AI
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Big Tech plans to spend between $364 billion and $400 billion on AI data centers, purchasing specialized AI hardware like GPUs, and supporting cloud computing/storage capacity. The final 2Q 2025 GDP report, released last week, reveals a surge in data center infrastructure spending from $9.5 billion in early 2020 to $40.4 billion in the second quarter of 2025. It’s largely due to an unprecedented investment boom driven by artificial intelligence (AI) and cloud computing. The increase highlights a monumental shift in capital expenditure by major tech companies.
Yet there are huge uncertainties about how far AI will transform scientific discovery and hypercharge technological advance. Tech financial analysts worry that enthusiasm for AI has turned into a bubble that is reminiscent of the mania around the internet’s infrastructure build-out boom from 1998-2000. During that time period, telecom network providers spent over $100 billion blanketing the country with fiber optic cables based on the belief that the internet’s growth would be so explosive that such massive investments were justified. The “talk of the town” during those years was the “All Optical Network,” with ultra-long haul optical transceiver, photonic switches and optical add/drop multiplexers. 27 years later, it still has not been realized anywhere in the world.
The resulting massive optical network overbuilding made telecom the hardest hit sector of the dot-com bust. Industry giants toppled like dominoes, including Global Crossing, WorldCom, Enron, Qwest, PSI Net and 360Networks.
However, a key difference between then and now is that today’s tech giants (e.g. hyperscalers) produce far more cash than the fiber builders in the 1990s. Also, AI is immediately available for use by anyone that has a high speed internet connection (via desktop, laptop, tablet or smartphone) unlike the late 1990s when internet users (consumers and businesses) had to obtain high-speed wireline access via cable modems, DSL or (in very few areas) fiber to the premises.
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Today, the once boring world of chips and data centers has become a raging multi-hundred billion dollar battleground where Silicon Valley giants attempt to one up each other with spending commitments—and sci-fi names. Meta CEO Mark Zuckerberg teased his planned “Hyperion” mega-data center with a social-media post showing it would be the size of a large chunk of Manhattan.
OpenAI’s Sam Altman calls his data-center effort “Stargate,” a reference to the 1994 movie about an interstellar time-travel portal. Company executives this week laid out plans that would require at least $1 trillion in data-center investments, and Altman recently committed the company to pay Oracle an average of approximately $60 billion a year for AI compute servers in data centers in coming years. That’s despite Oracle is not a major cloud service provider and OpenAI will not have the cash on hand to pay Oracle.
In fact, OpenAI is on track to realize just $13 billion in revenue from all its paying customers this year and won’t be profitable till at least 2029 or 2030. The company projects its total cash burn will reach $115 billion by 2029. The majority of its revenue comes from subscriptions to premium versions of ChatGPT, with the remainder from selling access to its models via its API. Although ~ 700 million people—9% of the world’s population—are weekly users of ChatGPT (as of August, up from 500 million in March), its estimated that over 90% use the free version. Also this past week:
- Nvidia plans to invest up to $100 billion to help OpenAI build data center capacity with millions GPUs.
- OpenAI revealed an expanded deal with Oracle and SoftBank , scaling its “Stargate” project to a $400 billion commitment across multiple phases and sites.
- OpenAI deepened its enterprise reach with a formal integration into Databricks — signaling a new phase in its push for commercial adoption.
Nvidia is supplying capital and chips. Oracle is building the sites. OpenAI is anchoring the demand. It’s a circular economy that could come under pressure if any one player falters. And while the headlines came fast this week, the physical buildout will take years to deliver — with much of it dependent on energy and grid upgrades that remain uncertain.
Another AI darling is CoreWeave, a company that provides GPU-accelerated cloud computing platforms and infrastructure. From its founding in 2017 until its pivot to cloud computing in 2019, Corweave was an obscure cryptocurrency miner with fewer than two dozen employees. Flooded with money from Wall Street and private-equity investors, it has morphed into a computing goliath with a market value bigger than General Motors or Target.
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Massive AI infrastructure spending will require tremendous AI revenue for pay-back:
David Cahn, a partner at venture-capital firm Sequoia, estimates that the money invested in AI infrastructure in 2023 and 2024 alone requires consumers and companies to buy roughly $800 billion in AI products over the life of these chips and data centers to produce a good investment return. Analysts believe most AI processors have a useful life of between three and five years.
This week, consultants at Bain & Co. estimated the wave of AI infrastructure spending will require $2 trillion in annual AI revenue by 2030. By comparison, that is more than the combined 2024 revenue of Amazon, Apple, Alphabet, Microsoft, Meta and Nvidia, and more than five times the size of the entire global subscription software market.
Morgan Stanley estimates that last year there was around $45 billion of revenue for AI products. The sector makes money from a combination of subscription fees for chatbots such as ChatGPT and money paid to use these companies’ data centers. How the tech sector will cover the gap is “the trillion dollar question,” said Mark Moerdler, an analyst at Bernstein. Consumers have been quick to use AI, but most are using free versions, Moerdler said. Businesses have been slow to spend much on AI services, except for the roughly $30 a month per user for Microsoft’s Copilot or similar products. “Someone’s got to make money off this,” he said.
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Why this time is different (?):
AI cheerleaders insist that this boom is different from the dot-com era. If AI continues to advance to the point where it can replace a large swath of white collar jobs, the savings will be more than enough to pay back the investment, backers argue. AI executives predict the technology could add 10% to global GDP in coming years.
“Training AI models is a gigantic multitrillion dollar market,” Oracle chairman Larry Ellison told investors this month. The market for companies and consumers using AI daily “will be much, much larger.”
The financing behind the AI build-out is complex. Debt is layered on at nearly every level. his “debt-fueled arms race” involves large technology companies, startups, and private credit firms seeking innovative ways to fund the development of data centers and acquire powerful hardware, such as Nvidia GPUs. Debt is layered across different levels of the AI ecosystem, from the large tech giants to smaller cloud providers and specialized hardware firms.
Alphabet, Microsoft, Amazon, Meta and others create their own AI products, and sometimes sell access to cloud-computing services to companies such as OpenAI that design AI models. The four “hyperscalers” alone are expected to spend nearly $400 billion on capital investments next year, more than the cost of the Apollo space program in today’s dollars. Some build their own data centers, and some rely on third parties to erect the mega-size warehouses tricked out with cooling equipment and power.
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Echoes of bubbles past:
History is replete with technology bubbles that pop. Optimism over an invention—canals, electricity, railroads—prompts an investor stampede premised on explosive growth. Overbuilding follows, and investors eat giant losses, even when a new technology permeates the economy. Predicting when a boom turns into a bubble is notoriously hard. Many inflate for years. Some never pop, and simply stagnate.
