Alan Weissberger – IEEE ComSoc Technology Blog

Verizon’s $1 Billion Google Dark Fiber Deal Highlights Importance of Optical Networks

Posted on July 24, 2026 by Alan Weissberger

Executive Summary:

Verizon’s reported a more-than-$1 billion dark fiber agreement with Google. It underscores how hyperscale AI growth is elevating dark fiber, route diversity, and high-capacity optical engineering into core industry priorities and is evidence that the network layer is becoming a central enabler of AI-scale computing. The deal, disclosed during Verizon’s Q2 2026 earnings call, is intended to connect Google data centers and support the transport demands of AI workloads.

From a telecom perspective, the significance lies in the shift from best-effort connectivity toward engineered optical infrastructure with explicit performance objectives. As hyperscalers expand distributed AI training and inference, the requirements for capacity, latency, route diversity, and operational control increasingly favor dark fiber over shared transport models.blogs.cisco+2

Why This Matters for Network Architecture:

Dark fiber gives the customer direct control over the optical layer, enabling custom design choices for line rates, protection schemes, and traffic engineering. That flexibility is especially relevant for large data-center interconnect environments, where traffic growth can quickly outpace conventional managed services.blogs.cisco+1

The Verizon-Google transaction also reinforces the role of long-haul and metro fiber as strategic infrastructure rather than commodity bandwidth. In practice, this places greater emphasis on fiber route resilience, diverse path design, and the ability to scale toward higher-capacity optical systems as AI clusters expand.blogs.cisco+1

Standards and Industry Implications:

While the deal itself is commercial, its implications touch several standards-adjacent concerns that are increasingly important to operators and vendors. These include high-capacity optical transport, inter-domain coordination, deterministic latency for distributed workloads, and the operational models needed to support AI-driven traffic growth.blogs.cisco+1

For IEEE ComSoc readers, the broader signal is that future network evolution may be shaped as much by AI infrastructure economics as by traditional access or mobility growth. The value proposition is moving toward fiber-based transport layers that can support hyperscale interconnect, cloud adjacency, and resilient backhaul for distributed computing environments.benton+1

Conclusions:

Verizon’s reported dark fiber deal with Google suggests that optical connectivity is no longer a passive enabler but a competitive differentiator in the data-center supply chain. It highlights a broader shift in network economics: AI growth is elevating fiber infrastructure from a supporting asset to a strategic enabler. For carriers, the message is clear — the winners in the AI era may be those that can pair scale, route control, and transport engineering with the capacity demands of hyperscale cloud buildouts.

Highlights from Ookla’s U.S. Speedtest Connectivity Report-Mobile & Fixed Networks

Posted on July 23, 2026 by Alan Weissberger

Executive Summary:

Ookla’s United States Speedtest Connectivity Report H1 2026 provides a detailed snapshot of network performance across the U.S. mobile and fixed broadband markets, based on Speedtest Intelligence data. The findings point to a competitive landscape in which T-Mobile continued to distinguish itself in mobile connectivity, while AT&T Fiber led the fixed broadband segment.

In the mobile network category, T-Mobile was named the Best Mobile Network overall and also claimed the Best 5G Network award for the first half of 2026. The “un-carrier” recorded a median download speed of 275.55 Mbps across all technologies combined, and a median 5G download speed of 314.38 Mbps. These results underscore the strong performance of T-Mobile’s network, particularly in 5G-centric use cases where throughput remains a key differentiator.

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T-Mobile recorded the best mobile network consistency in the United States, for all technologies combined and for 5G. Across all technologies, 89.7% of its samples met or exceeded the threshold of 5 Mbps download and 1 Mbps upload, while 79.3% of its 5G samples met or exceeded the higher threshold of 25 Mbps download and 3 Mbps upload.

For the fixed line networks, AT&T Fiber was recognized as the Best Internet provider and the Fastest Fixed Network in the United States during the same period. The oldest U.S. network operator posted a median download speed of 374.75 Mbps and a median upload speed of 320.65 Mbps, reflecting the growing importance of high-capacity fiber access in home broadband markets. As fixed and mobile connectivity continue to converge in consumer experience expectations, these metrics highlight the increasingly performance-driven nature of broadband competition.

The report also indicates that T-Mobile delivered the best gaming and video streaming experiences in the United States during 1H 2026, both across all technologies combined and in the 5G-only category. This is notable because it extends the company’s advantage beyond raw speed metrics into application-level quality, where latency sensitivity, jitter, and consistency can be as important as peak throughput.

At the city level, Lincoln, Nebraska recorded the fastest median mobile download speed among the most populous U.S. cities at 395.83 Mbps. In fixed broadband, Durham, NC led the group with a median download speed of 380.47 Mbps. These city-level results reinforce the fact that network performance varies significantly by market, even within a single national assessment, and that local deployment density and backhaul quality can materially influence user experience.

Editorial Analysis & Conclusions:

Taken together, the report suggests that U.S. network leadership in 1H 2026 was shaped by both access technology and service quality. Mobile operators are increasingly being judged not only by coverage and 5G speed, but also by end-user experience across demanding applications, while fiber providers continue to push the upper bounds of fixed-line performance. Ookla’s latest data therefore offers a useful benchmark for understanding where the U.S. connectivity market stands — and where competition is most intense.

The U.S. mobile market appears to be shifting from a simple coverage contest toward a performance-and-experience contest. T-Mobile’s lead in overall mobile, 5G, gaming, and video-streaming experience suggests that end-user quality is now being shaped by a combination of throughput, consistency, and 5G availability rather than raw coverage alone.
The results reinforce that 5G leadership is increasingly tied to how well operators can translate spectrum, radio access optimization, and transport capacity into sustained user experience. T-Mobile’s strong median download speeds across both blended and 5G-only measurements indicate that its network engineering is delivering not just peak performance, but performance that is visible in real-world usage patterns.
The fixed broadband results show that fiber remains the benchmark for high-capacity access in the U.S. AT&T Fiber’s strong download and upload figures illustrate why fiber continues to set the standard for symmetrical, high-throughput residential service, especially as cloud applications, video conferencing, and multi-device home traffic continue to grow.
Application-centric metrics are becoming more relevant in how networks are evaluated. The fact that T-Mobile led in gaming and video streaming experience suggests that operators are increasingly judged on latency-sensitive and consistency-sensitive workloads, not just speed-test averages. For ComSoc readers, that is a reminder that network KPIs are evolving in step with how people actually use the network.
At the city level, the report also highlights how uneven performance can be across markets. Lincoln’s leading mobile result and Durham’s leading fixed result suggest that local infrastructure, spectrum conditions, and deployment density still matter significantly even within a mature national market. This is especially relevant for planners and researchers interested in the last mile, metro-area capacity, and regional quality-of-experience gaps.

Overall, the report suggests that U.S. connectivity competition is entering a more mature phase in which operators are differentiated less by whether they can deliver 5G or fiber at all, and more by how well they can optimize those platforms for sustained, application-level performance. For IEEE Techblog readers, that makes the report useful not only for network operator rankings, but as a snapshot of where network engineering priorities are heading.

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References:

https://www.ookla.com/research/reports/united-states-speedtest-connectivity-report-h1-2026

https://www.ookla.com/resources/guides/speedtest-methodology

Ookla: AI workloads will force changes in 5G mobile network infrastructure

Ookla: AI platform reliability decreases as outages surge

Ookla on the Global D2D Market

Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area

Ookla: D2D satellite connectivity surged 24.5% during last 9 months; Starlink’s footprint expansion leads the way

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

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

Posted on July 20, 2026 by Alan Weissberger

Executive Sumary:

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

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

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

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

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

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

AI-Native Customer Experience Architecture:

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

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

Image Credit: Aisera

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

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

AI Across Commercial and Network Domains:

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

Marketing: AI-driven personalization is evolving toward real-time, context-aware engagement informed by both customer behavior and network conditions (e.g., location, QoS state, congestion). This aligns with event-driven architectures and customer data platforms integrated with network analytics (e.g., NWDAF exposure via APIs). “Personalisation shifts from broad audiences to the individual,” she said, adding that relevance is now “a prerequisite for attention.”
Sales: AI enables next-best-action and dynamic offer generation, incorporating network-aware insights such as service availability, slice characteristics (in 5G SA), and fiber capacity constraints. Integration with policy control (3GPP TS 23.203) and service orchestration frameworks supports closed-loop order capture and fulfillment. “That combination of higher conversion and lower friction is valuable,” she said.
Service: AI-driven assurance is transitioning from reactive fault management to predictive and intent-based service assurance across both wireless and wireline domains. Telemetry from RAN, transport, and fixed access networks feeds AI models that anticipate degradation and trigger remediation before customer impact. “Human agents still play a central role,” Cary said, “but they can be augmented with real-time recommendations, contextual history and autonomous processes that improve both speed and consistency.”

