Core Vendors” Report:
1. Market Share:
Five vendors control the 4G and 5G core market with a combined market share of 86.1% in 2025, down from 87.4% in 2024. The top three players collectively captured 70.9% of total revenue. With such a concentration of market power, each vendor is increasingly focused on asserting leadership, particularly as competition intensifies around the next-generation 5G core. However, measuring market leadership in this space has its challenges
In 2025, Huawei was the market leader. It was followed by Ericsson, ZTE, Nokia, NEC, and Samsung Electronics.
• Ericsson, Huawei, and ZTE captured a combined 70.9% of global core revenue in 2025, up from 68.3% in 2024. When Nokia and NEC are added, these top five companies generated 86.1% of total core revenue in 2025, up from 87.4% in 2024. This is very high but still less than the 95.4% captured by these vendors in 2020, which indicates that upcoming vendors are collectively gaining market share.
• Huawei gained 5.0 percentage points in market share thanks to a more favorable geographical mix and market share gains in emerging markets. Ericsson, ZTE, and Nokia, however, lost share.
• Market share remains an important component of vendor strategy. Some vendors deliberately trade short-term margins for increased share, leveraging competitive pricing to secure new business, especially within the 5G core space, which presents fresh monetization opportunities. Conversely, others may prioritize margin protection over top-line growth, consciously sacrificing share to maintain profitability. A single vendor may use both approaches depending on the geography, project scope, or timing.
Samsung Electronics and four other vendors including the new participant, AxyomCore, are in the upcoming mobile core vendors group. However, caution is required when benchmarking these vendors, given the relatively fewer network functions (NFs) that they develop and their smaller market reach compared with the larger players.
Note: For 4G and 5G core revenue, Omdia did not include Communications Service Providers (CSPs)’ spending in NF virtualization infrastructure (NFVi) or server and management software.
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2. Portfolio Competitiveness:
Nokia was ranked No. 1 for mobile core portfolio competitiveness with the Finland headquartered company a leader across all seven competitiveness categories: core portfolio breadth, cloud-native maturity, signaling, automation, core as a service, AI/ML and analytics, and implementations of other network functions. The recognition reflects Nokia’s continued investment in mobile core technologies that help operators modernize their networks for the AI era. Recent deployments include the world’s first commercial mobile telco service based on 5G Core SaaS; Core SaaS Edge enabling local breakout for roaming subscribers; core network resilience solutions; telecom core modernization programs, and mission-critical network upgrades supporting the IoT, rail and utilities sectors.
“Core networks are becoming the intelligence layer of modern communications, connecting cloud-native operations, AI-driven automation and application innovation. This recognition from Omdia highlights the breadth and maturity of our portfolio across every major category operators are using to evaluate connectivity partners who can help them move toward more autonomous, resilient and programmable networks.” Kal De, SVP, Core Networks, Nokia.
“Nokia continues to distinguish itself as a technology leader in core networks, with advanced capabilities in categories spanning cloud native maturity, automation, AI/ML and analytics, and Core as a Service. These are no longer optional innovations but strategic requirements for telecommunication providers pursuing greater operational efficiency, service agility and monetization opportunities. Nokia’s comprehensive approach demonstrates a deep understanding of both current operator challenges and the future direction of the telecom market. Roberto Kompany, Principal Analyst, Mobile Infrastructure, Omdia.
3. Selected Mobile Core Vendor Strategies:
Huawei and ZTE dominate the global 5G mobile core landscape, combined representing over half of the global market revenue. Unlike Western vendors who heavily rely on US-based hyperscalers (AWS, Microsoft Azure, Google Cloud) to realize a cloud core, Huawei and ZTE use an entirely different playbook. Their strategy relies on vertically integrated, proprietary Telco Clouds combined with intensive AI-native automation, while focusing geographically on domestic, Middle Eastern, African, and Asia-Pacific markets due to geopolitical restrictions in Europe and North America.
Huawei and ZTE sell the entire mobile core vertical stack: the underlying hardware, the cloud virtualization layer, and the mobile core software.
- Huawei’s Strategy: Huawei actively positions its Huawei Cloud and proprietary platform (Telco Intelligent Converged Cloud – TICC) as the direct alternative to AWS or Azure for global operators. Instead of validating their 5G core on American hyperscalers, they build end-to-end “Cloud-Network Synergy” environments using their own Kunpeng and Ascend chipsets.
- ZTE’s Strategy: ZTE deploys its 5G Common Core Solution. It uses their in-house Distributed Cloud infrastructure, which fully abstracts 2G/3G/4G/5G pipelines into a singular, containerized platform. They target extreme reliability through proprietary data layers (like their stateless cloud database) rather than outsourcing data management to a public cloud.
In sharp contrast, non-Chinese mobile core vendors (Nokia, Ericsson, Samsung) treat the mobile core as pure application software meant to run on hyperscaler public clouds.
Nokia’s core portfolio supports deployment models spanning private, public and hybrid cloud environments and helps operators simplify core operations through automation, AI-driven analytics, resilient architectures and network exposure capabilities. The company’s approach enables telecom providers and mission-critical enterprises to accelerate service innovation while reducing operational complexity and improving network agility. Nokia is a primary driver of the “Core on Multi-Cloud” strategy, intentionally designing its cloud-native 5G Standalone (SA) core to be completely infrastructure-agnostic.
- AWS: Nokia collaborates with AWS to deploy automated, cloud-native packet core and IMS voice functions natively on AWS infrastructure.
- Google Cloud: Nokia utilizes Google Cloud infrastructure to run its 5G core while integrating Google’s advanced Generative AI and data analytics tools for autonomous network operations.
- Microsoft Azure: Nokia tightly integrates its core applications with Azure’s carrier-grade hybrid cloud platforms, optimizing hosting configurations for low-latency enterprise and edge applications.
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- Google Cloud: Ericsson deeply collaborates with Google Cloud to evolve its cloud-native packet core stack, optimizing it to run on Google’s autonomous cloud infrastructure paired with Google’s Cloud TPUs and GPUs for network AI scaling.
- AWS: Ericsson partners with AWS to validate its 5G core functions on AWS Outposts, targeting hybrid cloud deployments for tier-1 operators.
- AWS: Samsung optimizes its cloud-native 5G Core on AWS to allow operators to quickly spin up network slices and private 5G instances.
- Microsoft Azure & Google Cloud: Samsung partners with both hyperscalers to deliver end-to-end virtualized network functions (VNFs) at the enterprise edge
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References:
https://www.nokia.com/asset/215526/
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Applying Zero Trust at the Wireless Edge: Securing Mixed WPA2 and WPA3 IoT Fleets
By Iftikhar Javed khan with Ajay Lotan Thakur
Abstract
Zero-trust architecture is a security model that eliminates implicit trust based on network location and instead requires every access request to be continuously authenticated, authorized, and validated before it is granted (NIST SP 800-207), Yet while zero-trust architecture is intentionally network-agnostic, practitioners still need concrete design patterns to apply its principles at the wireless edge. This challenge is acute in IoT deployments that span multiple hardware generations, where newer devices support WPA3 while legacy endpoints remain limited to WPA2 and may be unable to support 802.1X supplicants, certificate-based authentication, or endpoint posture agents.
Based on an anonymized critical-infrastructure sensor deployment, this article presents a control pattern that segments devices according to their maximum supported security capability rather than forcing the entire fleet into a single compatibility-mode WLAN. WPA3-capable devices are placed in a WPA3-enforced domain, while WPA2-only devices are confined to a tightly restricted legacy domain.
The pattern combines five controls: capability-aware wireless segmentation, per-device Multi-Pre-Shared Key credentials, least-privilege policy enforcement, RF-exposure reduction, and access-point-integrated wireless intrusion detection. The central argument is that WPA3 is an important wireless security control, but it is not, by itself, a zero-trust architecture. Instead, the article shows how zero-trust principles can be translated into practical controls for networks that cannot immediately replace every legacy endpoint.
The Core Principle: Capability Dictates Posture, Not the Reverse
Most wireless security design begins with a chosen standard and then asks how to make the device fleet conform to it. In a homogeneous estate that works. In a mixed-generation IoT fleet it fails, because the fleet contains devices that physically cannot meet modern baseline sensors that support only WPA2-Personal, cannot run an 802.1X supplicant, and cannot host a posture agent. When a single standard is imposed on such a fleet, one of two things happens: either the network is dragged down to the capability of its weakest device, or the weakest devices are quietly excluded and left unmanaged. Neither is zero trust.
The inversion this article argues for is simple: let each device’s maximum supported security capability determine which policy domain it belongs to, and architect the network around that reality rather than against it. A WPA3-capable sensor and a WPA2-only sensor are not two configurations of the same policy; they are two different risk profiles that deserve two different domains. Once capability is treated as the independent variable, the rest of the design segmentation, credentialing, least-privilege enforcement, RF exposure, and monitoring follows from it.
Step One Is Always Visibility
Before any of this can be designed, the fleet has to be seen. In practice, the first problem in a mixed-capability wireless estate is not choosing controls, it is not knowing, with confidence, what is actually associated with the network and what each device can support. A design built on assumptions about the fleet is a design built on sand.
Visibility therefore comes first, and it has two parts as shown in figure 1. The first is a wireless inventory: enumerating the devices present on the medium, their association state, their supported security modes (WPA2-only versus WPA3-capable, SAE support, Protected Management Frames), and their physical distribution. The second is monitoring the medium itself for what should not be access points and clients that are not part of the sanctioned fleet. Only once the estate is known can devices be grouped by capability and confined to the right domain; and only once the medium is continuously observed can the segmentation be trusted to hold over time.