The U.K.’s 19th-century railway mania was so large that over 7% of the country’s GDP went toward blanketing the country with rail. Between 1840 and 1852, the railway system nearly quintupled to 7,300 miles of track, but it only produced one-fourth of the revenue builders expected, according to Andrew Odlyzko,PhD, an emeritus University of Minnesota mathematics professor who studies bubbles. He calls the unbridled optimism in manias “collective hallucinations,” where investors, society and the press follow herd mentality and stop seeing risks.
He knows from firsthand experience as a researcher at Bell Labs in the 1990s. Then, telecom giants and upstarts raced to speculatively plunge tens of millions of miles of fiber cables into the ground, spending the equivalent of around 1% of U.S. GDP over half a decade.
Backers compared the effort to the highway system, to the advent of electricity and to discovering oil. The prevailing belief at the time, he said, was that internet use was doubling every 100 days. But in reality, for most of the 1990s boom, traffic doubled every year, Odlyzko found.
The force of the mania led executives across the industry to focus on hype more than unfavorable news and statistics, pouring money into fiber until the bubble burst.
“There was a strong element of self interest,” as companies and executives all stood to benefit financially as long as the boom continued, Odlyzko said. “Cautionary signs are disregarded.”
Kevin O’Hara, a co-founder of upstart fiber builder Level 3, said banks and stock investors were throwing money at the company, and executives believed demand would rocket upward for years. Despite worrying signs, executives focused on the promise of more traffic from uses like video streaming and games.
“It was an absolute gold rush,” he said. “We were spending about $110 million a week” building out the network.
When reality caught up, Level 3’s stock dropped 95%, while giants of the sector went bust. Much of the fiber sat unused for over a decade. Ultimately, the growth of video streaming and other uses in the early 2010s helped soak up much of the oversupply.
Worrying signs:
There are growing, worrying signs that the optimism about AI won’t pan out.
- MIT Media Lab (2025): The “State of AI in Business 2025” report found that 95% of custom enterprise AI tools and pilots fail to produce a measurable financial impact or reach full-scale production. The primary issue identified was a “learning gap” among leaders and organizations, who struggle to properly integrate AI tools and redesign workflows to capture value.
- A University of Chicago economics paper found AI chatbots had “no significant impact on workers’ earnings, recorded hours, or wages” at 7,000 Danish workplaces.
- Gartner (2024–2025): The research and consulting firm has reported that 85% of AI initiatives fail to deliver on their promised value. Gartner also predicts that by the end of 2025, 30% of generative AI projects will be abandoned after the proof-of-concept phase due to issues like poor data quality, lack of clear business value, and escalating costs.
- RAND Corporation (2024): In its analysis, RAND confirmed that the failure rate for AI projects is over 80%, which is double the failure rate of non-AI technology projects. Cited obstacles include cost overruns, data privacy concerns, and security risks.
OpenAI’s release of ChatGPT-5 in August was widely viewed as an incremental improvement, not the game-changing thinking machine many expected. Given the high cost of developing it, the release fanned concerns that generative AI models are improving at a slower pace than expected. Each new AI model—ChatGPT-4, ChatGPT-5—costs significantly more than the last to train and release to the world, often three to five times the cost of the previous, say AI executives. That means the payback has to be even higher to justify the spending.
Another hurdle: The chips in the data centers won’t be useful forever. Unlike the dot-com boom’s fiber cables, the latest AI chips rapidly depreciate in value as technology improves, much like an older model car. And they are extremely expensive.
“This is bigger than all the other tech bubbles put together,” said Roger McNamee, co-founder of tech investor Silver Lake Partners, who has been critical of some tech giants. “This industry can be as successful as the most successful tech products ever introduced and still not justify the current levels of investment.”
Other challenges include the growing strain on global supply chains, especially for chips, power and infrastructure. As for economy-wide gains in productivity, few of the biggest listed U.S. companies are able to describe how AI was changing their businesses for the better. Equally striking is the minimal euphoria some Big Tech companies display in their regulatory filings. Meta’s 10k form last year reads: “[T]here can be no assurance that the usage of AI will enhance our products or services or be beneficial to our business, including our efficiency or profitability.” That is very shaky basis on which to conduct a $300bn capex splurge.
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Conclusions:
Big tech spending on AI infrastructure has been propping up the U.S. economy, with some projections indicating it could fuel nearly half of the 2025 GDP growth. However, this contribution primarily stems from capital expenditures, and the long-term economic impact is still being debated. George Saravelos of Deutsche Bank notes that economic growth is not coming from AI itself but from building the data centers to generate AI capacity.
Once those AI factories have been built, with needed power supplies and cooling, will the productivity gains from AI finally be realized? How globally disseminated will those benefits be? Finally, what will be the return on investment (ROI) for the big spending AI companies like the hyperscalers, OpenAI and other AI players?
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References:
https://www.wsj.com/tech/ai/ai-bubble-building-spree-55ee6128
https://www.ft.com/content/6c181cb1-0cbb-4668-9854-5a29debb05b1
https://www.cnbc.com/2025/09/26/openai-big-week-ai-arms-race.html
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
AI Data Center Boom Carries Huge Default and Demand Risks
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SK Telecom forms AI CIC in-house company to pursue internal AI innovation
SK Telecom (SKT) is establishing an in-house independent company (CIC) that consolidates its artificial intelligence (AI) capabilities. Through AI CIC, SK Telecom plans to invest approximately 5 trillion won (US$3.5 billion) in AI over the next five years and achieve annual sales of over 5 trillion won ($3.5 billion) by 2030.
On September 25th, SK Telecom CEO Ryu Young-sang held a town hall meeting for all employees at the SKT Tower Supex Hall in Jung-gu, Seoul, announcing the launch of AI CIC to pursue rapid AI innovation. Ryu will concurrently serve as the CEO of SK CIC. SK Telecom plans to unveil detailed organizational restructuring plans for AI CIC at the end of October this year.
“We are launching AI CIC, a streamlined organizational structure, and will simultaneously pursue internal AI innovation, including internal systems, organizational culture, and enhancing employees’ AI capabilities. We will grow AI CIC to be the main driver of SK’s AI business and, furthermore, the core that leads the AI business for the entire SK Group. The AI CIC will establish itself as South Korea’s leading AI business operator in all fields of AI, including services, platforms, AI data centers and proprietary foundation models,” Ryu said.
The newly established AI CIC will be responsible for all the company’s AI-related functions and businesses. It is expected that SK Telecom’s business will be divided into mobile network operations (MNO) and AI, with AI CIC consolidating related businesses to enhance operational efficiency. Furthermore, AI CIC will actively participate in government-led AI projects, contributing to the establishment of a government-driven AI ecosystem. SKT said that reorganizing its services under one umbrella will “drive AI innovation that enhance business productivity and efficiency.”