Scaling Challenges in AI-Native Transformation:

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

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

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

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

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

Toward Fully AI-Native Operations:

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

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

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

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

Implementation Suggestions:

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

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

Establish a unified, real-time data fabric spanning customer, service, and network domains.
Deploy high-value AI use cases (e.g., next-best-action, churn prediction, predictive assurance) leveraging both IT and network telemetry.
Embed AI into execution workflows to enable closed-loop, intent-driven orchestration across the customer lifecycle.

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

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

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Editorial Analysis:

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

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

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

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

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References:

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

What is AI Native?

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

Comparing AI Native mode in 6G (IMT 2030) vs AI Overlay/Add-On status in 5G (IMT 2020)

SHIELD-6G with AI-native cyber threat intelligence platform to enhance cybersecurity for Europe’s future 6G networks

AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC

Ericsson and Intel collaborate to accelerate AI-Native 6G; other AI-Native 6G advancements at MWC 2026

NVIDIA and global telecom leaders to build 6G on open and secure AI-native platforms + Linux Foundation launches OCUDU

AT&T and Ericsson boost Cloud RAN performance with AI-native software running on Intel Xeon 6 SoC

Jio’s LEO satellite constellation authorized by IN-SPACe: 5 Tbps over India with 3GPP Rel 17 and 18 NTN Alignment

Posted on July 19, 2026 by Alan Weissberger

Executive Summary:

India’s space sector has taken another decisive step toward global competitiveness with the Indian National Space Promotion and Authorization Center (IN-SPACe) granting a key technical authorization to Reliance Jio for a proposed Low Earth Orbit (LEO) satellite constellation of approximately 1,600 satellites. The scale and ambition of the program place it firmly within the same category as leading non-terrestrial network (NTN) initiatives such as SpaceX’s Starlink, Amazon’s Project Kuiper, and Eutelsat OneWeb, while signaling India’s intent to build indigenous capability in space-based broadband infrastructure.

Constellation Scale and Architecture:

At ~1,600 satellites, Jio’s planned constellation is smaller than Starlink’s first-generation deployment (~4,400 satellites, with longer-term plans exceeding 10,000), but comparable to Amazon’s Project Kuiper (~3,236 satellites planned) and significantly larger than OneWeb’s first-generation system (648 satellites). This places Jio in an intermediate design space—large enough to deliver meaningful aggregate capacity and coverage, yet potentially more optimized for regional rather than fully global service.

The announced aggregate capacity of up to 5 Tbps suggests a high-throughput satellite (HTS) architecture leveraging aggressive frequency reuse and multi-spot beam designs. By comparison:

Starlink is estimated to already deliver tens of Tbps of global capacity, enabled by dense constellation scaling, advanced phased-array antennas, and increasingly, optical inter-satellite links (ISLs) [2.].
Kuiper targets multi-Tbps capacity with a strong emphasis on cloud integration via AWS, though it remains pre-commercial as of mid-2026.
OneWeb focuses more on enterprise, maritime, and government backhaul, with comparatively lower aggregate throughput but strong QoS guarantees.

Note 1. ISLs (Inter-Satellite Links) are direct communication connections between spacecraft in orbit, allowing them to route data to one another without first sending it down to an Earth station. This creates a dynamic space mesh network, which dramatically reduces data latency, increases coverage, and bypasses the need for costly ground gateways.

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A key technical question for Jio will be whether it incorporates optical ISLs in its initial deployment. Starlink’s Gen2 architecture relies heavily on ISLs for mesh networking and latency optimization, reducing dependence on ground gateway density. In contrast, OneWeb’s first-generation system lacks ISLs, relying instead on a dense ground station network. Jio’s architectural choice here will directly influence both latency performance and ground infrastructure cost.

Reliance Jio plans to deploy 1,600 LEO satellites to build a space-based communication network. AI-generated image via Business Standard.

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Spectrum Strategy and Link Budget Considerations:

Although Jio has not publicly disclosed its frequency plan, it is likely to align with Ku- and Ka-band allocations, consistent with global LEO broadband systems. Starlink and Kuiper both rely heavily on Ka-band for feeder links and Ku/Ka for user links, while also exploring V-band () for future capacity scaling.

For India-specific deployment, spectrum coordination presents both an opportunity and a constraint. Domestic prioritization could streamline regulatory approvals, but coexistence with incumbent satellite operators and terrestrial 5G services will require careful interference management. This is particularly relevant as 3GPP NTN bands increasingly intersect with traditional satellite allocations.

From a link budget perspective, enabling both fixed broadband and direct-to-device (D2D) services within the same constellation introduces competing design requirements. High-throughput broadband favors higher frequencies and larger user terminals, while D2D requires lower link margins, robust coding, and potentially sub-GHz or S-band spectrum to reach handheld devices.

Direct-to-Device and 3GPP NTN Alignment:

Jio’s emphasis on direct-to-device (D2D) connectivity places it at the forefront of a critical industry transition: the integration of NTN into the 3GPP ecosystem. Releases 17 and 18 define the foundational architecture for NTN support, including adaptations for:

Large propagation delays and Doppler shifts in LEO systems
Modified random access and timing advance procedures
Satellite-aware mobility and handover mechanisms
Power-efficient waveform adaptations for handheld devices

Starlink has taken an early lead in this domain through its partnership with T-Mobile, leveraging mid-band PCS spectrum to enable D2D messaging services. AST SpaceMobile, while not a direct LEO broadband competitor, has demonstrated high-throughput D2D links using large phased-array satellites. Apple’s emergency SOS feature (via Globalstar) represents a narrower but commercially successful implementation of NTN for consumer devices.

Jio’s differentiation may lie in tighter vertical integration with its terrestrial network. Unlike Starlink, which operates largely as an overlay network, Jio can embed NTN capabilities directly into its 5G—and eventually 6G—core architecture. This opens the door to unified authentication, billing, and service continuity across terrestrial and satellite domains, consistent with the 3GPP vision of seamless TN–NTN convergence.

Latency, Backhaul, and 5G/6G Integration:

Operating in LEO, Jio’s system can achieve round-trip latencies on the order of comparable to Starlink and significantly lower than geostationary systems (). With ISLs, latency for long-distance routes can even approach or outperform terrestrial fiber in certain scenarios, depending on routing efficiency.

For India, one of the most compelling use cases is satellite-based backhaul for rural and remote base stations. While fiber deployment remains uneven across the country, a LEO-based backhaul layer could enable rapid expansion of 5G coverage without the need for extensive terrestrial infrastructure. This aligns with ongoing 6G research, where integrated TN–NTN architectures are expected to support ubiquitous coverage and network resilience.

In comparison to Jio:

OneWeb has already established a strong position in cellular backhaul, including partnerships in emerging markets.
Starlink is increasingly targeting enterprise and mobility segments, including aviation and maritime.
Kuiper is expected to leverage AWS edge integration for enterprise and cloud-native applications.

Jio’s advantage lies in its domestic scale and control over both access and core network layers, enabling tighter optimization of end-to-end service delivery.

Manufacturing, Launch, and Economic Viability:

Deploying a 1,600-satellite constellation requires industrial-scale manufacturing and launch capabilities. SpaceX’s vertical integration—spanning satellite production and launch via Falcon 9 and Starship—has been a key enabler of Starlink’s rapid deployment. Amazon is pursuing a mixed launch strategy (ULA, Blue Origin, Arianespace), while OneWeb relied heavily on international launch providers.

Jio’s approach will likely depend on partnerships, potentially leveraging ISRO’s launch capabilities alongside commercial providers. However, achieving cost efficiency comparable to Starlink remains a significant challenge, particularly in satellite mass production and user terminal pricing.

User equipment (UE) economics will be especially critical for D2D services. While fixed terminals can subsidize higher costs, mass-market D2D requires integration into standard smartphones without significant cost premiums. This is an area where chipset ecosystem alignment—Qualcomm, MediaTek, and others—will play a निर्ण role.

Strategic and Geopolitical Implications:

Beyond technical considerations, Jio’s LEO initiative reflects broader geopolitical and industrial policy trends. India is positioning itself to reduce dependence on foreign satellite infrastructure while building domestic capability across the space value chain. This aligns with parallel efforts in semiconductor manufacturing, AI infrastructure, and 6G research.