This reframes the usual order of operations. Segmentation and credentialing are what most WLAN-security discussions start with, but they are the second move. The first move is establishing and maintaining an accurate inventory because you cannot correctly assign a device to a capability domain that you have not yet discovered, and you cannot detect a rogue or misclassified device without ongoing observation.
Figure 1: Visibility
Why WPA3 Alone Is Not Zero Trust
WPA3 materially improves the wireless link. Under WPA2-Personal the pairwise master key is derived directly from the passphrase, so an attacker who captures the 4-way handshake can mount an offline dictionary attack against it. WPA3-Personal replaces that PSK authentication with Simultaneous Authentication of Equals (SAE), a password-authenticated key agreement that establishes the pairwise master key without exposing a crackable value the 4-way handshake still runs afterwards to derive and install session keys, but it no longer leaks an offline-attackable target. Protected Management Frames additionally harden management traffic against deauthentication and disassociation abuse. These are real gains, and WPA3-capable devices should use them.
But link protection is not access control. Whether a sensor is on WPA2 or WPA3 says nothing about which application it may reach, whether it may talk to its neighbours, whether it can reach a management interface, or how anomalous behaviour is detected after it has associated. A device can hold a perfectly valid WPA3 credential and still be a compromised endpoint. Zero trust asks a different set of questions than “is the link encrypted”: what is this device, what is it allowed to do, and is it still behaving as expected. Answering those requires credential context, an explicit policy decision, an enforcement point, and continuous monitoring none of which WPA3 provides on its own.
Figure 2: Controls
Control 1 : Capability-Aware Wireless Segmentation
Devices are grouped by their maximum supported wireless security capability and assigned to distinct SSIDs and security zones accordingly: WPA3-capable sensors to a WPA3-enforced domain, WPA2-only devices to a separate, narrowly scoped legacy domain, each with its own firewall and access-control policy.
It is worth being precise about the alternative, because the single-SSID case is often overstated. A WPA2/WPA3 transition-mode SSID is technically possible and would preserve legacy compatibility. The problem is not that one SSID mechanically forces one shared WPA2 key; it is that a transition-mode SSID cannot enforce WPA3-only operation for capable devices, and it places endpoints with materially different risk profiles inside the same wireless policy domain. Separating by capability is a deliberate choice for policy separation and to prevent silent downgrade of capable devices, not a workaround for a technical impossibility. The SSID is not the security boundary; it is the first sorting step, with real enforcement occurring downstream at the firewall or microsegmentation gateway.
Control 2: Per-Device Credential Granularity
A single fleet-wide pre-shared key is replaced with a per-device or narrowly-scoped-group credential scheme, so that a compromised credential exposes one device or a small group rather than the entire fleet, and any one device can be revoked without re-keying everything.
The security benefit is credential granularity and accountability, not cryptographic identity. A pre-shared key remains a possession-based secret: holding it proves possession, not verified device identity, and device identity should never rest on a spoofable MAC address alone. The honest framing is that per-device keying shrinks blast radius and enables granular revocation and gradual rotation, a meaningful improvement over a shared key, but a step below certificate-based authentication, which constrained legacy sensors often cannot support. That gap is precisely why the surrounding controls (segmentation, least-privilege enforcement, monitoring) carry the rest of the load.
Control 3: Least-Privilege Policy Enforcement
This is the control that turns WLAN hardening into a zero-trust pattern, and it is the one most often missing. Each sensor is permitted to reach only the services its function requires, enforced downstream of the wireless layer at a firewall or microsegmentation gateway:
- Permitted: its designated telemetry collector, an authorized DNS resolver, an approved NTP source, and a required update service where applicable.
- Denied: general internet access, sensor-to-sensor communication, any access to wireless or infrastructure management interfaces, and any reach into user, server, or administrative networks.
- Logged: denied flows and policy violations, forwarded to central monitoring for correlation.
Stated plainly: a sensor should be able to send its readings to exactly one collector, resolve names, keep time, and nothing else. Segmentation decides which domain a device lives in; this layer decides what it is allowed to do once there. Without it, capability-aware SSIDs are just better-organized flat networks.
Control 4 : RF-Exposure Reduction
Because the exposure boundary of a wireless network is defined by radio propagation rather than by cabling, access-point placement, transmit power, and minimum data rates are optimized to reduce unnecessary signal propagation beyond the intended service area, validated through an RF survey rather than controller settings alone.
This is defense-in-depth, not a perimeter. RF exposure reduction lowers casual reachability, but a sensitive receiver or a directional antenna can still detect a usable signal outside the intended area, so it must never be presented as containment. Any power or rate change must also preserve required coverage, client uplink performance, roaming behaviour, redundancy, and application reliability; an over-aggressive reduction that breaks associations trades a small exposure gain for an availability loss.
Control 5 : Wireless Intrusion Detection and Continuous Monitoring
Continuous observation of the medium closes the loop opened by the visibility-first principle. Access-point-integrated WIDS/WIPS, or dedicated monitor-mode sensors, observe the over-the-air environment for conditions that IP-layer inspection cannot see, because rogue-AP and evil-twin detection depends on access to raw 802.11 management and control frames rather than post-association traffic.
A practical detection methodology for this environment prioritizes, in order:
- Rogue-AP identification: Unsanctioned access points advertising reachable SSIDs, the highest-priority wireless threat because it can bypass the entire wired policy stack.
- Evil-twin / SSID impersonation: Access points spoofing a legitimate SSID to lure client associations.
- Unauthorized or misclassified clients: Devices associating outside their expected capability domain, or appearing where the inventory says they should not be.
- Association and authentication anomalies: Repeated authentication failures, unexpected device appearance or movement, and deauthentication patterns consistent with over-the-air attack.
Wireless events are forwarded to a central monitoring or SIEM platform and correlated with firewall, authentication, and telemetry logs, so an over-the-air anomaly and a policy violation on the same device can be seen together. Where a network IDS is also used, its role is post-association IP-traffic inspection a separate function from over-the-air 802.11 monitoring, and the two should not be conflated
Reference Architecture
The controls compose into a single flow: capability determines domain, credential and policy determine access, and the medium is continuously observed.
Figure 3: Reference Architecture
Mapping Wireless Properties to Zero-Trust Principles
| Wireless Property | Control Applied | Zero-Trust Principle |
| Heterogeneous device capability | Capability-aware segmentation | Group by risk; do not downgrade capable devices. |
| Shared credential / broad blast radius | Per-device credential granularity | Bounded compromise; granular revocation. |
| Implicit post-association trust | Least-privilege policy enforcement | Explicit per-device authorization. |
| Propagation-defined exposure | RF-exposure reduction | Reduce reachable attack surface (defense-in-depth). |
| Unobserved medium | Visibility + WIDS/WIPS | Assume breach; continuous over-the-air monitoring. |
Applicability to Other IoT Environments
Although derived from a remote environmental-monitoring fleet, the pattern generalizes to any heterogeneous wireless estate that cannot hold every device to one standard healthcare and medical IoT combining modern and legacy or safety devices, building-management and facilities systems, industrial and operational-technology sensors, and smart-city or asset-tracking deployments acquired across long procurement cycles. In each, the same five questions apply, and in the same order: is the estate actually visible; can endpoints be separated by capability and risk; can credentials be scoped per device or small group; can access be restricted to explicitly authorized services; and is the wireless medium continuously monitored.
Limitations and Residual Risks
- WPA2-only legacy devices remain a residual risk until lifecycle replacement; the pattern bounds that risk, it does not eliminate it.
- Per-device pre-shared keys improve granularity but do not provide the assurance of certificate-based authentication.
- RF-exposure reduction lowers casual reachability but cannot prevent reception by sensitive or directional equipment.
- WIDS/WIPS provides detection, not guaranteed prevention; false positives require tuning and operational handling.
- Posture assessment is often limited on constrained endpoints, which is why segmentation and least-privilege enforcement carry more of the load.
- Long-term remediation should include lifecycle planning to retire devices that cannot meet the required security baseline.
Implications for Standards and Practice
As IoT-specific zero-trust guidance matures, the wireless access layer where many IoT deployments are, in practice, most exposed deserves treatment as a primary zero-trust surface rather than a downstream detail. The pattern here is deliberately buildable with widely available capabilities (WPA3/SAE, per-device keying, firewall or microsegmentation policy, AP-integrated WIDS/WIPS), which matters for constrained, hard-to-patch fleets that cannot absorb heavyweight agents. Recent research on trust-boundary management in heterogeneous, multi-radio IoT environments makes a compatible argument that current zero-trust frameworks assume relatively stable networks and under-address dynamic wireless conditions and points to the same conclusion: shared-medium risk, capability-aware segmentation, and wireless monitoring belong in IoT zero-trust frameworks as first-class concerns.
Conclusions
Mixed-capability wireless IoT fleets should not be collapsed into one lowest-common-denominator policy. The more robust approach inverts the usual order: let each device’s capability dictate its security domain, establish and maintain visibility of the medium first, and then apply capability-aware segmentation, per-device credentialing, least-privilege enforcement, RF-exposure reduction, and continuous wireless monitoring on top of that foundation. WPA3 is an important control within this pattern, but it is not the pattern itself. The result is a reusable, achievable design for organizations that must secure legacy and modern wireless IoT devices at the same time without waiting for a fleet-wide hardware refresh that may never come.