“Through this (AI CIC), we will play a central role in building a domestic AI-related ecosystem and become a company that contributes to the success of the national AI strategy,” Ryu said.
By integrating and consolidating dispersed AI technology assets, SKT plans to strengthen the role of the “AI platform” that supports AI technology/operations across the entire SK Group, including SKT, and also pursue a strategy to secure a flexible “AI model” to respond to the diverse AI needs of the government, industry, and private sectors.
In addition, SKT will accelerate the development of future growth areas (R&D) such as digital twins and robots, and the expansion of domestic and international partnerships based on AI full-stack capabilities.
Ryu Young-sang, CEO of SK Telecom, unveils the plans for the AI CIC
CEO Ryu said, “SK Telecom has secured various achievements such as securing 10 million Adot (AI enabled) subscribers, selecting an independent AI foundation model, launching the Ulsan AI DC, and establishing global partnerships through its transformation into an AI company over the past three years, and has laid the foundation for future leaps forward. We will achieve another AI innovation centered around the AI CIC to restore the trust of customers and the market and advance into a global AI company.”
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References:
https://www.businesskorea.co.kr/news/articleView.html?idxno=253124
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AI Data Center Boom Carries Huge Default and Demand Risks
“How does the digital economy exist?” asked John Medina, a senior vice president at Moody’s, who specializes in assessing infrastructure investments. “It exists on data centers.”
New investments in data centers to power Artificial Intelligence (AI) are projected to reach $3 trillion to $4 trillion by 2030, according to Nvidia. Other estimates suggest the investment needed to keep pace with AI demand could be as high as $7 trillion by 2030, according to McKinsey. This massive spending is already having a significant economic impact, with some analysis indicating that AI data center expenditure has surpassed the total impact from US consumer spending on GDP growth in 2025.
U.S. data center demand, driven largely by A.I., could triple by 2030, according to McKinsey. That would require data centers to make nearly $7 trillion in investment to keep up. OpenAI, SoftBank and Oracle recently announced a pact to invest $500 billion in A.I. infrastructure through 2029. Meta and Alphabet are also investing billions. Merely saying “please” and “thank you” to a chatbot eats up tens of millions of dollars in processing power, according to OpenAI’s chief executive, Sam Altman.
- OpenAI, SoftBank, and Oracle pledging to invest $500 billion in AI infrastructure through 2029.
- Nvidia and Intel collaborating to develop AI infrastructure, with Nvidia investing $5 billion in Intel stock.
- Microsoft spending $4 billion on a second data center in Wisconsin.
- Amazon planning to invest $20 billion in Pennsylvania for AI infrastructure.
Compute and Storage Servers within an AI Data Center. Photo credit: iStock quantic69
The spending frenzy comes with a big default risk. According to Moody’s, structured finance has become a popular way to pay for new data center projects, with more than $9 billion of issuance in the commercial mortgage-backed security and asset-backed security markets during the first four months of 2025. Meta, for example, tapped the bond manager Pimco to issue $26 billion in bonds to finance its data center expansion plans.
As more debt enters these data center build-out transactions, analysts and lenders are putting more emphasis on lease terms for third-party developers. “Does the debt get paid off in that lease term, or does the tenant’s lease need to be renewed?” Medina of Moody’s said. “What we’re seeing often is there is lease renewal risk, because who knows what the markets or what the world will even be like from a technology perspective at that time.”
Even if A.I. proliferates, demand for processing power may not. Chinese technology company DeepSeek has demonstrated that A.I. models can produce reliable outputs with less computing power. As A.I. companies make their models more efficient, data center demand could drop, making it much harder to turn investments in A.I. infrastructure into profit. After Microsoft backed out of a $1 billion data center investment in March, UBS wrote that the company, which has lease obligations of roughly $175 billion, most likely overcommitted.
Some worry costs will always be too high for profits. In a blog post on his company’s website, Harris Kupperman, a self-described boomer investor and the founder of the hedge fund Praetorian Capital, laid out his bearish case on A.I. infrastructure. Because the building needs upkeep and the chips and other technology will continually evolve, he argued that data centers will depreciate faster than they can generate revenue.
“Even worse, since losing the A.I. race is potentially existential, all future cash flow, for years into the future, may also have to be funneled into data centers with fabulously negative returns on capital,” he added. “However, lighting hundreds of billions on fire may seem preferable than losing out to a competitor, despite not even knowing what the prize ultimately is.”
It’s not just Silicon Valley with skin in the game. State budgets are being upended by tax incentives given to developers of A.I. data centers. According to Good Jobs First, a nonprofit that promotes corporate and government accountability in economic development, at least 10 states so far have lost more than $100 million per year in tax revenue to data centers. But the true monetary impact may never be truly known: Over one-third of states that offer tax incentives for data centers do not disclose aggregate revenue loss.
Local governments are also heralding the expansion of energy infrastructure to support the surge of data centers. Phoenix, for example, is expected to grow its data center power capacity by over 500 percent in the coming years — enough power to support over 4.3 million households. Virginia, which has more than 50 new data centers in the works, has contracted the state’s largest utility company, Dominion, to build 40 gigawatts of additional capacity to meet demand — triple the size of the current grid.
The stakes extend beyond finance. The big bump in data center activity has been linked to distorted residential power readings across the country. And according to the International Energy Agency, a 100-megawatt data center, which uses water to cool servers, consumes roughly two million liters of water per day, equivalent to 6,500 households. This puts strain on water supply for nearby residential communities, a majority of which, according to Bloomberg News, are already facing high levels of water stress.
“I think we’re in that era right now with A.I. models where it’s just who can make the bigger and better one,” said Vijay Gadepally, a senior scientist at the Lincoln Laboratory Supercomputing Center at the Massachusetts Institute of Technology. “But we haven’t actually stopped to think about, Well, OK, is this actually worth it?”
