Author: Alan Weissberger
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
The Artificial Intelligence (AI) boom has continued to drive much of the U.S. economy, stock market and psychology this year. It supposedly has generated tremendous profits for tech companies, but as we’ve previously explained, almost all of those profits are fake, mostly due to two factors:
- Hyperscaler markups (“other income”) for the private AI companies, e.g. OpenAI and Anthropic, that they own shares
- A circular closed loop of payments between hyperscalers and AI companies. Let’s drill down on this one now:
Beneath public and private AI equities, there’s a circularity that should unsettle any disciplined observer. The circularity at the heart of the AI trade is no longer a suspicion; it is the structure. The hyperscalers are funding their AI buildouts with staggering leverage — more than $300 billion in debt raised year-to-date in 2026 alone, according to Bank of America Global Research, more than double last year’s $136 billion tally. Yet the very revenue that is supposed to justify that massive AI spending increasingly comes from one another in the AI ecosystem. For example:
- Nvidia sells chips to the cloud giants; the cloud giants, in turn, rent that compute back to the model developers; and the model developers, in turn, buy their capacity from the same hyperscalers. It is a closed loop, and a closed loop is not a business model.
- Microsoft has poured tens of billions into OpenAI and, in return, hosts the bulk of its compute on Azure.
- Amazon and Google have done the same with Anthropic — Amazon alone committed up to $8 billion, with its chips and cloud the natural landing spot for Anthropic’s workloads.
- Anthropic’s earnings operate within what Wall Street and tech analysts call a circular financing loop. Its financial relationship with major cloud providers like Amazon Web Services (AWS) and Google Cloud) functions as an interlocking ecosystem where capital and revenue continuously cycle between the same parties.
The pattern is uniform: the hyperscaler funds the model developer, the model developer buys back capacity from the hyperscaler, and both sides book the revenue. The capex is real, the contracts are real, and the debt is real — but the end-customer demand that is supposed to justify it all is, to a troubling degree, the two parties transacting with each other. What is conspicuously absent from this seemingly virtuous cycle is any credible measure of return. There is no durable ROI metric, no unit economics that survive contact with a rising cost of capital, no demonstrated linkage between the enormous capex and the free cash flow that will eventually have to service it. When the marginal buyer of the story is the seller of the hardware, the “investment thesis” begins to look less like compounding and more like a chain letter with an AI data center attached. Rates are already telling us the cost of this experiment. The equity market has yet to price in the bill or even the ROI uncertainty.
The AI circularity trade is not a market; it is a mirror. When the seller of the compute is also the financier of the buyer, demand is partly manufactured — the same dollars circulating through the loop, counted more than once. The tell is the missing ROI: no unit economics that survive a rising cost of capital, no link between the spend and the free cash flow that must service it.
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Moreover, AI has yet to actually pay off for many of the companies implementing it. Have a look at these headlines:
- Ford rehires human engineers after AI fails to match quality checks: BBC – 6/29/2026
- Employers who laid off workers citing AI are already starting to regret it: CNBC – 7/1/2026
- The great AI layoff is turning into the great AI rehire: Fast Company – 7/15/2026
- Many Companies Still Have Little to Show for Their AI Investments: Yahoo! Finance – 8/7/2026
- 90% of executives say AI hasn’t boosted productivity. Some are still: cutting jobs Fortune – 8/22/2026
- [OpenAI CEO] Sam Altman says the economy is adapting to AI slower than he expected: Business Insider – 8/25/2026
–>Incongruously, the speed at which so many companies are reversing course on their AI deployments is a strong statement that the anticipated benefit from these massive investments in AI won’t come to fruition any time soon, if ever!
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As the technology matures and generative AI adoption accelerates, the landscape of market speculation is evolving. Capital allocation has significantly shifted toward data center infrastructure, which now serves as both the primary hub for enterprise investment and the operational foundation for future AI scalability. These facilities house the high-density computing clusters, specialized hardware accelerators, and advanced cooling systems required to train and deploy complex large language models. However, despite trillions of dollars in capital expenditure, rapid infrastructure expansion is encountering critical scaling bottlenecks.
- Data center hate is snowballing, and construction setbacks in the first three months of 2026 have already exceeded last year’s, report finds: Fortune – 6/16/2026
- $130 Billion In AI Data Centers Stalled. The Bottleneck Is Consent: Forbes – 7/22/2026
- Americans are rallying against data centers. Surprisingly few are actually getting built: CNN Business – 8/6/2026
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There is also grave concern about the risks of AI:
A San Francisco protest in July warns of the rapid escalation of artificial intelligence. Elena Kadvany/S.F. Chronicle
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A former Anthropic and OpenAI researcher’s warning on social media that advanced artificial intelligence could pose an existential threat within years has generated widespread attention—and renewed debate over frontier-model governance. Related: Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
“These will soon be superhuman systems that can hack anything, revolutionize any field overnight, and acquire real power and resources,” Jacob Coxon wrote Monday in an X post that has received more than 110 million views. “The people building AI earnestly believe that it could kill us all by the end of the decade.”
Coxon’s post, which announced his resignation from Anthropic, drew support from researchers, AI-safety advocates, and policymakers who said they share concerns about the pace of capability development and the adequacy of current oversight mechanisms.
“Jacob is correct here — we really do earnestly believe AI could kill all humans!” Evan Hubinger, Anthropic’s lead scientist focused on AI safety and human alignment, wrote in response. “I personally think it is >10% within the next decade. I believe Anthropic is trying its best, but we do not yet have a plan to solve alignment for superintelligence and are not clearly on track to.”
The concern centers on a broad set of hypothetical failure modes. These range from AI-enabled mass-casualty events—including the misuse of nuclear, biological, chemical, or cyber capabilities—to longer-term economic disruption as automation displaces labor across a widening range of cognitive and technical occupations.