At the same time, the global LEO market is becoming increasingly competitive and capacity-rich. The risk of oversupply, pricing pressure, and regulatory fragmentation is non-trivial. Jio’s success will depend not only on technical execution but also on its ability to carve out a differentiated market position—potentially focusing on South Asia, enterprise services, and tightly integrated telecom offerings.

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LEO Systems Serving India:

Key parameters of LEO constellations relevant to India’s satellite broadband market.

Parameter	Reliance Jio (IN-SPACe-approved)	Starlink (India)	Amazon Kuiper (India)	OneWeb / Eutelsat
Status in India	IN-SPACe technical nod, ITU filings to follow	Licensed with 600 Gbps cap	Capacity proposal ~3 Tbps, not yet technically cleared	Operating via prior satcom approvals
Satellites (global plan)	~1,600 LEO satellites (India-centric)	>5,000 deployed, >10,000 planned	3,236 planned	648 (Gen1 complete)
Aggregate capacity in India	4.5–5 Tbps over Indian territory	600 Gbps approved capacity	~3 Tbps proposed	Regional multi‑Gbps, enterprise/backhaul focused
Approximate altitude	LEO shells, ~500–1,200 km (India coverage)	LEO shells around ~540–570 km	LEO ~590–630 km	LEO ~1,200 km polar
Optical ISLs	Not disclosed / TBD	Active laser ISLs in newer sats	Expected but not fully specified publicly	None in Gen1; relies on gateways
Primary service focus	Nationwide broadband, mobile backhaul, potential D2D	Global consumer broadband and enterprise	Broadband plus AWS edge/cloud integration	Enterprise, maritime, aviation, government backhaul

Jio’s IN-SPACe-cleared system targets 4.5–5 Tbps over India with ~1,600 satellites, compared to Starlink’s currently approved 600 Gbps and Kuiper’s proposed 3 Tbps capacity in the Indian market.”

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Evolution of 3GPP NTN support from 5G NR to 5G‑Advanced and early 6G:

Jio’s IN‑SPACe‑approved constellation is architected to align with NR‑NTN capabilities introduced in 3GPP Rel‑17 and extended in Rel‑18, enabling integrated satellite–terrestrial broadband and potential direct‑to‑device services over India’s existing 5G and future 6G infrastructure. NTN support in 3GPP Releases:

Rel‑15 / Rel‑16 – Baseline 5G NR (Terrestrial)
- Label text: “TN‑only architecture; NR defined for terrestrial cells and standard mobility.”
Rel‑17 – Initial NTN Support
- Label text: “Introduction of NR‑NTN for LEO/GEO satellites and HAPS; adaptations for delay, Doppler, and satellite link budget.”
Rel‑18 – 5G‑Advanced NTN Enhancements
- Label text: “Improved NTN mobility, QoS, power efficiency; building blocks for direct‑to‑device scenarios and tighter TN–NTN integration.”
Beyond Rel‑18 / early 6G – Native TN–NTN Convergence
- Label text: “Unified terrestrial–satellite architecture, AI‑assisted RAN control, ubiquitous coverage; NTN treated as a first‑class component of 6G systems.”

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References:

https://www.business-standard.com/companies/news/reliance-jio-gets-in-space-approval-for-1-600-satellite-leo-network-126071700270_1.html

https://economictimes.indiatimes.com/industry/telecom/telecom-policy/reliance-jios-1600-leo-satellite-plan-gets-in-space-technical-nod-moves-closer-to-indias-first-homegrown-constellation/articleshow/132447556.cms?

European Consortium 5G NTN transmission paves the way for standards based direct to device (D2D) connectivity

Non-Terrestrial Networks (NTN) Tutorial: Architecture, Spectrum, and Technical Foundations

Non-Terrestrial Networks (NTNs): market, specifications & standards in 3GPP and ITU-R

Ookla: Starlink a viable competitor for hybrid 5G/NTN services due to network performance improvements and larger coverage area

From LPWAN to Hybrid Networks: Satellite and NTN as Enablers of Enterprise IoT – Part 2

Telecoms.com’s survey: 5G NTNs to highlight service reliability and network redundancy

Keysight Technologies Demonstrates 3GPP Rel-19 NR-NTN Connectivity in Band n252

ITU-R recommendation IMT-2020-SAT.SPECS from ITU-R WP 5B to be based on 3GPP 5G NR-NTN and IoT-NTN (from Release 17 & 18)

India approves backhaul satellite connectivity via VSAT for telecom services; BharatNet tender coming soon

Deutsche Telekom: Network APIs with RCS to Enhance AI Enterprise Network Engagements

Posted on July 16, 2026 by Alan Weissberger

The telecom industry’s network API (Applications Program Interface) journey is transitioning from capability exposure to measurable enterprise value creation. Initial efforts focused on standardizing and exposing network functions through developer-friendly interfaces. The current phase centers on driving adoption and embedding those capabilities into enterprise workflows with demonstrable ROI.

Deutsche Telekom’s Chathurangi Wickramasinghe, SVP Magenta Business API, frames this shift succinctly: “Building APIs is right now not the challenge. But scaling adoption is a challenge.” This reflects a broader industry inflection point where technical feasibility has largely been established through initiatives such as CAMARA (Linux Foundation), GSMA Open Gateway, and TM Forum Open APIs, but commercial scalability remains constrained by integration complexity, fragmented exposure models, and unclear enterprise value propositions.

Deutsche Telekom has been an active contributor to both standardization and commercialization of network APIs. Its MagentaBusiness API platform, launched in Germany in 2023 in collaboration with Vonage, aggregates communications and network APIs into a unified exposure layer. Initial service APIs—Quality-on-Demand (QoD), Device Status–Roaming, and Device Location—align with CAMARA-defined abstractions designed to ensure interoperability across operators and geographies. CAMARA’s northbound API framework, increasingly aligned with 3GPP Service-Based Architecture (SBA) principles, is intended to decouple application logic from underlying network complexity, enabling portability across multi-operator environments.

However, as Wickramasinghe emphasizes, enterprise demand is not driven by APIs per se but by business outcomes. “No customer wakes up in the morning and asks for APIs. They have real business challenges they want to solve.” These challenges increasingly map to identity assurance, fraud mitigation, customer engagement, and operational efficiency—domains where telecom networks possess unique, non-replicable data assets.

A representative use case is the integration of Rich Communication Services (RCS) with network APIs. RCS, specified by GSMA and widely deployed across Android ecosystems, enables branded and interactive messaging for enterprise engagement. When combined with network APIs such as Number Verify and SIM Swap, RCS workflows can incorporate real-time identity validation and fraud detection. This convergence is particularly relevant given persistent vulnerabilities in SMS-based OTP authentication, including SIM swap fraud and phishing attacks.

Deutsche Telekom’s commercial launch of Number Verify in Germany in 2024—implemented in collaboration with Vodafone and O2 Telefónica under GSMA Open Gateway—demonstrates early multi-operator federation. The API enables silent authentication by verifying a user’s MSISDN via network signaling, eliminating reliance on SMS OTP while reducing latency and attack surface. Similarly, the SIM Swap API allows enterprises, particularly in financial services, to query recent SIM change events before authorizing high-risk transactions. GSMA reports indicate that fraud losses linked to account takeover and identity compromise exceed tens of billions of dollars annually, reinforcing the economic rationale for such capabilities.

The relevance of network APIs is further amplified by the emergence of AI-native enterprise architectures. At MWC 2026, Deutsche Telekom positioned network APIs as foundational infrastructure for AI-driven applications, highlighting trusted network signals, real-time context, and programmable QoS as key enablers. This aligns with broader industry trends toward “Network as Code,” where network capabilities are abstracted and consumed dynamically by applications and, increasingly, by AI agents.

Wickramasinghe underscored that enterprise AI adoption is constrained by “trust, security and governance.” Network APIs can address these constraints by providing deterministic, operator-verified signals for identity, location, device state, and session quality. As agentic AI systems begin executing transactions and interacting autonomously with users and services, the integrity of these signals becomes critical. “They rely on trusted signals. They need [them] to carry out their tasks autonomously.”

This framing also clarifies the monetization pathway for operators. Network APIs represent a shift from connectivity-centric revenue models toward exposure of differentiated network intelligence. However, achieving scale requires three conditions: standardized interfaces (e.g., CAMARA-aligned APIs), low-friction developer onboarding (including SDKs and hyperscaler integrations), and direct alignment with high-value enterprise use cases such as fraud prevention, identity verification, and service assurance.