References:
What Wall Street Sees in the Data Center Boom – The New York Times
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Cisco CEO sees great potential in AI data center connectivity, silicon, optics, and optical systems
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
Lumen Technologies to connect Prometheus Hyperscale’s energy efficient AI data centers
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
According to Gartner, global AI spending will reach close to US$1.5 trillion this year and will top $2 trillion in 2026 as ongoing demand fuel IT infrastructure investment. This significant growth is driven by hyperscalers’ ongoing investments in AI-optimized data centers and hardware, such as GPUs, along with increased enterprise adoption and the integration of AI into consumer devices like smartphones and PCs.
| Market | 2024 | 2025 | 2026 |
| AI Services | 259,477 | 282,556 | 324,669 |
| AI Application Software | 83,679 | 172,029 | 269,703 |
| AI Infrastructure Software | 56,904 | 126,177 | 229,825 |
| GenAI Models | 5,719 | 14,200 | 25,766 |
| AI-optimized Servers (GPU and Non-GPU AI Accelerators) | 140,107 | 267,534 | 329,528 |
| AI-optimized IaaS | 7,447 | 18,325 | 37,507 |
| AI Processing Semiconductors | 138,813 | 209,192 | 267,934 |
| AI PCs by ARM and x86 | 51,023 | 90,432 | 144,413 |
| GenAI Smartphones | 244,735 | 298,189 | 393,297 |
| Total AI Spending | 987,904 | 1,478,634 | 2,022,642 |
Source: Gartner (September 2025)
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Hyperscaler Investments:
Cloud service providers are heavily investing in data centers and AI-optimized hardware to expand their services at scale. Amazon, Google and Microsoft are all ploughing massive sums into their cloud infrastructure, while reaping the benefits of AI-driven market growth, as Canalys’s latest data showed last week.
Businesses are increasingly investing in AI infrastructure and services, though there’s a shift towards using commercial off-the-shelf solutions with embedded GenAI features rather than solely developing custom solutions.
A growing number of consumer products, including smartphones and PCs, are incorporating AI capabilities by default, contributing to the overall spending growth. IDC forecasts GenAI smartphones* to reach 54% of the market by 2028, while Gartner projects nearly 100% of premium models to feature GenAI by 2029, driving significant increases in both shipments and end-user spending.
* A GenAI smartphone is a a mobile device featuring a system-on-a-chip (SoC) with a powerful Neural Processing Unit (NPU) capable of running advanced Generative Artificial Intelligence (GenAI) models directly on the device. It enables features like content creation, personalized assistants, and real-time task processing without needing constant cloud connectivity. These phones are designed to execute complex AI tasks faster, more efficiently, and with enhanced privacy compared to standard smartphones that rely heavily on the internet for such functions.
AI hardware, particularly GPUs and other AI accelerators, accounts for a substantial portion of the growth, with hyperscaler spending on these components nearly doubling, according to a story at CIO Drive.
About Gartner AI Use Case Insights:
Gartner AI Use Case Insights is an interactive tool that helps technology and business leaders efficiently discover, evaluate, and prioritize AI use cases to potentially pursue. Clients can search over 500 use cases (applications of AI in specific industries) and over 380 case studies (real world examples) based on industry, business function, and Gartner’s assessment of potential business value.
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AI spending is surging; companies accelerate AI adoption, but job cuts loom large
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Canalys & Gartner: AI investments drive growth in cloud infrastructure spending
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Ericsson integrates agentic AI into its NetCloud platform for self healing and autonomous 5G private networks
Ericsson is integrating agentic AI into its NetCloud platform to create self-healing and autonomous 5G private (enterprise) networks. This initiative upgrades the existing NetCloud Assistant (ANA), a generative AI tool, into a strategic partner capable of managing complex workflows and orchestrating multiple AI agents. The agentic AI agent aims to simplify private 5G adoption by reducing deployment complexity and the need for specialized administration. This new agentic architecture allows the new Ericsson system to interpret high-level instructions and autonomously assign tasks to a team of specialized AI agents.
Key AI features include:
- Agentic organizational hierarchy: ANA will be supported by multiple orchestrator and functional AI agents capable of planning and executing (with administrator direction). Orchestrator agents will be deployed in phases, starting with a troubleshooting agent planned in Q4 2025, followed by configuration, deployment, and policy agents planned in 2026. These orchestrators will connect with task, process, knowledge, and decision agents within an integrated agentic framework.
- Automated troubleshooting: ANA’s troubleshooting orchestrator will include automated workflows that address the top issues identified by Ericsson support teams, partners, and customers, such as offline devices and poor signal quality. Planned to launch in Q4 2025, this feature is expected to reduce downtime and customer support cases by over 20 percent.
- Multi-modal content generation: ANA can now generate dynamic graphs to visually represent trends and complex query results involving multiple data points.
- Explainable AI: ANA displays real-time process feedback, revealing steps taken by AI agents in order to enhance transparency and trust.
- Expanded AIOps insights: NetCloud AIOps will be expanded to provide isolation and correlation of fault, performance, configuration, and accounting anomalies for Wireless WAN and NetCloud SASE. For Ericsson Private 5G, NetCloud is expected to provide service health analytics including KPI monitoring and user equipment connectivity diagnostics. Planned availability Q4 2025.
Manish Tiwari, Head of Enterprise 5G, Ericsson Enterprise Wireless Solutions, adds: “With the integration of Ericsson Private 5G into the NetCloud platform, we’re taking a major step forward in making enterprise connectivity smarter, simpler, and adaptive. By building on powerful AI foundations, seamless lifecycle management, and the ability to scale securely across sites, we are providing flexibility to further accelerate digital transformation across industries. This is about more than connectivity: it is about giving enterprises the business-critical foundation they need to run IT and OT systems with confidence and unlock the next wave of innovation for their businesses.”
Pankaj Malhotra, Head of WWAN & Security, Ericsson Enterprise Wireless Solutions, says: “By introducing agentic AI into NetCloud, we’re enabling enterprises to simplify deployment and operations while also improving reliability, performance, and user experience. More importantly, it lays the foundation for our vision of fully autonomous, self-optimizing 5G enterprise networks, that can power the next generation of enterprise innovation.”
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OpenAI and Broadcom in $10B deal to make custom AI chips
Overview:
Late last October, IEEE Techblog reported that “OpenAI the maker of ChatGPT, was working with Broadcom to develop a new artificial intelligence (AI) chip focused on running AI models after they’ve been trained.” On Friday, the WSJ and FT (on-line subscriptions required) separately confirmed that OpenAI is working with Broadcom to develop custom AI chips, a move that could help alleviate the shortage of powerful processors needed to quickly train and release new versions of ChatGPT. OpenAI plans to use the new AI chip internally, according to one person close to the project, rather than make them available to external customers.
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Broadcom:
During its earnings call on Thursday, Broadcom’s CEO Hock Tan said that it had signed up an undisclosed fourth major AI developer as a custom AI chip customer, and that this new customer had committed to $10bn in orders. While Broadcom did not disclose the names of the new customer, people familiar with the matter confirmed OpenAI was the new client. Broadcom and OpenAI declined to comment, according to the FT. Tan said the deal had lifted the company’s growth prospects by bringing “immediate and fairly substantial demand,” shipping chips for that customer “pretty strongly” starting next year. “The addition of a fourth customer with immediate and fairly substantial demand really changes our thinking of what 2026 would be starting to look like,” Tan added.
