AI in Networks Market
AI wireless and fiber optic network technologies; IMT 2030 “native AI” concept
To date, the main benefit of AI for telecom has been to reduce headcount/layoff employees. Light Reading’s Iain Morris wrote, “Telecom operators and vendors, nevertheless, are already using AI as the excuse for thousands of job cuts made and promised. So far, those cuts have not brought any improvement in the sector’s fortunes. Meanwhile, ceding basic but essential skills to systems that hardly anyone understands seems incredibly risky.” Some say that will change with 6G/ IMT 2030, but that’s a long way off. Others point to AI RAN, but that has not gotten any real market traction with wireless telcos.
As Gen AI development accelerates, robust wireless and fiber optic network infrastructure will be essential to accommodate the substantial data and communication volume generated by AI systems. Initially, the existing network ecosystem—encompassing wireless, wireline, broadband, and satellite services—will absorb this traffic load. However, the expanding requirements of AI are anticipated to drive the future emergence of entirely new network architectures and communication paradigms.
For sure, AI needs massive, fast, reliable connectivity to function, driving demand for low latency optical networks and 6G/ IMT 2030, which AI itself will optimize, leading to better efficiency, security, resource management, and new services like real-time AR/VR, ultimately boosting telecom revenue and innovation across the entire digital ecosystem.
Source: Pitinan Piyavatin/Alamy Stock Photo
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- AI Backend Scale-Out and Scale-Up Networks: These are specialized, private networks within and across data centers designed to connect numerous GPUs and enable them to function as one massive compute resource. They utilize technologies like:
- InfiniBand: A long-standing high-bandwidth, low-latency technology that has become a top choice for connecting GPU clusters in AI training environments.
- Optimized Ethernet: Ethernet is gaining ground for AI workloads through the development of enhanced, open standards via the Ultra Ethernet Consortium (UEC). These enhancements aim to provide lossless, low-latency fabrics that can match or exceed InfiniBand’s performance at scale.
- High-Speed Optics: The use of 400 Gbps and 800 Gbps (and soon 1.6 Tbps) optical interconnects is critical for meeting the massive bandwidth and power requirements within and between AI data centers.
- Edge AI Networking: As AI inferencing (generating responses from AI models) moves closer to the end-user or device (e.g., in autonomous vehicles, smart hospitals, or factories), specialized edge networks are needed. These networks must ensure low latency and localized processing to enable real-time responses.
- AI-Native 6G Networks: The upcoming sixth-generation (6G) wireless networks are being designed with AI integration as a core principle, rather than an add-on.
- These networks are expected to be fully automated and self-evolving, using AI to optimize resource allocation, predict issues, and enhance security autonomously.
- They will support extremely high data rates (up to 1 Tbps), ultra-low latency (around 1 ms), and new technologies like AI-RAN (Radio Access Network) that integrate AI capabilities directly into the network infrastructure.
- More in next section below.
- Self-Evolving Networks: The ultimate goal is the development of “self-evolving networks” where AI agents manage and optimize the network infrastructure autonomously, adapting to new demands and challenges without human intervention.
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In IMT 2030/6G networks, AI will shift from being an “add-on” optimization tool (as in 5G) to a native, foundational component of the entire network architecture. This deep integration will enable the network to be self-organizing, highly efficient, and capable of supporting advanced AI applications as a service. Native AI for IMT-2030 (6G) means building AI directly into the network’s core architecture, making it AI-first and pervasive, rather than adding AI as an overlay; this enables self-optimizing, intelligent networks that can autonomously manage resources, provide ubiquitous AI services, and offer seamless, context-aware experiences with minimal human intervention, fundamentally transforming both network operations and user applications by 2030.
- Ubiquitous Intelligence: Embedding AI everywhere, enabling distributed intelligence for AI model training, inference, and deployment directly within the network infrastructure, extending to the network edge.
- Autonomous Operations: AI handles complex tasks like network optimization, resource allocation, and automated maintenance (O&M) in real-time, reducing reliance on manual intervention.
- AI-as-a-Service (AIaaS): The network transforms into a unified platform providing both communication and AI capabilities, making AI accessible for various applications.
- Intelligent Processing: AI drives functions across the air interface, resource management, and control planes for highly efficient operations.
- Data-Driven Automation: Leverages big data and real-time analytics to predict issues, optimize performance, and automate complex decision-making.
- Seamless User Experience: Moves beyond touchscreens to AI-driven interactions, offering more natural and contextual computing.
- Autonomous Operations: AI will enable self-monitoring, self-optimization, and self-healing networks, drastically reducing the need for human intervention in operation and maintenance (O&M).
- Dynamic Resource Management: ML algorithms will analyze massive amounts of network data in real-time to predict traffic patterns and user demands, dynamically allocating bandwidth, power, and computing resources to ensure optimal performance and energy efficiency.
- AI-Native Air Interface: AI/ML models will replace traditional, manually engineered signal processing blocks in the physical layer (e.g., channel estimation, beam management) to adapt dynamically to complex and time-varying wireless environments, improving spectral efficiency.
- Enhanced Security: AI will be critical for real-time threat detection and automated incident response across the hyper-connected 6G ecosystem, identifying anomalies and mitigating security risks that are not well understood by current systems.
- Digital Twins: AI will power the creation and management of real-time digital twins (virtual replicas) of the physical network, allowing for sophisticated simulations and testing of network changes before real-world deployment.
- Pervasive Edge AI: AI model training and inference will be distributed throughout the network, from the cloud to the edge (devices, base stations), reducing latency and enabling real-time, localized decision-making for applications like autonomous driving and industrial automation.
- Support for Advanced Use Cases: The massive data rates (up to 1 Tbps), ultra-low latency, and high reliability enabled by AI in 6G will facilitate new applications such as holographic communication, remote robotic surgery with haptic feedback, and collaborative robotics that were not feasible with 5G.
- Federated Learning: The network will support distributed machine learning techniques, such as federated learning, which allow AI models to be trained on local data across various devices without the need to centralize sensitive user data, thus ensuring data privacy and security.
- Integrated Sensing and Communication (ISAC): AI will process the rich environmental data gathered through 6G’s new sensing capabilities (e.g., precise positioning, motion detection, environmental monitoring), allowing the network to interact with and understand the physical world in a holistic manner for applications like smart city management or augmented reality.
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AI‑native air interface and RAN:
IMT‑2030 explicitly expects a new AI‑native air interface that uses AI/ML models for core PHY/MAC functions such as channel estimation, symbol detection/decoding, beam management, interference handling, and CSI feedback. This enables adaptive waveforms and link control that react in real time to channel and traffic conditions, going beyond deterministic algorithms in 5G‑Advanced.
At the RAN level, IMT‑2030 envisions “native‑AI enabled” architectures that are simpler but more intelligent, with data‑driven operation and distributed learning across gNBs, edge nodes, and devices. AI/ML will be applied end‑to‑end for resource allocation, mobility, energy optimization, and fault management, effectively turning the RAN into a self‑optimizing, self‑healing system.
Integrated AI and communication services:
The framework defines “Artificial Intelligence and Communication” (often phrased as Integrated AI and Communication) as a specific usage scenario where the network provides AI compute, model hosting, and inference as a service. Example use cases include IMT‑2030‑assisted automated driving, cooperative medical robotics, digital twins, and offloading heavy computation from devices to edge/cloud via the 6G network.
To support this, IMT‑2030 includes “applicable AI‑related capabilities” such as distributed data processing, distributed learning, AI model execution and inference, and AI‑aware scheduling as native capabilities of the system. Computing and data services (not just connectivity) are treated as integral IMT‑2030 components, especially at the edge for low‑latency, energy‑efficient AI workloads.
System intelligence and new use cases:
AI is central to several new IMT‑2030 usage scenarios beyond classic eMBB/mMTC/URLLC, including Immersive Communication, Integrated Sensing and Communication, and Integrated AI and Communication. In integrated sensing, AI fuses multi‑dimensional radio sensing data (position, motion, environment, even human behavior) to provide contextual awareness for applications like smart cities, industrial control, and XR.
Embedding intelligence across air interface, edge, and cloud is seen as necessary to manage 6G complexity and enable “Intelligence of Everything,” including real‑time digital twins and AIGC‑driven services. The vision is for the 6G/IMT‑2030 network to act as a distributed neural system that tightly couples communication, sensing, and computing.
IMT 2030 Goals:
- To create self-healing, self-optimizing networks that can adapt to diverse demands.