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References
- Cloud Security Alliance, Zero Trust Guidance for IoT https://cloudsecurityalliance.org/artifacts/zero-trust-guidance-for-iot
- IEEE Std 802.11-2024 (WLAN MAC and PHY base standard) https://standards.ieee.org/ieee/802.11/10548/
- Wi-Fi Alliance WPA3 certification documentation (SAE, Protected Management Frames)
- J. Shelby, “Zero Trust for Multi-RAT IoT: Trust Boundary Management in Heterogeneous Wireless Network Environments,” arXiv:2602.08989
- Rose, S., Borchert, O., Mitchell, S., and Connelly, S., Zero Trust Architecture, NIST Special Publication 800-207, National Institute of Standards and Technology, August 2020.
- https://www.cwnp.com/uploads/1660_cwnp-wp_revisiting-wpa-2-understanding-its-security-gaps-and-evaluating-the-move-to-wpa3_ver120224.pdf
About the author:
Iftikhar Javed khan is an enterprise security architect specializing in wireless security and zero-trust architecture for IoT and critical-infrastructure environments. He holds the Cisco CCIE and CWNP CWNE credentials and is a Senior Member of the IEEE. https://www.linkedin.com/in/iftikhar-j-03aa5533/
Ericsson and MediaTek Demonstrate 3GPP-Based GNSS RTK Positioning with Sub-30cm Accuracy Over a Commercial 5G Network
Ericsson and MediaTek have completed a world-first end-to-end demonstration of high-precision outdoor positioning over a commercial 5G network, using standardized 3GPP Global Navigation Satellite Systems (GNSS) Real-Time Kinematic (RTK) assistance. The trial achieved stable outdoor positioning accuracy below 30 cm using a handset’s integrated antenna, demonstrating a scalable path to decimeter-level positioning without proprietary positioning infrastructure or dedicated external GNSS hardware.
The demonstration validates how 5G networks can distribute GNSS RTK correction information efficiently to devices for industrial automation, autonomous mobility, drones, robotics, and other applications requiring reliable, mission-critical location awareness.
Image courtesy of Ericsson
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Technical highlights:
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Sub-30 cm positioning performance: The device achieved stable outdoor accuracy below 30 cm with its built-in antenna. With a geodetic-grade GNSS antenna, the same RTK approach can support centimeter-class positioning performance.
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3GPP-standardized architecture: The solution uses capabilities specified in 3GPP Release 16 for GNSS positioning assistance, enabling an interoperable architecture across compatible networks, positioning servers, chipsets, and devices.
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End-to-end unicast and broadcast validation: Both delivery modes were demonstrated end to end:
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Unicast GNSS RTK: LPP assistance messages are delivered through Secure User Plane Location (SUPL) over standard IP connectivity, enabling targeted delivery to individual UEs.
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Broadcast GNSS RTK: RTK assistance data is transmitted through Positioning System Information Blocks (PosSIBs), allowing common assistance information to be delivered simultaneously to all capable devices within a cell.
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Efficient scaling model: Broadcast delivery can reduce repeated transmission of identical RTK correction data in high-density areas, while unicast can be used for lower-traffic scenarios, individualized service handling, or LTE-connected devices. This supports flexible selection of the most efficient assistance-delivery method based on coverage, device population, and radio-resource conditions.
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Secured broadcast operation: While broadcast information is radio-accessible to devices in the coverage area, 3GPP mechanisms support encrypted positioning assistance. Authorized subscribers obtain the required decryption material through the 5G Core, including the Access and Mobility Management Function (AMF), protecting access to premium high-precision positioning services.
GNSS correction support:
The trial supports two standardized GNSS correction-data models:
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Observation State Representation (OSR): Provides corrections derived for a specific receiver or location context, including GNSS observation-related corrections.
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State Space Representation (SSR): Delivers correction parameters associated with GNSS satellite orbit, clock, bias, and atmospheric error states. SSR is particularly suitable for scalable service delivery because the common state information can be applied by many devices across a service area.
In both cases, the UE applies the received RTK assistance in real time to mitigate satellite, orbital, clock, ionospheric, tropospheric, and other GNSS error sources that limit conventional standalone GNSS accuracy.
Network and device implementation:
The test used Ericsson’s 5G Core, Ericsson Network Location (ENL), Ericsson 5G Advanced Location Services, and Ericsson RAN to generate, manage, and distribute OSR and SSR correction information through the mobile network. MediaTek validated the device side using its latest 5G modem technology with integrated GNSS capability, including real-time processing of advanced GNSS correction data with power-optimized implementation.
“This is a milestone for the 5G ecosystem,” said Johan Hultell, Head of Product RAN Software at Ericsson. “Together with MediaTek, we make high-precision location more accessible to device makers and enterprises, accelerating use in manufacturing, transport and beyond.”
Dr. HC Hwang, General Manager of Wireless Communication Systems and Partnerships at MediaTek, added: “The collaboration with Ericsson demonstrates how 5G Advanced technology can unlock new value for consumers and enterprises alike. Our modems with integrated GNSS are ready to support these advanced positioning services, paving the way for the next generation of intelligent devices.”
Technology backgrounder:
Conventional standalone GNSS typically provides positioning accuracy in the range of several meters. RTK improves this performance by using measurements from accurately surveyed reference stations to generate correction assistance. In the 3GPP architecture, this assistance is delivered through the cellular network using LPP-based positioning procedures and, where applicable, broadcast system information.
By standardizing the delivery of GNSS RTK assistance over 4G and 5G networks, operators can offer high-precision location capabilities at network scale. This creates a standards-based foundation for digitalized industrial operations, autonomous systems, intelligent transportation, asset tracking, and spatially aware consumer devices.
Accuracy to within tens of centimeters is a significant improvement compared to traditional GNSS, which is accurate to within several meters, and therefore paves the way for more advanced use cases. These include the safe operation of autonomous vehicles, drones and industrial robots in complex outdoor environments, Ericsson said. Complex environments encompasses places like factories, logistics hubs, ports, and mines etc.
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References:
https://www.ericsson.com/en/news/2026/8/ericsson-and-mediatek-raise-the-bar-for-location-accuracy
https://www.telecoms.com/5g-6g/ericsson-and-mediatek-put-precise-5g-positioning-through-its-paces
MediaTek overtakes Qualcomm in 5G smartphone chip market
MediaTek will use TSMC to make its Dimensity SoC’s in 2024
AT&T’s 600 MHz Deployment with Ericsson: Turning Low-Band Spectrum Into Coverage and Uplink Capacity
AT&T/Ericsson Demonstrate 5G-Based ISAC for Drone Detection at World Cup Stadium
Ericsson leads SK Telecom AI RAN vs. NVIDIA’s GPU centric AI RAN Alliance
AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era
Analysis: Ericsson’s leading role in French INTENTION 6G project
5G in Europe: Broad coverage but 5G SA cloud native core network lags other major markets (Table)
Europe’s 5G market status appears increasingly uneven. The region has achieved broad 5G population coverage, but its transition to the 5G Standalone (SA) core network—and the cloud-native 5G Core required to realize 5G features and capabilities—remains materially behind several major markets.
A recent workshop report from the EU-backed European 5G Observatory highlights stakeholder concern that European investment remains weighted toward radio-access coverage rather than toward core-network modernization. The underlying Observatory assessment also characterizes EU deployment as still predominantly NSA, meaning much of the installed 5G radio layer continues to rely on an LTE/EPC anchor rather than a service-based 5GC architecture. Participants estimated that only 18% of European 5G investment is directed to the core network, compared with 40% in China and 34% in the United States, South Korea, and Japan. Although European 5G core investment reportedly rose 31% from 2024, the prevailing view was that the next phase of 5G deployment must prioritize SA-capable core infrastructure rather than further expansion of basic 5G coverage.
This distinction is technically significant. Much of Europe’s existing 5G footprint remains based on Non-Standalone (NSA) deployment, in which 5G NR radio access is anchored to the LTE/Evolved Packet Core domain. The 3GPP-defined SA architecture instead pairs 5G NR with the 5G Core (5GC), including its service-based architecture specified principally in 3GPP TS 23.501 and TS 23.502. A full SA implementation enables functions that NSA cannot support as comprehensively, including end-to-end network slicing, native 5G QoS flows, ultra-low-latency service support, exposure of network capabilities through APIs, and more flexible edge and enterprise-service integration.
Europe’s aggregate 5G investment level is reported at 50.6%, substantially above the proportion allocated to the core but below China’s 72.0%, the United States’ 62.0%, and Japan’s 58.0%. The imbalance helps explain why headline coverage statistics do not yet translate into comparably mature 5G SA availability or widespread advanced-service monetization.
The Observatory report indicates that basic 5G coverage now reaches nearly all EU citizens, corresponding to 96.8% of households overall and 88.9% of rural households. However, deployment of SA-capable sites remains limited: on average, only 21.6% of European base stations are reported to operate in 5G SA mode, compared with 36% in the United States and 35% in China.
The gap becomes more pronounced when considering commercial service availability. While 21.6% of European base stations may be SA-capable, commercial SA availability to end users is reported at only 2.8%. India provides a notable contrast: the report places its SA-capable base-station share at 10%, below Europe’s, but commercial SA availability at 50%. The comparison suggests that Europe’s challenge is not only deployment of SA-capable RAN and 5GC infrastructure, but also the operationalization, device enablement, service launch, and commercial scaling of SA offerings.
Stakeholders attributed the low European availability figures partly to the current concentration of SA deployment in enterprise and business-to-business applications. Germany reportedly has the EU’s highest proportion of SA-capable base stations, at 63.2%, yet commercial availability stands at only 2.5%. Austria has the highest reported EU SA availability, although that figure remains modest at 8.7%.