Ecosystem developments in 2026 reinforce this trajectory. Nokia’s Network as Code platform includes Deutsche Telekom among its operator partners and highlights a deployment with Blocksport, where Number Verification enables seamless authentication in sports-oriented super apps. Nokia has also linked network APIs with agentic AI through collaboration with Google Cloud, aiming to make network capabilities programmatically accessible to enterprise AI systems via cloud-native interfaces. Hyperscaler involvement is particularly significant, as it addresses distribution and developer reach—historically a bottleneck for telecom API adoption.

From an architectural perspective, network APIs function as a control plane for trust. They expose network-derived assertions that cannot be replicated at the application layer: subscriber authenticity, SIM lifecycle events, device reachability, and network-validated location. In a zero-trust security paradigm and increasingly automated digital economy, these attributes become foundational primitives for secure transactions and interactions.

The concept of the “agentic network” extends beyond autonomous network operations (e.g., closed-loop assurance in 5G-Advanced and future 6G systems). It encompasses the ability of the network to provide verifiable, real-time context to external AI systems. As AI assumes greater responsibility for decision-making, customer engagement, and service orchestration, telecom operators are positioned to supply high-integrity signals that enhance reliability and reduce risk.

In this context, Wickramasinghe’s central argument is that network APIs achieve mainstream adoption not when they are marketed as technical interfaces, but when they are delivered as embedded, outcome-oriented capabilities. The transition from “API products” to “trusted outcomes”—fraud reduction, seamless authentication, and verified engagement—marks the critical step toward sustainable monetization and strategic relevance in the AI-driven digital ecosystem.

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Network API Specifications and Frameworks:

These specifications and frameworks collectively enable the convergence of RCS and network APIs, supporting use cases such as trusted customer engagement, silent authentication, and fraud mitigation in enterprise workflows.

CAMARA Project APIs (Linux Foundation)
- Provides northbound service API definitions aligned with GSMA Open Gateway.
- As of 2026, over 60 APIs are defined, including stable APIs such as:
  - Number Verification, SIM Swap, SIM Swap Subscriptions
  - Quality-on-Demand (QoD), QoS Profiles, QoS Provisioning
  - Device Reachability Status, Device Roaming Status, Device Swap
  - Location Verification, Location Retrieval, Geofencing Subscriptions
  - One Time Password SMS, Customer Insights, KYC (Know Your Customer) APIs
  - Simple Edge Discovery, Network Slice Booking, Traffic Influence
- APIs are hosted on GitHub and follow OpenAPI 3.0 schema conventions.telecomtv+2
GSMA Open Gateway API Descriptions
- Defines a universal set of northbound APIs for mobile network capabilities.
- Includes APIs such as Call Forwarding Signal, Device Identifier, Connected Network Type, Scam Signal, and Carrier Billing.
- Intended to ensure interoperability across operators and geographies, with CAMARA as the technical reference implementation.gsma+1
3GPP TS 23.222 – Common API Framework (CAPIF)
- Specifies architecture and procedures for exposing network capabilities via APIs within 5G systems.
- Defines API invoker, API provider, and CAPIF core functions for discovery, authentication, and logging.
- 3GPP TS 29.222 – Common API Framework for 3GPP Northbound APIs
- Defines RESTful API design principles and procedures for communication between Application Functions (AFs) and the 5G Core.
- Includes procedures for monitoring, device triggering, and traffic influence.
3GPP TS 29.522 – Network Exposure Function (NEF) Services
- Specifies northbound interfaces between the NEF and external AFs.
- Covers monitoring, device triggering, background data transfer, PFD management, traffic influence, and QoS session setup.
ETSI GS OSM 003 – RESTful API Specification and Testing
- Provides methodology for specifying and testing RESTful APIs in telecom contexts.
- Aligns with OpenAPI and includes guidelines for versioning, error handling, and security.
OMA RESTful Network API (OMA-TS-REST_NetAPI)
- Early specification for RESTful exposure of network services such as messaging, location, and payment.
- Predecessor to 3GPP CAPIF and NEF-based frameworks.

RCS (Rich Communication Services) Specifications:

GSMA RCC.71 – RCS Universal Profile Service Definition
- Defines the baseline service capabilities for RCS Universal Profile, including chat, file transfer, audio/video messaging, and group chat.
- Current version (v3.0) aligns with 3GPP IMS specifications and Android/Jibe ecosystem requirements.gsma
GSMA RCC.08 – Rich Communications Suite Endorsement of 3GPP TS 29.311
- Specifies interworking between RCS and 3GPP messaging services.
- Endorses relevant sections of 3GPP TS 29.311 for service-level interworking.gsma
3GPP TS 23.228 – IP Multimedia Subsystem (IMS)
- Core IMS architecture specification enabling RCS services over LTE and 5G.
- Defines session control, registration, and service delivery mechanisms.3gpp
3GPP TS 24.229 – IMS Signaling Flows and Procedures
- Specifies SIP-based signaling for IMS sessions, including RCS chat and multimedia sessions.3gpp
3GPP TS 29.311 – Interworking for Messaging Services
- Defines interworking between IMS messaging and external systems, including SMS and MMS gateways.gsma
ETSI TS 102 901 – Rich Communication Suite (RCS)
- European standard specifying RCS service architecture and capabilities.
- Aligns with GSMA RCS Universal Profile and 3GPP IMS framework.etsi
GSMA RCS Business Messaging (RBM) Guidelines
- Defines enterprise messaging use cases, A2P workflows, and brand verification mechanisms.
- Supports integration with network APIs for authentication and fraud prevention.5gamericas

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References:

https://www.rcrwireless.com/20260716/carriers/deutsche-telekom-ai-apis

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

Telefónica and Nokia partner to boost use of 5G SA network APIs

New venture to sell Network Application Programming Interfaces (APIs) on a global scale

AI-Era Cloud Network Transformation: A Reference Architecture and Implementation Roadmap

Fierce Network Research report examines telcos role in the AI economy and profiles early AI adopters

China’s big 3 telcos offer 5G Rich Communication Services (RCS)

Analysis: Nokia’s new AI-RAN platform and Standalone AI-RAN node with Nvidia GPUs

Posted on July 15, 2026 by Alan Weissberger

Nokia today announced what it describes as the first commercial AI-RAN platform, signaling a potentially significant inflection point in radio access network architecture. As AI workloads increasingly influence network design and operations, service providers are under pressure to deliver higher capacity, improved cost efficiency, and faster service innovation without depending on traditional hardware refresh cycles. Nokia’s AI-RAN platform is positioned to address these requirements by extracting greater uplink and downlink performance from existing spectrum and radio assets, while enabling a transition toward AI-native network architectures through software-centric evolution. Key take-aways:

Nokia launches the industry’s first commercial AI-RAN platform, turning AI-RAN from vision into reality and providing a practical path to AI-native networks.
Built on Nokia’s AI-native anyRAN software and NVIDIA’s Aerial AI-RAN platform, it will deliver more than 100% spectral efficiency gains by 2028, doubling the capacity of existing spectrum assets.
Nokia’s anyRAN software will support three new accelerated computing baseband platforms. In addition, its existing portfolio will be fully ORAN compliant so operators can modernize at their own pace.

“AI-RAN is the biggest innovation in radio in decades. AI-RAN makes the network intelligent, extends AI into the physical world, and allows telcos to get more from their existing infrastructure, including a software upgrade path to 6G. Nokia’s anyRAN software, powered by NVIDIA’s Aerial AI-RAN platform, unlocks greater performance from the spectrum operators already have and can be deployed with existing Nokia or ORAN-compliant radio units. For operators, that means more performance, better returns and faster delivery of new services,” said Justin Hotard, President and Chief Executive Officer at Nokia.

The platform is built on Nokia’s AI-native network architecture and leverages NVIDIA’s accelerated computing stack to enable AI-driven radio optimization. Initial results indicate spectral efficiency gains exceeding 20% through AI-enhanced radio resource management and signal processing techniques. Nokia’s roadmap targets up to 50% gains by 2027 and greater than 100% by 2028, with the objective of increasing capacity in dense deployments while reducing cost per bit and improving user experience.

“Telecommunications is entering the AI era — the radio access network is the next AI infrastructure,” said Jensen Huang, CEO and founder of NVIDIA. “Together with Nokia, we are bringing NVIDIA CUDA and AI into the baseband, transforming RAN into a planet-scale AI computer. This is a generational shift for operators — unlocking more capacity and efficiency from today’s spectrum while creating the foundation for new AI services and the 6G era.”