- To enable new AI-driven applications, from intelligent digital twins to advanced immersive experiences.
- To build a truly intelligent communication fabric that supports a hyper-connected, AI-enhanced world.
Summary table: AI’s roles in IMT‑2030:
| Dimension | AI role in IMT‑2030 |
|---|---|
| Air interface | AI‑native PHY/MAC for channel estimation, decoding, beamforming, interference control. |
| RAN/core architecture | Native‑AI enabled, data‑driven, self‑optimizing/self‑healing network functions. |
| Compute and data services | Built‑in edge/cloud compute for AI training, inference, and data processing. |
| Usage scenarios | Dedicated “Integrated AI and Communication” plus AI‑rich sensing and immersive use cases. |
| Applications and ecosystems | Support for digital twins, automated driving, robotics, AIGC, and industrial automation. |
In summary, AI in IMT‑2030 is both an internal engine for network intelligence and an exported capability the network offers to verticals, making 6G effectively AI‑native end‑to‑end.
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References:
https://www.lightreading.com/ai-machine-learning/the-lessons-of-pluribus-for-telecom-s-genai-fans
https://www.ericsson.com/en/reports-and-papers/white-papers/ai-native
https://www.5gamericas.org/wp-content/uploads/2024/08/ITUs-IMT-2030-Vision_Id.pdf
ITU-R WP 5D Timeline for submission, evaluation process & consensus building for IMT-2030 (6G) RITs/SRITs
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
Ericsson and e& (UAE) sign MoU for 6G collaboration vs ITU-R IMT-2030 framework
Nokia and Rohde & Schwarz collaborate on AI-powered 6G receiver years before IMT 2030 RIT submissions to ITU-R WP5D
NTT DOCOMO successful outdoor trial of AI-driven wireless interface with 3 partners
Verizon’s 6G Innovation Forum joins a crowded list of 6G efforts that may conflict with 3GPP and ITU-R IMT-2030 work
ITU-R WP5D IMT 2030 Submission & Evaluation Guidelines vs 6G specs in 3GPP Release 20 & 21
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
Highlights of 3GPP Stage 1 Workshop on IMT 2030 (6G) Use Cases
Draft new ITU-R recommendation (not yet approved): M.[IMT.FRAMEWORK FOR 2030 AND BEYOND]
Analysis: OpenAI and Deutsche Telekom launch multi-year AI collaboration
Deutsche Telekom (DT) has formalized a strategic, multi-year collaboration with OpenAI to integrate advanced artificial intelligence (AI) solutions across its internal operations and customer engagement platforms. The partnership aims to co-develop “simple, personal, and multi-lingual AI experiences” focused on enhancing communication and productivity. Initial pilot programs are slated for deployment in Q1 2026. AI will also play a larger role in customer care, internal copilots, and network operations as the Group advances toward more autonomous, self-healing networks.DT plans a company-wide rollout of ChatGPT Enterprise, leveraging AI to streamline core functions including:
- Customer Care: Deploying sophisticated virtual assistants to manage billing inquiries, service outages, plan modifications, roaming support, and device troubleshooting [1].
- Internal Operations: Utilizing AI copilots to increase internal efficiency.
- Network Management: Optimizing core network provisioning and operations.
- Sovereign Cloud (2021): DT’s T-Systems division partnered with Google Cloud to offer sovereign cloud services.
- T Cloud Suite (Early 2025): The launch of a comprehensive suite providing sovereign public, private, and AI cloud options leveraging hybrid infrastructure.
- Industrial AI Cloud (Early 2025): A collaboration with Nvidia to build a dedicated industrial AI data center in Munich, scheduled for Q1 2026 operations.
- Edge AI compute services for enterprises.
- Vertical AI solutions tailored for healthcare, retail, and manufacturing sectors.
- Integrated private 5G and AI bundles for industrial logistical hubs.
“Telcos – if they execute – will have a big play in the edge inferencing space as well as providing hosting and colo services that can host domain specific SLMs that need to be run closer to the user data,” he said. “Furthermore, telcos will play a role in connectivity services across Neocloud providers such as CoreWeave, Lambda Labs, Digital Ocean, Vast.AI etc. OpenAI does not want to lose the opportunity to partner with telcos so they are striking early,” Nag added.
Other Voices:
- Roger Entner notes the model is highly applicable to European incumbents (e.g., Orange, Telefonica) due to the relative scarcity of existing AI data centers in the region, allowing operators to fill a critical infrastructure gap. Conversely, the model is less viable for U.S. operators, where hyperscalers already dominate the extensive data center market.
- AvidThink Founder and colleague Roy Chua cautions that while DT presents a robust “reference blueprint,” replicating this strategy requires significant scale, substantial financial investment, and regulatory alignment—factors not easily accessible to all network operators.
- Futurum Group VP and Practice Lead Nick Patience told Fierce Network, “This deal elevates DT from being a user of AI to being a co-developer, which is pretty significant. DT is one of the few operators building a full-stack AI story. This is an example of OpenAI treating telcos as high-scale distribution and data channels – customer care, billing, network telemetry, national reach and government relationships. This suggests OpenAI is deliberately building an operator channel in key regions (U.S., Korea, EU) but still in partnership with existing cloud and infra providers rather than displacing them.”
OpenAI has established significant partnerships with several telecom network providers and related technology companies to integrate AI into network operations, enhance customer experience, and develop new AI-native platforms. Those deals and collaborations include:
- T-Mobile: T-Mobile has a multi-year agreement with OpenAI and is actively testing the integration of AI (specifically IntentCX) into its business operations for customer service improvements. T-Mobile is also collaborating with Nokia and Nvidia on AI-RAN (Radio Access Network) technologies for 6G innovation.
- SK Telecom (SKT): SK Telecom has an in-house AI company and collaborates with OpenAI and other AI leaders like Anthropic to enhance its AI capabilities, build sovereign AI infrastructure, and explore new services for its customers in South Korea and globally. They are also reportedly integrating Perplexity into their offerings.
- Deutsche Telekom (DT): DT is partnering with OpenAI to offer ChatGPT Enterprise across its business to help teams work more effectively, improve customer service, and automate network operations.
- Circles: This global telco technology company and OpenAI announced a strategic global collaboration to build a fully AI-native telco SaaS platform, which will first launch in Singapore. The platform aims to revolutionize the consumer experience and drive operational efficiencies for telcos worldwide.
- Rakuten: Rakuten and OpenAI launched a strategic partnership to develop AI tools and a platform aimed at leveraging Rakuten’s Open RAN expertise to revolutionize the use of AI in telecommunications.
- Orange: Orange is working with OpenAI to drive new use cases for enterprise needs, manage networks, and enable innovative customer care solutions, including those that support African regional languages.
- Indian Telecoms (Reliance Jio, Airtel): Telecom providers in India are integrating AI tools from companies like Google and Perplexity into their mobile subscriptions, providing millions of users access to advanced intelligence resources.
- Nokia & Nvidia: In a broader industry collaboration, Nvidia invested $1 billion in Nokia to add Nvidia-powered AI-RAN products to Nokia’s portfolio, enabling telecom service providers to launch AI-native 5G-Advanced and 6G networks. This partnership also includes T-Mobile US for testing.
Conclusions:
With more than 261 million mobile customers globally, Deutsche Telekom provides a strong foundation to bring AI into everyday use at scale. The new collaboration marks the next step in Deutsche Telekom’s AI journey – moving from early pilots to large-scale products that make AI useful for everyone
References:
https://www.telekom.com/en/media/media-information/archive/openai-and-telekom-collaborate-1100164
https://www.telekom.com/en/company/companyprofile/company-profile-625808
Deutsche Telekom: successful completion of the 6G-TakeOff project with “3D networks”
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Deutsche Telekom offers 5G mmWave for industrial customers in Germany on 5G SA network
Deutsche Telekom migrates IP-based voice telephony platform to the cloud
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
OpenAI and Broadcom in $10B deal to make custom AI chips
Custom AI Chips: Powering the next wave of Intelligent Computing
OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
OpenAI announces new open weight, open source GPT models which Orange will deploy
OpenAI partners with G42 to build giant data center for Stargate UAE project
Reuters & Bloomberg: OpenAI to design “inference AI” chip with Broadcom and TSMC
AI infrastructure spending boom: a path towards AGI or speculative bubble?
by Rahul Sharma, Indxx with Alan J Weissberger, IEEE Techblog
Introduction:
The ongoing wave of artificial intelligence (AI) infrastructure investment by U.S. mega-cap tech firms marks one of the largest corporate spending cycles in history. Aggregate annual AI investments, mostly for cloud resident mega-data centers, are expected to exceed $400 billion in 2025, potentially surpassing $500 billion by 2026 — the scale of this buildout rivals that of past industrial revolutions — from railroads to the internet era.[1]
At its core, this spending surge represents a strategic arms race for computational dominance. Meta, Alphabet, Amazon and Microsoft are racing to secure leadership in artificial intelligence capabilities — a contest where access to data, energy, and compute capacity are the new determinants of market power.