This enterprise emphasis is understandable. A 5G SA network provides the architectural basis for differentiated connectivity services specified across the 3GPP 5G system framework, including network slicing and dedicated QoS treatment. In particular, 3GPP TS 23.501 defines network-slice concepts and service requirements, while 3GPP TS 23.503 specifies policy and charging-control mechanisms that can support service differentiation. Private 5G networks, campus deployments, industrial automation, logistics, utilities, and critical-infrastructure applications are therefore among the most immediate candidates for SA-led value creation.
Workshop participants nevertheless agreed that broader 5G SA deployment, together with private-network expansion, will be necessary if Europe is to capture the full economic value of 5G. That requires investment not only in 5GC functions, but also in cloud infrastructure, transport capacity and synchronization, orchestration, security, operational automation, and interoperable exposure frameworks. In practical terms, the opportunity is to shift from a coverage-centric 5G model to one capable of delivering programmable, assured, and differentiated connectivity services.
The European Commission established the 5G Observatory in 2018 as an evidence-gathering and policy-support mechanism. The Commission reports that the EU had reached 75.3% harmonized spectrum assignment by 2025, with several Member States approaching or achieving full assignment. Spectrum progress is important, but it does not by itself ensure SA maturity: operators must still convert spectrum assets and broad NR coverage into commercially available 5GC-based services.
The investment challenge is substantial. A GSMA assessment published earlier this year estimates that Europe will require approximately $550 billion in mobile-network investment over the next decade, while operators may have access to only about $312 billion. That implies an investment gap of roughly $238 billion—one that could constrain Europe’s ability to close the SA, cloudification, and advanced-network-services gap with leading global markets.
Mr. Johannes Theiss, DG CNECT Head of Sector for Advanced Networking Technologies and Applications at the European Commission noted that 5G standalone (SA) will be crucial on the road towards 6G, though further work is needed to develop indicators that will be both meaningful and manageable. As preparations begin for how 6G progress will eventually be measured, the experience of tracking 5G provides a useful basis for identifying what worked well and what should be approached differently.
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5G SA -Europe vs RoW Comparison:
A 5G SA system requires both 5G NR radio access and a 3GPP 5G Core (5GC), rather than NR anchored to an LTE EPC as in NSA. The relevant architecture is defined principally in 3GPP TS 23.501 and associated procedures in TS 23.502. Accordingly, the table is best interpreted as an indication of how extensively each market has extended SA capability into the RAN—not as a direct count of deployed cores.
The EU’s 5G SA deployment footprint represented 20.9% of all mobile base stations in 2025, compared with 36.2% in the United States, 34.8% in China, 26.3% in Japan, and 26.2% in South Korea
5G SA deployment proxy – 5G SA base stations as a share of all mobile base stations (Source: Perplexity.ai):
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References:
Ookla: Uneven 5G deployment in Europe, 5G SA remains sluggish; Ofcom: 28% of UK connections on 5G with only 2% 5G SA
Ookla: Europe severely lagging in 5G SA deployments and performance
Dell’Oro: Telecom carriers are on a 5G SA spending spree with more to come
GSA: 5G Non Terrestrial Networks, 5G SA and 5G Advanced gain momentum
Dell’Oro: Mobile Core Networks +15% in 2025; Ookla: Global Reality Check on 5G SA and 5G Advanced in 2026
Dell’Oro: RAN market stable, Mobile Core Network market +14% Y/Y with 72 5G SA core networks deployed
AT&T deploys nationwide 5G SA while Verizon lags and T-Mobile leads
Impact of optical component shortages & bottlenecks explained + Hyperscaler’s CAPEX
LightCounting’s August 2026 market and component report highlights how severe physical supply shortages for high-speed optical components are reshaping data-center architectures and forcing cloud builders to expand their vendor pools from the traditional 2-3 suppliers up to 5-7 specialized hardware manufacturers. Qualifying and managing all of them is a new challenge.
- Applied Optoelectronics, Macom, and MaxLinear have already reported accelerating growth in Q2 2026 and we expect to see more examples in the upcoming earnings reports.
- Coherent and Lumentum have also reported improvements in growth rates for the last quarter, catching up with Eoptolink and Innolight (reporting at the end of August). Eoptolink has already disclosed a sharp increase in the profits expected for Q2. Accelink and CIG also report sharply higher profits.
- Tower Semiconductor reported triple-digit growth (y-o-y) in silicon photonics revenue in Q2-2026.
- GlobalFoundries plans to double its silicon photonics business in 2026.
- Cisco reported “a remarkable 28% y-o-y increase” in the networking segment revenues, up from 25% in Q1. Cisco reported three new hyperscale AI design wins and 40% growth in orders during the quarter. The company also reported record orders for campus networking – up 20% y-o-y.
- Arista Networks reported Q2 2026 revenue of $3.04 billion, marking its first-ever $3 billion quarter. This represented a roughly 38% year-over-year increase. Management also raised its full-year 2026 revenue growth outlook to 40%.
- Calix reported 21% y-o-y growth in Q2 and guided for another 15% in the current quarter. Growth in company’s revenue is attributed to sales of broadband equipment and AI-enhanced software to telecom operators.
- Extreme Networks reported 14% y-o-y growth in product sales, in part driven by success of its agentic AI networking platform.
Surging data-center traffic driven by AI queries has created acute optical component shortages, fundamentally altering supply chain qualifications for hyper-dense network switches and interconnects. The primary optical component bottleneck is a severe production shortage of Indium Phosphide (InP) laser chips and EML (Electro-Absorption Modulated Laser) components, which are failing to keep pace with a projected 53% surge in total optical transceiver and hardware demand, reaching $39 billion. Current demand for high-speed datacom optics is outstripping available supply by roughly 30%, forcing hyperscalers and system vendors to fundamentally restructure their supply chains. More details in the Addendum at the end of this article.
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Dell’Oro Group says that the rise of agentic AI and inference workloads is driving new demand and introducing network requirements that differ from those associated with training workloads. This shift is leading to significant expansion of front-end networks to support the broader buildout of AI infrastructure. We estimate that more than half of the growth in Front-end Data Center Switch sales over the next few years will be driven by AI-related opportunities. These deployments represent net-new expansion opportunities for both established vendors and new market entrants.
Accton, Arista, Celestica, Cisco, HPE/Juniper, H3C, Huawei, NVIDIA and others—emerge as the primary beneficiaries of this growth, according to the market research company.
“As AI infrastructure shifts from large-scale training to inference and agentic workflows, there is an increasing demand for general-purpose infrastructure, and expanded front-end network requirements,” said Sameh Boujelbene, Vice President at Dell’Oro Group. “The traditional assumption of a 10-to-1 ratio of XPU to CPU no longer applies across all deployments, with some environments moving closer to a 1-to-1 ratio. CPUs are becoming increasingly important for workloads orchestration and data movement. Additionally, networking for KV caching storage rack is also needed for inferencing applications,” added Boujelbene.
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As for the network providers/ISPs:
- Verizon announced a dark-fiber deal with Google to support the hyperscaler’s data center traffic growth. The deal is valued at over $1 billion and Verizon said there will be other deals announced before year-end.
- Deutsche Telekom said its AI factory for Germany’s public institutions and businesses that opened in February, has already sold the site’s 10,000 Blackwell GPUs capacity. DT is now looking at increasing capacity by another 20,000 GPUs.
- Comcast cites that its upstream broadband traffic is growing at 2.5x the rate of downstream data, which it links to AI-driven queries.
- AT&T is seeing rapid increases in large-scale data traffic requiring high-capacity metro and intercity fiber infrastructure.
Capex of Telecom Service Providers is still expected to be flat (or down 1%) in 2026, but AT&T and Comcast reported 16% and 20% y-o-y growth in capex for Q2, respectively. Both companies are investing more in broadband access. Verizon’s capex was also up, but only by 5% in Q2.
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Hyperscaler CAPEX:
The chart below illustrates the capital intensity of the TOP 4 Cloud companies. It is up sharply: Meta is already at 51% and Microsoft is at 46% in Q2. Oracle (not included in the figure) would have been off the chart at above 80% in Q1. The company reports earnings in early September and the key question is how it intends to finance future investments. Some moderation in spending growth is well needed. Amazon is the largest spender with $54.2 billion for the quarter, up 73% y-o-y. Amazon also increased guidance for 2026 capex from $200 billion to $220 billion.
Recent $ Trillion fundraising activities of Anthropic and OpenAI, supported by Nvidia, were widely covered yet both companies continue to lose money. Some financial experts refer to these activities as the “future for financial engineering.” Any innovation comes with some risk, but we all hope for the best.
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Addendum: Optical Network Bottlenecks Explained:
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- InP Epitaxial Production Limits: The primary physical constraint lies in the fabrication and epitaxy capacity for Indium Phosphide wafers used to build high-frequency lasers, where specialized manufacturing equipment (such as MOCVD systems) remains heavily backordered.
- Electrical Trace and Power Walls: Inside massive AI clusters, traditional pluggable optical modules face severe latency, thermal, and signal-integrity walls due to long copper electrical traces running from switch ASICs to the optical engine.
- Packaging and Testing Complexities: Shifting from legacy designs to dense 800G, 1.6T, and co-packaged optics (CPO) introduces intricate multi-die integration and hybrid bonding hurdles that slow down final module validation and packaging yields.
- Expanding Vendor Pools: Cloud builders are aggressively expanding their sourcing lists, moving from a concentrated pool of 2–3 legacy optical vendors out to 5–7 specialized component manufacturers to secure allocation.
- Ecosystem Pivot to Photonics: Major semiconductor players (such as Marvell’s acquisition of Celestial AI) and optical fabricators are pouring capital into silicon photonics and optical interconnect architectures to bypass standard copper and discrete laser constraints.
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- Mechanism: LPO removes the power-hungry Digital Signal Processor (DSP) and clock/data recovery (CDR) chips directly from inside the optical transceiver module.
