AI
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Tech firms are spending hundreds of billions of dollars on advanced AI chips and data centers, not just to keep pace with a surge in the use of chatbots such as ChatGPT, Gemini and Claude, but to make sure they’re ready to handle a more fundamental and disruptive shift of economic activity from humans to machines. The final bill may run into the trillions. The financing is coming from venture capital, debt and, lately, some more unconventional arrangements that have raised concerns among top industry executives and financial asset management firms.
At Italian Tech Week in Turin on October 3, 2025, Amazon founder Jeff Bezos said this about artificial intelligence, “This is a kind of industrial bubble, as opposed to financial bubbles.” Bezos differentiated this from “bad” financial or housing bubbles, which cause harm. Bezos’s comparison of the current AI boom to a historical “industrial bubble” highlights that, while speculative, it is rooted in real, transformative technology.
“It can even be good, because when the dust settles and you see who are the winners, societies benefit from those investors,” Bezos said. “That is what is going to happen here too. This is real, the benefits to society from AI are going to be gigantic.”
He noted that during bubbles, everything (both good and bad investments) gets funded. When these periods of excitement come along, investors have a hard time distinguishing the good ideas from the bad, he said, adding this is “probably happening today” with AI investments. “Investors have a hard time in the middle of this excitement, distinguishing between the good ideas and the bad ideas,” Bezos said of the AI industry. “And that’s also probably happening today,” he added.
- A “good” kind of bubble: He explained that during industrial bubbles, excessive funding flows to both good and bad ideas, making it hard for investors to distinguish between them. However, the influx of capital spurs significant innovation and infrastructure development that ultimately benefits society once the bubble bursts and the strongest companies survive.
- Echoes of the dot-com era: Bezos drew a parallel to the dot-com boom of the 1990s, where many internet companies failed, but the underlying infrastructure—like fiber-optic cable—endured and led to the creation of companies like Amazon.
- Gigantic benefits: Despite the market frothiness, Bezos reiterated that AI is “real” and its benefits to society “are going to be gigantic.”
- Sam Altman (OpenAI): The CEO of OpenAI has stated that he believes “investors as a whole are overexcited about AI.” In In August, the OpenAI CEO told reporters the AI market was in a bubble. When bubbles happen, “smart people get overexcited about a kernel of truth,” Altman warned, drawing parallels with the dot-com boom. Still, he said his personal belief is “on the whole, this would be a huge net win for the economy.”
- David Solomon (Goldman Sachs): Also speaking at Italian Tech Week, the Goldman Sachs CEO warned that a lot of capital deployed in AI would not deliver returns and that a market “drawdown” could occur.
- Mark Zuckerberg (Meta): Zuckerberg has also acknowledged that an AI bubble exists. The Meta CEO acknowledged that the rapid development of and surging investments in AI stands to form a bubble, potentially outpacing practical productivity and returns and risking a market crash. However, he would rather “misspend a couple hundred billion dollars” on AI development than be late to the technology.
- Morgan Stanley Wealth Management’s chief investment officer, Lisa Shalett, warned that the AI stock boom was showing “cracks” and was likely closer to its end than its beginning. The firm cited concerns over negative free cash flow growth among major AI players and increasing speculative investment. Shalett highlighted that free cash flow growth for the major cloud providers, or “hyperscalers,” has turned negative. This is viewed as a key signal of the AI capital expenditure cycle’s maturity. Some analysts estimate this growth could shrink by about 16% over the next year.
Bezos’s remarks come as some analysts express growing fears of an impending AI market crash.
- Underlying technology is real: Unlike purely speculative bubbles, the AI boom is driven by a fundamental technology shift with real-world applications that will survive any market correction.
- Historical context: Some analysts believe the current AI bubble is on a much larger scale than the dot-com bubble due to the massive influx of investment.
- Significant spending: The level of business spending on AI is already at historic levels and is fueling economic growth, which could cause a broader economic slowdown if it were to crash.
- Potential for disruption: The AI industry faces risks such as diminishing returns for costly advanced models, increased competition, and infrastructure limitations related to power consumption.
Ian Harnett argues, the current bubble may be approaching its “endgame.” He wrote in the Financial Times:
“The dramatic rise in AI capital expenditure by so-called hyperscalers of the technology and the stock concentration in US equities are classic peak bubble signals. But history shows that a bust triggered by this over-investment may hold the key to the positive long-run potential of AI.