Image credit: © Dado Ruvic/Reuters
HSBC analysts have recently noted that they expect to see a much higher growth rate from Broadcom’s custom chip business compared with Nvidia’s chip business in 2026. Nvidia continues to dominate the AI silicon market, with “hyperscalers” still representing the largest share of its customer base. While Nvidia doesn’t disclose specific customer names, recent filings show that a significant portion of their revenue comes from a small number of unidentified direct customers, which likely are large cloud providers like Microsoft, Amazon, Alphabet (Google), and Meta Platforms.
In August, Broadcom launched its Jericho networking chip, which is designed to help speed up AI computing by connecting data centers as far as 60 miles apart. By August, Broadcom’s market value had surpassed that of oil giant Saudi Aramco, making the chip firm the world’s seventh-largest publicly listed company.
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Open AI:
OpenAI CEO Sam Altman has been saying for months that a shortage of graphics processing units, or GPUs, has been slowing his company’s progress in releasing new versions of its flagship chatbot. In February, Altman wrote on X that ChatGPT-4.5, its then-newest large language model, was the closest the company had come to designing an AI model that behaved like a “thoughtful person,” but there were very high costs that came with developing it. “We will add tens of thousands of GPUs next week and roll it out to the plus tier then. (hundreds of thousands coming soon, and i’m pretty sure y’all will use every one we can rack up.)”
In recent years, OpenAI has relied heavily on so-called “off the shelf” GPUs produced by Nvidia, the biggest player in the chip-design space. But as demand from large AI firms looking to train increasingly sophisticated models has surged, chip makers and data-center operators have struggled to keep up. The company was one of the earliest customers for Nvidia’s AI chips and has since proven to be a voracious consumer of its AI silicon.
“If we’re talking about hyperscalers and gigantic AI factories, it’s very hard to get access to a high number of GPUs,” said Nikolay Filichkin, co-founder of Compute Labs, a startup that buys GPUs and offers investors a share in the rental income they produce. “It requires months of lead time and planning with the manufacturers.”
To solve this problem, OpenAI has been working with Broadcom for over a year to develop a custom chip for use in model training. Broadcom specializes in what it calls XPUs, a type of semiconductor that is designed with a particular application—such as training ChatGPT—in mind.
Last month, Altman said the company was prioritizing compute “in light of the increased demand from [OpenAI’s latest model] GPT-5” and planned to double its compute fleet “over the next 5 months.” OpenAI also recently struck a data-center deal with Oracle that calls for OpenAI to pay more than $30 billion a year to the cloud giant, and signed a smaller contract with Google earlier this year to alleviate computing shortages. It is also embarking on its own data-center construction project, Stargate, though that has gotten off to a slow start.
OpenAI’s move follows the strategy of tech giants such as Google, Amazon and Meta, which have designed their own specialized custom chips to run AI workloads. The industry has seen huge demand for the computing power to train and run AI models.
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References:
https://www.ft.com/content/e8cc6d99-d06e-4e9b-a54f-29317fa68d6f
https://www.wsj.com/tech/ai/openai-broadcom-deal-ai-chips-5c7201d2
Reuters & Bloomberg: OpenAI to design “inference AI” chip with Broadcom and TSMC
Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble
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
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SoftBank’s Transformer AI model boosts 5G AI-RAN uplink throughput by 30%, compared to a baseline model without AI
Softbank has developed its own Transformer-based AI model that can be used for wireless signal processing. SoftBank used its Transformer model to improve uplink channel interpolation which is a signal processing technique where the network essentially makes an educated guess as to the characteristics and current state of a signal’s channel. Enabling this type of intelligence in a network contributes to faster, more stable communication, according to SoftBank. The Japanese wireless network operator successfully increased uplink throughput by approximately 20% compared to a conventional signal processing method (the baseline method). In the latest demonstration, the new Transformer-based architecture was run on GPUs and tested in a live Over-the-Air (OTA) wireless environment. In addition to confirming real-time operation, the results showed further throughput gains and achieved ultra-low latency.
Editor’s note: A Transformer model is a type of neural network architecture that emerged in 2017. It excels at interpreting streams of sequential data associated with large language models (LLMs). Transformer models have also achieved elite performance in other fields of artificial intelligence (AI), including computer vision, speech recognition and time series forecasting. Transformer models are lightweight, efficient, and versatile – capable of natural language processing (NLP), image recognition and wireless signal processing as per this Softbank demo.
Significant throughput improvement:
- Uplink channel interpolation using the new architecture improved uplink throughput by approximately 8% compared to the conventional CNN model. Compared to the baseline method without AI, this represents an approximately 30% increase in throughput, proving that the continuous evolution of AI models leads to enhanced communication quality in real-world environments.
Higher AI performance with ultra-low latency:
- While real-time 5G communication requires processing in under 1 millisecond, this demonstration with the Transformer achieved an average processing time of approximately 338 microseconds, an ultra-low latency that is about 26% faster than the convolution neural network (CNN) [1.] based approach. Generally, AI model processing speeds decrease as performance increases. This achievement overcomes the technically difficult challenge of simultaneously achieving higher AI performance and lower latency. Editor’s note: Perhaps this can overcome the performance limitations in ITU-R M.2150 for URRLC in the RAN, which is based on an uncompleted 3GPP Release 16 specification.
Note 1. CNN-based approaches to achieving low latency focus on optimizing model architecture, computation, and hardware to accelerate inference, especially in real-time applications. Rather than relying on a single technique, the best results are often achieved through a combination of methods.
Using the new architecture, SoftBank conducted a simulation of “Sounding Reference Signal (SRS) prediction,” a process required for base stations to assign optimal radio waves (beams) to terminals. Previous research using a simpler Multilayer Perceptron (MLP) AI model for SRS prediction confirmed a maximum downlink throughput improvement of about 13% for a terminal moving at 80 km/h.*2
In the new simulation with the Transformer-based architecture, the downlink throughput for a terminal moving at 80 km/h improved by up to approximately 29%, and by up to approximately 31% for a terminal moving at 40 km/h. This confirms that enhancing the AI model more than doubled the throughput improvement rate (see Figure 1). This is a crucial achievement that will lead to a dramatic improvement in communication speeds, directly impacting the user experience.
The most significant technical challenge for the practical application of “AI for RAN” is to further improve communication quality using high-performance AI models while operating under the real-time processing constraint of less than one millisecond. SoftBank addressed this by developing a lightweight and highly efficient Transformer-based architecture that focuses only on essential processes, achieving both low latency and maximum AI performance. The important features are:
(1) Grasps overall wireless signal correlations
By leveraging the “Self-Attention” mechanism, a key feature of Transformers, the architecture can grasp wide-ranging correlations in wireless signals across frequency and time (e.g., complex signal patterns caused by radio wave reflection and interference). This allows it to maintain high AI performance while remaining lightweight. Convolution focuses on a part of the input, while Self-Attention captures the relationships of the entire input (see Figure 2).