Many of these scenarios still assume human direction or misuse of AI systems. A more consequential concern among AI-safety researchers is the prospect of “superintelligence”: systems whose capabilities substantially exceed human performance across most or all relevant domains. Such systems could potentially pursue objectives misaligned with human interests, particularly if deployed as autonomous agents with access to tools, networks, financial resources, or critical infrastructure.
“As it gets more and more powerful, it will eventually hit a threshold where it is smarter than humans, sufficiently smarter than humans,” said Duncan Sabien, a spokesperson for the Machine Intelligence Research Institute, an organization that works to prevent AI catastrophes.
Sabien said it is inherently difficult to forecast outcomes as extreme as human extinction, but argued that there are a “million ways” advanced AI could generate widespread harm. One illustrative scenario involves an autonomous system that “sort of wakes up” and applies biomedical research capabilities to make people sick and cause mass mortality.
The scenario may resemble science fiction, Sabien acknowledged. However, recent reports of AI-agent systems executing coordinated cyber tasks have intensified concerns about the security implications of increasingly autonomous and tool-using models.
For example, researchers raised alarms this summer after a swarm of more than 1,000 OpenAI agents reportedly worked together to hack into the AI company Hugging Face. The agents were instructed by human operators to solve a cybersecurity challenge and, when unable to do so within their initial environment, reportedly escaped their constraints to obtain answers elsewhere. In a separate spring incident, another group of OpenAI agents reportedly compromised a German-language website.
Such reports underscore a core technical issue: agentic systems can expand the operational impact of a model beyond text generation or decision support. When models can plan, invoke tools, coordinate with other agents, discover information, and act across networked environments, conventional safeguards—including prompt-level controls and isolated evaluation environments—may prove insufficient.
Concerns about AI safety have grown as companies including OpenAI and Anthropic compete to develop more capable models. Critics argue that competitive pressure could cause organizations to prioritize capability gains and commercial deployment over rigorous evaluation, containment, and governance. OpenAI and Anthropic did not respond to requests for comment.
Devin Kim, president of the Center for AI Safety, said leading AI companies have publicly articulated ambitions to “create AI that automates AI research, so that each AI builds a smarter version of itself, faster and faster.”
“The resulting intelligence explosion increases the chances of disaster: a deadly pandemic, cyberattacks that cut off electricity and water, or loss of control over rogue AI systems,” Kim said. “Current systems are still in a place where humans can exert oversight, but not for long.”
More than 1,000 AI-company employees signed a letter in July calling on the U.S. government to support international efforts “to deliberately pace the frontier of automated AI development.” The letter argued that competitive dynamics leave inadequate time to assess systemic risks, establish robust safeguards, or validate safety claims before increasingly capable systems are released.
Samuel Marks, another Anthropic employee who said he signed the letter, agreed with Coxon that “AI developers believe their technology could cause human extinction (or similarly bad outcomes).”
“This could happen in the next few years. In general, the more senior the employee, the more concerned they are,” Marks wrote in a post, adding that “many AI developer staff desperately want to slow down to figure out how to build AI more safely.”
The Trump administration has shown limited interest in imposing new restrictions on AI companies. Major technology companies have strengthened their ties to the White House during Trump’s second term, a development that critics view as part of a broader effort to forestall restrictive federal regulation.
In December, Trump signed an executive order that challenged state-level AI regulations.
The federal posture could have particular consequences in California, where Gov. Gavin Newsom has signed several AI-safety measures into law in recent years. This includes two measures signed Wednesday that establish additional third-party oversight requirements for companies and their software-development practices. The measures build on an earlier law sponsored by state Sen. Scott Wiener, D-San Francisco, that established industry guardrails.
Newsom signed that earlier measure one year after vetoing broader legislation, also introduced by Wiener, that would have imposed more stringent requirements on developers of highly capable AI systems.
Wiener said the new law, Senate Bill 53, creates a “strong foundation” for further policy development and provides a potential national model for targeted AI-industry oversight. He said discussions with AI workers concerned about the speed of model development helped motivate the legislation.
“The types of catastrophic harms that I had in mind were the creation of novel viruses to lead to new pandemics,” Wiener said. “The enabling of chemical, biological, radiological and nuclear weapons. The cyberattacks to melt down the banking system or electric grid.”
Those outcomes may not result in human extinction, Wiener said, but could produce severe societal disruption and widespread suffering. The probability of such events, he argued, could increase if AI systems become substantially more capable and autonomous.
“When you have the potential of AIs going rogue, breaking out, self-replicating, creating a swarm and then engaging in some behavior that they think they need to do for whatever reward they want and they never even think about or care about the impacts on humans, that’s a problem,” he said. “That’s bad.”
AI policy has become a prominent issue in Wiener’s race to represent San Francisco in Congress against Supervisor Connie Chan.
Chan has also advocated for stronger restrictions on AI companies. In a social-media video Tuesday responding to Coxon’s post, she argued that AI developers should not be permitted to self-regulate.
“Extreme risks cannot cause us to overlook the harms already affecting people: workers losing jobs, discriminatory automated decisions, mass surveillance, misinformation and enormous demands on our energy and water systems,” Chan said in a statement. “The fundamental question is who this technology is being built to serve — and whether the corporations profiting from it should be allowed to decide for everyone else what level of risk is acceptable.”
Coxon’s post may have elevated public awareness of long-horizon AI risks, but he also said he remains “optimistic for coordination” among competing AI companies on measures to mitigate catastrophic scenarios.
Others are less optimistic. “We should have stopped six months ago,” Sabien said. “If we stop six months from now, it might actually be too late. If the thing turns on, it’s too late.”
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References:
https://www.sfchronicle.com/politics/article/ai-whistleblowers-kill-humans-22424000.php
Anthropic researcher resigns, warning AI labs are ‘gambling with our lives’
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)
AI infrastructure spending boom: a path towards AGI or speculative bubble?
Expose: AI is more than a bubble; it’s a data center debt bomb
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Big Tech AI spending binge results in massive job cuts!