- The Fix: Raw, linear analog signals are driven straight from the switch or network interface card (NIC) ASIC.
- Benefits: Slashes module power consumption by 50% or more and cuts latency while preserving the hot-swappable, multi-vendor front-panel pluggable form factor.
- Trade-off: Requires host ASICs with advanced analog front-end and signal-equalization capabilities to process the raw electrical signals.
- Mechanism: CPO moves the optical engine (which converts electrical signals into light) off the front-panel cage and places it directly onto the same substrate or interposer as the switch or accelerator ASIC.
- The Fix: Electrical traces shrink from centimeters down to mere millimeters, completely bypassing lossy copper-clad circuit boards and high-power SerDes requirements.
- Benefits: Reduces optical-interface power consumption by up to 75% and maximizes bandwidth density for ultra-dense GPU scale-up fabrics.
- Trade-off: Reworks serviceability—if an optical engine fails, the repair domain shifts from a simple two-minute transceiver swap to board- or switch-level replacement
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References:
Agentic AI and Inference to Supercharge Front-End Networks Growth, According to Dell’Oro Group
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Goldman Sachs report: Optical Networking is the next mega trend in AI infrastructure
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
Meta’s “Iris” AI Chip for MTIA: Implications for Telecom-Grade Optical Networking, DCI and High Capacity Ethernet Fabrics
Oriole Networks photonic networking platform to be integrated with AMD GPUs/CPUs for next-gen AI data center fabrics
Cheap Chinese AI Models: Unappreciated Threat to U.S. Hyperscaler AI Dominance
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Disclaimer: Perplexity.ai was used for research and analysis in this article.
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Executive Summary:
This past Monday, six giant Wall Street asset managers, private-equity firms and banks announced an effort to raise $500 billion to keep fueling the A.I. boom by financing more data centers, power plants and chips. The proposed platform would direct capital to Nvidia customers—including AI startups and data-center operators—at precisely the point when many have struggled to obtain funding through ordinary credit channels.
We take that as a huge warning sign for the proposed AI investments. Here’s why: If the underlying projects offered clearly proven cash flows, predictable utilization and collateral with durable value, lenders would not need a specially assembled consortium, headline-scale commitments and Nvidia’s direct involvement to make the loans happen. The initiative appears designed to overcome a financing bottleneck created by the extraordinary gap between AI infrastructure spending and demonstrated AI revenue.
This proposed $500 billion AI-financing initiative is less a validation of durable AI economics than an admission that the sector’s spending plans have outgrown its customers’ ability—or willingness—to finance them conventionally. Rather than demonstrating independently sustainable demand, the arrangement risks extending an investment cycle increasingly dependent on vendor-enabled credit, opaque commitments and financial engineering.
The firms—Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR—said they were working together to come up with that huge sum to lend to Nvidia’s customers, including the start-ups that use the company’s chips in data centers to develop and operate A.I. software. These customers, Nvidia said, have been struggling to secure financing for chips and data centers.
Nvidia will connect its customers with one of the six lenders, which will provide financing that could range from loans to credit. The financing will be “at attractive rates,” Nvidia said in a blog post.
In practical terms, this is a vendor-financing mechanism, even if the capital technically comes from third parties. Nvidia is helping its customers obtain the money required to buy Nvidia-dependent infrastructure. The more readily startups and data-center operators can borrow, the more equipment they can order; the more equipment they order, the more Nvidia can sell. That does not mean the demand is fictitious, but it does make it harder to distinguish independent end-user demand from demand supported by an ecosystem that is financing itself.
Circularity is the principal concern. When suppliers, investors, cloud providers, AI labs and lenders all have financial incentives to keep capital circulating within the same small group of counterparties, reported growth can look more robust than the ultimate economics justify. The risk is not simply that projects fail individually. It is that a shortfall in AI-service revenues, utilization or pricing could spread simultaneously through hardware vendors, AI developers, cloud operators, private-credit vehicles and the securities backed by their cash flows. Circular financing can blur the line between real external demand and investment-funded purchases.
Executives from the lenders joined Jensen Huang, Nvidia’s CEO & founder, for an unusual, extended interview on CNBC, where they talked up their new, seemingly insatiable desire to finance infrastructure for A.I. Huang said on the air. He added that “A.I. labs” and “A.I. start-ups” would have access to the financing. He did not name those companies or whether Nvidia would receive any money as part of the effort. David M. Solomon, Goldman’s chief executive, said the consortium was Mr. Huang’s idea.
“We need to raise this money as fast as possible,” said Larry Fink, BlackRock’s chief executive. He also stated: “There’s quite a bit of negativity around A.I. and data centers right now, but let’s be clear: This is going to be creating a huge amount of jobs.”
Skeptical Analysis:
The urgency in those statements deserves more scrutiny. Speed is not a substitute for underwriting, and job-creation claims do not establish that investments will meet their cost of capital. The industry is attempting to finance assets whose useful economic life may be much shorter and more uncertain than that of traditional infrastructure. A GPU fleet can lose competitiveness rapidly when a new architecture, memory standard or systems design emerges. Its resale value can fall sharply if capacity demand weakens. Treating such hardware as collateral comparable to a toll road, utility asset or long-lived building is a major assumption—not an established fact.
The announcement punctuates a head rush on Wall Street and in Silicon Valley into anything that even vaguely resembles A.I. The stocks of tech giants and chipmakers have soared for most of this year, and a pair of the biggest names in the space, Anthropic and OpenAI, are expected to file for initial public offerings that could value them at $1 trillion apiece.
But soaring equity valuations and enormous projected IPO valuations do not answer the basic return-on-investment question: who will pay enough, for long enough, to justify the total cost of the data centers, power plants, networking, GPUs and debt now being assembled? AI vendors may generate impressive revenue growth while still failing to earn enough to cover the depreciation, energy, financing and replacement costs of the infrastructure required to produce it.
Nvidia’s financing narrative appears to conflate broad interest in AI-enabled services with broad, independent demand for capital-intensive AI infrastructure. Governments, enterprises and startups may all seek access to AI capabilities, but the current demand for hyperscale GPU clusters, dedicated power capacity and purpose-built “AI factories” remains concentrated among a relatively small group of frontier-model developers and cloud platforms.
That distinction matters because broad adoption of AI applications does not automatically translate into economically sustainable demand for vast new data-center capacity. Enterprises can consume AI through APIs, hosted platforms, smaller models and open-source software without owning—or financing—dedicated high-density compute infrastructure. Nvidia itself describes demand as spanning enterprises, startups, governments, nations and AI labs, but the critical question for investors is how much of that demand converts into durable, contracted infrastructure revenue rather than experimentation, pilots or subsidized consumption.
As Bloomberg reported last week, OpenAI accounted for somewhere between 50% and 70% of Microsoft’s AI revenue in the 12 months to 30 June.
That degree of concentration is significant. Microsoft disclosed $24.1 billion in sales from OpenAI during the year ended in June; outside estimates place that at more than half, and perhaps about 70%, of Microsoft’s AI-related revenue. Much of the revenue reflects OpenAI’s spending on Microsoft cloud and model-development services, meaning a substantial portion of the apparent AI revenue base may be generated within a tightly linked commercial relationship rather than by a diversified population of external enterprise customers.
That could be problematic if the picture is similar at other hyperscalers and at other frontier AI companies like Anthropic, for example. A market in which a handful of loss-making model developers drive an outsized share of cloud and infrastructure revenue is inherently more fragile than one supported by a broad base of profitable end users. It exposes infrastructure providers and lenders to customer concentration, correlated capital spending and the possibility that a reduction in financing at one frontier lab quickly reduces demand across the supply chain.
Indeed, as has been reported by multiple correspondents – most notably staunch AI critic Ed Zitron – LLM makers like OpenAI are losing money hand over fist and can only charge so much for tokens before customers either curb their usage or switch to open source models. Zitron wrote:
“It’s estimated that 70% or more of the AI revenues of Microsoft, Google, and Amazon were from either OpenAI or Anthropic. UBS estimated that next year, Anthropic and OpenAI’s compute spend would be 48% of all Google Cloud revenues — which means that they likely account for even more than 70% of its AI revenues.”
The commercial challenge is not whether frontier models have value- they do. It is whether they can deliver that value at prices that exceed the combined costs of training, inference, electricity, networking, cloud capacity, depreciation and ongoing model development. If token pricing rises too far, customers may reduce usage, shift workloads to lower-cost models, or use open-source alternatives. If pricing remains low, frontier-model providers may struggle to cover their infrastructure bills. Open-source fine-tuning can materially reduce costs for specialized enterprise workloads, reinforcing the competitive pressure on premium proprietary-model pricing.
The most plausible downside is that the technology becomes broadly useful but insufficiently profitable to support today’s extreme capital intensity. That outcome would leave the sector with too much high-cost capacity, thin margins, declining GPU collateral values and lenders dependent on assumptions about utilization and cash flows that have not yet been tested through a downturn. The foundational financial risk is straightforward: AI-service revenues may prove insufficient to service the fixed obligations incurred to construct the infrastructure. Notably, Nvidia stock dipped modestly on Monday after The Financial Times reported that the company was nearing the mammoth financing deal.
While the contours of the arrangements were announced Monday afternoon, details remained scarce. A joint news release referred only to “memorandums of understanding” to “create dedicated pools of capital at significant scale.”
That language is important. Memorandums of Understanding (MoU’s) are not equivalent to binding, fully funded commitments. Until investors know the actual terms—capital committed, leverage permitted, collateral requirements, Nvidia’s role in losses, loan maturities, customer concentration limits and underwriting standards—the $500 billion figure is better understood as an ambitious financing target than as validated capital deployment. Reporting has described the initiative as a multiyear target rather than cash already committed, while questions remain about the scale of any Nvidia backstop.