Until recently, the missing ingredient was the rapid build-out of physical capital. This is now firmly in place, echoing the capex boom seen in the late-1990s bubble in telecommunications, media and technology stocks. That scaling of the internet and mobile telephony was central to sustaining ‘blue sky’ earnings expectations and extreme valuations, but it also led to the TMT bust.”
Today’s AI capital expenditure (capex) is increasingly being funded by debt, marking a notable shift from previous reliance on cash reserves. While tech giants initially used their substantial cash flows for AI infrastructure, their massive and escalating spending has led them to increasingly rely on external financing to cover costs.
This is especially true of Oracle, which will have to increase its capex by almost $100 billion over the next two years for their deal to build out AI data centers for OpenAI. That’s an annualized growth rate of some 47%, even though Oracle’s free cash flow has already fallen into negative territory for the first time since 1990. According to a recent note from KeyBanc Capital Markets, Oracle may need to borrow $25 billion annually over the next four years. This comes at a time when Oracle is already carrying substantial debt and is highly leveraged. As of the end of August, the company had around $82 billion in long-term debt, with a debt-to-equity ratio of roughly 450%. By comparison, Alphabet—the parent company of Google—reported a ratio of 11.5%, while Microsoft’s stood at about 33%. In July, Moody’s revised Oracle’s credit outlook to negative from, while affirming its Baa2 senior unsecured rating. This negative outlook reflects the risks associated with Oracle’s significant expansion into AI infrastructure, which is expected to lead to elevated leverage and negative free cash flow due to high capital expenditures. Caveat Emptor!
References:
https://fortune.com/2025/10/04/jeff-bezos-amazon-openai-sam-altman-ai-bubble-tech-stocks-investing/
https://www.ft.com/content/c7b9453e-f528-4fc3-9bbd-3dbd369041be
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI Data Center Boom Carries Huge Default and Demand Risks
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will the wave of AI generated user-to/from-network traffic increase spectacularly as Cisco and Nokia predict?
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
RtBrick survey: Telco leaders warn AI and streaming traffic to “crack networks” by 2030
https://fortune.com/2025/09/19/zuckerberg-ai-bubble-definitely-possibility-sam-altman-collapse/
https://finance.yahoo.com/news/why-fears-trillion-dollar-ai-130008034.html
Huawei to Double Output of Ascend AI chips in 2026; OpenAI orders HBM chips from SK Hynix & Samsung for Stargate UAE project
With sales of Nvidia AI chips restricted in China, Huawei Technologies Inc. plans to make about 600,000 of its 910C Ascend chips next year, roughly double this year’s output, people familiar with the matter told Bloomberg. The China tech behemoth will increase its Ascend product line in 2026 to as many as 1.6 million dies – the basic silicon component that’s packaged as a chip.
Huawei had struggled to get those products to potential customers for much of 2025, because of U.S. sanctions. Yet if Huawei and its partner Semiconductor Manufacturing International Corp. (SMIC) can hit that ambitious AI chip manufacturing target, it suggest self sufficiency which will remove some of the bottlenecks that’ve hindered not just its AI business.
The projections for 2025 and 2026 include dies that Huawei has in inventory, as well as internal estimates of yields or the rate of failure during production, the people said. Shares in SMIC and rival chipmaker Hua Hong Semiconductor Ltd. gained more than 4% in Hong Kong Tuesday, while the broader market stayed largely unchanged.
Huawei Ascend branding at a trade show i China. Photographer: Ying Tang/Getty Images
Chinese AI companies from Alibaba Group Holding Ltd. to DeepSeek need millions of AI chips to develop and operate AI services. Nvidia alone was estimated to have sold a million H20 chips in 2024.
What Bloomberg Economics Says:
Huawei’s reported plan to double AI-chip output over the next year suggests China is making real progress in working around US export controls. Yet the plan also exposes the limitations imposed by US controls: Node development remains stuck at 7 nanometers, and Huawei will continue to rely on stockpiles of foreign high-bandwidth memory amid a lack of domestic production.
From Beijing’s perspective, Huawei’s production expansion represents another move in an ongoing back-and-forth with the West over semiconductor access and self-sufficiency. The priority remains accelerating indigenization of critical technologies while steadily pushing back against Western controls.