AI Spending & Debt Financing:
Leading technology firms are racing to secure dominance in compute capacity — the new cornerstone of digital power:
- Meta plans to spend $72 billion on AI infrastructure in 2025.
- Alphabet (Google) has expanded its capex guidance to $91–93 billion.[3]
- Microsoft and Amazon are doubling data center capacity, while AWS will drive most of Amazon’s $125 billion 2026 investment.[4]
- Even Apple, typically conservative in R&D, has accelerated AI infrastructure spending.
Their capex is shown in the chart below:
Analysts estimate that AI could add up to 0.5% to U.S. GDP annually over the next several years. Reflecting this optimism, Morgan Stanley forecasts $2.9 trillion in AI-related investments between 2025 and 2028. The scale of commitment from Big Tech is reshaping expectations across financial markets, enterprise strategies, and public policy, marking one of the most intense capital spending cycles in corporate history.[2]
Meanwhile, OpenAI’s trillion-dollar partnerships with Nvidia, Oracle, and Broadcom have redefined the scale of ambition, turning compute infrastructure into a strategic asset comparable to energy independence or semiconductor sovereignty.[5]
Growth Engine or Speculative Bubble?
As Big Tech pours hundreds of billions of dollars into AI infrastructure, analysts and investors remain divided — some view it as a rational, long-term investment cycle, while others warn of a potential speculative bubble. Yet uncertainty remains — especially around Meta’s long-term monetization of AGI-related efforts.[8]
Some analysts view this huge AI spending as a necessary step towards achieving Artificial General Intelligence (AGI) – an unrealized type of AI that possesses human-level cognitive abilities, allowing it to understand, learn, and adapt to any intellectual task a human can. Unlike narrow AI, which is designed for specific functions like playing chess or image recognition, AGI could apply its knowledge to a wide range of different situations and problems without needing to be explicitly programmed for each one.
Other analysts believe this is a speculative bubble, fueled by debt that can never be repaid. Tech sector valuations have soared to dot-com era levels – and, based on price-to-sales ratios, are well beyond them. Some of AI’s biggest proponents acknowledge the fact that valuations are overinflated, including OpenAI chairman Bret Taylor: “AI will transform the economy… and create huge amounts of economic value in the future,” Taylor told The Verge. “I think we’re also in a bubble, and a lot of people will lose a lot of money,” he added.
Here are a few AI bubble points and charts:
- AI-related capex is projected to consume up to 94% of operating cash flows by 2026, according to Bank of America.[6]
- Over $75 billion in AI-linked corporate bonds have been issued in just two months — a signal of mounting leverage. Still, strong revenue growth from AI services (particularly cloud and enterprise AI) keeps optimism alive.[7]
- Meta, Google, Microsoft, Amazon and xAI (Elon Musk’s company) are all using off-balance-sheet debt vehicles, including special-purpose vehicles (SPVs) to fund part of their AI investments. A slowdown in AI demand could render the debt tied to these SPVs worthless, potentially triggering another financial crisis.
- Alphabet’s (Google’s parent company) CEO Sundar Pichai sees “elements of irrationality” in the current scale of AI investing which is much more than excessive investments during the dot-com/fiber optic built-out boom of the late 1990s. If the AI bubble bursts, Pichai said that no company will be immune, including Alphabet, despite its breakthrough technology, Gemini, fueling gains in the company’s stock price.
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From Infrastructure to Intelligence:
Executives justify the massive spend by citing acute compute shortages and exponential demand growth:
- Microsoft’s CFO Amy Hood admitted, “We’ve been short on capacity for many quarters” and confirmed that the company will increase its spending on GPUs and CPUs in 2026 to meet surging demand.
- Amazon’s Andy Jassy noted that “every new tranche of capacity is immediately monetized”, underscoring strong and sustained demand for AI and cloud services.
- Google reported billions in quarterly AI revenue, offering early evidence of commercial payoff.
Macro Ripple Effects – Industrializing Intelligence:
AI data centers have become the factories of the digital age, fueling demand for:
- Semiconductors, especially GPUs (Nvidia, AMD, Broadcom)
- Cloud and networking infrastructure (Oracle, Cisco)
- Energy and advanced cooling systems for AI data centers (Vertiv, Schneider Electric, Johnson Controls, and other specialists such as Liquid Stack and Green Revolution Cooling).
| Company Name | Core Expertise | Key Solutions for AI Data Centers |
|---|---|---|
| Vertiv | Critical infrastructure (power & cooling) | Offers full-stack solutions with air and liquid cooling, power distribution units (PDUs), and monitoring systems, including the AI-ready Vertiv 360AI portfolio. |
| Schneider Electric | Energy management & automation | Provides integrated power and thermal management via its EcoStruxure platform, specializing in modular and liquid cooling solutions for HPC and AI applications. |
| Johnson Controls | HVAC & building solutions | Offers integrated, energy-efficient solutions from design to maintenance, including Silent-Aire cooling and YORK chillers, with a focus on large-scale operations. |
| Eaton | Power management | Specializes in electrical distribution systems, uninterruptible power supplies (UPS), and switchgear, which are crucial for reliable energy delivery to high-density AI racks. |
- LiquidStack: A leader in two-phase and modular immersion cooling and direct-to-chip systems, trusted by large cloud and hardware providers.
- Green Revolution Cooling (GRC): Pioneers in single-phase immersion cooling solutions that help simplify thermal management and improve energy efficiency.
- Iceotope: Focuses on chassis-level precision liquid cooling, delivering dielectric fluid directly to components for maximum efficiency and reduced operational costs.
- Asetek: Specializes in direct-to-chip (D2C) liquid cooling solutions and rack-level Coolant Distribution Units (CDUs) for high-performance computing.
- CoolIT Systems: Known for its custom direct liquid cooling technologies, working closely with server OEMs (Original Equipment Manufacturers) to integrate cold plates and CDUs for AI and HPC workloads.
–>This new AI ecosystem is reshaping global supply chains — but also straining local energy and water resources. For example, Meta’s massive data center in Georgia has already triggered environmental concerns over energy and water usage.
Global Spending Outlook:
- According to UBS, global AI capex will reach $423 billion in 2025, $571 billion by 2026 and $1.3 trillion by 2030, growing at a 25% CAGR during the period 2025-2030.
Compute demand is outpacing expectations, with Google’s Gemini saw 130 times rise in AI token usage over the past 18 months, highlighting soaring compute and Meta’s infrastructure needs expanding sharply.[9]
Conclusions:
The AI infrastructure boom reflects a bold, forward-looking strategy by Big Tech, built on the belief that compute capacity will define the next decade’s leaders. If Artificial General Intelligence (AGI) or large-scale AI monetization unfolds as expected, today’s investments will be seen as visionary and transformative. Either way, the AI era is well underway — and the race for computational excellence is reshaping the future of global markets and innovation.
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Footnotes:
[1] https://www.investing.com/news/stock-market-news/ai-capex-to-exceed-half-a-trillion-in-2026-ubs-4343520?utm_medium=feed&utm_source=yahoo&utm_campaign=yahoo-www
[2] https://www.venturepulsemag.com/2025/08/01/big-techs-400-billion-ai-bet-the-race-thats-reshaping-global-technology/#:~:text=Big%20Tech’s%20$400%20Billion%20AI%20Bet:%20The%20Race%20That’s%20Reshaping%20Global%20Technology,-3%20months%20ago&text=The%20world’s%20largest%20technology%20companies,enterprise%20strategy%2C%20and%20public%20policy.