During their television interview, the lenders’ executives alluded vaguely to “yield-based products” and even securitization, or the creation of bonds that would divvy up the revenue from A.I. labs into risky and less risky categories. There were several references to A.I. as a new asset class and to allowing smaller investors an opportunity to invest in debt backed by the data centers.
This is where the proposal moves from aggressive investment to potential systemic-risk creation. Securitization can distribute risk, but it does not eliminate it; it can instead diffuse difficult-to-value exposure across a wider investor base. Packaging AI-data-center debt into yield products may make financing more available, but it also risks obscuring the quality of the underlying cash flows, the degree of correlated exposure among borrowers and the vulnerability of rapidly depreciating hardware collateral. The more complex the capital stack becomes, the greater the danger that investors mistake engineered liquidity for genuine economic value.
Executive Quotes:
“NVIDIA has reached an important milestone. We began by building chips; today, we are helping create a new class of productive, investable infrastructure: AI factories,” said Jensen Huang, founder and CEO of NVIDIA. “In AI, compute is revenue. NVIDIA compute is uniquely suited for this role. It is broadly adopted, flexible across models and workloads, fungible and transferable across customers and operators, and continuously improved through CUDA software — extending its useful life and improving its economics over time. It is supported by a deep global ecosystem of developers, customers and offtakers. That is why we are bringing the world’s leading long-term capital providers together to independently underwrite AI infrastructure. These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country in the age of AI.”
“Modern compute has emerged as a scarce, mission-critical asset class with compelling investment characteristics that is positioned to drive significant long-term economic growth and productivity gains,” said Apollo President Jim Zelter. “The combination of NVIDIA’s proprietary technology ecosystem and Apollo’s flexible, long-term capital base provides a strong foundation to support the next stage of the AI buildout as part of the broader Global Industrial Renaissance.”
“The AI buildout will require unprecedented investment and a skilled workforce to turn that investment into the infrastructure that will help power future growth,” said Larry Fink, Chairman and CEO of BlackRock. “This partnership deepens our relationship with NVIDIA, including through the AI Infrastructure Partnership, and brings together NVIDIA’s leadership in accelerated computing with BlackRock’s ability to connect long-term capital to essential infrastructure. Together, we can help deliver the compute capacity that companies need to grow and create more jobs, supporting the continued growth of the U.S. and global economies, while creating attractive, long-term investment opportunities for our clients.”
“NVIDIA has created extraordinary demand for its compute through an intense focus on customer value and versatile technology,” said Jon Gray, President and COO of Blackstone. “We continue to be enormous investors globally across the NVIDIA ecosystem, and this announcement further underscores our confidence in their platform and the future of AI infrastructure.”
“As our strategic partner, NVIDIA is enabling us to scale AI factories. We are excited about further collaboration to build and fund the backbone of AI globally,” said Bruce Flatt, CEO of Brookfield. “With demand for large-scale AI compute growing significantly as adoption scales across industries, compute is fast becoming the essential layer of infrastructure and a core pillar of the Brookfield AI infrastructure strategy.”
“We’re in a pivotal moment of a historic AI investment cycle. NVIDIA’s full-stack platform is in high demand and uniquely positioned at the center of that global buildout,” said David Solomon, Chairman and CEO of Goldman Sachs. “Our investment and distribution roles reflect our confidence in NVIDIA’s leadership, and we’re excited for the new opportunity to create a market for credit backed by NVIDIA compute.”
“Compute has become a critical infrastructure asset. As we’ve scaled our approach to digital infrastructure, we’ve learned that delivery, not ambition, is the hard part. That’s why we’re excited to build on our strategic partnership with NVIDIA, a founding investor in Helix Digital Infrastructure, to bring together NVIDIA’s accelerated computing platform with KKR’s long-duration capital, infrastructure expertise and capital markets capabilities to turn growing demand into real capacity at extraordinary scale,” said Joe Bae and Scott Nuttall, Co-Chief Executive Officers of KKR.
“This is the very beginning — like what it was when I started in the mortgage-backed securities market in the 1970s,” Mr. Fink said. “I look upon this as a next future for financial engineering.”
That analogy should be treated as cautionary, not reassuring. Financial engineering can expand access to capital and spread risk efficiently when assets have transparent valuations, stable cash flows and conservative underwriting. It becomes dangerous when it is used to finance unproven revenue models, rapidly obsolescing assets and demand forecasts that must remain exceptionally optimistic simply to justify the initial investment.
Huge Risks Explained:
If AI demand stalls, Nvidia faces a sharp reversal in hardware demand and a potentially damaging credit overhang, while lenders could be left financing underutilized data centers secured by equipment whose value can decline far faster than conventional infrastructure. The common vulnerability is that the same uncertain AI revenue streams would be expected to support chip purchases, data-center leases, project debt and securitized investment products. Nvidia is especially exposed to risks:
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Order cancellations and lower pricing power. Cloud providers, AI labs and startups would likely slow GPU orders, defer deployments or renegotiate capacity commitments. Nvidia could face weaker revenue growth, inventory risk and pressure on the high margins that have supported its valuation.
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Vendor-financing and counterparty risk. If Nvidia is arranging or backstopping financing for customers buying its systems, a demand slowdown could turn what appeared to be hardware sales into indirect credit exposure. Customers unable to earn adequate returns from AI services may struggle to repay loans used to buy Nvidia equipment. The risk is magnified where the company’s commercial success depends on borrowers gaining access to financing in the first place.247wallst+1
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Collateral impairment. GPUs are not durable, slow-depreciating infrastructure assets. A new chip generation, a shift toward more efficient models, or weak utilization can materially reduce the resale value of installed systems. If lenders rely on those systems as collateral, a default could leave them holding equipment worth substantially less than the loan balance—and Nvidia could face lower demand for both new and prior-generation products. Moody’s identifies rapid capacity expansion and fast-changing chips, cooling and computing architectures as sources of overbuilding and technology-obsolescence risk.moodys
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Feedback-loop risk. A slowdown could create a negative cycle: AI customers reduce spending; Nvidia’s sales weaken; lenders become more cautious; financing availability tightens; customers cut orders further. Where vendors, customers and capital providers are financially intertwined, the decline can be more abrupt than a normal inventory correction.
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Equity-valuation risk. Nvidia’s market value reflects unusually strong assumptions about the longevity of AI spending, margins and growth. A reassessment of those assumptions could compress the valuation sharply even if Nvidia remains profitable. BIS warns that disappointment in AI returns could trigger a sudden financing pullback and turn the capex boom into a prolonged investment bust.bis
Lenders’ exposure:
The most immediate lender risk is a mismatch between long-lived debt obligations and unstable, technology-dependent revenue. A data center might be financed over many years, but its GPU fleet may need continual upgrades to remain competitive—requiring additional capital expenditure before the original debt is repaid. Moody’s notes that this combination of increasing capital intensity, uncertain compute requirements and structured finance can pressure developers, landlords and investors through execution, renewal and refinancing risk.moodys
Construction and power risks:
A demand slowdown could arrive before projects enter service. That creates a particularly difficult situation for lenders because construction interest, cost overruns and power-reservation charges may accumulate before revenue begins.
Permitting delays, local opposition, water constraints and electricity-grid limitations further compound this exposure. Reuters reported that lenders increasingly treat project readiness—including approvals, permits and local community support—as a credit factor because delays can increase costs, jeopardize covenants and prevent projects from reaching revenue-generating operation.
System-wide scenario:
The more serious scenario is a correlated unwind:
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AI applications fail to deliver enough monetizable demand.
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AI labs and cloud providers reduce compute commitments.
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Data-center utilization and expected rental income fall.
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Borrowers cannot service or refinance project debt.
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GPUs and related infrastructure lose collateral value.
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Losses reach private-credit funds, banks, securitized vehicles, insurers and institutional investors.
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New financing becomes unavailable, causing further cuts in infrastructure orders and Nvidia sales.
This would not necessarily resemble the 2008 banking crisis: some first-loss exposure sits outside regulated banks. But Chicago Booth research estimates that a severe re-rating of AI-related debt could still produce roughly $60 billion to $140 billion in realized credit losses, alongside large equity-market effects.
What matters most:
The decisive question is not whether AI is useful or whether data centers remain necessary. It is whether cash-paying end users will generate sufficient, durable revenue to justify the full cost of the hardware, power, real estate, construction and financing now being committed. If that answer is no, the sector may discover that it financed capacity—not returns.
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From Google’s Gemini:
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- The Revenue Void: If OpenAI or Anthropic defers or cancels capacity commitments, hyperscalers are left with energized Megawatts (MW) that have no immediate, high-paying tenant.
- Stranded Capital: The specialized power, liquid cooling systems, and electrical equipment tailored for dense AI clusters cannot easily be repurposed for traditional cloud workloads without lowering returns on invested capital (ROIC).
| Financial Metric | Baseline Forecast (Bull Case) | Partial Monetization Reset | Severe Crash / Structural Downside |
|---|---|---|---|
| Cloud Revenue Growth | Accelerated (driven by AI APIs & enterprise seats) | Flattens out as enterprise customers consolidate suppliers | Decelerates sharply; circular cloud revenue loops collapse |
| CapEx Infrastructure | Full execution of Dell’Oro’s ~200 GW build out by 2030 | 20–30% of capacity commitments deferred or resized | Mass cancellations of orders; multi-year build freezes |
| Operating Margins | Expands as inference costs decline relative to software prices | Compresses due to underutilized GPU clusters and high DCPI fixed costs | Severe contraction; massive asset write-downs and depreciation drag |
| ROIC | Historic highs driven by high-density compute demand | Diluted; longer payback periods for specialized data centers | Tanks; billions in stranded physical capital and obsolete hardware |
References:
https://www.nytimes.com/2026/08/10/business/ai-nvidia-lenders-500-billion.html
https://www.wheresyoured.at/dont-look-up/
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)
Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies
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?