(2) Preserves physical information of wireless signals
While it is common to normalize input data to stabilize learning in AI models, the architecture features a proprietary design that uses the raw amplitude of wireless signals without normalization. This ensures that crucial physical information indicating communication quality is not lost, significantly improving the performance of tasks like channel estimation.
(3) Versatility for various tasks
The architecture has a versatile, unified design. By making only minor changes to its output layer, it can be adapted to handle a variety of different tasks, including channel interpolation/estimation, SRS prediction, and signal demodulation. This reduces the time and cost associated with developing separate AI models for each task.
The demonstration results show that high-performance AI models like Transformer and the GPUs that run them are indispensable for achieving the high communication performance required in the 5G-Advanced and 6G eras. Furthermore, an AI-RAN that controls the RAN on GPUs allows for continuous performance upgrades through software updates as more advanced AI models emerge, even after the hardware has been deployed. This will enable telecommunication carriers to improve the efficiency of their capital expenditures and maximize value.
Moving forward, SoftBank will accelerate the commercialization of the technologies validated in this demonstration. By further improving communication quality and advancing networks with AI-RAN, SoftBank will contribute to innovation in future communication infrastructure. The Japan based conglomerate strongly endorsed AI RAN at MWC 2025.
References:
https://www.softbank.jp/en/corp/news/press/sbkk/2025/20250821_02/
https://www.telecoms.com/5g-6g/softbank-claims-its-ai-ran-tech-boosts-throughput-by-30-
https://www.telecoms.com/ai/softbank-makes-mwc-25-all-about-ai-ran
https://www.ibm.com/think/topics/transformer-model
https://www.itu.int/rec/R-REC-M.2150/en
Softbank developing autonomous AI agents; an AI model that can predict and capture human cognition
Dell’Oro Group: RAN Market Grows Outside of China in 2Q 2025
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
Nvidia AI-RAN survey results; AI inferencing as a reinvention of edge computing?
OpenAI announces new open weight, open source GPT models which Orange will deploy
Deutsche Telekom and Google Cloud partner on “RAN Guardian” AI agent
RtBrick survey: Telco leaders warn AI and streaming traffic to “crack networks” by 2030
Respondents to a RtBrick survey of 200 senior telecom decision makers in the U.S., UK, and Australia finds that network operator leaders are failing to make key decisions and lack the motivation to change. The report exposes urgent warnings from telco engineers that their networks are on a five-year collision course with AI and streaming traffic. It finds that 93% of respondents report a lack of support from leadership to deploy disaggregated network equipment. Key findings:
- Risk-averse leadership and a lack of skills are the top factors that are choking progress.
- Majority are stuck in early planning, while AT&T, Deutsche Telekom, and Comcast lead large-scale disaggregation rollouts.
- Operators anticipate higher broadband prices but fear customer backlash if service quality can’t match the price.
- Organizations require more support from leadership to deploy disaggregation (93%).
- Complexity around operational transformation (42%), such as redesigning architectures and workflows.
- Critical shortage of specialist skills/staff (38%) to manage disaggregated systems.
The survey finds that almost nine in ten operators (87%) expect customers to demand higher broadband speeds by 2030, while roughly the same (79%) state their customers expect costs to increase, suggesting they will pay more for it. Yet half of all leaders (49%) admit they lack complete confidence in delivering services at a viable cost. Eighty-four percent say customer expectations for faster, cheaper broadband are already outpacing their networks, while 81% concede their current architectures are not well-suited to handling the future increases in bandwidth demand, suggesting they may struggle with the next wave of AI and streaming traffic.
“Senior leaders, engineers, and support staff inside operators have made their feelings clear: the bottleneck isn’t capacity, it’s decision-making,” said Pravin S Bhandarkar, CEO and Founder of RtBrick. “Disaggregated networks are no longer an experiment. They’re the foundation for the agility, scalability, and transparency operators need to thrive in an AI-driven, streaming-heavy future,” he added noting the intent to deploy disaggregation as per this figure:
However, execution continues to trail ambition. Only one in twenty leaders has confirmed they’re “in deployment” today, while 49% remain stuck in early-stage “exploration”, and 38% are still “in planning”. Meanwhile, big-name operators such as AT&T, Deutsche Telekom, and Comcast are charging ahead and already actively deploying disaggregation at scale, demonstrating faster rollouts, greater operational control, and true vendor flexibility. Here’s a snapshot of those activities:
- AT&T has deployed an open, disaggregated routing network in their core, powered by DriveNets Network Cloud software on white-box bare metal switches and routers from Taiwanese ODMs, according to Israel based DriveNets. DriveNets utilizes a Distributed Disaggregated Chassis (DDC) architecture, where a cluster of bare metal switches act as a single routing entity. That architecture has enabled AT&T to accelerate 5G and fiber rollouts and improve network scalability and performance. It has made 1.6Tb/s transport a reality on AT&T’s live network.
- Deutsche Telekom has deployed a disaggregated broadband network using routing software from RtBrick running on bare-metal switch hardware to provide high-speed internet connectivity. They’re also actively promoting Open BNG solutions as part of this initiative.
- Comcast uses network cloud software from DriveNets and white-box hardware to disaggregate their core network, aiming to increase efficiency and enable new services through a self-healing and consumable network. This also includes the use of disaggregated, pluggable optics from multiple vendors.
Nearly every leader surveyed also claims their organization is “using” or “planning to use” AI in network operations, including for planning, optimization, and fault resolution. However, nine in ten (93%) say they cannot unlock AI’s full value without richer, real-time network data. This requires more open, modular, software-driven architecture, enabled by network disaggregation.
“Telco leaders see AI as a powerful asset that can enhance network performance,” said Zara Squarey, Research Manager at Vanson Bourne. “However, the data shows that without support from leadership, specialized expertise, and modern architectures that open up real-time data, disaggregation deployments may risk further delays.”
When asked what benefits they expect disaggregation to deliver, operators focused on outcomes that could deliver the following benefits:
- 54% increased operational automation
- 54% enhanced supply chain resilience
- 51% improved energy efficiency
- 48% lower purchase and operational costs
- 33% reduced vendor lock-in
Transformation priorities align with those goals, with automation and agility (57%) ranked first, followed by vendor flexibility (55%), supply chain security (51%), cost efficiency (46%) and energy usage and sustainability (47%).