Image credit: Nokia

A key element of the offering is a software subscription model that enables operators to access ongoing AI-driven enhancements, feature updates, and performance improvements independent of hardware upgrade cycles. Nokia expects pilot deployments to begin by the end of the year, with broader commercial availability targeted for 2027. The roadmap incorporates NVIDIA’s programmable merchant silicon to support continued performance scaling and feature evolution.

“Nokia’s AI-RAN launch represents an important step in bringing AI-RAN from industry vision to commercial reality. The addition of the new AI-RAN node alongside the AirScale capacity plug-in unit and cloud-native deployment options gives operators practical choices for adopting AI-native networks based on their existing infrastructure and transformation goals. By combining AI-accelerated computing with a software-defined architecture and a clear product roadmap, Nokia is helping operators unlock greater capacity, improve network economics and accelerate the transition toward AI-native RAN,” commented Rémy Pascal, Practice Leader, Mobile Infrastructure at Omdia.

Light Reading’s Iain Morris wrote:

Nokia’s vision, outlined during an exclusive interview with Light Reading, is that the Nvidia-based hardware products announced today and available from next year will potentially last customers deep into the 6G era. That cannot be said of the latest Nokia hardware in commercial use, based on custom silicon provided by Marvell Technology, according to Atkinson. The same would be true of the latest hardware from rival Ericsson, he believes: “It will get them into 6G, but it won’t see them long into 6G.”

The Nokia pivot to GPUs sets up a riveting clash between the two Nordic companies. Ericsson remains firmly attached to its own custom silicon, and its top executives think writing code for CUDA, Nvidia’s software platform, could make Nokia a victim of “vendor lock-in,” imprisoned by a single vendor’s hardware.

For telcos interested in general-purpose processors and the greater freedom they promise, Ericsson instead offers a set of “virtual” RAN products. While based today on Intel’s central processing units (CPUs), the same software can run on CPUs from other chipmakers after minimal tweaks, according to Ericsson. Only a small amount of code remains hardware-dependent, it says.

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Nokia’s AI-RAN platform is based on a common, software-defined architecture integrating its anyRAN software with NVIDIA’s accelerated computing. The platform supports 4G, 5G, and forward evolution toward 6G, while maintaining compliance with Open RAN specifications to enable multi-vendor interoperability. Operators can select from multiple deployment options aligned with their installed base and transformation strategy, while leveraging a unified software roadmap.

For network operators with existing Nokia AirScale deployments, the introduction of a GPU-accelerated capacity plug-in unit provides a relatively low-friction upgrade path. This approach integrates accelerated computing into the current RAN footprint, enabling step-function capacity improvements while preserving prior infrastructure investments. Nokia also highlights support for AI-optimized merchant silicon, including contributions from partners such as Marvell, reflecting a broader ecosystem strategy for AI-RAN evolution.

Nokia is also introducing a GPU-based standalone AI-RAN node designed for flexible deployment across diverse network environments. The platform supports 4G, 5G, and future 6G workloads and can be deployed as a discrete node, in clustered configurations, or integrated with existing AirScale systems as a logical baseband. This enables scalable deployment of AI-native capacity while maintaining architectural flexibility.

For network operators pursuing cloud-native RAN strategies, Nokia’s AI-RAN capabilities extend to GPU-enabled COTS server platforms delivered through ecosystem partners. This approach supports deployment on standardized, accelerated infrastructure while aligning with open and secure supply chain principles. It combines cloud-native operational models with the performance requirements of AI-intensive RAN workloads.

Nokia’s AI-RAN introduces a shift from hardware-centric lifecycle management to a software-driven innovation model. Through its subscription-based framework, operators gain continuous access to evolving AI algorithms, spectral efficiency enhancements, and network optimization capabilities. This model enables ongoing improvements in performance, efficiency, security, and resilience without requiring discrete hardware upgrades, thereby improving total cost of ownership and extending asset lifecycles.

By combining AI-accelerated computing, software-defined RAN architecture, and an open ecosystem approach, Nokia’s AI-RAN platform is intended to provide operators with a scalable pathway to increased capacity, improved economic performance, and continuous innovation as networks evolve toward the 6G era.

References:

https://www.globenewswire.com/news-release/2026/07/15/3327480/0/en/nokia-defines-the-next-era-of-radio-with-the-industry-s-first-ai-native-ran-platform.html

https://www.nokia.com/newsroom/nokia-defines-the-next-era-of-radio-with-the-industrys-first-ai-native-ran-platform/

https://www.nokia.com/radio-access/ai-ran/

https://www.lightreading.com/6g/nokia-says-long-term-6g-is-not-doable-without-nvidia

European Consortium 5G NTN transmission paves the way for standards based direct to device (D2D) connectivity

Posted on July 14, 2026 by Alan Weissberger

Executive Summary:

Satellite connectivity advanced meaningfully this past week as the European Trantor consortium reported the first 5G NTN transmission over a Hispasat satellite on July 8th. This is an important step because it moves NTN from proof-of-concept demonstrations toward a standards-based implementation path aligned with 3GPP’s non-terrestrial network work. In telecom terms, interoperability is the real gating factor: NTN only becomes architecturally relevant if it can integrate cleanly with 3GPP-defined access, mobility, and service procedures rather than remaining a proprietary satellite overlay.

From a technical perspective, the signal here is that NTN is evolving beyond its initial role as satellite backhaul for remote coverage and into direct-to-device (D2D) access using standard cellular devices and network functions. That transition brings a new set of engineering challenges: synchronization and timing, mobility management, spectrum coordination, terminal power efficiency, and seamless handover between terrestrial and non-terrestrial domains. The “pre-6G” label is appropriate because these developments point to a converged terrestrial-plus-space access architecture, not a standalone satellite niche.

Sanford Bernstein’s warning that direct-to-device satellite can increase competitive pressure on terrestrial network operators is credible because it erodes one of the incumbents’ traditional advantages: exclusive control over wide-area coverage. If NTN systems can support messaging, emergency connectivity, and eventually broader mobile services, then operators face substitution pressure in segments where they historically monetized coverage gaps, roaming resilience, and service continuity. This does not displace terrestrial networks, but it does reduce the ability of carriers to price certain coverage and resilience attributes as premium differentiators.

The most likely industry response is partnership rather than confrontation. Mobile operators will probably position NTN as a complementary resilience layer for coverage extension, disaster recovery, IoT continuity, and premium service tiers, rather than as a replacement for terrestrial RAN investment. At the same time, vendors and standards bodies will continue pushing multi-orbit, multi-band, and multi-vendor interoperability as the condition for commercial viability. For editorial purposes, the key question is whether NTN matures as an operator-integrated extension of the mobile network or as an adjacent service layer that partially bypasses terrestrial incumbents.

3GPP Evolution to 6G:

Image Credit: Ericsson

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Key Technology Takeaways:

The first 5G NTN transmission over a Hispasat satellite marks a meaningful step from lab validation to standards-aligned deployment.
3GPP NTN work in Release 19 is the key enabler because interoperability, not just link feasibility, will determine commercial viability.
ITU-R SWG 4B1 – Satellites in Next Generation Access Technologies will likely rubber stamp 3GPP NTN specifications which will then become ITU-R recommendations.
NTN is evolving from satellite backhaul for remote coverage into direct device access for standard cellular endpoints.
The hardest technical problems are shifting toward timing, mobility, spectrum coordination, device power efficiency, and seamless terrestrial/non-terrestrial handover.
“Pre-6G” is the right framing because NTN is becoming part of a hybrid terrestrial-plus-space access architecture.
Direct-to-device satellite services can pressure terrestrial operators by reducing their exclusive control over last-mile coverage and resilience.
The most likely carrier strategy is partnership and bundling, using NTN for coverage extension, disaster recovery, and IoT continuity rather than full substitution.
Multi-orbit, multi-band, and multi-vendor interoperability will be essential if NTN is to become a durable commercial platform.

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References:

https://www.hispasat.com/en/press-room/press-releases/archivo-2026/491/the-trantor-project-has-successfully-completed-its-work-on-the-development-of-5g-advanced-and-pre-6g-satellite-networks

https://www.3gpp.org/technologies/ntn-overview

https://www.ericsson.com/en/blog/2024/10/ntn-payload-architecture

AT&T/Ericsson Demonstrate 5G-Based ISAC for Drone Detection at World Cup Stadium

Posted on July 13, 2026 by Alan Weissberger

AT&T and Ericsson recently demonstrated the potential of 5G as a platform for integrated sensing and communications (ISAC) [1.] in support of critical infrastructure protection and public safety. The demonstration, conducted at a World Cup stadium near Dallas, TX highlights how cellular networks can evolve into dual-function systems that provide both connectivity and environmental sensing.