[3] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[4] https://www.investing.com/analysis/meta-plunged-12-amazon-jumped-11–same-ai-race-different-economics-200669410
[5] https://www.cnbc.com/2025/10/15/a-guide-to-1-trillion-worth-of-ai-deals-between-openai-nvidia.html
[6] https://finance.yahoo.com/news/bank-america-just-issued-stark-152422714.html
[7] https://news.futunn.com/en/post/64706046/from-cash-rich-to-collective-debt-how-does-wall-street?level=1&data_ticket=1763038546393561
[8] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[9] https://finance.yahoo.com/news/ai-capex-exceed-half-trillion-093015889.html
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About the Author:
Rahul Sharma is President & Co-Chief Executive Officer at Indxx – a provider of end-to-end indexing services, data and technology products. He has been instrumental in leading the firm’s growth since 2011. Raul manages Indxx’s Sales, Client Engagement, Marketing and Branding teams while also helping to set the firm’s overall strategic objectives and vision.
Rahul holds a BS from Boston College and an MBA with Beta Gamma Sigma honors from Georgetown University’s McDonough School of Business.
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References:
Curmudgeon/Sperandeo: New AI Era Thinking and Circular Financing Deals
Expose: AI is more than a bubble; it’s a data center debt bomb
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
FT: Scale of AI private company valuations dwarfs dot-com boom
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
AI Data Center Boom Carries Huge Default and Demand Risks
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Reuters: US Department of Energy forms $1 billion AI supercomputer partnership with AMD
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Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
According to Dell’Oro VP Stefan Pongratz, Nokia has outlined a clear plan to arrest its declining RAN revenue share (see chart below), with NVIDIA now a central pillar of that strategy. The partnership is designed to deliver AI RAN [1.] while meeting wireless network operators’ near-term constraints and concerns on performance, power, and TCO (Total Cost of Ownership). IEEE Techblog has noted in many past blog posts that telcos have huge doubts about AI RAN which implies they won’t buy into that new RAN architecture.
This is especially relevant considering the monumental failure of multi-vendor Open RAN which was promoted as a game changer, but has dismally failed to attain that vision.
Note 1. AI RAN is a mobile RAN architecture where AI and machine learning are embedded into the RAN software and underlying compute platform to optimize how the network is planned, configured, and operated. It is being pushed by NVIDIA to get its GPUs into 5G, 5G Advanced and 6G base stations and other wireless network equipment in the RAN.
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Nokia aims to use collaboration with NVIDIA (which invested $1B in the Finland based company) to stabilize its RAN market share in the near term and create a platform for long-term growth in AI-native 5G-Advanced and 6G networks. The timing—following a dense cadence of disclosures at NVIDIA’s GPU Technology Conference and Nokia’s Capital Markets Day—makes this an ideal time to reassess the scope of the joint announcements, the RAN implications, and Nokia’s broader competitive posture in an increasingly concentrated market.
Both companies share a belief that telecom networks will evolve from best-effort connectivity into a distributed compute fabric underpinning autonomous machines, self-driving vehicles, humanoids, and industrial digital twins. From that perspective, the RAN becomes an “AI grid” that executes and orchestrates AI workloads at the edge, enabling massive numbers of latency-sensitive, bandwidth-intensive AI use cases.
Unlike prior attempts to penetrate the RAN market with its GPUs, NVIDIA is now taking a more pragmatic approach, explicitly targeting parity with incumbent, purpose-built RAN equipment based on performance, power, and TCO rather than leading with speculative multi-tenant or new-revenue narratives. Nokia, acutely aware of wireless telco risk tolerance, is positioning the solution so that the ROI must be justifiable on a pure RAN basis, with additional AI and edge-compute upside treated as optional rather than foundational.
A quick recap of NVIDIA’s entry into RAN: Based on the announcement and subsequent discussions, our understanding is that NVIDIA will invest $1 B in Nokia and that NVIDIA-powered AI-RAN products will be incorporated into Nokia’s RAN portfolio starting in 2027 (with trials beginning in 2026). While RAN compute—which represents less than half of the $30B+ RAN market—is immaterial relative to NVIDIA’s $4+ T market cap, the potential upside becomes more meaningful when viewed in the context of NVIDIA’s broader telecom ambitions and its $165 B in trailing-twelve-month revenue.
With a deployed base of more than 1 million BTS, Nokia is prioritizing three migration vectors to GPU/AI-RAN, in order of expected impact:
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Purpose-built D-RAN [2.], by inserting a new card into existing AirScale slots.
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D-RAN vRAN [3.], using COTS servers at the cell site.
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Cloud RAN [4.] or vRAN, using centralized COTS infrastructure.
This approach aligns with wireless network operators’ desire to sweat existing AirScale assets while minimizing operational disruption.
Note 2. Purpose-built D-RAN is a distributed RAN architecture where the baseband processing runs on dedicated, vendor-specific hardware at or very close to the cell site, rather than on generic COTS servers. It is “purpose-built” because the silicon, boards, and software stack are tightly integrated and optimized for RAN performance, power efficiency, and footprint, not general-purpose compute.
Note 3. vRAN or virtual RAN is a technology that virtualizes the functions of a cellular network’s radio access network, moving them from dedicated hardware to software running on general-purpose servers. This approach makes mobile networks more flexible, scalable, and cost-efficient by replacing proprietary hardware with software on common-off-the-shelf (COTS) hardware.
Note 4. Cloud RAN (C-RAN) is a centralized cellular network architecture that uses cloud computing to virtualize and process radio access network (RAN) functions. This architecture centralizes baseband units in a “BBU hotel,” allowing for more flexible and scalable network management, efficient resource allocation, and improved network performance. It allows operators to pool resources, adjust capacity based on demand, and support new services, which is a key enabler for 5G networks.
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In this model, the Distributed Unit, and often the higher-layer functions, are physically collocated with the radio unit at the site, making each site a largely self-contained RAN node. This contrasts with Cloud RAN or vRAN, where baseband functions are centralized or virtualized on shared cloud infrastructure, and with cloud/AI-RAN approaches that rely on GPUs or other general-purpose accelerators instead of custom RAN hardware.
The macro-RAN market (baseband plus radio) is roughly a 30 billion USD annual opportunity, with on the order of 1–2 million macro sites shipped per year. In that context, operators have limited appetite to pay more than 10,000 USD for a GPU per sector, even if software-led benefits accumulate over time, which is why NVIDIA is signaling GPU pricing in line with ARC-Compact but at roughly double the capacity and Nokia is targeting 48–50% gross margins in Mobile Infrastructure by 2028, slightly above the current run-rate.
If the TCO and performance-per-watt gap versus custom silicon continues to narrow, the partnership could materially influence AI-RAN and Cloud-RAN trajectories while also supporting Nokia’s margin expansion goals. AI-RAN was already expected to scale to roughly one-third of the RAN market by 2029; Nokia’s decision to lean harder into GPUs amplifies this structural shift without fundamentally changing the long-term 6G direction.
In the near term, GPU-enabled D-RAN using empty AirScale slots is expected to dominate deployments, reflecting operators’ preference for incremental, site-level upgrades. At the same time, the Nokia-NVIDIA partnership is not expected to meaningfully alter the overall Cloud RAN vs. D-RAN mix, Open RAN adoption (slow or non-existent) , or the trajectory of multi-tenant RAN, which remain more dependent on network operator architecture and commercial decisions than on a single vendor–silicon alignment.
Nokia plans to remain disciplined and focus on areas where it can differentiate and unlock value—particularly through software/faster innovation cycles via its recently announced partnership with NVIDIA. The company sees meaningful opportunities to capture incremental share in North America, Europe, India, and select APAC markets. And it is already off to a solid start— we estimate that Nokia’s 1Q25–3Q25 RAN revenue share outside North America improved slightly relative to 2024. Following this stabilization phase, Nokia is betting that its investments will pay off and that it will be well-positioned to lead with AI-native networks and 6G.
Nokia’s objective is clear: stabilize RAN in the short term, then grow by leading in AI-native networks and 6G over the longer horizon. Success now hinges on Nokia’s ability to operationalize the GPU-based RAN roadmap at scale and on NVIDIA’s ability to deliver carrier-grade economics and performance—turning the AI-RAN narrative into production-grade, repeatable deployments.
Nokia sees meaningful opportunities to capture incremental RAN market share in North America, Europe, India, and select APAC markets. And it is already off to a solid start— we estimate that Nokia’s 1Q25–3Q25 RAN revenue share outside North America improved slightly relative to 2024. Following this stabilization phase, Nokia is betting that its investments will pay off and that it will be well-positioned to lead with AI-native networks and 6G.