Telefónica incorporates AI for businesses voice communications vs. 3GPP/ITU specifications
Executive Summary:
Telefónica España made an announcement this week which indicates that Voice could be an important AI monetization opportunity for telcos. The Spain based telecom group is positioning its business voice portfolio around a key differentiator: the ability to embed AI-enabled capabilities directly into conventional fixed and mobile telephony, without requiring enterprises to migrate users or workflows to a separate communications platform. It is incorporating generative AI features into its network for things like call transcription and summarization, which it says is will transform “every voice conversation into usable, structured and actionable information,” as week as virtual assistants on fixed-line and mobile.
Targeted at large enterprises, public-sector organizations, and mid-sized businesses, the enhanced portfolio is intended to shorten call-response times, increase the proportion of calls handled, and convert voice interactions into structured, actionable business information. Telefónica reports that the AI-enabled tools can reduce time spent managing calls by an average of 60%, enabling organizations to handle a higher volume of customer interactions.
Telefónica has integrated artificial intelligence across its business voice offerings—from basic mobile services to advanced PBX and cloud-based telephony platforms—as part of its evolution toward intelligent voice communications. The proposal incorporates generative-AI functions within the Telefónica network, including call transcription, automated summarization, and virtual-agent capabilities. These functions are designed to preserve information that might otherwise remain unstructured within voice conversations, while helping organizations reduce missed opportunities and improve operational responsiveness.
Javier Pascual, Director of Product, Pre-sales and Provisioning at Telefónica Spain, said:
“We are the only operator that offers intelligent transcription and summarization of calls over fixed and mobile voice, making us the best way for companies to access digital technologies. This pioneering solution, which integrates generative AI into standard telephony, allows our clients to summarize and transcribe calls, as well as integrate 100% of virtual agents using natural language, thus improving productivity and agility.”
Cross-Portfolio Intelligent Voice:
Telefónica’s approach spans enterprise, public-administration, corporate-mobile, and mid-market customer segments. It applies to traditional and cloud-based voice solutions, including:
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Centrex IP, Telefónica’s converged fixed-mobile business voice platform.
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Centrex 365, a Microsoft-based cloud voice offering integrated with collaboration tools.
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Enterprise mobile voice services.
A core capability is AI-based transcription and summarization of calls. By transforming voice conversations into searchable and structured records, the feature can support knowledge capture, customer-service follow-up, compliance-related documentation, and analytics workflows.
The company is also introducing Centrex AI, a virtual-agent capability based on advanced language models. Centrex AI is designed to support next-generation generative-AI interactions across channels beyond voice and to integrate with customer business applications. The virtual agents are intended to interpret natural-language requests in context, automate repetitive interactions, and provide faster, more consistent responses.
Telefónica states that the platform supports more than 100 languages and can operate continuously, enabling 24/7 multilingual customer engagement.
Operational and Vertical Use Cases:
Telefónica reports that the AI-enabled capabilities can improve agent efficiency by as much as 60% by reducing time devoted to repetitive tasks. The company also cites potential increases of more than 10% in the number of interactions managed, reflecting improved call-handling capacity.
Initial use cases focus on healthcare, public administration, retail, and industrial enterprises:
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In healthcare, a WhatsApp-based AI agent can schedule appointments, provide immediate confirmations, and support multilingual exchanges.
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For municipal governments, voice agents can address common citizen queries in multiple languages and route calls to the appropriate department.
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For automotive dealerships, virtual agents can help manage service appointments and customer inquiries related to vehicle sales.
By integrating generative AI functions into the existing voice network and service portfolio, Telefónica is seeking to extend intelligent automation to established telephony environments rather than treating AI communications as a standalone application layer.
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Editorial Analysis:
Telefónica’s offer as an operator-integrated, proprietary AI overlay on existing fixed/mobile and cloud voice services, rather than as a service defined by 3GPP or ITU. The business voice offering builds on standardized fixed/mobile voice and cloud-telephony foundations, while its generative-AI functions—call transcription, summarization, and virtual-agent integration—appear to be operator- and vendor-implemented capabilities. Current 3GPP work provides enabling mechanisms for AI/ML in 5G systems, whereas ITU-R’s AI-related IMT work addresses radio-network evolution rather than AI-enhanced enterprise telephony.3GPP.
Relevant 3GPP specifications:
3GPP’s AI/ML work is primarily focused on network and RAN optimization, AI/ML model transfer and lifecycle management, data collection, and interoperability. Notably, 3GPP has stated that it does not plan to standardize the AI/ML models themselves; it instead standardizes the supporting mechanisms and controls.3gpp
ITU-R versus ITU-T:
ITU-R: There are no ITU-R Recommendations specifically governing AI-based telephony, generative-AI call summarization, or virtual agents. This is consistent with ITU-R’s mission: spectrum, radio propagation, and IMT radio-interface frameworks. Its IMT-2030/6G work includes integrated AI and communication as a broad capability area, but that concerns wireless-system capabilities such as distributed training and inference—not enterprise voice-service features.
ITU-T: This is the more relevant ITU sector for AI telephony and conversational AI, although its work is still largely horizontal rather than specific to IMS/PSTN calling:
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ITU-T F.748.46 (2025) specifies requirements and evaluation methods for AI agents based on large-scale pre-trained models. Its scope includes recognition, comprehension, dialogue, generation, and reasoning—capabilities directly relevant to virtual voice agents.
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ITU-T E.AIQ, Framework for quality evaluation of conversational AI systems, is under study in Study Group 12. It proposes KPIs and an “AI Quotient” approach for assessing AI systems in relation to QoS and QoE.itu
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ITU-T Y.3178 defines a functional framework for AI-based network-service provisioning in future networks.itu
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ITU-T Y.3661 (2025) specifies an architecture and mechanisms for customer-oriented intelligent network operation, including AI-supported recognition of user intent; this is adjacent to, but not a telephony-service specification
References:
Telefónica incorpora la IA a todas sus comunicaciones de voz para empresas
https://www.telecoms.com/ai/telef-nica-upgrades-business-voice-services-with-integrated-ai
Vodafone Spain (Zegona), MasOrange and Telefonica in possible RANco joint venture
Telefónica and Nokia partner to boost use of 5G SA network APIs
Ericsson and O2 Telefónica demo Europe’s 1st Cloud RAN 5G mmWave FWA use case
Telefónica launches 5G SA in >700 towns and cities in Spain
Telefónica and Nokia partner to boost use of 5G SA network APIs
Enable-6G: Yet another 6G R&D effort spearheaded by Telefónica de España
Dell’Oro: Enterprise PON Deployments expected to increase 844% year-over-year
According to a new Dell’Oro Group report, “PON in the Data Center and Premise Advanced Research Report“ recently published, total 2026 Data Center PON equipment revenues are expected to increase 844% year-over-year (Y/Y), driven by hyperscalers looking to use the point-to-multipoint technologies to reduce the cabling and power consumption requirements of their out-of-band management networks.
“PON technologies are increasingly moving from traditional residential networks to enterprise and data center applications, providing additional growth opportunities for PON equipment providers,” said Jeff Heynen, Vice President of Broadband Access and Home Networking market research at Dell’Oro Group. “We see hyperscalers and enterprises, both large and small, increasingly deploying PON technologies for passive fiber distribution that is lower cost and that maintains its value far longer than traditional copper infrastructure,” added Heynen.
Additional highlights from the PON in the Data Center and Premise Advanced Research Report:
- Total cumulative spending on data center PON equipment from 2026 to 2030 is expected to exceed $3 billion, as hyperscalers, neocloud providers, and colocation providers all deploy PON for their out-of-band and infrastructure management networks.
- Enterprises are increasingly deploying Passive Optical LAN (POL) as the long-term benefits of increased speeds and lower operational costs outweigh the costs of deploying fiber in the building.
- Chinese operators continue to deploy tens of millions of master and subtended ONTs to deliver fiber-to-the-room (FTTR) services to their residential broadband customers.
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Editorial Analysis:
This extremely bullish forecast points to a potentially important new use case for PON: not as a replacement for the high-bandwidth, low-latency Ethernet fabric that interconnects servers and storage, but as an economical physical layer for the separate networks used to monitor, provision, and recover data-center infrastructure. In that role, a passive optical distribution architecture can consolidate fiber runs and avoid electrically powered aggregation equipment in parts of the management network, potentially simplifying expansion and reducing operational overhead.
Dell’Oro’s projected 844% year-over-year revenue increase should be read in the context of an early-stage market: the percentage reflects rapid adoption from a comparatively small base rather than an indication that PON will displace mainstream data-center switching. Nevertheless, the report’s forecast of more than $3 billion in cumulative 2026–2030 spending indicates that hyperscale, neocloud, and colocation operators are sufficiently interested to make data-center PON a material adjacent market for OLT, ONT, and ONU suppliers.
The enterprise opportunity is somewhat different. Passive Optical LAN can extend fiber deeper into commercial buildings, with optical terminals serving end-user areas rather than relying entirely on copper horizontal cabling and access switches. The principal trade-off is front-loaded installation complexity—especially where fiber pathways must be added or upgraded—against the prospect of longer infrastructure life, higher available access speeds, and lower energy use over the building lifecycle. Dell’Oro also includes enterprise/MDU POL and business FTTR applications in its five-year forecast coverage, suggesting that it views these segments as part of the same widening PON equipment ecosystem.