Note 1. Integrated Sensing and Communication (ISAC) is a flagship 6G/IMT 2030 capability that unifies mobile communication and environmental sensing into a single network. By using the same infrastructure, spectrum, and waveforms, 6G systems will act as spatially aware platforms. It allows networks to detect, track, and image objects while transmitting data. ITU-R officially designated ISAC as one of the six core usage scenarios in the IMT-2030 (6G) framework.

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In this trial, AT&T and Ericsson (its primary RAN equipment supplier) employed 5G-based network sensing to detect and track unmanned aerial vehicles (UAVs) operating at altitudes between 300 and 400 feet in authorized airspace as they approached AT&T Stadium in Arlington, Texas. The approach reflects a joint communication and sensing (JCAS) paradigm, in which existing radio access network (RAN) infrastructure is leveraged for situational awareness without the need for dedicated radar or sensing overlays.

Ericsson deployed Massive MIMO radios across multiple sites to establish a “multi-static sensing configuration,” enabling spatial diversity and improved detection performance. By combining “sensing-enabled radio transmissions with advanced signal processing and AI-enabled sensing algorithms,” the system detected, localized, and tracked drones in real time. This capability exploits the propagation characteristics of RF signals used for communication, enabling object detection and tracking within the coverage footprint of the network.

Although no match was scheduled during the demonstration, AT&T Stadium has been a primary venue during the tournament and will host the semi-final between France and Spain later this week, providing a representative high-density and security-sensitive deployment context.

Cellular Sensing for Drone Mitigation:

Image Credit: AT&T

Unauthorized UAV activity has posed ongoing challenges for public safety authorities during the tournament. On match days, drone operations are prohibited within a one-nautical-mile radius of stadiums and up to 1,000 feet above ground level, according to the Federal Aviation Administration.

Reporting from the tech demo in Arlington, NBC 5 DFW indicated that U.S. authorities have detected approximately 1,500 drones and confiscated more than 700 across World Cup venues, including 53 in the vicinity of AT&T Stadium.

AT&T and Ericsson position cellular-based sensing as a complementary capability to existing counter-UAV systems deployed by law enforcement. While the Arlington Police Department indicated it was not directly involved in the demonstration, it acknowledged ongoing evaluation of emerging technologies that could enhance future operational capabilities, according to NBC 5 DFW.

Quotes:

Ildefonso de la Cruz, senior principal analyst at Omdia (owned by Informa-UK), characterized the demonstration as strategically timed and situated, noting its alignment with global attention on the World Cup and upcoming large-scale events such as the 2028 Summer Olympic Games in Los Angeles. “This demonstration shows that robust cellular infrastructure is the foundation to build reliable next-generation critical services for public safety and other critical infrastructure verticals,” he stated.

“As networks evolve, the opportunity is not just to prepare for 6G someday, but to begin introducing important building blocks now,” said Dyon Agnew, SVP and Head of Customer Unit AT&T, Ericsson Americas. “This demonstration with AT&T shows a product roadmap in action: using advanced 5G capabilities today to explore how sensing and connectivity can work together, then evolving those capabilities over time as the path to 6G becomes clearer.”

“Integrated sensing is an important part of the road to 6G, and this work helps show how we can start bringing that future to life right now,” said Yigal Elbaz, SVP and Network CTO, AT&T. “By working with Ericsson, we are exploring how advanced wireless networks can add sensing capabilities to connectivity in ways that could support safer operations, smarter venues, and stronger customer experiences, while creating a path to evolve these capabilities responsibly over time.”

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Technical Takeaways:

Demonstrates early-stage ISAC/JCAS capabilities using commercial 5G Massive MIMO infrastructure, with implications for 6G-native sensing architectures.
Validates multi-static sensing configurations in cellular deployments, improving detection accuracy through spatial diversity.
Highlights the role of AI-driven signal processing in extracting sensing information from communication waveforms.
Suggests a pathway to cost-efficient sensing by reusing existing RAN assets, avoiding dedicated radar infrastructure.
Reinforces the potential for cellular networks to support public safety and critical infrastructure monitoring as a value-added service layer.

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What this roadmap will enable over time:

Help event and facility teams improve planning and staffing by providing broader visibility into how vehicles move through large environments.
Enhance coordination around temporary event infrastructure and logistics by adding network-based environmental awareness alongside connectivity.
Support a wide-area drone awareness system for public-sector stakeholders, improving visibility into low-altitude drone activity as the low-altitude economy develops across cities and regions.
Inform the evolution of future 5G and 6G capabilities as sensing and communications mature together for large venues, enterprises, governments, and public-sector environments.

Conclusions:

AT&T and Ericsson will continue exploring how sensing capabilities can be introduced pragmatically using existing network foundations, then advanced over time as standards, ecosystems, and market needs develop.

The goal is to help shape a practical path where future 6G/IMT 2030 capabilities are not treated as a distant leap, but as an evolution that can begin delivering value well before full 6G/IMT 2030 commercialization.

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References:

https://about.att.com/story/2026/att-ericsson-drone-detection.html

https://www.lightreading.com/5g/att-and-ericsson-demo-5g-sensing-for-drone-detection

https://www.linkedin.com/feed/update/urn:li:activity:7481460850159943680/ by Yigal Elbaz, of AT&T

https://www.itu.int/en/ITU-R/study-groups/rsg5/rwp5d/imt-2030/pages/default.aspx

Analysis: Cohere’s $28M U.S. DoD FutureG ISAC contract; OTFS vs OFDM; 6G-NR/IMT 2030 RIT standards outlook

Analysis & Implications of the Communications Cybersecurity Information Sharing and Analysis Center (C2 ISAC)

Network X Americas: AT&T and Comcast reveal huge AI impact on network operations

Analysis: AT&T’s $250B network investment to advance U.S. connectivity

AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era

Meta’s “Iris” AI Chip for MTIA: Implications for Telecom-Grade Optical Networking, DCI and High Capacity Ethernet Fabrics

Posted on July 11, 2026 by Alan Weissberger

Executive Summary:

According to Reuters, Meta Platforms (previously known as Facebook) plans to start manufacturing an artificial intelligence (AI) chip in ‌September as part of its plan to boost overall computing power to 14 gigawatts in 2027. The social media firm’s data center chip, code-named “Iris,” is part of a four-generation project for Meta Training and Inference Accelerators (MTIA) that it will design in-house. The plan is to use custom-built silicon to improve the AI that powers its Facebook and Instagram social media platforms.

This move by Meta marks a pivotal moment in hyperscaler AI infrastructure strategy. This vertical integration play, executed through a multi-vendor supply chain (Broadcom design, TSMC manufacturing, Samsung RAM, SanDisk storage, Sumitomo fiber-optic equipment), has profound implications for telecom-grade optical networking, data center interconnect (DCI), and high-capacity Ethernet fabrics.

For IEEE Techblog readers focused on network architecture, standards, and infrastructure economics, the Meta MTIA story illuminates three critical trends:

Hyperscaler silicon sovereignty as a cost and performance lever.
Scaling challenge of 14 GW AI compute for optical transport and DCI.
The emerging “Network Supercycle” driven by agentic AI workloads as per Cisco.

Image Credit: Meta Platforms

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The MTIA “Iris” Roadmap: Accelerating AI Silicon Cadence:

Meta’s Meta Training and Inference Accelerator (MTIA) program—now in its third generation with “Iris”—is pursuing an aggressive development cadence: a new chip every six months through 2027. This contrasts sharply with the industry-standard 12–18 month cadence for AI accelerators from NVIDIA, AMD, and even hyperscaler custom silicon programs (Google TPU, AWS Trainium)

Key MTIA milestones:

MTIA v1 (2024): First-generation training/inference chip, proof-of-concept for Meta’s internal AI workloads
MTIA v2 (early 2026): Performance and efficiency improvements, scaled deployment for Llama model training
MTIA v3 “Iris” (September 2026): Production ramp, targeting higher throughput and lower power per inference
MTIA v4 (2027): Next-generation architecture, expected to integrate advanced packaging, higher-bandwidth memory, and improved interconnect topologies

This cadence is not merely a technical achievement—it’s a strategic signal. Meta is betting that in-house silicon, even if initially less performant than NVIDIA’s H100/B100 or AMD’s MI300X, can deliver better total cost of ownership (TCO) when optimized for Meta’s specific workloads (Llama LLMs, recommendation systems, ad targeting).