References:
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Indosat Ooredoo Hutchison, Nokia and Nvidia AI-RAN research center in Indonesia amongst telco skepticism
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
AI RAN Alliance selects Alex Choi as Chairman
Expose: AI is more than a bubble; it’s a data center debt bomb
GSMA, ETSI, IEEE, ITU & TM Forum: AI Telco Troubleshooting Challenge + TelecomGPT: a dedicated LLM for telecom applications
The GSMA — along with ETSI, IEEE GenAINet, the ITU, and TM Forum — today opened an innovation challenge calling on telco operators, AI researchers, and startups to build large-language models (LLMs) capable of root-cause analysis (RCA) for telecom network faults. The AI Telco Troubleshooting Challenge is supported by Huawei, InterDigital, NextGCloud, RelationalAI, xFlowResearch and technical advisors from AT&T.
The new competition invites teams to submit AI models in three categories: Generalization to New Faults will assess the best performing LLMs for RCA; Small Models at the Edge will evaluate lightweight edge-deployable models; and Explainability/Reasoning will focus on the AI systems that clearly explain their reasoning. Additional categories will include securing edge-cloud deployments and enabling AI services for application developers.
The goal is to deliver AI tools that help operators automatically identify, diagnose, and (eventually) remediate network problems — potentially reducing both downtime and operational costs. This marks a concrete step toward turning “telco-AI” from pilot projects into operational infrastructure.
As telecom networks scale (5G, 5G-Advanced, edge, IoT), faults and failures become costlier. Automating fault detection and troubleshooting with AI could significantly boost network resilience, reduce manual labor, and enable faster recovery from outages.
“Large Language Models have become instrumental in the pursuit of autonomous, resilient and adaptive networks,” said Prof. Merouane Debbah, General Chair of IEEE GenAINet ETI. “Through this challenge, we are tackling core research and engineering challenges, such as generalisation to unseen network faults, interpretability and edge-efficient AI, that are vital for making AI-native telecom infrastructures a reality. IEEE GenAINet ETI is proud to support this initiative, which serves as a testbed for future-ready innovations across the global telco ecosystem.”
“ITU’s global AI challenges connect innovators with computing resources, datasets, and expert mentors to nurture AI innovation ecosystems worldwide,” said Seizo Onoe, Director of the ITU Telecommunication Standardization Bureau. “Crowdsourcing new solutions and creating conditions for them to scale, our challenges boost business by helping innovations achieve meaningful impact.”
“The future of telecoms depends on the autonomation of network resiliency – shifting from static infrastructure to AI-driven, context-aware, self-optimising networks. TM Forum’s AI-Native Blueprint provides the architectural foundation to make this reality, and the AI Telco Troubleshooting Challenge aligns perfectly to support the industry in moving beyond isolated pilots to production-grade resilient autonomation,” said Guy Lupo, AI and Data Mission lead at TM Forum.
The initiative builds on recent breakthroughs in applying AI to network operations, leveraging curated datasets such as TeleLogs and benchmarking frameworks developed by GSMA and its partners under the GSMA Open-Telco LLM Benchmarks community, which includes a leaderboard that highlights how various LLMs perform on telco-specific use cases.
“Network faults cost operators millions annually and root cause analysis is a critical pain point for operators,” said Louis Powell, Director of AI Technologies at GSMA. “By harnessing AI models capable of reasoning and diagnosing unseen faults, the industry can dramatically improve reliability and reduce operational costs. Through this challenge, we aim to accelerate the development of LLMs that combine reasoning, efficiency and scalability.”
“We are encouraged by the upside of this challenge after our team at AT&T fine-tuned a 4-billion-parameter small language model that topped all other evaluated models on the GSMA Open-Telco LLM Benchmarks (TeleLogs RCA task), including frontier models such as GPT-5, Claude Sonnet 4.5 and Grok-4,” said Andy Markus, Chief Data Officer at AT&T. “This challenge has the right mix of an important business problem and a technical opportunity, and we welcome the industry’s collaboration to take it to the next level.”
The AI Telco Troubleshooting Challenge is open for submissions on the 28th November and it closes on 1st February 2026, with the winners announced at a dedicated prize-giving session at MWC26 Barcelona.
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Separately, the GSMA Foundry and Khalifa University announced a strategic collaboration to develop “TelecomGPT,” a dedicated LLM for telecom applications, plus an Open-Telco Knowledge Graph based on 3GPP specifications.
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These assets are intended to help the industry overcome limitations of general-purpose LLMs, which often struggle with telecom-specific technical contexts. PR Newswire+2Mobile World Live+2
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The plan: make TelecomGPT and related knowledge tools available for operators, vendors and researchers to accelerate AI-driven telco innovations. PR Newswire+1
Why it matters: A specialized “telco-native” LLM could improve automation, operations, R&D and standardization efforts — for example, helping operators configure networks, analyze logs, or build AI-powered services. It represents a shift toward embedding AI more deeply into core telecom infrastructure and operations.
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About GSMA
The GSMA is a global organization unifying the mobile ecosystem to discover, develop and deliver innovation foundational to positive business environments and societal change. Our vision is to unlock the full power of connectivity so that people, industry, and society thrive. Representing mobile operators and organizations across the mobile ecosystem and adjacent industries, the GSMA delivers for its members across three broad pillars: Connectivity for Good, Industry Services and Solutions, and Outreach. This activity includes advancing policy, tackling today’s biggest societal challenges, underpinning the technology and interoperability that make mobile work, and providing the world’s largest platform to convene the mobile ecosystem at the MWC and M360 series of events.
We invite you to find out more at gsma.com
About ETSI
ETSI is one of only three bodies officially recognized by the European Union as a European Standards Organization (ESO). It is an independent, not-for-profit body dedicated to ICT standardisation. With over 900 member organizations from more than 60 countries across five continents, ETSI offers an open and inclusive environment for members representing large and small private companies, research institutions, academia, governments, and public organizations. ETSI supports the timely development, ratification, and testing of globally applicable standards for ICT‑enabled systems, applications, and services across all sectors of industry and society. More on: etsi.org
About IEEE GenAINet
The aim of the IEEE Large Generative AI Models in Telecom Emerging Technology Initiative (GenAINet ETI) is to create a dynamic platform of research and innovation for academics, researchers, and industry leaders to advance the research on large generative AI in Telecom, through collaborative efforts across various disciplines, including mathematics, information theory, wireless communications, signal processing, networking, artificial intelligence, and more. More on: https://genainet.committees.comsoc.org
About ITU
The International Telecommunication Union (ITU) is the United Nations agency for digital technologies, driving innovation for people and the planet with 194 Member States and a membership of over 1,000 companies, universities, civil society, and international and regional organizations. Established in 1865, ITU coordinates the global use of the radio spectrum and satellite orbits, establishes international technology standards, drives universal connectivity and digital services, and is helping to make sure everyone benefits from sustainable digital transformation, including the most remote communities. From artificial intelligence (AI) to quantum, from satellites and submarine cables to advanced mobile and wireless broadband networks, ITU is committed to connecting the world and beyond. Learn more: www.itu.int
About TM Forum
TM Forum is an alliance of over 800 organizations spanning the global connectivity ecosystem, including the world’s top ten Communication Service Providers (CSPs), top three hyperscalers and Network Equipment Providers (NEPs), vendors, consultancies and system integrators, large and small. We provide a place for our Members to collaborate, innovate, and deliver lasting change. Together, we are building a sustainable future for the industry in connectivity and beyond. To find out more, visit: www.tmforum.org
References:
The AI Telco Troubleshooting Challenge Launches to Transform Network Reliability
AI Telco Troubleshooting Challenge global launch webinar
GSMA Vision 2040 study identifies spectrum needs during the peak 6G era of 2035–2040
Gartner: Gen AI nearing trough of disillusionment; GSMA survey of network operator use of AI
NTT DOCOMO successful outdoor trial of AI-driven wireless interface with 3 partners
NTT DOCOMO has successfully executed the world’s premier outdoor field trial of real-time transceiver systems leveraging artificial intelligence (AI)-driven wireless technology, a critical advancement for sixth-generation (6G) mobile communications (AKA IMT 2030).
Conducted in collaboration with parent company NTT, Inc. (NTT), Nokia Bell Labs, and SK Telecom Co., Ltd, the field trials were held across three sites in Yokosuka City, Kanagawa Prefecture. The results validated that the application of AI optimized system throughput (transmission speed), achieving up to a 100% improvement over conventional, non-AI methods under identical environmental conditions, effectively doubling communication speeds.