About the research:
The ‘State of Disaggregation’ research was independently conducted by Vanson Bourne in June 2025 and commissioned by RtBrick to identify the primary drivers and barriers to disaggregated network rollouts. The findings are based on responses from 200 telecom decision makers across the U.S., UK, and Australia, representing operations, engineering, and design/Research and Development at organizations with 100 to 5,000 or more employees.
References:
https://www.rtbrick.com/state-of-disaggregation-report-2
https://drivenets.com/blog/disaggregation-is-driving-the-future-of-atts-ip-transport-today/
Disaggregation of network equipment – advantages and issues to consider
NTT Data and Google Cloud partner to offer industry-specific cloud and AI solutions
NTT Data and Google Cloud plan to combine their expertise in AI and the cloud to offer customized solutions to accelerate enterprise transformation across sectors including banking, insurance, manufacturing, retail, healthcare, life sciences and the public sector.. The partnership will include agentic AI solutions, security, sovereign cloud and developer tools. This collaboration combines NTT DATA’s deep industry expertise in AI, cloud-native modernization and data engineering with Google Cloud’s advanced analytics, AI and cloud technologies to deliver tailored, scalable enterprise solutions.
With a focus on co-innovation, the partnership will drive industry-specific cloud and AI solutions, leveraging NTT DATA’s proven frameworks and best practices along with Google Cloud’s capabilities to deliver customized solutions backed by deep implementation expertise. Significant joint go-to-market investments will support seamless adoption across key markets.
According to Gartner®, worldwide end-user spending on public cloud services is forecast to reach $723 billion in 2025, up from $595.7 billion in 2024.1 The use of AI deployments in IT and business operations is accelerating the reliance on modern cloud infrastructure, highlighting the critical importance of this strategic global partnership.
“This collaboration with Google Cloud represents a significant milestone in our mission to drive innovation and digital transformation across industries,” said Marv Mouchawar, Head of Global Innovation, NTT DATA. “By combining NTT DATA’s deep expertise in AI, cloud-native modernization and enterprise solutions with Google Cloud’s advanced technologies, we are helping businesses accelerate their AI-powered cloud adoption globally and unlock new opportunities for growth.”
“Our partnership with NTT DATA will help enterprises use agentic AI to enhance business processes and solve complex industry challenges,” said Kevin Ichhpurani, President, Global Partner Ecosystem at Google Cloud. “By combining Google Cloud’s AI with NTT DATA’s implementation expertise, we will enable customers to deploy intelligent agents that modernize operations and deliver significant value for their organizations.”
Photo Credit: Phil Harvey/Alamy Stock Photo
In financial services, this collaboration will support regulatory compliance and reporting through NTT DATA solutions like Regla, which leverage Google Cloud’s scalable AI infrastructure. In hospitality, NTT DATA’s Virtual Travel Concierge enhances customer experience and drives sales with 24×7 multilingual support, real-time itinerary planning and intelligent travel recommendations. It uses the capabilities of Google’s Gemini models to drive personalization across more than 3 million monthly conversations.
Key focus areas include:
- Industry-specific agentic AI solutions: NTT DATA will build new industry solutions that transform analytics, decision-making and client experiences using Google Agentspace, Google’s Gemini models, secure data clean rooms and modernized data platforms.
- AI-driven cloud modernization: Accelerating enterprise modernization with Google Distributed Cloud for secure, scalable modernization built and managed on NTT DATA’s global infrastructure, from data centers to edge to cloud.
- Next-generation application and security modernization: Strengthening enterprise agility and resilience through mainframe modernization, DevOps, observability, API management, cybersecurity frameworks and SAP on Google Cloud.
- Sovereign cloud innovation: Delivering secure, compliant solutions through Google Distributed Cloud in both air-gapped and connected deployments. Air-gapped environments operate offline for maximum data isolation. Connected deployments enable secure integration with cloud services. These scenarios meet data sovereignty and regulatory demands in sectors such as finance, government and healthcare without compromising innovation.
- Google Distributed Cloud sandbox environment: Google Distributed Cloud sandbox environment is a digital playground where developers can build, test and deploy industry-specific and sovereign cloud deployments. This sandbox will help teams upskill through hands-on training and accelerate time to market with Google Distributed Cloud technologies through preconfigured, ready-to-deploy templates.
NTT DATA will support these innovations through a full-stack suite of services including advisory, building, implementation and ongoing hosting and managed services.
By combining NTT DATA’s proven blueprints and delivery expertise with Google Cloud’s technology, the partnership will accelerate the development of repeatable, scalable solutions for enterprise transformation. At the heart of this innovation strategy is Takumi, NTT DATA’s GenAI framework that guides clients from ideation to enterprise-wide deployment. Takumi integrates seamlessly with Google Cloud’s AI stack, enabling rapid prototyping and operationalization of GenAI use cases.
This initiative expands NTT DATA’s Smart AI Agent Ecosystem, which unites strategic technology partnerships, specialized assets and an AI-ready talent engine to help clients deploy and manage responsible, business-driven AI at scale.
Accelerating global delivery with a dedicated Google Cloud Business Group:
To achieve excellence, NTT DATA has established a dedicated global Google Cloud Business Group comprising thousands of engineers, architects and advisory consultants. This global team at NTT DATA will work in close collaboration with Google Cloud teams to help clients adopt and scale AI-powered cloud technologies.
NTT DATA is also investing in advanced training and certification programs ensuring teams across sales, pre-sales and delivery are equipped to sell, secure, migrate and implement AI-powered cloud solutions. The company aims to certify 5,000 engineers in Google Cloud technology, further reinforcing its role as a leader in cloud transformation on a global scale.
Additionally, both companies are co-investing in global sales and go-to-market campaigns to accelerate client adoption across priority industries. By aligning technical, sales and marketing expertise, the companies aim to scale transformative solutions efficiently across global markets.
This global partnership builds on NTT DATA and Google Cloud’s 2024 co-innovation agreement in APAC. In addition it further strengthens NTT DATA’s acquisition of Niveus Solutions, a leading Google Cloud specialist recognized with three 2025 Google Cloud Awards – “Google Cloud Country Partner of the Year – India”, “Google Cloud Databases Partner of the Year – APAC” and “Google Cloud Country Partner of the Year – Chile,” further validating NTT DATA’s commitment to cloud excellence and innovation.