Broadcom + TSMC: A Multi-Vendor Supply Chain Play:

Meta’s MTIA program is not a pure in-house design effort. The company is partnering with Broadcom for chip design and TSMC for advanced-node manufacturing (likely 5nm or 3nm process). This hybrid approach—hyperscaler architectural control with foundry and design partner execution—is becoming the dominant model for AI silicon:techcrunch

Hyperscaler	Design Partner	Foundry	Notes
Meta	Broadcom	TSMC	MTIA v3 “Iris” production Sept 2026
Google	In-house + Broadcom	TSMC	TPU v5e/v5p, Trillium (TPU v6)
Amazon	Annapurna Labs (acquired)	TSMC	Trainium2, Inferentia2
Microsoft	In-house + AMD	TSMC	Maia 100, limited deployment
NVIDIA	In-house	TSMC	H100, B100, Rubin (2026)

Why this matters: The multi-vendor AI chip supply chain is becoming a critical dependency for telecom-grade infrastructure. Broadcom’s involvement in both Meta’s MTIA and Apple’s $30B RF/FBAR deal (announced July 7–8, 2026) positions the company as a central player in both AI compute and 5G/6G RF ecosystems. For network architects, this means tracking Broadcom’s packaging, interconnect, and I/O roadmaps—not just NVIDIA’s.

14 GW Computing Target: The Optical and DCI Challenge:

Meta’s internal memo, reported by Reuters on July 9, 2026, outlines a target of 14 GW of computing capacity by 2027. To put this in perspective:

14 GW ≈ 14 large nuclear power plants (each ~1 GW)
Current hyperscaler data center power draw: ~50–100 GW total across all hyperscalers (2025 estimate)
Meta’s 2025 data center power: ~10–12 GW (estimated)
Growth rate: ~15–20% CAGR in hyperscaler power draw, but Meta is targeting a step-function increase

This is not just a compute scaling story—it’s an optical transport and DCI scaling story. Each GW of AI compute requires:

High-bandwidth optical interconnect within data centers (400G/800G/1.6T Ethernet, optical circuit switching)
Long-haul DCI between data center campuses (coherent 800G/1.6T, subsea cable systems)
Power and cooling infrastructure (liquid cooling, direct-to-chip, immersion)
Fiber-optic cabling and fiber-optic equipment (Sumitomo, Corning, Prysmian)

Optical Transport Implications:

Meta’s 14 GW target implies a massive buildout of optical infrastructure. Key considerations for IEEE ComSoc readers:

Intra-DC Optical Fabrics: AI clusters (e.g., 10K–100K GPU/TPU/MTIA nodes) require non-blocking, low-latency optical fabrics. Meta’s 2025–2026 data center designs likely use:
- 800G/1.6T optical transceivers (OSFP, QSFP-DD)
- Optical circuit switching (OCS) for dynamic bandwidth allocation (e.g., Oriole Networks PRISM, Google Apollo)
- Co-packaged optics (CPO) and near-packaged optics (NPO) for power efficiency
Inter-DC DCI: Meta operates multiple data center campuses globally (U.S., Europe, Asia). Connecting these for AI workload distribution requires:
- Coherent 800G/1.6T DCI (400ZR/ZR+, OpenROADM)
- Subsea cable systems (e.g., Meta’s 2024–2026 investments in transatlantic and transpacific cables)
- Terragraph-inspired metro fiber for regional campus interconnects
Fiber-Optic Equipment: Meta’s supply chain includes Sumitomo Electric for fiber-optic equipment, per the July 2026 memo. Sumitomo is a key supplier of:reuters
- Optical amplifiers (EDFA)
- Optical switches and ROADMs
- Fiber-optic cables and connectors

Standards relevance: IEEE 802.3 (Ethernet), IEEE 802.1 (Time-Sensitive Networking), and ITU-T G.709 (OTN) are all directly impacted by Meta’s custom AI chip development program.

Cost Reduction vs. NVIDIA/AMD: The Vertical Integration Calculus & Why Hyperscalers Are Building Their Own AI Chips:

Meta’s MTIA program is part of a broader hyperscaler trend: vertical integration in AI silicon. The economic rationale is straightforward:

NVIDIA H100/B100 pricing: $30K–$40K per GPU (2025–2026 list prices)
AMD MI300X pricing: $20K–$30K per accelerator (2025–2026)
Hyperscaler custom silicon TCO: 30–50% lower than NVIDIA/AMD at scale, despite lower peak performance

Meta’s internal analysis (per their July 2026 internal memo) likely shows that MTIA v3 “Iris” can deliver comparable inference throughput per dollar to NVIDIA H100 for Llama workloads, even if peak FLOPS are lower. This is because:

Workload-specific optimization: MTIA is tuned for Meta’s LLM architectures (Llama 2/3/4), recommendation systems, and ad targeting—not general-purpose AI training.
Supply chain control: Meta can negotiate better TSMC wafer pricing, avoid NVIDIA’s 20–30% gross margin, and reduce dependency on a single vendor.
Software stack integration: Meta can optimize PyTorch, Llama inference libraries, and Meta’s internal AI frameworks for MTIA, reducing software overhead.

NVIDIA’s AI Chip “tax” vs. Hyperscaler Pushback:

NVIDIA’s dominance in AI accelerators (80–90% market share in 2025) has created what hyperscalers call the “NVIDIA tax”: premium pricing, limited supply, and software lock-in (CUDA ecosystem). Meta’s MTIA, Google’s TPU, Amazon’s Trainium, and Microsoft’s Maia are all attempts to reduce this dependency.

This is analogous to the telecom industry’s historical pushback against Cisco/Juniper proprietary switching ASICs. Open networking (Barefoot Tofino, Broadcom StrataXGS, P4 programmability) and disaggregated hardware (white-box switches, SONiC NOS) emerged as responses. AI silicon is following a similar path: disaggregation, open software stacks, and multi-vendor supply chains.

Full AI Infrastructure Stack Diversification: Samsung, SanDisk, Sumitomo:

Meta’s July 2026 memo outlines a fully diversified AI infrastructure stack:

AI accelerators: Meta MTIA (Broadcom design, TSMC fab)
DRAM: Samsung (HBM3/HBM3e for high-bandwidth memory)
Storage: SanDisk (NVMe SSDs, QLC/TLC NAND for model checkpoints and data lakes)
Fiber-optic equipment: Sumitomo (optical amplifiers, switches, cables)
Networking: Broadcom (Ethernet switches, NICs), potentially NVIDIA (Spectrum-X, Quantum InfiniBand for some clusters)

This diversification is not just about cost—it’s about supply chain resilience. The 2020–2023 chip shortage, U.S.-China trade tensions, and Taiwan geopolitics have made hyperscalers acutely aware of single-vendor risk.

Telecom relevance: This mirrors the telecom industry’s shift from Cisco/Juniper monolithic routers to disaggregated white-box switches, open optical line systems, and multi-vendor RAN (O-RAN, vRAN). The AI infrastructure stack is undergoing a similar transformation.

The “Network Supercycle” Narrative: AI Compute as a WAN Traffic Driver:

Cisco executives have framed agentic AI workloads as driving a new infrastructure investment wave, with AI inference projected to account for ~25% of total WAN traffic by 2035. Meta’s 14 GW target is a concrete manifestation of this thesis.

Key implications for WAN and DCI:

Bursty, Low-Latency Uplink Traffic: Agentic AI (e.g., autonomous coding agents, multi-agent collaboration) requires high uplink capacity, low latency, and guaranteed connectivity—exactly the traffic patterns Ookla’s July 2026 report highlighted as stressors for 5G networks.
East-West DCI Traffic: AI training and inference workloads require massive data movement between storage, compute, and memory across data center campuses. This drives demand for:
- Coherent 800G/1.6T DCI
- Optical circuit switching for dynamic bandwidth allocation
- Subsea cable systems for intercontinental AI workload distribution
Token/Byte Monetization: Huawei’s July 2026 AI-centric network vision includes “token/byte” monetization strategies for AI-driven services in the upper-6 GHz band. Meta’s AI infrastructure buildout is the supply-side enabler for this demand-side monetization.techblog.comsoc+1

Nokia’s “Physical AI” Warning:

Nokia’s “Physical AI” study (covered in earlier Techblog posts) warns that high-volume, low-latency uplink traffic from physical AI applications (e.g., robotics, autonomous systems) may require a fundamental RAN redesign. Meta’s 14 GW target is a parallel data center-side manifestation of this trend: AI workloads are reshaping both RAN and DCI/optical architectures.techblog.comsoc+1

Standards and Interoperability:

Meta’s MTIA “Iris” and 14 GW target have direct implications for several IEEE and standards activities:

IEEE 802.3 (Ethernet):

800G/1.6T Ethernet: IEEE 802.3df (800G/1.6T) and IEEE 802.3dj (1.6T/3.2T) are critical for AI cluster fabrics.
Power over Ethernet (PoE) for AI racks: Higher-power PoE standards may be needed for AI accelerator racks and liquid-cooled systems.