Wireless communication quality can be compromised by fluctuations in radio propagation environments, leading to unstable connections. To mitigate this challenge, the partners developed “AI-AI technology,” which applies AI to both the transmitting and receiving ends of the wireless interface. This system dynamically optimizes modulation and demodulation schemes based on prevailing radio conditions, facilitating stable communication across diverse use cases. The efficacy of this technology had previously been confirmed in indoor environments.
The recent field trials aimed to verify the technology’s stable performance in complex outdoor settings, where radio conditions are subject to greater variability from factors such as temperature, weather, and physical obstructions.
This innovative AI wireless technology was evaluated across three distinct outdoor courses with varying propagation conditions, including the presence of obstacles and terminal mobility:
- Course 1: A public road featuring gentle curves, with a test vehicle traveling up to 40 km/h.
- Course 2: An environment with partial signal obstructions.
- Course 3: A road with minimal obstructions, with a test vehicle traveling up to 60 km/h.
In all test scenarios, the technology demonstrated its ability to compensate for signal degradation, confirming enhanced communication speeds. Specifically, in the highly complex propagation conditions of Course 1, the AI-AI technology yielded an average throughput improvement of 18% and a maximum increase of 100% compared to traditional methods.
These findings enable higher-speed data transmission for users and allow network operators to enhance spectrum efficiency and deliver superior quality of service (QoS). The successful outdoor validation marks a significant milestone toward the practical implementation of 6G systems, which promise a combination of high wireless transmission efficiency and reduced power consumption. NTT DOCOMO remains committed to refining this technology under a wide range of conditions and accelerating R&D efforts toward 6G realization, while simultaneously collaborating with global partners on 6G standardization (in 3GPP and ITU-R WP5D) and deployment.
This new technology will be featured at the NTT R&D FORUM 2025 hosted by NTT, scheduled from November 19–21 and November 25–26, 2025.
These three AI-wireless field trials represent the latest joint effort stemming from the collaborative AI research partnership of DOCOMO, parent NTT, Nokia Bell Labs, and SK Telecom Co, which was established at Mobile World Congress (MWC) in February 2024.
NTT Docomo has forged additional 6G alliances with a range of partners, including Ericsson, domestic Japanese suppliers Fujitsu and NEC, and testing specialists Keysight Technologies and Rohde & Schwarz.
This year has seen an increase in partnerships among Korean and Japanese operators. Earlier this month, KDDI‘s research partnership with Nokia Bell Labs was announced, focusing on achieving 6G energy efficiency and enhanced network resilience. Samsung and SoftBank entered into a memorandum of understanding (MoU) last month to co-develop prospective next-generation technologies, encompassing 6G, AI-driven Radio Access Networks (AI RAN), and Large Telecom Models (LTMs).
In a separate MoU signed in March, KT‘s and Samsung’s collaboration was formalized to jointly advance 6G antenna technology. Additionally, KT has maintained a separate research engagement with Nokia centered on semantic communications research.
About NTT DOCOMO:
NTT DOCOMO, Japan’s leading mobile operator with over 91 million subscribers, is one of the global leaders in 3G, 4G and 5G mobile network technologies.
Under the slogan “Bridging Worlds for Wonder & Happiness,” DOCOMO is actively collaborating with global partners to expand its business scope from mobile services to comprehensive solutions, aiming to deliver unsurpassed value and drive innovation in technology and communications, ultimately to support positive change and advancement in global society.
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References:
https://www.docomo.ne.jp/english/info/media_center/pr/2025/1117_00.html
https://www.docomo.ne.jp/english/
https://www.lightreading.com/6g/ntt-docomo-doubles-6g-throughput-in-ai-trials
NTT DOCOMO OREX brand offers a pre-integrated solution for Open RAN
NTT’s IOWN provides ultra low latency and energy efficiency in Japan and Hong Kong
NTT & Yomiuri: ‘Social Order Could Collapse’ in AI Era
NTT Docomo will use its wireless technology to enter the metaverse
Indosat Ooredoo Hutchison, Nokia and Nvidia AI-RAN research center in Indonesia amongst telco skepticism
Indosat Ooredoo Hutchison (Indosat) Nokia, and Nvidia have officially launched the AI-RAN Research Centre in Surabaya, a strategic collaboration designed to advance AI-native wireless networks and edge AI applications across Indonesia. This collaboration, aims to support Indonesia’s digital transformation goals and its “Golden Indonesia Vision 2045.” The facility will allow researchers and engineers to experiment with combining Nokia’s RAN technologies with Nvidia’s accelerated computing platforms and Indosat’s 5G network.
According to the partners, the research facility will serve as a collaborative environment for engineers, researchers, and future digital leaders to experiment, learn, and co-create AI-powered solutions. Its work will centre on integrating Nokia’s advanced RAN technologies with Nvidia’s accelerated computing platforms and Indosat’s commercial 5G network. The three companies view the project as a foundation for AI-driven growth, with applications spanning education, agriculture, and healthcare.
The AI-RAN infrastructure enables high-performance software-defined RAN and AI workloads on a single platform, leveraging Nvidia’s Aerial RAN Computer 1 (ARC-1). The facility will also act as a distributed computing extension of Indosat’s sovereign AI Factory, a national AI platform powered by Nvidia, creating an “AI Grid” that connects datacentres and distributed 5G nodes to deliver intelligence closer to users.
Nezar Patria, vice minister of communication and digital affairs of the Republic of Indonesia said: “The inauguration of the AI-RAN Research Centre marks a concrete step in strengthening Indonesia’s digital sovereignty. The collaboration between the government, industry, and global partners such as Indosat, Nokia, and Nvidia demonstrates that Indonesia is not merely a user but also a creator of AI technology. This initiative supports the acceleration of the Indonesia Emas 2045 vision by building an inclusive, secure, and globally competitive AI ecosystem.”
Vikram Sinha, president director and CEO of Indosat Ooredoo Hutchison said: “As Indonesia accelerates its digital transformation, the AI-RAN Research Centre reflects Indosat’s larger purpose of empowering Indonesia. When connectivity meets compute, it creates intelligence, delivered at the edge, in a sovereign manner. This is how AI unlocks real impact, from personalised tutors for children in rural areas to precision farming powered by drones. Together with Nokia and Nvidia, we’re building the foundation for AI-driven growth that strengthens Indonesia’s digital future.”
From a network perspective, the project demonstrates how AI-RAN architectures can optimize wireless network performance, energy efficiency, and scalability through machine learning–based radio signal processing.
Ronnie Vasishta, senior vice president of telecom at Nvidia added: “The AI Grid is the biggest opportunity for telecom providers to make AI as ubiquitous as connectivity and distribute intelligence at scale by tapping into their nationwide wireless networks.”
Pallavi Mahajan, chief technology and AI officer at Nokia said: “This initiative represents a major milestone in our journey toward the future of AI-native networks by bringing AI-powered intelligence into the hands of every Indonesian.”
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Wireless Telcos are Skeptical about AI-RAN:
According to Light Reading, the AI RAN uptake is facing resistance from telcos. The problem is Nvidia’s AI GPUs are very costly and not GPUs power-efficient enough to reside in wireless base stations, central offices or even small telco data centers.
Nvidia references 300 watts for the power consumption of ARC-Pro, which is much higher than the peak of 40 watts that Qualcomm claimed more than two years ago for its own RAN silicon when supporting throughput of 24 Gbit/s. How ARC-Pro would measure up on a like-for-like basis in a commercial network is obviously unclear.
Nvidia also claims a Gbit/s-per-watt performance “on par with” today’s traditional custom silicon. Yet the huge energy consumption of GPU-filled telco data centers does not bear that out.
“Is there a case for a wide-area indiscriminate rollout? I am not sure,” said Verizon CTO Yago Tenorio, during the Brooklyn 6G Summit, another telecom event, last week. “It depends on the unit cost of the GPU, on the power efficiency of the GPU, and the main factor will always be just doing what’s best for the basestation. Don’t try to just overcomplicate the whole thing monetizing that platform, as there are easier ways to do it.”
“We have no way to justify a business case like that,” said Bernard Bureau, the vice president of wireless strategy for Canada’s Telus, at FYUZ. “Our COs [central offices] are not necessarily the best places to run a data center. It would mean huge investments in space and power upgrades for those locations, and we’ve got sunk investment that can be leveraged in our cell sites.”