China’s large-scale FTTR deployments provide a useful volume counterweight to these specialized data-center and enterprise applications. Master and subtended ONT architectures enable operators to extend fiber connectivity from the residence gateway to individual rooms, creating another demand source for optical endpoints and related PON equipment. Together, these developments suggest that future PON market growth will depend increasingly on diversification beyond conventional residential FTTH—while also requiring vendors to address application-specific management, installation, and interoperability requirements.
The Dell’Oro Group PON in the Data Center and Premise Advanced Research Report includes 5-year market forecasts for PON Optical Line Terminals (OLTs), and PON Optical Network Terminals (ONTs) and Optical Network Units (ONUs) used in Data Center [Out-of-band management (OOBM), infrastructure management (DCIM)], Enterprise/MDU [Passive Optical LAN (POL), Fiber-to-the-room for business (FTTR-B)] , and Fiber-to-the room (FTTR) applications. To purchase this report, please contact us by email at [email protected].
References:
PON in Data Centers Expected to Grow at 52 Percent CAGR from 2026-2030, According to Dell’Oro Group
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Analysis: Broadcom’s end-to-end 50G PON Edge AI portfolio with WiFi 8 support
Highlights of FiberConnect 2024: PON-related products dominate
Nokia and Google Fiber trial 50G PON – first in the U.S.
Nokia and Hong Kong Broadband Network Ltd deploy 25G PON
HKT is first to deploy 50G PON technology in Hong Kong
AT&T’s 600 MHz Deployment with Ericsson: Turning Low-Band Spectrum Into Coverage and Uplink Capacity
Executive Summary:
AT&T has selected Ericsson to supply 600 MHz dual-band radios for deployment of the low-band spectrum it recently acquired from EchoStar. The equipment choice is notable not simply because it activates new spectrum, but because it enables AT&T to introduce uplink-enhancing eight-receiver (8RX) technology across its low-band holdings for the first time. AT&T says it will disclose rollout timing and commercial-availability plans later.
—>By selecting Ericsson’s dual-band radios for its newly acquired 600 MHz holdings, AT&T is moving from spectrum ownership to the more consequential task of operationalizing low-band capacity across its entire RAN.
Spectrum Is Not a Network:
The announcement illustrates an elementary but sometimes overlooked fact of mobile-network economics: a spectrum license is not yet a network capability. It becomes one only when an operator has compatible radios, antennas, baseband and transport resources, site access, configuration, optimization, and a viable deployment program.
AT&T’s EchoStar acquisition delivered approximately 20 MHz of nationwide 600 MHz spectrum alongside 30 MHz of 3.45 GHz mid-band spectrum. The mid-band component could be put into service comparatively quickly, while the 600 MHz component required radio equipment that AT&T had not previously deployed at scale. That hardware gap makes the Ericsson selection an operational milestone rather than a routine vendor purchase.lightreading+1
Ericsson’s selected radios support both 600 MHz Band 71 and 700 MHz Band 14—the latter associated with FirstNet. This dual-band approach potentially lets AT&T refresh or augment low-band radio infrastructure while adding a new frequency layer, rather than treating the 600 MHz deployment as a stand-alone overlay.fierce-network
Why 600 MHz Matters:
Spectrum below 1 GHz is valuable principally for propagation rather than for peak throughput. A 600 MHz signal can generally cover a wider geographic area and penetrate buildings better than mid-band spectrum, which makes it especially useful for rural coverage, suburban edge coverage, indoor service, and reliability-oriented capacity layers. AT&T and Ericsson characterize the deployment as improving capacity, reliability, coverage, and overall network performance.ericsson+1
That does not mean 600 MHz should be viewed as a substitute for mid-band 5G capacity. With a nationwide block of roughly 20 MHz, AT&T’s 600 MHz spectrum is unlikely to match the raw throughput potential of wider mid-band channels. Its strategic value lies instead in extending a dependable coverage foundation to locations where higher-frequency spectrum either reaches fewer users per site or suffers greater indoor loss.
In this respect, the acquisition and radio deployment form a complementary two-layer strategy. The 3.45 GHz spectrum can add substantial metropolitan and suburban capacity, while 600 MHz strengthens the wide-area and in-building layer beneath it. The relevant measure of success will therefore not be a single peak-speed benchmark, but whether AT&T can improve user experience at the cell edge, indoors, and in markets where macro-site density is inherently limited.
The 8RX Uplink Dimension:
The technically distinctive part of the announcement is AT&T’s plan to deploy 8RX technology across low bands for the first time. In simplified terms, 8RX refers to use of eight receive paths at the base station. This can improve the network’s ability to receive signals transmitted by user equipment, improving uplink link budget, interference handling, and potentially uplink capacity or consistency. 8RX improves uplink because the base station has eight receive branches—rather than the four normally used for low-band FDD radios—to hear and separate signals sent by handsets. More receive branches improve receiver diversity, spatial interference rejection, and link budget, especially for weak uplink transmissions at the cell edge or indoors.
This matters because mobile usage is no longer overwhelmingly downlink-centric. Consumers upload high-resolution video, participate in real-time communications, share content, use cloud applications, and increasingly interact with AI-enabled services that may generate meaningful upstream traffic. Enterprises also depend on upstream performance for field video, surveillance, industrial devices, connected vehicles, and edge-to-cloud telemetry.
Low-band FDD deployments commonly use four receiver paths; moving to 8RX can therefore increase the base-station receive capability at the coverage layer where user devices are most likely to operate at weak-signal conditions. Light Reading reported that AT&T views the Ericsson radio selection as a means to introduce this uplink-enhancing configuration across its low-band spectrum, although performance results and deployment scope remain to be demonstrated in the field.
The important caveat is that 8RX does not create spectrum. Its contribution is to improve how effectively the network uses the uplink resources it has, particularly where coverage, interference, or link budget constrain the user’s transmitted signal. The realized benefit will depend on radio design, antenna implementation, spectrum configuration, device capability, traffic mix, and local RF conditions. See Addendum.
A Modernization Program, Not an Isolated Upgrade:
AT&T frames the effort as part of a wider network-modernization program. The company says upgraded Ericsson infrastructure has already delivered up to two-times faster average speeds in relevant areas, a 10% reduction in dropped or blocked calls, fewer slow-speed incidents, and lower uplink interference; these are operator-reported figures and should not be generalized to the forthcoming 600 MHz layer until independently validated.
The deployment also fits AT&T’s stated Open RAN direction. In 2023, AT&T said it intended for 70% of its wireless traffic to flow over open-capable platforms by late 2026, with Ericsson among the suppliers supporting its modernization path. The 600 MHz deployment itself should not be conflated with an Open RAN announcement, but it will take place within a RAN estate being progressively modernized for greater openness and flexibility.
What to Watch Next:
The immediate question is deployment execution. AT&T has not yet released commercial launch dates, market sequencing, site counts, or device-support details for its new 600 MHz spectrum. Those disclosures will determine whether this is primarily a targeted coverage investment, a broad nationwide low-band overlay, or a phased modernization program extending over several years.lightreading+1
The more substantive test will be whether AT&T can translate a high-value spectrum acquisition into measurable improvements in rural availability, indoor coverage, cell-edge performance, and uplink experience. The Ericsson selection is the necessary first step: it turns an underutilized spectrum asset into a deployable radio-network program. The ultimate value will come from execution at scale.
Addendum- “8RX” Explained:
In this AT&T/Ericsson deployment, 8RX means eight radio-frequency receive chains at the network side. The handset still transmits its normal uplink signal; the cell site has more antenna/receiver observations from which to recover it.
This differs from 8RX in a handset or fixed-wireless terminal, where it generally improves the device’s downlink reception. Here, the direction is reversed: the base station’s added receive capability benefits device-to-network traffic.
Uplink mechanisms:
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Diversity gain: The eight branches experience somewhat different fading and multipath conditions. Combining them makes it less likely that a deep fade on one path causes decoding failure.
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Array/combining gain: When receive paths can be coherently combined, the desired UE signal arrives with a stronger effective signal-to-noise-plus-interference ratio. In an idealized case, doubling the number of equivalent receive branches from four to eight can provide roughly 3 dB of additional combining gain, though the field result depends on antenna correlation, propagation, and implementation.
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Interference suppression: More antenna observations give the receiver more spatial degrees of freedom to distinguish a desired UE from co-channel interferers. This can improve uplink SINR and allow more robust—or, when conditions permit, higher-order—uplink modulation and coding.
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Better cell-edge operation: The uplink is often the limiting direction in wide-area low-band coverage because UE transmit power is tightly constrained. Improving the base-station receiver makes a low-power device more likely to maintain service from a building, rural location, or cell edge.
Why it is significant at 600 MHz:
600 MHz’s principal advantage is coverage: it propagates farther and penetrates buildings more effectively than higher-frequency spectrum. But broad downlink coverage can expose an uplink asymmetry—the device may receive the cell reliably yet lack sufficient transmit power for an equally strong return path.
8RX specifically addresses that asymmetry. AT&T says its Ericsson radios will enable uplink-enhancing 8RX across low bands for the first time, while Light Reading notes that low-band FDD networks such as 600 MHz have typically used 4RX radios and that 8RX had been relatively uncommon below 1 GHz.
Conceptually, with NN receive branches, the base station adheres to this equation:
References:
https://about.att.com/story/2026/att-ericsson-enhance-wireless-nationwide.html
https://www.lightreading.com/5g/at-t-puts-600mhz-to-work-with-ericsson-for-coverage-and-uplink
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