“We’re excited to see the strengthened partnership between NTT DATA and Google Cloud, which continues to deliver measurable impact. Their combined expertise has been instrumental in migrating more than 380 workloads to Google Cloud to align with our cloud-first strategy,” said José Luis González Santana, Head of IT Infrastructure, Carrefour. “By running SAP HANA on Google Cloud, we have consolidated 100 legacy applications to create a powerful, modernized e-commerce platform across 200 hypermarkets. This transformation has given us the agility we need during peak times like Black Friday and enabled us to launch new services faster than ever. Together, NTT DATA and Google Cloud are helping us deliver more connected, seamless experiences for our customers,”
About NTT DATA:
NTT DATA is a $30+ billion trusted global innovator of business and technology services. We serve 75% of the Fortune Global 100 and are committed to helping clients innovate, optimize and transform for long-term success. As a Global Top Employer, we have experts in more than 50 countries and a robust partner ecosystem of established and start-up companies. Our services include business and technology consulting, data and artificial intelligence, industry solutions, as well as the development, implementation and management of applications, infrastructure and connectivity. We are also one of the leading providers of digital and AI infrastructure in the world. NTT DATA is part of NTT Group, which invests over $3.6 billion each year in R&D to help organizations and society move confidently and sustainably into the digital future.
Resources:
https://www.nttdata.com/global/en/news/press-release/2025/august/081300
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AI Meets Telecom: Automating RF Plumbing Diagrams at Scale
By Chode Balaji with Ajay Lotan Thakur
In radio frequency (RF) circuit design, an RF plumbing diagram is a visual representation of how components such as antennas, amplifiers, filters, and cables are physically interconnected to manage RF signal flow across a network node. Unlike logical or schematic diagrams, these diagrams emphasize signal routing, cable paths, and component connectivity, ensuring spectrum compliance and accurate transmission behavior.
In this article, I introduce an AI-powered automation platform designed to generate RF plumbing diagrams for complex telecom deployments, which dramatically reduces manual effort and engineering errors. The system has been field-tested within a major telecom provider’s RF design workflow, showing measurable reduction in design time and increased compliance, where it has cut design time from hours to minutes while standardizing outputs across markets. We discuss the architecture of the platform, its real-world use cases, and the broader implications for network scalability and compliance in next-generation RF deployments.
Introduction
In RF circuit and system design, an RF plumbing diagram is a critical visual blueprint that shows how physical components—antennas, cables, combiners, duplexers, and power sources—are interconnected to manage signal flow across a network node. Unlike logical network schematics, these diagrams emphasize actual deployment wiring, routing, and interconnection details across multiple frequency bands and sectors.
As networks become increasingly dense and distributed, especially with 5G and Open RAN architectures, RF plumbing diagrams have grown in both complexity and importance. Yet across the industry, they are still predominantly created using manual methods—introducing inconsistency, delay, and high operational cost [1].
Challenges with Manual RF Documentation
Creating RF plumbing diagrams manually demands deep subject matter expertise, detailed knowledge of hardware interconnections, and alignment with region-specific compliance standards. Each diagram can take hours to complete, and even minor errors—such as incorrect port mappings or misaligned frequency bands—can result in service degradation, failed field validations, or regulatory non-compliance. In some cases, incorrect diagrams have delayed spectrum audits or triggered failed E911 checks, which are critical in public safety contexts.
Compliance requirements often vary by country due to differences in spectrum licensing, environmental limits, and emergency services integration. For example, the U.S. mandates specific RF configuration standards for E911 systems [3], while European operators must align with ETSI guidelines [4].
According to industry discussions on automation in telecom operations [1], reducing manual overhead and standardizing documentation workflows is a key goal for next-generation network teams.
System Overview – AI Powered Diagram Generation
To streamline this process, we developed CERTA RFDS—a system that automates RF plumbing diagram generation using input configuration data. CERTA ingests band and sector mappings, node configurations, and passive element definitions, then applies business logic to render a complete, standards-aligned diagram.
The system is built as a cloud-native microservice and can be integrated into OSS workflows or CI/CD pipelines used by RF planning teams. Its modular engine outputs standardized SVG/PDFs and maintains design versioning aligned with audit requirements.
This system aligns with automation trends seen in AI-native telecom operations [1] and can scale to support edge-native deployments as part of broader infrastructure-as-code workflows.
Deployment and Public Availability
The CERTA RFDS system has been internally validated within major telecom design teams and is now available publicly for industry adoption. It has demonstrated consistent savings in engineering time—reducing diagram effort from 2–4 hours to under 5 minutes per node—while improving compliance through template consistency. These results and the underlying platform were presented at the IEEE International Conference on Emerging and Advanced Information Systems (EEAIS) [5]. (Note – Paper is presented at EEAIS 2025; publication pending)
Output Showcase and Engineering Impact
Below is a sample RF plumbing diagram generated by the CERTA platform for a complex LTE and UMTS multi-sector node. The system automatically determines feed paths, port mappings, and labeling conventions based on configuration metadata.
As 5G networks continue to roll out globally, RF plumbing diagrams are becoming even more complex due to increased densification, the use of small cells, and the incorporation of mmWave technologies. The AI-driven automation framework we developed is fully adaptable to 5G architecture. It supports configuration planning for high-frequency spectrum bands, MIMO antenna arrangements, and ensures that E911 and regulatory compliance standards are maintained even in ultra-dense urban deployments. This makes the system a valuable asset in accelerating the design and validation processes for next generation 5G infrastructure.
Figure 1. AI-Generated RF Plumbing Diagram from CERTA RFDS: Illustrating dual-feed, multi-sector layout for LTE and UMTS deployment.
Benefits include:
- 90%+ time savings per node
- Consistency across regions and engineering teams
- Simplified field validation and compliance review
Future Scope
CERTA RFDS is being extended to support:
- GIS visualization of RF components with geo-tagged layouts
- Integration with planning systems for real-time topology generation
- LLM-based auto-summary of node-level changes for audit documentation
Conclusion
RF plumbing diagrams are fundamental to reliable telecom deployment and compliance. By shifting from manual workflows to intelligent automation, systems like CERTA RFDS enable engineers and operators to scale with confidence, consistency, and speed—meeting the challenges of modern wireless networks.
Abbreviation
- CERTA RFDS – Cognitive Engineering for Rapid RFDS Transformation & Automation
- RFDS – Radio Frequency Data Sheet
- GIS – Geographic Information System
- LLM – Large Language Model
- OSS – Operations Support System
- MIMO – Multiple Input Multiple Output
- RF – Radio Frequency
Reference
[1] ZTE’s Vision for AI-Native Infrastructure and AI-Powered Operations
[5] IEEE EEAIS 2025 Conference, “CERTA RFDS: Automating RF Plumbing Diagrams at Scale,”
About Author
Balaji Chode is an AI Solutions Architect at UBTUS, where he leads telecom automation initiatives including the design and deployment of CERTA RFDS. He has contributed to large-scale design and automation platforms across telecom and public safety, authored multiple peer-reviewed articles, and filed several patents.
“The author acknowledges the use of AI-assisted tools for language refinement and formatting”