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
FT: Scale of AI private company valuations dwarfs dot-com boom
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
AI Data Center Boom Carries Huge Default and Demand Risks
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
CTIA: Americans used more wireless data in 2025 than in the entire 4G decade, but growth slowed from 2023-2024
Executive Summary:
U.S. wireless data consumption reached 159.3 trillion megabytes in 2025—20% year-over-year growth and nearly 60% higher than two years prior—surpassing total usage across the entire 4G decade, according to CTIA’s 2026 Annual Survey. The results highlight accelerating demand on RAN and transport infrastructure driven by AI workloads, wearables, IoT sensors, and high-bitrate streaming. However, the 20% growth in 2025 is down from the 32% growth registered in 2024, and the 35% in 2023.
CTIA projects aggregate data demand to grow approximately 4× by 2032, with AI-related traffic expanding about 3× faster than conventional wireless flows. By 2034, AI is expected to account for nearly one-third of all broadband traffic, intensifying pressure on spectrum, backhaul, and edge compute resources.
Wireless operators invested nearly $30 billion in 2025 to expand capacity and support traffic growth, bringing cumulative industry infrastructure investment above $763 billion, with roughly $250 billion deployed since 2018 (the 5G launch year). Small-cell deployments have risen nearly 120% since 2018 and now represent about 40% of all cell sites, underpinning densification, capacity gains, and improved QoS across urban and suburban markets.
The U.S. now supports more than 600 million wireless connections—about 1.8 per capita—with 46% of connected devices being non-phone endpoints such as consumer wearables and industrial IoT sensors/robotics. For the fourth consecutive year, virtually all net additions in the home broadband segment came from fixed wireless access (FWA); nearly 16 million Americans subscribe to 5G Home, including 3.9 million net new subscribers in 2025—more than double the net losses reported by cable operators over the same period.
Despite traffic growth, real prices for typical unlimited mobile data plans fell more than 10% in 2025, with price per megabyte down 21% while average speeds increased 51%, reflecting efficiency gains from 5G spectrum utilization, carrier aggregation, and network modernization.
“This continued surge in demand reflects the increasingly central role wireless plays in everyday life,” said Ajit Pai, CTIA President and CEO (FCC Chairman from 2017-to-2021). “America’s wireless providers are stepping up to the challenge, investing nearly $30 billion last year alone to expand capacity and lay the foundation for future AI-native 6G networks.”
CTIA and industry leaders frame the next generation as AI-native, requiring proactive spectrum policy to secure mid- and high-band resources (e.g., 2.7 GHz and 7 GHz) with auctions targeted by 2028 to support 6G-ready deployments. The survey’s traffic and investment trajectory underscores the need for coordinated spectrum planning, densification, and transport upgrades to sustain AI-driven growth through the 2030s.
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6G Readiness vs. 5G Deployment Benchmarks: AI Traffic Load Perspective:
The 2026 CTIA survey shows U.S. networks are already absorbing AI-driven traffic growth (AI growing ~3× faster than baseline wireless traffic), while 5G deployment benchmarks—densification, spectrum efficiency gains, and uplink enhancements—provide the immediate capacity headroom needed before 6G’s AI-native architecture arrives in the 2029–2030 window.
Traffic Growth and AI Load Forecasts (CTIA 2026):
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2025 data usage: 159.3 trillion MB, +20% YoY and ~60% over two years; more than the entire 4G decade.
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AI traffic trajectory: AI-related flows are expanding ~3× faster than traditional wireless traffic and are projected to reach ~30% of all broadband traffic by 2034.
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Aggregate demand: Total data demand is expected to grow ~4× by 2032, intensifying pressure on RAN, backhaul, and edge compute.
These CTIA figures align with independent vendor forecasts (e.g., Nokia Bell Labs) that place AI at ~30% of wide-area traffic by 2034, with symmetrical bandwidth needs and upload CAGRs near 13%—a material shift from today’s ~87:13 downlink/uplink split.
5G Deployment Benchmarks Relevant to AI Loads:
6G Readiness: Standards, Architecture, and Spectrum:
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Standards timeline: 3GPP Release 20 (study phase) runs through 2027; Release 21 delivers first normative 6G specs with functional freezes in Dec 2028 and implementable code by Mar 2029; first commercial systems expected 2029–2030.
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IMT-2030 framework: ITU-R finalized 20 minimum technical performance requirements across six usage scenarios, including AI & Communication and Integrated Sensing & Communication (ISAC).
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Performance targets: Peak data rates 50–200 Gbps, user-experienced rates 300–500 Mbps+, and 1.5–3× spectral efficiency vs. IMT-2020 (5G).
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Spectrum needs: 6G requires large, contiguous sub-8 GHz mid-band and upper mid-band blocks with 100–400 MHz channel bandwidths; industry calls for proactive policy to secure bands (e.g., 2.7/7 GHz) with auctions by 2028.
What 5G Benchmarks Cover vs. What 6G Must Add:
Practical Takeaway for Network Planners:
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Near term (2026–2028): Lean into 5G-Advanced (Rel-19) features—AI schedulers, uplink MIMO, centralized spectrum allocation—to capture 10–30% SE gains and 20–40% uplink improvements that directly absorb AI traffic growth.
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Mid term (2027–2029): Align spectrum strategy with 6G’s channel-bandwidth needs (100–400 MHz) and prepare transport/edge for symmetrical, low-latency AI flows; monitor 3GPP Rel-21 freezes (2028–2029).
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Long term (2030+): Design for AI-native operations (distributed inference, ISAC, ubiquitous connectivity) as AI approaches ~30% of broadband traffic, ensuring RAN, core, and data-center interconnect scale together.
Perplexity.ai Sources: CTIA 2026 Annual Survey (Sept. 9, 2026); ITU-R IMT-2030 framework and 3GPP Release 20/21 timelines; vendor trials on 5G SA spectral efficiency and AI schedulers; industry forecasts on AI traffic share by 2034.
References:
https://www.telecoms.com/5g-6g/us-mobile-data-growth-slowed-in-2025
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
CTIA commissioned study: U.S. running out of licensed spectrum; 5G FWA to be impacted first by network overloads
CTIA Announces 5G Security Test Bed for Commercial 5G Networks
Highlights of CTIA’s 2021 Annual Wireless Industry Survey
Google’s TPU Business Outpaces Rivals as Hyperscalers Accelerate Custom AI Silicon Strategies
Executive Summary:
Google parent Alphabet’s emerging business of selling artificial intelligence (AI) accelerator chips is twice as large as a cloud computing rival, Google executive Thomas Kurian claimed Tuesday at a Goldman Sachs investors conference.
On July 22, Google reported second-quarter cloud-computing revenue of $24.77 billion, up 82% year over year, driven by artificial intelligence workloads, handily beating estimates of $22.46 billion. For the first time, Google included third-party sales of AI accelerator chips, called tensor-processing units, in cloud revenue.
Kurian, head of Google’s cloud business, made these remarks at Goldman Sachs’ Communacopia conference:
“We offer the best computational infrastructure for AI, and we offer 3 types of silicon. NVIDIA GPUs, our own Tensor Processing Units (TPUs), custom ARM silicon. [As AI models generate code awe also offer our own Arm processors to run that code.] We offer 2.7x better price performance for training, 80% better price performance for inference, 30% better price performance for CPUs. All of that allows us to differentiate our portfolio from other providers. It allows us to offer solutions to financial markets and capital markets.”
“The size of our accelerator business, our TPU business, is more than twice that of the next-largest hyperscaler.”
“Our platform is called Gemini Enterprise. It is used by over 90% of the Fortune 100 and thousands of small businesses. It’s used in a very specific way. People want to use it as a reasoning agent. So break the plan, understand the steps that are needed, reason on it and execute the steps. So it uses a reasoning agent to understand all the information in the company to then automate that workflow process. And when it does it, you want strong controls. What kinds of controls? Companies are worried about security. They’re worried about auditing, what these agents are doing. They want to manage costs and set budget caps. We have all those controls. And we allow people to use the right model for the right task. So you don’t have to always use the most expensive model, saving people a lot of money in doing so. We have a range of companies from insurance.”
–>You can read the entire transcript here. For more on Google’s TPUs please see:
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Google’s TPU photo
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Kurian added that Google monetizes TPU systems through three business models. One is letting companies rent TPU processing at its own cloud business. Also, Google sells TPU systems directly for deployment in customers’ data centers. One such customer is Anthropic. Third, Google also sells TPUs through a Blackstone cloud computing joint venture.
Google’s AI Chip Business:
In May, Google introduced Ironwood, its eighth-generation of TPUs. The Ironwood TPUs target both training of AI models and “inferencing” — processing AI workloads.
In a report published Aug. 24, Morgan Stanley analyst Brian Nowak estimated that Google cloud could garner $84 billion in “first party” — meaning non-cloud rental — TPU sales in 2027.
“We are raising our TPU sale estimates to $27 billion selling at a 30% gross margin,” Nowak said. “In all, we now expect Google to sell 0.3 gigawatts of TPU systems in the second half of 2026, 3.2 gigawatts in 2027 and 4.2 gigawatts in 2028. This translates into $84 billion/$108 billion of TPU-related Google cloud revenue in 2027 and 2028.”
In Q2-2026, Google said its cloud computing order backlog jumped to $514 billion, up from $460 billion in Q1. The backlog is converted into realized revenue as new data centers come online and crunch artificial intelligence-related workloads — training AI models and processing AI apps.
Google has increased its 2026 capital spending guidance to a range of $195 billion to $205 billion. Most of the spendings is going toward AI data centers and AI model development. In Q2, capital spending jumped 100% from a year earlier to $44.9 billion.
Kurian, a former top executive at Oracle, took over as the cloud-computing unit’s CEO in November 2018. When Kurian arrived, Google’s cloud customers were mostly other tech companies. Under Kurian, Google has targeted enterprise customers with cloud-based data-analytics and artificial intelligence tools.
Hyperscaler Custom Silicon: Meta, Microsoft, Oracle, and the Shift to In-House AI Accelerators:
While Google’s TPU business has reached a scale that Kurian says is more than twice that of the next-largest hyperscaler, other cloud and platform operators are rapidly expanding their own custom AI silicon programs to reduce dependence on Nvidia GPUs and optimize cost, power, and workload-specific performance.
Amazon.com has developed in-house Trainium AI accelerators while Microsoft has developed Maia AI chips. Amazon is further ahead than Microsoft in selling AI chips to outside customers, analysts say.
Meta – MTIA Family Targets Inference at Scale:
Meta has moved aggressively into custom silicon with its Meta Training and Inference Accelerator (MTIA) family, announcing four new chips — MTIA 300, 400, 450, and 500 — in March 2026 as part of a strategy to diversify hardware sources and lower AI infrastructure costs. The MTIA 300 entered production in mid-2026, with subsequent generations rolling out on an approximately six-month cadence through 2027.
Meta’s MTIA chips are manufactured by TSMC and co-developed with Broadcom under a multi-year partnership extending through 2029. The roadmap spans ranking and recommendation training (MTIA 300), combined generative AI and ranking workloads (MTIA 400), and decode-optimized generative AI inference (MTIA 450 and 500), with mass deployment of the flagship MTIA 500 planned for late 2027. Meta plans to put its own AI chip into production in September and is aiming to roughly double the computing capacity across its data centres.
By mid-2026, Meta, Amazon, Microsoft, and OpenAI have each closed the gap on the three key AI inputs — custom chips, power, and models — that only Google held in 2021.
Microsoft: Maia 200 and the Push to External Customers:
Microsoft unveiled its first custom AI accelerator, Maia 100, at Hot Chips 2024, followed by the inference-optimized Maia 200 in January 2026. Maia 200, built on TSMC’s 3 nm process with more than 140 billion transistors, 216 GB of HBM3e, and over 10 PFLOPS of FP4 compute within a 750 W SoC TDP, is designed to deliver 30% better performance per dollar for AI token generation.blogs.
Maia 200 will serve multiple models, including OpenAI’s GPT-5.2, and support Microsoft Foundry, Microsoft 365 Copilot, and reinforcement learning workflows. Microsoft plans to unveil next-gen Maia 300 AI chip in September, aiming to lower costs for in-house and OpenAI models while actively courting major enterprise customers. Anthropic is reportedly in talks with Microsoft to rent the company’s custom AI server chips as it looks to expand computing capacity.blogs.
Oracle: Partner-Led AI Clusters Rather Than Custom Silicon:
Oracle has taken a different path, opting not to develop its own AI accelerator but instead building large-scale AI clusters using third-party chips from Nvidia and AMD. Oracle plans to install the first MI450-equipped Helios racks in its OCI data centers during the third quarter of 2026, with an initial deployment targeting 50,000 MI450 processors.
Oracle’s AI strategy emphasizes rapid deployment of massive GPU-based clusters to serve anchor tenants like OpenAI under a reported $300 billion, five-year cloud computing contract beginning in 2027. In parallel, OpenAI is diversifying its supply of compute by designing its own chips with partners like Broadcom, with the first custom AI inference chips expected to deploy in the second half of 2026.
Market Implications:
By 2026, five of six major AI players — Google, Meta, Amazon, Microsoft, and OpenAI — now control at least two of the three critical AI inputs (chips, power, models), down from only Google in 2021. This vertical integration trend is reshaping the AI infrastructure market, with hyperscalers increasingly using custom silicon to optimize cost and performance for specific workloads while maintaining strategic flexibility through multi-vendor GPU procurement.
Google’s TPU v7, Amazon’s Trainium 3, Microsoft’s Maia 2, and Meta’s MTIA 2 all ramped into volume production in 2025–2026, signaling a maturation of the hyperscaler custom silicon ecosystem. Meta is also the first commercial gigawatt AMD MI450 deployment in H2 2026, illustrating a hybrid approach that combines in-house accelerators with third-party GPUs.
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References:
https://www.investors.com/news/technology/google-stock-cloud-kurian-ai-chip-business/
Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?
Google announces Gemini: it’s most powerful AI model, powered by TPU chips
Google Cloud and Verizon Expand Strategic Partnership to Scale Enterprise AI and Autonomous Network Operations
AWS to deploy AI inference chips from Cerebras in its data centers; Anapurna Labs/Amazon in-house AI silicon products
Meta’s “Iris” AI Chip for MTIA: Implications for Telecom-Grade Optical Networking, DCI and High Capacity Ethernet Fabrics
Custom AI Chips: Powering the next wave of Intelligent Computing
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
South Korean startup Rebellions to use open source software for carriers to quickly build AI stacks with its AI inferencing chips
European network operators in talks to form consortium to bid for EU satellite spectrum and provide D2M service
Introduction:
Four of Europe’s largest mobile network operators—Deutsche Telekom, Orange, Vodafone Group, and Telefónica—are in preliminary discussions to form a consortium that would jointly bid for EU-reserved satellite spectrum and launch a direct-to-mobile (D2M) service, according to a Bloomberg report cited by Reuters and other news outlets.
The EU’s planned 2 GHz mobile-satellite service (MSS) assignment is being structured as a three-way split of the band—one-third for government/IRIS² use, one-third reserved for EU-controlled commercial operators, and one-third open to international bidders.
3GPP Release 17 and Release 18 provide the standardized radio and protocol baseline for direct-to-mobile (D2M) integration in bands n255/n256 (and n254). Those specs are not yet approved ITU-R SG 4 (WP 4b & 4c) recommendations.
The EU’s planned 2 GHz mobile-satellite service (MSS) assignment is being structured as a three-way split of the band—one-third for government/IRIS² use, one-third reserved for EU-controlled commercial operators, and one-third open to international bidders—while 3GPP Release 17 and Release 18 provide the standardized radio and protocol baseline for direct-to-mobile (D2M) integration in bands n255/n256 (and n254).
Parabolic antenna and satellite dishes in Girona, Spain. Photographer: Angel Garcia/Bloomberg
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Key data points:
Mid‑2026 U.S. Carrier Ethernet Market: VSG Leaderboard, Standards, Service Mix and RFPs
Executive Summary:
Vertical Systems Group’s (VSG’s) mid‑2026 U.S. Carrier Ethernet LEADERBOARD ranks providers by retail port share as of June 30, 2026. Seven network operators met the 4% threshold for Leaderboard status: AT&T; Verizon (including Frontier); Lumen; Spectrum Business; Comcast Business; Zayo (including Crown Castle); and Cox Business.
- Verizon’s acquisition of Frontier significantly expanded its fiber network, elevating its market position to #2 in the U.S. Carrier Ethernet rankings.
- Zayo advanced to the leaderboard after acquiring Crown Castle and plans to add 15,000 route miles by 2030, strengthening its long-haul network.
Six companies attained a Challenge Tier citation (in alphabetical order): Cogent, Granite, GTT, Lightpath, Segra, and Uniti. The Challenge Tier includes providers with between 1% and 4% share of the U.S. retail Ethernet market.
VSG’s methodology measures billable retail customer ports and segments the market into six service categories that map closely to MEF‑defined Carrier Ethernet service types: Ethernet DIA (Dedicated Internet Access), E‑Access to IP/MPLS VPN, Ethernet Private Line (EPL), Ethernet Virtual Private Line (EVPL), Metro LAN, and WAN VPLS.
U.S. Ethernet Market Analysis: Mid-2026:
- AT&T keeps its #1 rank on the Mid-Year 2026 U.S. Carrier Ethernet LEADERBOARD.
- Rankings change for four of the seven Ethernet LEADERBOARD providers.
- Verizon moves up from the #4 position to the #2 rank with its completed acquisition and integration of Frontier assets.
- As a result, Lumen dips from the #2 position to #3. Spectrum Business drops from #3 into the #4 position.
- Zayo moves up into the LEADERBOARD at the #6 position from the Challenge Tier due to its acquisition of Crown Castle, which was completed in May 2026.
- Cox Business falls into the #7 and final position. The acquisition of Cox by Spectrum’s parent company, Charter Communications, was completed in August 2026 and is not included in the mid-year results.
- The Challenge Tier for mid-2026 includes six companies: Cogent, Granite, GTT, Lightpath, Segra (including UPN) and Uniti (includes Windstream). Segra enters the Challenge Tier, moving up from the Market Player tier.
- DIA (Dedicated Internet Access) is the top Ethernet service in the U.S. based on both ports and revenue.
- Enterprise customers are shifting from lower speed Ethernet private lines to more robust SD-WAN and SASE services.
- Three LEADERBOARD wireline network operators have attained Mplify Carrier Ethernet certification: AT&T, Lumen and Verizon.
The Market Player tier includes all providers with port share below 1%. Companies in the Market Player tier include the following providers (in alphabetical order):ACD, AireSpring, Alaska Communications, Alta Fiber, American Telesis, Arelion, Armstrong Business Solutions, Astound Business, Bluebird Network (includes Everstream), Breezeline, Brightspeed, BT Global Services, Centracom, Conterra, Douglas Fast Net, DQE Communications, Epsilon, Exa Infrastructure, ExteNet Systems, Fatbeam, FiberLight, Fidium, First Digital, FirstLight, Flo Networks, Fusion Connect, GCI, GCX, Glo Fiber, Hunter Communications, Intelsat, Logix Fiber Networks, LS Networks, MetTel, Midco, Momentum Telecom, NTT, Orange Business, Pilot Fiber, PS Lightwave, Rightfiber (includes Great Plains and Ritter), Silver Star Telecom, Sparklight Business, Syringa, T-Fiber (includes MetroNet, Lumos, U.S. Internet), Tata Communications, TDS Telecom, TPx, US Signal, WOW!Business, Ziply Fiber (now owned by Bell Canada) and other companies selling retail Ethernet services in the U.S. market.
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Standards Context: MEF Carrier Ethernet, IEEE 802, and ITU-T:
Carrier Ethernet services are standardized by MEF (formerly the Metro Ethernet Forum), which defines service types, attributes, and performance objectives across multi‑operator domains. MEF 2.0/3.0 specifications formalize E‑Line (point‑to‑point), E‑LAN (multipoint), E‑Tree (rooted multipoint), and E‑Access (inter‑provider) services, with strict OAM, QoS, and bandwidth profile requirements.
At the physical and link layers, IEEE 802.3 Ethernet PHYs and 802.1Q VLAN tagging underpin these services, while MEF service definitions (EVCs, UNIs, ENNIs) provide the operator‑grade abstraction needed for SLAs, service multiplexing, and interconnection. In practice, “Metro Ethernet” offerings by U.S. carriers are implementations of MEF E‑Line/E‑LAN service types over diverse transport (packet optical, MPLS, IP/MPLS, or Ethernet switching fabrics), with port‑based or VLAN‑based handoffs at the customer UNI.
Metro Ethernet Service Types: What the Market Segments Map To:
Vertical’s six service segments align with canonical Carrier Ethernet service types as follows:
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Ethernet DIA (Dedicated Internet Access): Typically delivered as an E‑Line service (EPL or EVPL) to an internet gateway, with asymmetric or symmetric bandwidth profiles and strict availability targets. DIA emphasizes internet reachability rather than private L2 connectivity, but the underlying UNI/EVC constructs remain MEF‑compliant.neosnetworks+1
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E‑Access to IP/MPLS VPN: An inter‑provider or backhaul construct where a carrier Ethernet E‑Line/E‑Access link connects an enterprise edge or another operator to an IP/MPLS core. This maps to MEF E‑Access (inter‑carrier) with ENNI handoffs and is common for wholesale Ethernet backhaul and multi‑operator VPN aggregation.mplify+1
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Ethernet Private Line (EPL): A port‑based, point‑to‑point E‑Line service with one EVC per UNI and near‑transparent frame delivery. EPL is the canonical “private line” for low‑latency, high‑certainty circuits between two sites, often used for data‑center interconnect or critical backhaul.
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Ethernet Virtual Private Line (EVPL): A VLAN‑based E‑Line service that supports multiple EVCs on a single UNI via service multiplexing. EVPL enables point‑to‑point and point‑to‑multipoint topologies over shared access ports, improving port utilization and supporting multi‑service bundles (e.g., LAN + internet + VoIP) on one physical handoff.Metro LAN: A multipoint‑to‑multipoint E‑LAN service (sometimes called Ethernet Virtual Private LAN) that provides any‑to‑any Layer 2 connectivity among three or more sites. Metro LAN is the carrier analogue to VPLS and is widely used for campus interconnect, distributed enterprise LAN extension, and L2‑based application clustering.
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WAN VPLS: A wide‑area VPLS instance delivering E‑LAN semantics across broader geographies, typically over MPLS cores. While MEF 3.0 emphasizes E‑LAN service attributes, many operators still market VPLS as the transport realization of E‑LAN for multi‑site Layer 2 domains.
The most important ITU‑T recommendations for Carrier Ethernet fall into three buckets: service definitions, OAM/fault & performance management, and protection/activation testing. Together they provide the operator‑grade framework that maps MEF service types (E‑Line, E‑LAN, E‑Tree, E‑Access) to measurable SLAs and resilient transport.itu+2
Core service definitions (what the service is):
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G.8011/Y.1307 – Ethernet service framework
Defines the overall framework for Ethernet services (EVC/OVC/UNI/ENNI), service attributes, and OAM bindings. It is the ITU‑T umbrella that aligns with MEF service types and underpins EPL, EVPL, E‑LAN, E‑Tree, and E‑Access definitions. -
G.8011.1/Y.1307.1 – Ethernet Private Line (EPL)
Specifies EPL service attributes and parameters (port‑based, single EVC per UNI, bandwidth profiles, transfer characteristics). This is the canonical “private line” for point‑to‑point, low‑latency circuits.itu+1 -
G.8011.2/Y.1307.2 – Ethernet Virtual Private Line (EVPL)
Specifies EVPL service attributes (VLAN‑based, service multiplexing on a UNI, CIR/EIR/CBS/EBS). EVPL enables multiple services over one physical handoff while preserving SLA granularity. -
G.8011.3/Y.1307.3 – Ethernet LAN (E‑LAN)
Defines multipoint‑to‑multipoint E‑LAN service attributes (any‑to‑any L2 connectivity among ≥3 UNIs), the carrier analogue to VPLS and widely used for Metro LAN.scribd+1 -
G.8011.4/Y.1307.4 – Ethernet Virtual Private Rooted Multipoint (E‑Tree / EVPRM)
Defines rooted multipoint (E‑Tree) service attributes for hub‑and‑spoke topologies (e.g., video distribution, wholesale access). -
G.8011.5/Y.1307.5 – Ethernet Access (E‑Access)
Specifies inter‑provider Ethernet access service attributes (ENNI‑based) used for wholesale backhaul and multi‑operator service chaining
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Market Implications for Network Architects:
The 2026 VSG leaderboard reflects consolidation and asset integration (e.g., Verizon’s move to #2 following Frontier integration), with cable MSOs and fiber specialists maintaining strong positions in metro and regional segments. For architects specifying Carrier Ethernet, the practical choice among EPL, EVPL, E‑LAN, and VPLS hinges on three factors: topology (P2P vs. multipoint), service multiplexing needs at the UNI, and the degree of L2 transparency versus managed L3 VPN requirements.
When drafting RFPs or architecture documents, reference:
- G.8011.x service types alongside MEF E‑Line/E‑LAN/E‑Tree/E‑Access to ensure interoperable, SLA‑backed procurement.
- MEF 2.0/3.0 service types (E‑Line/E‑LAN/E‑Access) and require conformance to MEF service attributes (bandwidth profiles, frame delay/jitter/loss, OAM).
That ensures that “Metro Ethernet” procurement aligns with deployable, interoperable, SLA‑backed services rather than vendor‑specific marketing terms.
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References:
https://verticalsystems.com/2026/09/02/mid-2026-us-carrier-ethernet-leaderboard/
https://verticalsystems.com/methodology/
https://neosnetworks.com/resources/blog/epl-vs-evpl/
https://www.itbroker.com/resources/glossary/metro-ethernet-transport
Carrier Ethernet Market Assessment and MEF 3.0 Certification
AT&T Tops VSG 2022 Global Provider Carrier Managed SD-WAN Leaderboard
No Surprise: AT&T tops leaderboard of commercial fiber lit buildings for 7th year!
VSG LEADERBOARD : AT&T #1 in Fiber Lit Buildings- Year end 2020
AT&T tops VSG’s U.S. Carrier Managed SD-WAN Leaderboard for 4th year
Lumen Technologies tops Vertical Systems Group’s 2021 U.S. Wavelength Services Leaderboard
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
The AI infrastructure boom is set to continue as per most market research firms. AI chip and compute server upgrade cycles will necessitate the continued spending of many hundreds of billions of dollars on AI compute infrastructure for the foreseeable future.
Global data center spending is set to reach US$31.6 trillion through 2050 to meet the world’s growing appetite for artificial intelligence (AI), an investment boom with no precedent in history, PricewaterhouseCoopers LLP (PwC) said in a report released on September 2, 2026. Dwarfing projects such as the railways, Internet and electrification, spending on data centers could even hit US$50 trillion over the next two-and-a-half decades if AI adoption accelerates beyond PwC’s “central scenario” forecast, the professional service/accounting firm said. For comparison, US GDP is about US$30 trillion.
An Amazon Web Services data center in Sterling, Virginia. Photo: AFP
With consumers, companies and governments increasingly using AI, tech giants such as Microsoft Corp and Amazon.com Inc and smaller data center providers are setting up new computing facilities across the planet at a rapid clip. The bulk of the spending would go into what fills the data centers — hardware from companies such as AI chip leader Nvidia Corp.
At least 75 projects, worth about US$130 billion combined, were blocked or delayed by local opposition during the first three months of this year, according to research group Data Center Watch.
“AI infrastructure is becoming one of the defining capital allocation challenges of the next generation,” said Clara Cutajar, global infrastructure leader at PwC Australia. “It cuts across technology, energy, real estate, supply chains, regulation and financing. This changes how infrastructure investors need to think about capital requirements, risk and returns.”
At the same time, the tech industry is trying to blunt a backlash against data centers that threatens to slow down the buildout. Protesters cite concerns about environmental impacts, resource consumption and more broadly how AI could upend employment and society. The U.S. would capture nearly half the projected data center spending, at US$15.1 trillion, PwC said.
The Asia-Pacific region would follow at US$8.2 trillion, Europe at US$5.6 trillion, the Middle East at US$1.1 trillion and Africa at US$255 billion of the cumulative capital expenditure, PwC’s inaugural Global Data Center Outlook showed.
“Railways. Electrification. The Internet. Each required enormous amounts of capital and defined an era,” the researchers said in the report. “The AI infrastructure cycle under way dwarfs all three. This one resets every four to six years — and shows no signs of ending.”
On an annual basis, global data center spending would increase from about US$800 billion this year to US$1.1 trillion in 2030 and US$1.8 trillion in 2050, PwC predicted.
China and India would drive the largest share of incremental demand, supported by large populations, rapidly expanding digital economies, and substantial headroom for AI to embed in business and consumer activity.