Light Reading’s Iain Morris wrote, “Besides turning COs into data centers, operators would need to invest in fiber connections between those facilities and their masts.”
How much spectral efficiency can be gained by using Nvidia GPUs as RAN silicon?
“It’s debatable if it’s going to improve the spectral efficiency by 50% or even 100%. It depends on the case,” said Tenorio. Whatever the exact improvement, it would be “really good” and is something the industry needs, he told the audience.
In April, Nokia’s rival Ericsson said it had tested “AI-native” link adaptation, a RAN algorithm, in the network of Bell Canada without needing any GPU. “That’s an algorithm we have optimized for decades,” said Per Narvinger, the head of Ericsson’s mobile networks business group. “Despite that, through a large language model, but a really small one, we gained 10% of spectral efficiency.”
Before Nvidia invested in Nokia, the latter claimed to have sufficient AI and machine-learning capabilities in the custom silicon provided by Marvell Technology, its historical supplier of 5G base station chips.
Executives at Cohere Technology praises Nvidia’s investment in Nokia, seeing it as an important AI spur for telecom. Yet their own software does not run on Nvidia GPUs. It promises to boost spectral efficiency on today’s 5G networks, massively reducing what telcos would have to spend on new radios. It has won plaudits from Vodafone’s Pignatelli as well as Bell Canada and Telstra, both of which have invested in Cohere. The challenge is getting the kit vendors to accommodate a technology that could hurt their own sales. Regardless, Bell Canada’s recent field trials of Cohere have used a standard Dell server without GPUs.
Finally, if GPUs are so critical in AI for RAN, why has neither Ericsson or Samsung using Nvidia GPU’s in their RAN equipment?
Morris sums up:
“Currently, the AI-RAN strategy adopted by Nokia looks like a massive gamble on the future. “The world is developing on Nvidia,” Vasishta told Light Reading in the summer, before the company’s share price had gained another 35%. That vast and expanding ecosystem holds attractions for RAN developers bothered by the diminishing returns on investment in custom silicon.”
“Intel’s general-purpose chips and virtual RAN approach drew interest several years ago for all the same reasons. But Intel’s recent decline has made Nvidia shine even more brightly. Telcos might not have to worry. Nvidia is already paying a big 5G vendor (Nokia) to use its technology. For a company that is so outrageously wealthy, paying a big operator to deploy it would be the next logical step.
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References:
https://capacityglobal.com/news/indosat-nokia-and-nvidia-launch-ai-ran-research-centre-in-indonesia/
https://www.telecoms.com/ai/indosat-nokia-and-nvidia-open-ai-ran-research-centre-in-indonesia
https://www.lightreading.com/5g/nokia-and-nvidia-s-ai-ran-plan-hits-telco-resistance
https://resources.nvidia.com/en-us-aerial-ran-computer-pro
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
Dell’Oro: AI RAN to account for 1/3 of RAN market by 2029; AI RAN Alliance membership increases but few telcos have joined
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
The case for and against AI-RAN technology using Nvidia or AMD GPUs
AI RAN Alliance selects Alex Choi as Chairman
IDC Report: Telecom Operators Turn to AI to Boost EBITDA Margins
Worldwide spending on telecommunication and pay TV services will reach $1,532 billion in 2025, representing an increase of +1.7% year-on-year, according to the International Data Corporation (IDC) Worldwide Semiannual Telecom Services Tracker. The latest forecast is slightly more optimistic compared to the forecast published earlier this year, as it assumes a 0.1 percentage point higher growth of the total market value.
“The regional dynamics remain mixed, with inflationary effects, competition, and Average Revenue per User (ARPU) trends playing a central role in shaping market trajectories,” said Kresimir Alic, research director, Worldwide Telecom Services at IDC.
Global telecom operators are strategically adopting AI to drive significant business improvements across several key areas. The integration of AI technology is enhancing network operations, refining customer service interactions, and strengthening fraud prevention mechanisms which are reduce losses, reinforcing customer trust and regulatory compliance. With AI accelerating time-to-market for new services, telecoms can better monetize emerging technologies like 5G and edge computing.
“In the longer term, as AI continues to evolve, it will be increasingly recognized not as a mere technological enhancement, but as a strategic enabler poised to drive sustainable growth for telecommunications operators,” according to the report. This strategic adoption is accelerating time-to-market for new services, enabling better monetization of technologies like 5G and edge computing (which requires a 5G SA core network). It represents cautious optimism for a global connectivity services market that has been stagnant for many years.
- Network Planning and Operations: AI is heavily used to optimize network performance and manage the complexity of modern networks, including 5G and future 6G technologies. This involves:
- Predictive Maintenance: Anticipating hardware failures and network issues to ensure uninterrupted service and reduce downtime.
- Automation and Orchestration: Automating complex tasks and managing physical, virtual, and containerized network functions.
- Energy Efficiency: Making intelligent choices about radio access network (RAN) energy consumption and resource allocation to increase efficiency.
- Customer Experience (CX) and Service: Enhancing customer engagement and service is a top priority. This is achieved through:
- Personalized Services: Analyzing customer behavior and preferences to offer tailored products and marketing campaigns.
- Intelligent Virtual Assistants/Chatbots: Automating customer interactions and improving self-service capabilities.
- Churn Reduction: Using AI to predict customer churn and implement retention strategies.
- Business Efficiency and Productivity: Operators are focused on driving agility and productivity across the organization. This includes:
- Employee Productivity: Streamlining workflows and automating tasks using generative AI (GenAI) and agentic AI.
- Cost Reduction: Driving efficiency in operations to lower overall costs.
- Fraud Prevention: Deploying AI-enhanced systems to detect and mitigate fraud, protecting revenue streams and customer trust.
- New Revenue Opportunities: AI is seen as a cornerstone for developing new services, such as AI-as-a-Service, and monetizing existing network assets like 5G capabilities.
| Global Regional Services Revenue and Year-on-Year Growth (revenues in $B) | |||
| Global Region | 2024 Revenue | 2025 Revenue | 25/24
Growth |
| Americas | $568 | $574 | 1.0% |
| Asia/Pacific | $476 | $481 | 1.0% |
| EMEA | $462 | $477 | 3.2% |
| Grand Total | $1,507 | $1,532 | 1.7% |
| Source: IDC Worldwide Semiannual Services Tracker – 1H 2025 | |||
Mobile continues to dominate, driven by rising data consumption and the expansion of M2M applications, which are offsetting declines in traditional voice and messaging revenues.
Fixed data services are also expected to grow steadily, fueled by increasing demand for high-bandwidth connectivity.
In summary, IDC forecasts that the global connectivity services market is projected to grow at a compound annual rate of 1.5% over the next five years, maintaining a cautiously optimistic outlook. As highlighted by recent IMF forecasts, the overall market environment is expected to be less stimulating than in previous years, shaped by rising protectionism and persistent economic uncertainty in key regions. While declining inflation may ease cost pressures, it is also likely to reduce the inflation-driven boost to telecom service spending seen in recent cycles. Political instability in areas such as Eastern Europe and the Middle East adds further complexity to the growth landscape. Most notably, saturation in mature telecom markets continues to be the primary constraint on expansion, limiting upside potential in traditional service segments.
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About IDC Trackers:
IDC Tracker products provide accurate and timely market size, vendor share, and forecasts for hundreds of technology markets from more than 100 countries around the globe. Using proprietary tools and research processes, IDC’s Trackers are updated on a semiannual, quarterly, and monthly basis. Tracker results are delivered to clients in user-friendly excel deliverables and on-line query tools.
For more information about IDC’s Worldwide Semiannual Telecom Services Tracker, please contact Kathy Nagamine at 650-350-6423 or [email protected].
About IDC:
International Data Corporation (IDC) is the premier global provider of market intelligence, advisory services, and events for the information technology, telecommunications, and consumer technology markets. With more than 1,000 analysts worldwide, IDC offers global, regional, and local expertise on technology and industry opportunities and trends in over 100 countries. IDC’s analysis and insight helps IT professionals, business executives, and the investment community to make fact-based technology decisions and to achieve their key business objectives. Founded in 1964, IDC is the world’s leading media, data and marketing services company that activates and engages the most influential technology buyers. To learn more about IDC, please visit www.idc.com. Follow IDC on Twitter at @IDC and LinkedIn.