IEEE 802.1 (Time-Sensitive Networking):

Deterministic Ethernet for AI: Low-latency, jitter-free traffic for AI inference may require TSN profiles or new deterministic Ethernet extensions.

IEEE 802.15 (Wireless Personal Area Networks):

AI-native wireless for edge inference: Meta’s MTIA may eventually extend to edge inference (e.g., AR/VR, metaverse), requiring low-power, high-bandwidth wireless standards.

ITU-T and OIF:

Coherent DCI: ITU-T G.709 (OTN), G.709.x (coherent OTN), and OIF 400ZR/ZR+ are critical for inter-DCI.
Open optical line systems: OpenROADM, OpenCable, and disaggregated optical line systems are relevant for hyperscaler DCI builds.

O-RAN and AI-RAN Alliance:

AI-for-RAN vs. AI-on-RAN: Meta’s AI infrastructure could eventually support AI-on-RAN workloads (running AI inference on RAN/edge infrastructure), aligning with the AI-RAN Alliance’s vision.

Competitive Landscape – How Meta’s MTIA Compares:

Hyperscaler	AI Accelerator	Design Partner	Foundry	Production Timeline	Notes
Meta	MTIA v3 “Iris”	Broadcom	TSMC	Sept 2026	14 GW target by 2027
Google	TPU v6 “Trillium”	In-house + Broadcom	TSMC	2025–2026	10x performance vs. TPU v4
Amazon	Trainium2	Annapurna Labs	TSMC	2025–2026	4x performance vs. Trainium1
Microsoft	Maia 100	In-house + AMD	TSMC	2025 (limited)	Limited deployment, hybrid with NVIDIA
NVIDIA	B100, Rubin	In-house	TSMC	2025–2026	Dominant market share, CUDA ecosystem

Key takeaway: Meta’s MTIA is not the most performant AI accelerator, but it’s part of a broader hyperscaler strategy to reduce NVIDIA dependency, control TCO, and optimize for specific workloads.

Conclusions – The AI Infrastructure Stack as a Telecom-Grade Opportunity:

Meta’s MTIA “Iris” chip and 14 GW computing target are not just hyperscaler news—they are telecom-grade infrastructure news. For IEEE ComSoc readers, the implications are clear:

Optical transport and DCI will scale dramatically to support 14 GW of AI compute, creating demand for 800G/1.6T coherent optics, optical circuit switching, and subsea cable systems.
Hyperscaler silicon sovereignty is reshaping the AI accelerator market, with direct implications for Broadcom, TSMC, and the broader semiconductor supply chain.
The “Network Supercycle” is real, driven by agentic AI workloads that require high uplink capacity, low latency, and guaranteed connectivity.
Standards bodies (IEEE, ITU-T, OIF, O-RAN) must track AI infrastructure trends to ensure interoperability, performance, and cost efficiency.

For telecom network architects, optical engineers, and standards professionals, the Meta MTIA story is a call to action: AI infrastructure is the next frontier for telecom-grade networking. The question is not whether telecom and AI will converge—it’s how quickly and effectively the industry can adapt.

References:

https://www.reuters.com/world/asia-pacific/meta-put-ai-chip-into-production-september-it-looks-double-computing-capacity-2026-07-09/

Meta’s new AI chips will begin production in September

Cisco Execs: New “Network Supercycle” as Agentic AI Workloads Reshape Telecom Infrastructure

Ookla: AI workloads will force changes in 5G mobile network infrastructure

Nokia’s AI Applications Study: “Physical AI” may require RAN redesign to support high‑volume, low‑latency uplink traffic

Ookla: AI platform reliability decreases as outages surge

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

Dell’Oro: AI RAN revenue forecast: $35B from 2026-to-2030; 3 types of AI RAN explained

AI-RAN and Agentic AI get real: Ericsson, Nokia, Verizon & other operators enter into a new network automation era

AI-RAN Reality Check: hype vs hesitation, shaky business case, no specific definition, no standards?

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

Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments

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

Ookla: AI workloads will force changes in 5G mobile network infrastructure

Posted on July 7, 2026 by Alan Weissberger

Introduction:

Ookla’s latest research study examines how AI use cases will stress 5G mobile networks, relative to standard internet traffic. The report, based on Speedtest Intelligence® data across 22 markets, evaluates metrics like upload capacity, latency under load, and cloud infrastructure pathways (see graphs below). Using Speedtest 5G data from 2025 across 22 markets and 86 operators in North America, Europe, Asia Pacific, the Middle East, and Latin America, it measures upload capacity, latency under load, and the quality of the path to the cloud. It also shows where current 5G falls short of what AI actually demands.

Analysis:

Ookla’s report argues that 5G network evaluation is entering a new phase: raw download speed is no longer enough to describe user experience or network capability in an AI-driven era. The more relevant indicators are upload performance, latency, consistency, and resilience, because AI-heavy applications tend to be interactive, symmetric, and sensitive to delay. The report’s timing is important because it reframes 5G from a consumer mobile broadband service into an infrastructure question for AI workloads. That shift matters for network operators, because uplink and latency have historically received less attention than headline download rates in market rankings and public messaging.

Here’s the lead-in (emphasis added):

“AI has changed what a good mobile network looks like, and the metric the industry has marketed for two decades — peak download speed — no longer predicts it. The networks that top the download charts are often not the ones best prepared for AI traffic. Whether an AI application feels instant or breaks depends in large part on how much a network can upload, how it holds up under load, and how consistently it reaches the cloud, and on those measures, different networks come out on top. This report rebuilds the industry’s download-led scorecard around what AI actually asks of a network, and shows where today’s 5G mobile networks are ready and where they fall short. AI traffic is not one thing. Text chat, conversational voice, multimodal and AR vision, generated video, and agentic activity each load the network differently, and most of them lean on parts of the network that download speed never tested. The change AI brings is less about raw capacity, which operators have expanded for years, than about the shape of the traffic — heavier on upload, always on, and bursty, rather than download-led and session-based.”

A few high-level takeaways for the U.S. market include:

Although the United States ranks among the strongest on overall network performance, it sits at 5.1% for the proportion of network capacity allocated to the uplink, which is the lowest in the dataset.
The U.S. upload share has contracted, declining from 8.0% to 5.1% between 2023 and 2025.
The U.S. market top network operators fall short of the 20 Mbps upload target required for AR and multimodal AI.
For baseline network responsiveness, the U.S. records a multi-server latency of 50.5 ms, missing the target of less than 50 ms for text-based large language models (LLMs).

Technical Implications:

Ookla’s framing implicitly favors 5G SA, 5G Advanced, and edge-assisted architectures, since these are the network generations most likely to improve latency determinism and support more efficient uplink behavior. It also suggests that future benchmarking should include workload-aware tests, not just conventional speed tests, because AI applications stress networks differently from video streaming or web browsing. The report has immediate relevance for markets where 5G download speeds look strong but uplink and latency remain weaker, because those networks may appear healthy under older metrics while still underperforming for AI use cases. That is a useful lens for comparing operators, especially where regulators and carriers are beginning to discuss AI readiness as part of national digital infrastructure strategy.

Conclusions:

With the rise of AI workloads, mobile network measurement is becoming application-specific. The central question is no longer just “How fast is 5G?” but “How well does the network support AI-era traffic patterns, especially interactive and uplink-heavy traffic?” In this new context, metrics such as upload capacity, latency consistency, and service resilience are becoming just as important as peak downlink speed. For operators, this implies that competitive advantage will increasingly depend on how well the network supports real-time, bidirectional, and latency-sensitive applications, rather than how well it performs on legacy consumer benchmarks.

Traditional speed tests still matter, but they are increasingly insufficient as a proxy for user experience in an AI-native environment. In practice, the networks that win will be those that can deliver symmetry, resilience, and predictable latency across real workloads, not merely impressive headline throughput.

Ookla Charts: