AI/ML
HPE cost reduction campaign with more layoffs; 250 AI PoC trials or deployments
Hewlett Packward Enterprise (HPE) is going through yet another restructuring to reduce costs and to capitalize on the AI use cases it’s been developing. HPE’s workforce reduction program announced in March 2025 was to reduce its headcount of around 61,000 by 2,500 and to have another reduction of 500 people by attrition, over a period of 12 to 18 months, eliminating about $350 million in annual costs when it is said and done. The plan is to have this restructuring done by the end of this fiscal year, which comes to a close at the end of October. The headcount at the end of Q2 Fiscal Year 2025 was 59,000, so the restructuring is proceeding apace and this is, by the way, the lowest employee count that HPE’s enterprise business has had since absorbing Compaq in the wake of the Dot Com Bust in 2001.
The company, which sells IT servers, network communications equipment and cloud services, employed about 66,000 people in 2017, not long after it was created by the bi-section of Hewlett-Packard (with the PC- and printer-making part now called HP Inc). By the end of April this year, the number of employees had dropped to 59,000 – “the lowest we have seen as an independent company,” said HPE chief financial officer of Marie Myers, on the company’s Wednesday earnings call (according to this Motley Fool transcript)– after 2,000 job cuts in the last six months. By the end of October, under the latest plans, HPE expects to have shed another 1,050 employees.
Weak profitability of its server and cloud units is why HPE now attaches such importance to intelligent edge. HPE’s networking division today encompasses the Aruba enterprise Wi-Fi business along with more recent acquisitions such as Athonet, an Italian developer of core network software for private 5G. It accounts for only 15% of sales but a huge 41% of earnings, which makes it HPE’s most profitable division by far, with a margin of 24%.
Customer growth is slowing at HPE’s GreenLake cloud services division. Only 1,000 customers added in the quarter, bringing to total to 42,000 worldwide. The annualized run rate for the GreenLake business inched up to $2.2 billion, compared to $2.1 billion in Q1 F2025 and from $1.5 billion a year ago. It is in this area that HPE plans to accelerate it’s AI growth, via Nvidia’s AI/GPU chips.
Source: HPE
With respect to the Juniper Networks acquisition, there is a possibility that the $14 billion deal may collapse. A legal battle in court is due to begin on July 9th, but Neri talked on the analyst call about exploring “a number of other options if the Juniper deal doesn’t happen.”
Photo Credit: HPE
Apparently, artificial intelligence (AI) is allowing HPE to eject staff it once needed. It has apparently worked with Deloitte, a management consultancy, to create AI “agents” based on Nvidia’s technology and its own cloud. Let loose in finance, those agents already seem to be taking over some jobs. “This strategic move will transform our executive reporting,” said Myers. “We’re turning data into actionable intelligence, accelerating our reporting cycles by approximately 50% and reducing processing costs by an estimated 25%. Our ambition is clear: a leaner, faster and more competitive organization. Nothing is off limits.”
HPE CEO Anthony Neris AI comments on yesterday’s earnings call:
Ultimately, it comes down to the mix of the business with AI. And that’s why we take a very disciplined approach across the AI ecosystem, if you will. And what I’m really pleased in AI is that this quarter, one-third of our orders came from enterprise, which tend to come with higher margin because there is more software and services attached to that enterprise market. Then you have to pay attention also to working capital. Working capital is very important because in some of these deals, you are deploying a significant amount of capital and there is a time between the capital deployment and the revenue profit recognition. So that’s why, it is a technology transition, there is a business transition, and then there’s a working capital transition. But I’m pleased with the progress we made in Q2.
The fact is that we have more than 250 use cases where we are doing PoCs (Proof of Concepts) or already deploying AI. In fact, more than 40 are already in production. And we see the benefits of that across finance, global operations, marketing, as well as services. So that’s why we believe there is an opportunity to accelerate that improvement, not just by reducing the workforce, but really becoming nimbler and better at everything we do.
- About Hewlett Packard Enterprise (HPE):
HP Enterprise (HPE) is a large US based business and technology services company. HPE was founded on 1 November 2015 as part of splitting of the Hewlett-Packard company. The company has over 240,000 employees and the headquarters are based in Palo Alto, CA (as of 2016).
HPE operates in 60 countries, centered in the metropolitan areas of Dallas-Fort Worth; Detroit; Des Moines and Clarion, Iowa; Salt Lake City; Indianapolis; Winchester, Kentucky; Tulsa, Oklahoma; Boise, Idaho; and Northern Virginia in the United States. Other major locations are as follows: Argentina, Colombia, Costa Rica, India, Brazil, Mexico, the United Kingdom, Australia, Canada, Egypt, Germany, New Zealand, Hungary, Spain, Slovakia, Israel, South Africa, Italy, Malaysia and the Philippines.
HPE has four major operating divisions: Enterprise Group, which works in servers, storage, networking, consulting and support; Services; Software; and Financial Services. In May 2016, HPE announced it would sell its Enterprise Services division to one of its competitors, Computer Sciences Corporation (CSC).
References:
Dell’Oro: RAN revenue growth in 1Q2025; AI RAN is a conundrum
Dell’Oro Group just completed its 1Q-2025 Radio Access Network (RAN) report. Initial findings suggest that after two years of steep declines, market conditions improved in the quarter. Preliminary estimates show that worldwide RAN revenue, excluding services, stabilized year-over-year, resulting in the first growth quarter since 1Q-2023. Author Stefan Pongratz attributes the improved conditions to favorable regional mix and easy comparisons (investments were very low same quarter lasts year), rather than a change to the fundamentals that shape the RAN market.
Pongratz believes the long-term trajectory has not changed. “While it is exciting that RAN came in as expected and the full year outlook remains on track, the message we have communicated for some time now has not changed. The RAN market is still growth-challenged as regional 5G coverage imbalances, slower data traffic growth, and monetization challenges continue to weigh on the broader growth prospects,” he added.
Vendor rankings haven’t changed much in several years, as per this table:
Additional highlights from the 1Q 2025 RAN report:
– Strong growth in North America was enough to offset declines in CALA, China, and MEA.
– The picture is less favorable outside of North America. RAN, excluding North America, recorded a fifth consecutive quarter of declines.
– Revenue rankings did not change in 1Q 2025. The top 5 RAN suppliers (4-Quarter Trailing) based on worldwide revenues are Huawei, Ericsson, Nokia, ZTE, and Samsung.
– The top 5 RAN (4-Quarter Trailing) suppliers based on revenues outside of China are Ericsson, Nokia, Huawei, Samsung, and ZTE.
– The short-term outlook is mostly unchanged, with total RAN expected to remain stable in 2025 and RAN outside of China growing at a modest pace.
Dell’Oro Group’s RAN Quarterly Report offers a complete overview of the RAN industry, with tables covering manufacturers’ and market revenue for multiple RAN segments including 5G NR Sub-7 GHz, 5G NR mmWave, LTE, macro base stations and radios, small cells, Massive MIMO, Open RAN, and vRAN. The report also tracks the RAN market by region and includes a four-quarter outlook. To purchase this report, please contact us by email at [email protected]
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Separately, Pongrantz says “there is great skepticism about AI’s ability to reverse the flat revenue trajectory that has defined network operators throughout the 4G and 5G cycles.”
The 3GPP AI/ML activities and roadmap are mostly aligned with the broader efficiency aspects of the AI RAN vision, primarily focused on automation, management data analytics (MDA), SON/MDT, and over-the-air (OTA) related work (CSI, beam management, mobility, and positioning).
Current AI/ML activities align well with the AI-RAN Alliance’s vision to elevate the RAN’s potential with more automation, improved efficiencies, and new monetization opportunities. The AI-RAN Alliance envisions three key development areas: 1) AI and RAN – improving asset utilization by using a common shared infrastructure for both RAN and AI workloads, 2) AI on RAN – enabling AI applications on the RAN, 3) AI for RAN – optimizing and enhancing RAN performance. Or from an operator standpoint, AI offers the potential to boost revenue or reduce capex and opex.
While operators generally don’t consider AI the end destination, they believe more openness, virtualization, and intelligence will play essential roles in the broader RAN automation journey.
Operators are not revising their topline growth or mobile data traffic projections upward as a result of AI growing in and around the RAN. Disappointing 4G/5G returns and the failure to reverse the flattish carrier revenue trajectory is helping to explain the increased focus on what can be controlled — AI RAN is currently all about improving the performance/efficiency and reducing opex.
Since the typical gains demonstrated so far are in the 10% to 30% range for specific features, the AI RAN business case will hinge crucially on the cost and power envelope—the risk appetite for growing capex/opex is limited.
The AI-RAN business case using new hardware is difficult to justify for single-purpose tenancy. However, if the operators can use the resources for both RAN and non-RAN workloads and/or the accelerated computing cost comes down (NVIDIA recently announced ARC-Compact, an AI-RAN solution designed for D-RAN), the TAM could expand. For now, the AI service provider vision, where carriers sell unused capacity at scale, remains somewhat far-fetched, and as a result, multi-purpose tenancy is expected to account for a small share of the broader AI RAN market over the near term.
In short, improving something already done by 10% to 30% is not overly exciting. However, suppose AI embedded in the radio signal processing can realize more significant gains or help unlock new revenue opportunities by improving site utilization and providing telcos with an opportunity to sell unused RAN capacity. In that case, there are reasons to be excited. But since the latter is a lower-likelihood play, the base case expectation is that AI RAN will produce tangible value-add, and the excitement level is moderate — or as the Swedes would say, it is lagom.
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Editor’s Note:
ITU-R WP 5D is working on aspects related to AI in the Radio Access Network (RAN) as part of its IMT-2030 (6G) recommendations. IMT-2030 is expected to consider an appropriate AI-native new air interface that uses to the extent practicable, and proved demonstrated actionable AI to enhance the performance of radio interface functions such as symbol detection/decoding, channel estimation etc. An appropriate AI-native radio network would enable automated and intelligent networking services such as intelligent data perception, supply of on-demand capability etc. Radio networks that support applicable AI services would be fundamental to the design of IMT technologies to serve various AI applications, and the proposed directions include on-demand uplink/sidelink-centric, deep edge, and distributed machine learning.
In summary:
- ITU-R WP5D recognizes AI as one of the key technology trends for IMT-2030 (6G).
- This includes “native AI,” which encompasses both AI-enabled air interface design and radio network for AI services.
- AI is expected to play a crucial role in enhancing the capabilities and performance of 6G networks.
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References:
Dell’Oro: Private RAN revenue declines slightly, but still doing relatively better than public RAN and WLAN markets
ITU-R WP 5D reports on: IMT-2030 (“6G”) Minimum Technology Performance Requirements; Evaluation Criteria & Methodology
https://www.itu.int/dms_pubrec/itu-r/rec/m/R-REC-M.2160-0-202311-I!!PDF-E.pdf
McKinsey: AI infrastructure opportunity for telcos? AI developments in the telecom sector
A new report from McKinsey & Company offers a wide range of options for telecom network operators looking to enter the market for AI services. One high-level conclusion is that strategy inertia and decision paralysis might be the most dangerous threats. That’s largely based on telco’s failure to monetize past emerging technologies like smartphones and mobile apps, cloud networking, 5G-SA (the true 5G), etc. For example, global mobile data traffic rose 60% per year from 2010 to 2023, while the global telecom industry’s revenues rose just 1% during that same time period.
“Operators could provide the backbone for today’s AI economy to reignite growth. But success will hinge on effectively navigating complex market dynamics, uncertain demand, and rising competition….Not every path will suit every telco; some may be too risky for certain operators right now. However, the most significant risk may come from inaction, as telcos face the possibility of missing out on their fair share of growth from this latest technological disruption.”
McKinsey predicts that global data center demand could rise as high as 298 gigawatts by 2030, from just 55 gigawatts in 2023. Fiber connections to AI infused data centers could generate up to $50 billion globally in sales to fiber facilities based carriers.
Pathways to growth -Exploring four strategic options:
- Connecting new data centers with fiber
- Enabling high-performance cloud access with intelligent network services
- Turning unused space and power into revenue
- Building a new GPU as a Service business.
“Our research suggests that the addressable GPUaaS [GPU-as-a-service] market addressed by telcos could range from $35 billion to $70 billion by 2030 globally.” Verizon’s AI Connect service (described below), Indosat Ooredoo Hutchinson (IOH), Singtel and Softbank in Asia have launched their own GPUaaS offerings.
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Recent AI developments in the telecom sector include:
- The AI-RAN Alliance, which promises to allow wireless network operators to add AI to their radio access networks (RANs) and then sell AI computing capabilities to enterprises and other customers at the network edge. Nvidia is leading this industrial initiative. Telecom operators in the alliance include T-Mobile and SoftBank, as well as Boost Mobile, Globe, Indosat Ooredoo Hutchison, Korea Telecom, LG UPlus, SK Telecom and Turkcell.
- Verizon’s new AI Connect product, which includes Vultr’s GPU-as-a-service (GPUaaS) offering. GPU-as-a-service is a cloud computing model that allows businesses to rent access to powerful graphics processing units (GPUs) for AI and machine learning workloads without having to purchase and maintain that expensive hardware themselves. Verizon also has agreements with Google Cloud and Meta to provide network infrastructure for their AI workloads, demonstrating a focus on supporting the broader AI economy.
- Orange views AI as a critical growth driver. They are developing “AI factories” (data centers optimized for AI workloads) and providing an “AI platform layer” called Live Intelligence to help enterprises build generative AI systems. They also offer a generative AI assistant for contact centers in partnership with Microsoft.
- Lumen Technologies continues to build fiber connections intended to carry AI traffic.
- British Telecom (BT) has launched intelligent network services and is working with partners like Fortinet to integrate AI for enhanced security and network management.
- Telus (Canada) has built its own AI platform called “Fuel iX” to boost employee productivity and generate new revenue. They are also commercializing Fuel iX and building sovereign AI infrastructure.
- Telefónica: Their “Next Best Action AI Brain” uses an in-house Kernel platform to revolutionize customer interactions with precise, contextually relevant recommendations.
- Bharti Airtel (India): Launched India’s first anti-spam network, an AI-powered system that processes billions of calls and messages daily to identify and block spammers.
- e& (formerly Etisalat in UAE): Has launched the “Autonomous Store Experience (EASE),” which uses smart gates, AI-powered cameras, robotics, and smart shelves for a frictionless shopping experience.
- SK Telecom (Korea): Unveiled a strategy to implement an “AI Infrastructure Superhighway” and is actively involved in AI-RAN (AI in Radio Access Networks) development, including their AITRAS solution.
- Vodafone: Sees AI as a transformative force, with initiatives in network optimization, customer experience (e.g., their TOBi chatbot handling over 45 million interactions per month), and even supporting neurodiverse staff.
- Deutsche Telekom: Deploys AI across various facets of its operations
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A recent report from DCD indicates that new AI models that can reason may require massive, expensive data centers, and such data centers may be out of reach for even the largest telecom operators. Across optical data center interconnects, data centers are already communicating with each other for multi-cluster training runs. “What we see is that, in the largest data centers in the world, there’s actually a data center and another data center and another data center,” he says. “Then the interesting discussion becomes – do I need 100 meters? Do I need 500 meters? Do I need a kilometer interconnect between data centers?”
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References:
https://www.datacenterdynamics.com/en/analysis/nvidias-networking-vision-for-training-and-inference/
https://opentools.ai/news/inaction-on-ai-a-critical-misstep-for-telecos-says-mckinsey
Bain & Co, McKinsey & Co, AWS suggest how telcos can use and adapt Generative AI
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
The case for and against AI-RAN technology using Nvidia or AMD GPUs
Telecom and AI Status in the EU
Major technology companies form AI-Enabled Information and Communication Technology (ICT) Workforce Consortium
AI RAN Alliance selects Alex Choi as Chairman
AI Frenzy Backgrounder; Review of AI Products and Services from Nvidia, Microsoft, Amazon, Google and Meta; Conclusions
AI sparks huge increase in U.S. energy consumption and is straining the power grid; transmission/distribution as a major problem
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
NEC’s new AI technology for robotics & RAN optimization designed to improve performance
MTN Consulting: Generative AI hype grips telecom industry; telco CAPEX decreases while vendor revenue plummets
Amdocs and NVIDIA to Accelerate Adoption of Generative AI for $1.7 Trillion Telecom Industry
SK Telecom and Deutsche Telekom to Jointly Develop Telco-specific Large Language Models (LLMs)
Wedbush: Middle East (Saudi Arabia and UAE) to be next center of AI infrastructure boom
The next major area of penetration for the AI revolution appears to be the Middle East, Wedbush analysts say in a research note. Analyst Dan Ives said the rapid expansion of artificial intelligence infrastructure in the Middle East marks a “watershed moment” for U.S. tech companies, driven by major developments in Saudi Arabia and the United Arab Emirates. President Trump was recently there to negotiate a deal to have the U.S. tech sector make data centers, supercomputers, software, and overall infrastructure for a massive AI buildout in Saudi Arabia and the U.A.E in the coming years, the analysts say. Saudi Arabia is now due to get 18,000 Nvidia chips for a massive data center while the U.A.E. has Trump’s support to guild the largest data center outside of the U.S., two factors that should start an era of new growth for the U.S. tech sector and be a game-changer for the industry, the analysts say.
“We believe the market opportunity in Saudi Arabia and UAE alone could over time add another $1 trillion to the broader global AI market in the coming year,” Wedbush said. “No Nvidia chips for China… red carpet rollout for the Kingdom,” the firm wrote, contrasting Middle East expansion with chip export restrictions affecting Beijing. Ives called the momentum in the region “a bullish indicator that further shows the U.S. tech’s lead in this 4th Industrial Revolution.” He said that Nvidia CEO Jensen Huang was “the Godfather of AI” and this author totally agrees. Without Nvidia [1.] AI-GPT chips there would be no AI compute servers in the massive data centers now being built.
Wedbush believes Saudi Arabia, the UAE, and Qatar are now on the “priority list” for U.S. tech, with regional demand for AI chips, software, robotics, and data centers expected to surge over the next decade. ……………………………………………………………………………………………………………………………………………………………………………………………
Note 1. Nvidia should see its trend of strong revenue growth continue, but consensus estimates may not fully account for the recent H20 export restriction, Raymond James’ Srini Pajjuri and Grant Li say in a research note. The analysts expect revenue growth between $4 billion and $5 billion during the past six quarters to continue on strong ramps for its Blackwell chip, but they note that the restrictions on the H20 chips present a roughly $4 billion headwind, leaving them to expect limited sequential growth in 2Q. “That said, we fully expect management to sound bullish on 2H given the strong hyperscale capex trends and recent AI diffusion rule changes,” say the analysts. Nvidia is scheduled to report 1QFY26 results on May 28th.
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Last week, Saudi Arabia unveiled a series of blockbuster AI partnerships with US chip makers, cloud infrastructure providers, and software developers this week, signaling its ambition to become a global AI hub.
Image Credit: Adam Flaherty / Shutterstock.com
Leveraging its $940 billion Public Investment Fund (PIF) and strategic location, Saudi Arabia is forming partnerships to create sovereign AI infrastructure including advanced data centers and Arabic large language models. Google, Oracle, and Salesforce are deepening AI and cloud commitments in Saudi Arabia that will support Vision 2030, a 15-year program to diversify the country’s economy. Within that, the $100 billion Project Transcendence aims to put the kingdom among the top 15 countries in AI by 2030.
The deals include a $20 billion commitment from Saudi firm DataVolt for AI data centers and energy infrastructure in the US and an $80 billion joint investment by Google, DataVolt, Oracle, Salesforce, AMD, and Uber in technologies across both nations, according to a White House fact sheet.
References:
https://finance.yahoo.com/news/wedbush-ives-sees-ai-boom-123710639.html
https://www.wsj.com/tech/tech-media-telecom-roundup-market-talk-87c22df6
US companies are helping Saudi Arabia to build an AI powerhouse
Ericsson and ACES partner to revolutionize indoor 5G connectivity in Saudi Arabia
Ericsson and e& (UAE) sign MoU for 6G collaboration vs ITU-R IMT-2030 framework
e& UAE sets new world record with fastest 5G speed of 30.5Gbps
ITU World Radiocommunication Conference 2023 opens in Dubai, UAE
Sources: AI is Getting Smarter, but Hallucinations Are Getting Worse
Recent reports suggest that AI hallucinations—instances where AI generates false or misleading information—are becoming more frequent and present growing challenges for businesses and consumers alike who rely on these technologies. More than two years after the arrival of ChatGPT, tech companies, office workers and everyday consumers are using A.I. bots for an increasingly wide array of tasks. But there is still no way of ensuring that these systems produce accurate information.
A groundbreaking study featured in the PHARE (Pervasive Hallucination Assessment in Robust Evaluation) dataset has revealed that AI hallucinations are not only persistent but potentially increasing in frequency across leading language models. The research, published on Hugging Face, evaluated multiple large language models (LLMs) including GPT-4, Claude, and Llama models across various knowledge domains.
“We’re seeing a concerning trend where even as these models advance in capability, their propensity to hallucinate remains stubbornly present,” notes the PHARE analysis. The comprehensive benchmark tested models across 37 knowledge categories, revealing that hallucination rates varied significantly by domain, with some models demonstrating hallucination rates exceeding 30% in specialized fields.
Hallucinations are when AI bots produce fabricated information and present it as fact. Photo Credit: More SOPA Images/LightRocket via Getty Images
Today’s A.I. bots are based on complex mathematical systems that learn their skills by analyzing enormous amounts of digital data. These systems use mathematical probabilities to guess the best response, not a strict set of rules defined by human engineers. So they make a certain number of mistakes. “Despite our best efforts, they will always hallucinate,” said Amr Awadallah, the chief executive of Vectara, a start-up that builds A.I. tools for businesses, and a former Google executive.
“That will never go away,” he said. These AI bots do not — and cannot — decide what is true and what is false. Sometimes, they just make stuff up, a phenomenon some A.I. researchers call hallucinations. On one test, the hallucination rates of newer A.I. systems were as high as 79%.
Amr Awadallah, the chief executive of Vectara, which builds A.I. tools for businesses, believes A.I. “hallucinations” will persist.Credit…Photo credit: Cayce Clifford for The New York Times
AI companies like OpenAI, Google, and DeepSeek have introduced reasoning models designed to improve logical thinking, but these models have shown higher hallucination rates compared to previous versions. For more than two years, those companies steadily improved their A.I. systems and reduced the frequency of these errors. But with the use of new reasoning systems, errors are rising. The latest OpenAI systems hallucinate at a higher rate than the company’s previous system, according to the company’s own tests.
For example, OpenAI’s latest models (o3 and o4-mini) have hallucination rates ranging from 33% to 79%, depending on the type of question asked. This is significantly higher than earlier models, which had lower error rates. Experts are still investigating why this is happening. Some believe that the complex reasoning processes in newer AI models may introduce more opportunities for errors.
Others suggest that the way these models are trained might be amplifying inaccuracies. For several years, this phenomenon has raised concerns about the reliability of these systems. Though they are useful in some situations — like writing term papers, summarizing office documents and generating computer code — their mistakes can cause problems. Despite efforts to reduce hallucinations, AI researchers acknowledge that hallucinations may never fully disappear. This raises concerns for applications where accuracy is critical, such as legal, medical, and customer service AI systems.
The A.I. bots tied to search engines like Google and Bing sometimes generate search results that are laughably wrong. If you ask them for a good marathon on the West Coast, they might suggest a race in Philadelphia. If they tell you the number of households in Illinois, they might cite a source that does not include that information. Those hallucinations may not be a big problem for many people, but it is a serious issue for anyone using the technology with court documents, medical information or sensitive business data.
“You spend a lot of time trying to figure out which responses are factual and which aren’t,” said Pratik Verma, co-founder and chief executive of Okahu, a company that helps businesses navigate the hallucination problem. “Not dealing with these errors properly basically eliminates the value of A.I. systems, which are supposed to automate tasks for you.”
For more than two years, companies like OpenAI and Google steadily improved their A.I. systems and reduced the frequency of these errors. But with the use of new reasoning systems, errors are rising. The latest OpenAI systems hallucinate at a higher rate than the company’s previous system, according to the company’s own tests.
The company found that o3 — its most powerful system — hallucinated 33% of the time when running its PersonQA benchmark test, which involves answering questions about public figures. That is more than twice the hallucination rate of OpenAI’s previous reasoning system, called o1. The new o4-mini hallucinated at an even higher rate: 48 percent.
When running another test called SimpleQA, which asks more general questions, the hallucination rates for o3 and o4-mini were 51% and 79%. The previous system, o1, hallucinated 44% of the time.
In a paper detailing the tests, OpenAI said more research was needed to understand the cause of these results. Because A.I. systems learn from more data than people can wrap their heads around, technologists struggle to determine why they behave in the ways they do.
“Hallucinations are not inherently more prevalent in reasoning models, though we are actively working to reduce the higher rates of hallucination we saw in o3 and o4-mini,” a company spokeswoman, Gaby Raila, said. “We’ll continue our research on hallucinations across all models to improve accuracy and reliability.”
Tests by independent companies and researchers indicate that hallucination rates are also rising for reasoning models from companies such as Google and DeepSeek.
Since late 2023, Mr. Awadallah’s company, Vectara, has tracked how often chatbots veer from the truth. The company asks these systems to perform a straightforward task that is readily verified: Summarize specific news articles. Even then, chatbots persistently invent information. Vectara’s original research estimated that in this situation chatbots made up information at least 3% of the time and sometimes as much as 27%.
In the year and a half since, companies such as OpenAI and Google pushed those numbers down into the 1 or 2% range. Others, such as the San Francisco start-up Anthropic, hovered around 4%. But hallucination rates on this test have risen with reasoning systems. DeepSeek’s reasoning system, R1, hallucinated 14.3% of the time. OpenAI’s o3 climbed to 6.8%.
Sarah Schwettmann, co-founder of Transluce, said that o3’s hallucination rate may make it less useful than it otherwise would be. Kian Katanforoosh, a Stanford adjunct professor and CEO of the upskilling startup Workera, told TechCrunch that his team is already testing o3 in their coding workflows, and that they’ve found it to be a step above the competition. However, Katanforoosh says that o3 tends to hallucinate broken website links. The model will supply a link that, when clicked, doesn’t work.
AI companies are now leaning more heavily on a technique that scientists call reinforcement learning. With this process, a system can learn behavior through trial and error. It is working well in certain areas, like math and computer programming. But it is falling short in other areas.
“The way these systems are trained, they will start focusing on one task — and start forgetting about others,” said Laura Perez-Beltrachini, a researcher at the University of Edinburgh who is among a team closely examining the hallucination problem.
Another issue is that reasoning models are designed to spend time “thinking” through complex problems before settling on an answer. As they try to tackle a problem step by step, they run the risk of hallucinating at each step. The errors can compound as they spend more time thinking.
“What the system says it is thinking is not necessarily what it is thinking,” said Aryo Pradipta Gema, an A.I. researcher at the University of Edinburgh and a fellow at Anthropic.
New research highlighted by TechCrunch indicates that user behavior may exacerbate the problem. When users request shorter answers from AI chatbots, hallucination rates actually increase rather than decrease. “The pressure to be concise seems to force these models to cut corners on accuracy,” the TechCrunch article explains, challenging the common assumption that brevity leads to greater precision.
References:
https://www.nytimes.com/2025/05/05/technology/ai-hallucinations-chatgpt-google.html
The Confidence Paradox: Why AI Hallucinations Are Getting Worse, Not Better
https://www.forbes.com/sites/conormurray/2025/05/06/why-ai-hallucinations-are-worse-than-ever/
https://techcrunch.com/2025/04/18/openais-new-reasoning-ai-models-hallucinate-more/
Goldman Sachs: Big 3 China telecom operators are the biggest beneficiaries of China’s AI boom via DeepSeek models; China Mobile’s ‘AI+NETWORK’ strategy
Telecom sessions at Nvidia’s 2025 AI developers GTC: March 17–21 in San Jose, CA
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
Does AI change the business case for cloud networking?
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Ericsson’s sales rose for the first time in 8 quarters; mobile networks need an AI boost
U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
Damage of U.S. Export Controls and Trade War with China:
The U.S. big tech sector, especially needs to know what the rules of the trade game will be looking ahead instead of the on-again/off-again Trump tariffs and trade war with China which includes 145% tariffs and export controls on AI chips from Nvidia, AMD, and other U.S. semiconductor companies.
The latest export restriction on Nvidia’s H20 AI chips are a case in point. Nvidia said it would record a $5.5 billion charge on its quarterly earnings after it disclosed that the U.S. will now require a license for exporting the company’s H20 processors to China and other countries. The U.S. government told the chip maker on April 14th that the new license requirement would be in place “indefinitely.”
Nvidia designed the H20 chip to comply with existing U.S. export controls that limit sales of advanced AI processors to Chinese customers. That meant the chip’s capabilities were significantly degraded; Morgan Stanley analyst Joe Moore estimates the H20’s performance is about 75% below that of Nvidia’s H100 family. The Commerce Department said it was issuing new export-licensing requirements covering H20 chips and AMD’s MI308 AI processors.
Big Chinese cloud companies like Tencent, ByteDance (TikTok’s parent), Alibaba, Baidu, and iFlytek have been left scrambling for domestic alternatives to the H20, the primary AI chip that Nvidia had until recently been allowed to sell freely into the Chinese market. Some analysts suggest that H20 bulk orders to build a stockpile were a response to concerns about future U.S. export restrictions and a race to secure limited supplies of Nvidia chips. The estimate is that there’s a 90 days supply of H20 chips, but it’s uncertain what China big tech companies will use when that runs out.
The inability to sell even a low-performance chip into the Chinese market shows how the trade war will hurt Nvidia’s business. The AI chip king is now caught between the world’s two superpowers as they jockey to take the lead in AI development.
Nvidia CEO Jensen Huang “flew to China to do damage control and make sure China/Xi knows Nvidia wants/needs China to maintain its global ironclad grip on the AI Revolution,” the analysts note. The markets and tech world are tired of “deal progress” talks from the White House and want deals starting to be inked so they can plan their future strategy. The analysts think this is a critical week ahead to get some trade deals on the board, because Wall Street has stopped caring about words and comments around “deal progress.”
- Baidu is developing its own AI chips called Kunlun. It recently placed an order for 1,600 of Huawei’s Ascend 910B AI chips for 200 servers. This order was made in anticipation of further U.S. export restrictions on AI chips.
- Alibaba (T-Head) has developed AI chips like the Hanguang 800 inference chip, used to accelerate its e-commerce platform and other services.
- Cambricon Technologies: Designs various types of semiconductors, including those for training AI models and running AI applications on devices.
- Biren Technology: Designs general-purpose GPUs and software development platforms for AI training and inference, with products like the BR100 series.
- Moore Threads: Develops GPUs designed for training large AI models, with data center products like the MTT KUAE.
- Horizon Robotics: Focuses on AI chips for smart driving, including the Sunrise and Journey series, collaborating with automotive companies.
- Enflame Technology: Designs chips for data centers, specializing in AI training and inference.
“With Nvidia’s H20 and other advanced GPUs restricted, domestic alternatives like Huawei’s Ascend series are gaining traction,” said Doug O’Laughlin, an industry analyst at independent semiconductor research company SemiAnalysis. “While there are still gaps in software maturity and overall ecosystem readiness, hardware performance is closing in fast,” O’Laughlin added. According to the SemiAnalysis report, Huawei’s Ascend chip shows how China’s export controls have failed to stop firms like Huawei from accessing critical foreign tools and sub-components needed for advanced GPUs. “While Huawei’s Ascend chip can be fabricated at SMIC, this is a global chip that has HBM from Korea, primary wafer production from TSMC, and is fabricated by 10s of billions of wafer fabrication equipment from the US, Netherlands, and Japan,” the report stated.
Huawei’s New AI Chip May Dominate in China:
Huawei Technologies plans to begin mass shipments of its advanced 910C artificial intelligence chip to Chinese customers as early as next month, according to Reuters. Some shipments have already been made, people familiar with the matter said. Huawei’s 910C, a graphics processing unit (GPU), represents an architectural evolution rather than a technological breakthrough, according to one of the two people and a third source familiar with its design. It achieves performance comparable to Nvidia’s H100 chip by combining two 910B processors into a single package through advanced integration techniques, they said. That means it has double the computing power and memory capacity of the 910B and it also has incremental improvements, including enhanced support for diverse AI workload data.
Conclusions:
The U.S. Commerce Department’s latest export curbs on Nvidia’s H20 “will mean that Huawei’s Ascend 910C GPU will now become the hardware of choice for (Chinese) AI model developers and for deploying inference capacity,” said Paul Triolo, a partner at consulting firm Albright Stonebridge Group.
The markets, tech world and the global economy urgently need U.S. – China trade negotiations in some form to start as soon as possible, Wedbush analysts say in a research note today. The analysts expect minimal or no guidance from tech companies during this earnings season as they are “playing darts blindfolded.”
References:
https://qz.com/china-six-tigers-ai-startup-zhipu-moonshot-minimax-01ai-1851768509#
https://www.huaweicloud.com/intl/en-us/
Goldman Sachs: Big 3 China telecom operators are the biggest beneficiaries of China’s AI boom via DeepSeek models; China Mobile’s ‘AI+NETWORK’ strategy
Telecom sessions at Nvidia’s 2025 AI developers GTC: March 17–21 in San Jose, CA
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
FT: Nvidia invested $1bn in AI start-ups in 2024
Omdia: Huawei increases global RAN market share due to China hegemony
Huawei’s “FOUR NEW strategy” for carriers to be successful in AI era
Telecom sessions at Nvidia’s 2025 AI developers GTC: March 17–21 in San Jose, CA
Nvidia’s annual AI developers conference (GTC) used to be a relatively modest affair, drawing about 9,000 people in its last year before the Covid outbreak. But the event now unofficially dubbed “AI Woodstock” is expected to bring more than 25,000 in-person attendees!
Nvidia’s Blackwell AI chips, the main showcase of last year’s GTC (GPU Technology Conference), have only recently started shipping in high volume following delays related to the mass production of their complicated design. Blackwell is expected to be the main anchor of Nvidia’s AI business through next year. Analysts expect Nvidia Chief Executive Jensen Huang to showcase a revved-up version of that family called Blackwell Ultra at his keynote address on Tuesday.
March 18th Update: The next Blackwell Ultra NVL72 chips, which have one-and-a-half times more memory and two times more bandwidth, will be used to accelerate building AI agents, physical AI, and reasoning models, Huang said. Blackwell Ultra will be available in the second half of this year. The Rubin AI chip, is expected to launch in late 2026. Rubin Ultra will take the stage in 2027.
Nvidia watchers are especially eager to hear more about the next generation of AI chips called Rubin, which Nvidia has only teased at in prior events. Ross Seymore of Deutsche Bank expects the Rubin family to show “very impressive performance improvements” over Blackwell. Atif Malik of Citigroup notes that Blackwell provided 30 times faster performance than the company’s previous generation on AI inferencing, which is when trained AI models generate output. “We don’t rule out Rubin seeing similar improvement,” Malik wrote in a note to clients this month.
Rubin products aren’t expected to start shipping until next year. But much is already expected of the lineup; analysts forecast Nvidia’s data-center business will hit about $237 billion in revenue for the fiscal year ending in January of 2027, more than double its current size. The same segment is expected to eclipse $300 billion in annual revenue two years later, according to consensus estimates from Visible Alpha. That would imply an average annual growth rate of 30% over the next four years, for a business that has already exploded more than sevenfold over the last two.
Nvidia has also been haunted by worries about competition with in-house chips designed by its biggest customers like Amazon and Google. Another concern has been the efficiency breakthroughs claimed by Chinese AI startup DeepSeek, which would seemingly lessen the need for the types of AI chip clusters that Nvidia sells for top dollar.
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Telecom Sessions of Interest:
Wednesday Mar 19 | 2:00 PM – 2:40 PM
Delivering Real Business Outcomes With AI in Telecom [S73438]
In this session, executives from three leading telcos will share their unique journeys of embedding AI into their organizations. They’ll discuss how AI is driving measurable value across critical areas such as network optimization, customer experience, operational efficiency, and revenue growth. Gain insights into the challenges and lessons learned, key strategies for successful AI implementation, and the transformative potential of AI in addressing evolving industry demands.
Thursday Mar 20 | 11:00 AM – 11:40 AM PDT
AI-RAN in Action [S72987]
Thursday Mar 20 | 9:00 AM – 9:40 AM PDTHow Indonesia Delivered a Telco-led Sovereign AI Platform for 270M Users [S73440]
Thursday Mar 20 | 3:00 PM – 3:40 PM PDT
Driving 6G Development With Advanced Simulation Tools [S72994]
Thursday Mar 20 | 2:00 PM – 2:40 PM PDT
Thursday Mar 20 | 4:00 PM – 4:40 PM PDT
Pushing Spectral Efficiency Limits on CUDA-accelerated 5G/6G RAN [S72990]
Thursday Mar 20 | 4:00 PM – 4:40 PM PDT
Enable AI-Native Networking for Telcos with Kubernetes [S72993]
Monday Mar 17 | 3:00 PM – 4:45 PM PDT
Automate 5G Network Configurations With NVIDIA AI LLM Agents and Kinetica Accelerated Database [DLIT72350]
Learn how to create AI agents using LangGraph and NVIDIA NIM to automate 5G network configurations. You’ll deploy LLM agents to monitor real-time network quality of service (QoS) and dynamically respond to congestion by creating new network slices. LLM agents will process logs to detect when QoS falls below a threshold, then automatically trigger a new slice for the affected user equipment. Using graph-based models, the agents understand the network configuration, identifying impacted elements. This ensures efficient, AI-driven adjustments that consider the overall network architecture.
We’ll use the Open Air Interface 5G lab to simulate the 5G network, demonstrating how AI can be integrated into real-world telecom environments. You’ll also gain practical knowledge on using Python with LangGraph and NVIDIA AI endpoints to develop and deploy LLM agents that automate complex network tasks.
Prerequisite: Python programming.
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References:
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
The case for and against AI-RAN technology using Nvidia or AMD GPUs
FT: Nvidia invested $1bn in AI start-ups in 2024
Quartet launches “Open Telecom AI Platform” with multiple AI layers and domains
At Mobile World Congress 2025, Jio Platforms (JPL), AMD, Cisco, and Nokia announced the Open Telecom AI Platform, a new project designed to pioneer the use of AI across all network domains. It aims to provide a centralized intelligence layer that can integrate AI and automation into every layer of network operations.
The AI platform will be large language model (LLM) agnostic and use open APIs to optimize functionality and capabilities. By collectively harnessing agentic AI and using LLMs, domain-specific SLMs and machine learning techniques, the Telecom AI Platform is intended to enable end-to-end intelligence for network management and operations. The founding quartet of companies said that by combining shared elements, the platform provides improvements across network security and efficiency alongside a reduction in total cost of ownership. The companies each bring their specific expertise to the consortium across domains including RAN, routing, AI compute and security.
Jio Platforms will be the initial customer. The Indian telco says it will be AI-agnostic and use open APIs to optimize functionality and capabilities. It will be able to make use of agentic AI, as well as large language models (LLMs), domain-specific small language models (SLMs), and machine learning techniques.
“Think about this platform as multi-layer, multi-domain. Each of these domains, or each of these layers, will have their own agentic AI capability. By harnessing agentic AI across all telco layers, we are building a multimodal, multidomain orchestrated workflow platform that redefines efficiency, intelligence, and security for the telecom industry,” said Mathew Oommen, group CEO, Reliance Jio.
“In collaboration with AMD, Cisco, and Nokia, Jio is advancing the Open Telecom AI Platform to transform networks into self-optimising, customer-aware ecosystems. This initiative goes beyond automation – it’s about enabling AI-driven, autonomous networks that adapt in real time, enhance user experiences, and create new service and revenue opportunities across the digital ecosystem,” he added.
On top of Jio Platforms’ agentic AI workflow manager is an AI orchestrator which will work with what is deemed the best LLM. “Whichever LLM is the right LLM, this orchestrator will leverage it through an API framework,” Oomen explained. He said that Jio Platforms could have its first product set sometime this year.
Under the terms of the agreement, AMD will provide high-performance computing solutions, including EPYC CPUs, Instinct GPUs, DPUs, and adaptive computing technologies. Cisco will contribute networking, security, and AI analytics solutions, including Cisco Agile Services Networking, AI Defense, Splunk Analytics, and Data Center Networking. Nokia will bring expertise in wireless and fixed broadband, core networks, IP, and optical transport. Finally, Jio Platforms Limited (JPL) will be the platform’s lead organizer and first adopter. It will also provide global telecom operators’ initial deployment and reference model. 
The Telecom AI Platform intends to share the results with other network operators (besides Jio).
“We don’t want to take a few years to create something. I will tell you a little secret, and the secret is Reliance Jio has decided to look at markets outside of India. As part of this, we will not only leverage it for Jio, we will figure out how to democratize this platform for the rest of the world. Because unlike a physical box, this is going to be a lot of virtual functions and capabilities.”
AMD represents a lower-cost alternative to Intel and Nvidia when it comes to central processing units (CPUs) and graphics processing units (GPUs), respectively. For AMD, getting into a potentially successful telco platform is a huge success. Intel, its arch-rival in CPUs, has a major lead with telecom projects (e.g. cloud RAN and OpenRAN), having invested massive amounts of money in 5G and other telecom technologies.
AMD’s participation suggests that this JPL-led group is looking for hardware that can handle AI workloads at a much lower cost then using NVIDIA GPUs.
“AMD is proud to collaborate with Jio Platforms Limited, Cisco, and Nokia to power the next generation of AI-driven telecom infrastructure,” said Lisa Su, chair and CEO, AMD. “By leveraging our broad portfolio of high-performance CPUs, GPUs, and adaptive computing solutions, service providers will be able to create more secure, efficient, and scalable networks. Together we can bring the transformational benefits of AI to both operators and users and enable innovative services that will shape the future of communications and connectivity.”
Jio will surely be keeping a close eye on the cost of rolling out this reference architecture when the time comes, and optimizing it to ensure the telco AI platform is financially viable.
“Nokia possesses trusted technology leadership in multiple domains, including RAN, core, fixed broadband, IP and optical transport. We are delighted to bring this broad expertise to the table in service of today’s important announcement,” said Pekka Lundmark, President and CEO at Nokia. “The Telecom AI Platform will help Jio to optimise and monetise their network investments through enhanced performance, security, operational efficiency, automation and greatly improved customer experience, all via the immense power of artificial intelligence. I am proud that Nokia is contributing to this work.”
Cisco chairman and CEO Chuck Robbins said: “This collaboration with Jio Platforms Limited, AMD and Nokia harnesses the expertise of industry leaders to revolutionise networks with AI.
“Cisco is proud of the role we play here with integrated solutions from across our stack including Cisco Agile Services Networking, Data Center Networking, Compute, AI Defence, and Splunk Analytics. We look forward to seeing how the Telecom AI Platform will boost efficiency, enhance security, and unlock new revenue streams for service provider customers.”
If all goes well, the Open Telecom AI Platform could offer an alternative to Nvidia’s AI infrastructure, and give telcos in lower-ARPU markets a more cost-effective means of imbuing their network operations with the power of AI.
References:
https://www.telecoms.com/ai/jio-s-new-ai-club-could-offer-a-cheaper-route-into-telco-ai
Does AI change the business case for cloud networking?
For several years now, the big cloud service providers – Amazon Web Services (AWS), Microsoft Azure, and Google Cloud – have tried to get wireless network operators to run their 5G SA core network, edge computing and various distributed applications on their cloud platforms. For example, Amazon’s AWS public cloud, Microsoft’s Azure for Operators, and Google’s Anthos for Telecom were intended to get network operators to run their core network functions into a hyperscaler cloud.
AWS had early success with Dish Network’s 5G SA core network which has all its functions running in Amazon’s cloud with fully automated network deployment and operations.
Conversely, AT&T has yet to commercially deploy its 5G SA Core network on the Microsoft Azure public cloud. Also, users on AT&T’s network have experienced difficulties accessing Microsoft 365 and Azure services. Those incidents were often traced to changes within the network’s managed environment. As a result, Microsoft has drastically reduced its early telecom ambitions.
Several pundits now say that AI will significantly strengthen the business case for cloud networking by enabling more efficient resource management, advanced predictive analytics, improved security, and automation, ultimately leading to cost savings, better performance, and faster innovation for businesses utilizing cloud infrastructure.
“AI is already a significant traffic driver, and AI traffic growth is accelerating,” wrote analyst Brian Washburn in a market research report for Omdia (owned by Informa). “As AI traffic adds to and substitutes conventional applications, conventional traffic year-over-year growth slows. Omdia forecasts that in 2026–30, global conventional (non-AI) traffic will be about 18% CAGR [compound annual growth rate].”
Omdia forecasts 2031 as “the crossover point where global AI network traffic exceeds conventional traffic.”
Markets & Markets forecasts the global cloud AI market (which includes cloud AI networking) will grow at a CAGR of 32.4% from 2024 to 2029.
AI is said to enhance cloud networking in these ways:
- Optimized resource allocation:
AI algorithms can analyze real-time data to dynamically adjust cloud resources like compute power and storage based on demand, minimizing unnecessary costs. - Predictive maintenance:
By analyzing network patterns, AI can identify potential issues before they occur, allowing for proactive maintenance and preventing downtime. - Enhanced security:
AI can detect and respond to cyber threats in real-time through anomaly detection and behavioral analysis, improving overall network security. - Intelligent routing:
AI can optimize network traffic flow by dynamically routing data packets to the most efficient paths, improving network performance. - Automated network management:
AI can automate routine network management tasks, freeing up IT staff to focus on more strategic initiatives.
The pitch is that AI will enable businesses to leverage the full potential of cloud networking by providing a more intelligent, adaptable, and cost-effective solution. Well, that remains to be seen. Google’s new global industry lead for telecom, Angelo Libertucci, told Light Reading:
“Now enter AI,” he continued. “With AI … I really have a power to do some amazing things, like enrich customer experiences, automate my network, feed the network data into my customer experience virtual agents. There’s a lot I can do with AI. It changes the business case that we’ve been running.”
“Before AI, the business case was maybe based on certain criteria. With AI, it changes the criteria. And it helps accelerate that move [to the cloud and to the edge],” he explained. “So, I think that work is ongoing, and with AI it’ll actually be accelerated. But we still have work to do with both the carriers and, especially, the network equipment manufacturers.”
Google Cloud last week announced several new AI-focused agreements with companies such as Amdocs, Bell Canada, Deutsche Telekom, Telus and Vodafone Italy.
As IEEE Techblog reported here last week, Deutsche Telekom is using Google Cloud’s Gemini 2.0 in Vertex AI to develop a network AI agent called RAN Guardian. That AI agent can “analyze network behavior, detect performance issues, and implement corrective actions to improve network reliability and customer experience,” according to the companies.
And, of course, there’s all the buzz over AI RAN and we plan to cover expected MWC 2025 announcements in that space next week.
https://www.lightreading.com/cloud/google-cloud-doubles-down-on-mwc
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
The case for and against AI-RAN technology using Nvidia or AMD GPUs
Generative AI in telecom; ChatGPT as a manager? ChatGPT vs Google Search
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
Deutsche Telekom and Google Cloud today announced a new partnership to improve Radio Access Network (RAN) operations through the development of a network AI agent. Built using Gemini 2.0 in Vertex AI from Google Cloud, the agent can analyze network behavior, detect performance issues, and implement corrective actions to improve network reliability, reduce operational costs, and enhance customer experiences.
Deutsche Telekom says that as telecom networks become increasingly complex, traditional rule-based automation falls short in addressing real-time challenges. The solution is to use Agentic AI which leverages large language models (LLMs) and advanced reasoning frameworks to create intelligent agents that can think, reason, act, and learn independently.
The RAN Guardian agent, which has been tested and verified at Deutsche Telekom, collaborates in a human-like manner, detecting network anomalies and executing self-healing actions to optimize RAN performance. It will be exhibited at next week’s Mobile World Congress (MWC) in Barcelona, Spain.
–>This cooperative initiative appears to be a first step towards building autonomous and self-healing networks.
In addition to Gemini 2.0 in Vertex AI, the RAN Guardian also uses CloudRun, BigQuery, and Firestore to help deliver:
- Autonomous RAN performance monitoring: The RAN Guardian will continuously analyze key network parameters in real time to predict and detect anomalies.
- AI-driven issue classification and routing: The agent will identify and prioritize network degradations based on multiple data sources, including network monitoring data, inventory data, performance data, and coverage data.
- Proactive network optimization: The agent will also recommend or autonomously implement corrective actions, including resource reallocation and configuration adjustments.
“By combining Deutsche Telekom’s deep telecom expertise with Google Cloud’s cutting-edge AI capabilities, we’re building the next generation of intelligent networks,” said Angelo Libertucci, Global Industry Lead, Telecommunications, Google Cloud. “This means fewer disruptions, faster speeds, and an overall enhanced mobile experience for Deutsche Telekom’s customers.”
“Traditional network management approaches are no longer sufficient to meet the demands of 5G and beyond. We are pioneering AI agents for networks, working with key partners like Google Cloud to unlock a new level of intelligence and automation in RAN operations as a step towards autonomous, self-healing networks” said Abdu Mudesir, Group CTO, Deutsche Telekom.
Mr. Mudesir and Google Cloud’s Muninder Sambi will discuss the role of AI agents in the future of network operations at MWC next week.
References:
https://www.telecoms.com/ai/deutsche-telekom-and-google-cloud-team-up-on-ai-agent-for-ran-operations
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
The case for and against AI-RAN technology using Nvidia or AMD GPUs
AI RAN Alliance selects Alex Choi as Chairman
AI sparks huge increase in U.S. energy consumption and is straining the power grid; transmission/distribution as a major problem