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References:
https://my.idc.com/getdoc.jsp?containerId=prUS53913925&
https://my.idc.com/getdoc.jsp?containerId=prEUR253369525
https://my.idc.com/getdoc.jsp?containerId=US53765725
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Nokia and Rohde & Schwarz collaborate on AI-powered 6G receiver years before IMT 2030 RIT submissions to ITU-R WP5D
Nokia and the test and measurement firm Rohde & Schwarz have created and successfully tested a “6G” radio receiver that uses AI technologies to overcome one of the biggest anticipated challenges of 6G network rollouts, coverage limitations inherent in 6G’s higher-frequency spectrum.
–>This is truly astonishing as ITU-R WP5D doesn’t even plan to evaluate 6G RIT/SRITs till February 2027 when the first submissions are invited to be presented.
Nokia Bell Labs developed the receiver and validated it using 6G test equipment and methodologies from Rohde & Schwarz. The two companies will unveil a proof-of-concept receiver at the Brooklyn 6G Summit on November 6, 2025. Nokia says, “the machine learning capabilities in the receiver greatly boost uplink distance, enhancing coverage for future 6G networks. This will help operators roll out 6G over their existing 5G footprints, reducing deployment costs and accelerating time to market.”
Image Credit: Rohde & Schwarz
Nokia Bell Labs and Rohde & Schwarz have tested this new AI receiver under real world conditions, achieving uplink distance improvements over today’s receiver technologies ranging from 10% to 25%. The testbed comprises an R&S SMW200A vector signal generator, used for uplink signal generation and channel emulation. On the receive side, the newly launched FSWX signal and spectrum analyzer from Rohde & Schwarz is employed to perform the AI inference for Nokia’s AI receiver. In addition to enhancing coverage, the AI technology also demonstrates improved throughput and power efficiency, multiplying the benefits it will provide in the 6G era.
“One of the key issues facing future 6G deployments is the coverage limitations inherent in 6G’s higher-frequency spectrum. Typically, we would need to build denser networks with more cell sites to overcome this problem. By boosting the coverage of 6G receivers, however, AI technology will help us build 6G infrastructure over current 5G footprints,” said Peter Vetter, President, Core Research, Bell Labs, Nokia.
“Rohde & Schwarz is excited to collaborate with Nokia in pioneering AI-driven 6G receiver technology. Leveraging more than 90 years of experience in test and measurement, we’re uniquely positioned to support the development of next-generation wireless, allowing us to evaluate and refine AI algorithms at this crucial pre-standardization stage. This partnership builds on our long history of innovation and demonstrates our commitment to shaping the future of 6G,” said Michael Fischlein, VP, Spectrum & Network Analyzers, EMC and Antenna Test, Rohde & Schwarz.
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Last month, Nokia teamed up with rival kit vendor Ericsson to work on video coding standardization in preparation for 6G. The project, which also involved Berlin’s Fraunhofer Heinrich Hertz Institute (HHI), demonstrated a new video codec which they claim has higher compression efficiency than the current standards (H.264/AVC, H.265/HEVC, and H.266/VVC) without significantly increasing complexity, and its wider aim is to strengthen Europe’s role in next generation standardization, we were told at the time.
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About Nokia:
At Nokia, we create technology that helps the world act together.
As a B2B technology innovation leader, we are pioneering networks that sense, think and act by leveraging our work across mobile, fixed and cloud networks. In addition, we create value with intellectual property and long-term research, led by the award-winning Nokia Bell Labs, which is celebrating 100 years of innovation.
With truly open architectures that seamlessly integrate into any ecosystem, our high-performance networks create new opportunities for monetization and scale. Service providers, enterprises and partners worldwide trust Nokia to deliver secure, reliable and sustainable networks today – and work with us to create the digital services and applications of the future
About Rohde & Schwarz:
Rohde & Schwarz is striving for a safer and connected world with its Test & Measurement, Technology Systems and Networks & Cybersecurity Divisions. For over 90 years, the global technology group has pushed technical boundaries with developments in cutting-edge technologies. The company’s leading-edge products and solutions empower industrial, regulatory and government customers to attain technological and digital sovereignty. The privately owned, Munich-based company can act independently, long-term and sustainably. Rohde & Schwarz generated a net revenue of EUR 3.16 billion in the 2024/2025 fiscal year (July to June). On June 30, 2025, Rohde & Schwarz had more than 15,000 employees worldwide.
References:
ITU-R WP5D IMT 2030 Submission & Evaluation Guidelines vs 6G specs in 3GPP Release 20 & 21
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Nvidia pays $1 billion for a stake in Nokia to collaborate on AI networking solutions
Highlights of Nokia’s Smart Factory in Oulu, Finland for 5G and 6G innovation
Verizon’s 6G Innovation Forum joins a crowded list of 6G efforts that may conflict with 3GPP and ITU-R IMT-2030 work
Qualcomm CEO: expect “pre-commercial” 6G devices by 2028
NGMN: 6G Key Messages from a network operator point of view
Market research firms Omdia and Dell’Oro: impact of 6G and AI investments on telcos
Market research firm Omdia (owned by Informa) this week forecast that 6G and AI investments are set to drive industry growth in the global communications market. As a result, global communications providers’ revenue is expected to reach $5.6 trillion by 2030, growing at a 6.2% CAGR from 2025. Investment momentum is also expected to shift toward mobile networks from 2028 onward, as tier 1 markets prepare for 6G deployments. Telecoms capex is forecast to reach $395 billion by 2030, with a 3.6% CAGR, while technology capex will surge to $545 billion, reflecting a 9.3% CAGR.
Fixed telecom capex will gradually decline due to market saturation. Meanwhile, AI infrastructure, cloud services, and digital sovereignty policies are driving telecom operators to expand data centers and invest in specialized hardware.
Key market trends:
- CP capex per person will increase from $74 in 2024 to $116 in 2030, with CP capex reaching 2.5% of global GDP investment.
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Capital intensity in telecom will decline until 2027, then rise due to mobile network upgrades.
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Regional leaders in revenue and capex include North America, Oceania & Eastern Asia, and Western Europe, with Central & Southern Asia showing the highest growth potential.
Dario Talmesio, research director at Omdia said, “telecom operators are entering a new phase of strategic investment. With 6G on the horizon and AI infrastructure demands accelerating, the connectivity business is shifting from volume-based pricing to value-driven connectivity.”
Omdia’s forecast is based on a comprehensive model incorporating historical data from 67 countries, local market dynamics, regulatory trends, and technology migration patterns.
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Separately, Dell’Oro Group sees 6G capex ramping around 2030, although it warns that the RAN market remains flat, “raising key questions for the industry’s future.” Cumulative 6G RAN investments over the 2029-2034 period are projected to account for 55% to 60% of the total RAN capex over the same forecast period.
“Our long-term position and characterization of this market have not changed,” said Stefan Pongratz, Vice President of RAN and Telecom Capex research at Dell’Oro Group. “The RAN network plays a pivotal role in the broader telecom market. There are opportunities to expand the RAN beyond the traditional MBB (mobile broadband) use cases. At the same time, there are serious near-term risks tilted to the downside, particularly when considering the slowdown in data traffic,” continued Pongratz.
Additional highlights from Dell’Oro’s October 2025 6G Advanced Research Report:
- The baseline scenario is for the broader RAN market to stay flat over the next 10 years. This is built on the assumption that the mobile network will run into utilization challenges by the end of the decade, spurring a 6G capex ramp dominated by Massive MIMO systems in the Sub-7GHz/cm Wave spectrum, utilizing the existing macro grid as much as possible.
- The report also outlines more optimistic and pessimistic growth scenarios, depending largely on the mobile data traffic growth trajectory and the impact beyond MBB, including private wireless and FWA (fixed wireless access).
- Cumulative 6G RAN investments over the 2029-2034 period are projected to account for 55 to 60 percent of the total RAN capex over the same forecast period.
Dell’Oro Group’s 6G Advanced Research Report offers an overview of the RAN market by technology, with tables covering manufacturers’ revenue for total RAN over the next 10 years. 6G RAN is analyzed by spectrum (Sub-7 GHz, cmWave, mmWave), by Massive MIMO, and by region (North America, Europe, Middle East and Africa, China, Asia Pacific Excl. China, and CALA). To purchase this report, please contact by email at [email protected].
References:
https://www.lightreading.com/6g/6g-momentum-is-building
6G Capex Ramp to Start Around 2030, According to Dell’Oro Group
https://www.lightreading.com/6g/6g-course-correction-vendors-hear-mno-pleas
https://www.lightreading.com/6g/what-at-t-really-wants-from-6g