– Michael Deng, analyst
While Huawei’s new AI silicon promises massive performance gains it has several shortcomings, especially the lack of a developer community comparable to Nvidia’s CUDA ecosystem. A Chinese tech executive said Nvidia’s biggest advantage wasn’t its advanced chips but the ecosystem built around CUDA, its parallel computing architecture and programming model. The exec called for the creation of a Chinese version of CUDA that can be used worldwide.
Also, Huawei is playing catchup by progressively going open source. It announced last month that its Ascend and AI training toolkit CANN, its Mind development environment and Pangu models would all be open source by year-end.
Huawei chairman Eric Xu said in an interview the company had given the “ecosystem issue” a great deal of thought and regarded the transition to open source as a long-term project. “Why keep it hidden? If it’s widely used, an ecosystem will emerge; if it’s used less, the ecosystem will disappear,” he said.
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At its customer event in Shanghai last month, Huawei revealed that it planned to spend 15 billion Chinese yuan (US$2.1 billion) annually over the next five years on ecosystem development and open source computing.
Xu announced a series of new Ascend chips – the 950, 960 and 970 – to be rolled out over the next three years. He foreshadowed a new series of massive Atlas SuperPoD clusters – each one a single logical machine made up of multiple physical devices that can work together – and also announced Huawei’s unified bus interconnect protocol, which allows customers to stitch together compute power across multiple data centers.
Xu acknowledged that Huawei’s single Ascend chips could not match Nvidia’s, but said the SuperPoDs were currently the world’s most powerful and will remain so “for years to come.” But the scale of its SuperPOD architecture points to its other shortcoming – the power consumption of these giant compute arrays.
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Separately, OpenAI has made huge memory chip agreements with South Korea’s SK Hynix and Samsung, the world’s two biggest semiconductor memory manufacturers. The partnership, aimed at locking up HBM ((High Bandwidth Memory) [1.] chip supply for the $400 billion Stargate AI infrastructure project, is estimated to be worth more than 100 trillion Korean won (US$71.3 billion) for the Korean chipmakers over the next four years. The two companies say they are targeting 900,000 DRAM wafer starts per month – more than double the current global HBM capacity.
Note 1. HBM is a specialized type of DRAM that uses a unique 3D vertical stacking architecture and Through-Silicon Via (TSV) technology to achieve significantly higher bandwidth and performance than traditional, flat DRAM configurations. HBM uses standard DRAM “dies” stacked vertically, connected by TSVs, to create a more densely packed, high-performance memory solution for demanding applications like AI and high-performance computing.
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“These partnerships will focus on increasing the supply of advanced memory chips essential for next-generation AI and expanding data center capacity in Korea, positioning Samsung and SK as key contributors to global AI infrastructure and supporting Korea’s ambition to become a top-three global AI nation.” OpenAI said.
The announcement followed a meeting between President Lee Jae-myung, Samsung Electronics Executive Chairman Jay Y. Lee, SK Chairman Chey Tae-won, and OpenAI CEO Sam Altman at the Presidential Office in Seoul.
Through these partnerships, Samsung Electronics and SK hynix plan to scale up production of advanced memory chips, targeting 900,000 DRAM wafer starts per month at an accelerated capacity rollout, critical for powering OpenAI’s advanced AI models.
OpenAI also signed a series of agreements today to explore developing next-generation AI data centers in Korea. These include a Memorandum of Understanding (MoU) with the Korean Ministry of Science and ICT (MSIT) specifically to evaluate opportunities for building AI data centers outside the Seoul Metropolitan Area, supporting balanced regional economic growth and job creation across the country.
The agreements signed today also include a separate partnership with SK Telecom to explore building an AI data center in Korea, as well as an agreement with Samsung C&T, Samsung Heavy Industries, and Samsung SDS to assess opportunities for additional data center capacity in the country.
References:
https://www.lightreading.com/ai-machine-learning/huawei-sets-itself-as-china-s-go-to-for-ai-tech
OpenAI orders $71B in Korean memory chips
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U.S. export controls on Nvidia H20 AI chips enables Huawei’s 910C GPU to be favored by AI tech giants in China
Huawei launches CloudMatrix 384 AI System to rival Nvidia’s most advanced AI system
China gaining on U.S. in AI technology arms race- silicon, models and research
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Despite U.S. sanctions, Huawei has come “roaring back,” due to massive China government support and policies
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
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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
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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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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
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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
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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