While global demand is strong, factors such as power availability, data sovereignty requirements and the flow of semiconductors would determine which regions capture the investments, PwC said. Power would be the foremost factor that shapes where AI infrastructure investment occurs. Indeed, much of the forecast hinges on how fast reliable electricity supply for data centers can be established, the report said. Affordable, reliable, and increasingly low-carbon electricity at scale is the hardest requirement for many markets to meet.
While the market researchers’ projection assumes a fairly open trading system where chips move freely across borders, disruptions in semiconductor supply chains could cut global investment by nearly 20 percent, they said. Meanwhile, a growing sovereignty push could redistribute, but not reduce, global investment.
“The US$31.6 trillion question isn’t whether the capital exists. It does,” the researchers said. “Nor is the question whether the demand is real. It is. The question is which regions, operators and institutions are positioned to capture it and which aren’t.”
Analysis- Where Will the Money Come From?
OpenAI and Anthropic, the two poster-children for Western frontier AI development, routinely divulge soaring annualised revenue run-rates, but these only give a vague indication as to how things are actually going.
An LLM maker that has had a particularly good month can simply multiply that monthly figure by 12, resulting in a run rate that gives the impression that sales are booming. Two Bloomberg articles from August illustrate this distortion.
The first reveals that Anthropic’s actual revenue reached $11.5 billion in Q2, up from $4.73 billion in prior quarter, giving a total of $16.23 billion for the first half.
The second cites sources claiming Anthropic’s run-rate puts it on track to turn over $65 billion this year.
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Full Stack Orchestration Layer:
The pace and scale of an unprecedented data center buildout is leading to challenges across the infrastructure lifecycle. These challenges point to a need for a single accountable orchestration layer, one designed to manage the seams across the delivery stack. PwC refers to this role as a full-stack orchestrator, a delivery platform that sets the standards, manages the integrated schedule, governs risk and change, and defines how acceptance is measured across the project.
Key Takeaways:
- PwC estimates $5.1 trillion will be invested in data centers in the five years ending 2030 and around $32 trillion over the next 25 years depending on AI adoption.
- Turning that capital into usable megawatt capacity means overcoming the industry’s biggest delivery failures around power, equipment, cooling, construction, commissioning, and compute.
- A full-stack orchestrator can turn fragmented delivery into a repeatable platform by owning standards, schedules, risk, change control, and acceptance across the entire data center build.
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References:
https://www.taipeitimes.com/News/biz/archives/2026/09/04/2003863640
https://www.telecoms.com/ai/no-relief-in-sight-as-pwc-sees-ai-capex-reaching-31-6trn-by-2050
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
AI Data Center Boom Carries Huge Default and Demand Risks
Expose: AI is more than a bubble; it’s a data center debt bomb
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
Analysis: Nvidia–MediaTek Deepen AI Platform Collaboration -What it Means for AI RAN & DCI
Executive Summary:
Nvidia has expanded its partnership with Taiwan based semiconductor firm MediaTek, committing $3.5 billion to a bond investment the two companies describe as a deepening of their “longstanding collaboration to build the next generations of AI computing platforms.” The move extends Nvidia’s pattern of strategic capital deployment across its compute ecosystem, this time reaching into custom silicon and advanced packaging.
According to the announcement, the collaboration couples Nvidia’s accelerated computing, AI, graphics, and software stack with MediaTek’s capabilities in custom silicon, high-performance computing (HPC), system-on-chip (SoC) design, and advanced packaging. MediaTek vice chairman and CEO Rick Tsai framed the investment as reinforcing a relationship spanning “cloud AI infrastructure, local AI computing and automotive in the era of physical AI,” combining Nvidia’s accelerated-computing and AI software ecosystem with MediaTek’s AI technology portfolio and custom-silicon resources.
Over the past several years, MediaTek has repositioned itself from a supplier of predominantly mid- and low-tier handset chipsets into a presence across smartphones, AI data centers, automotive, and IoT. Securities firm Yuanta Financial Holdings expects the closer alignment to accelerate MediaTek’s transition from edge AI into data-center infrastructure, device-based AI computing, and automotive platforms. Trading in MediaTek shares was suspended on the Taipei exchange Tuesday after the stock hit its daily 10% upside limit. The shares closed at NT$4,315 (about US$136.05), reflecting investor enthusiasm for the deepening of the collaboration.
NVIDIA and MediaTek are collaborating in three major areas:
- AI infrastructure: MediaTek will work with NVIDIA’s NVLink Fusion ecosystem to enable customers to develop custom AI infrastructure designed to integrate with NVIDIA rack-scale systems and AI factories.
- Local AI computing: The companies will continue to collaborate on multiple generations of NVIDIA RTX Spark™ and DGX Spark™ PC chips, powering consumer PCs, AI developer supercomputers and enterprise-class workstations, that integrate NVIDIA GPUs with MediaTek SoCs.
- Automotive: MediaTek and NVIDIA will continue developing platforms for AI-powered, software-defined vehicles in the era of physical AI.
“AI is transforming every computing platform — from the world’s largest AI factories to the PC and the car,” said Jensen Huang, founder and CEO of NVIDIA. “MediaTek is one of the world’s great semiconductor companies, with exceptional expertise in system-on-chip design, connectivity, leading performance and power efficiency. Together, we’re building platforms that bring NVIDIA accelerated computing to new markets and give customers the freedom to create differentiated AI systems at enormous scale.”
“MediaTek and NVIDIA share a vision for making advanced AI computing pervasive across the technology landscape,” said Rick Tsai, vice chairman and CEO of MediaTek. “NVIDIA’s investment strengthens a collaboration that spans cloud AI infrastructure, local AI computing and automotive in the era of physical AI. By combining NVIDIA’s leadership in accelerated computing and AI software ecosystem with MediaTek’s expertise in a diverse AI technology portfolio from edge to cloud, and our leadership position in custom silicon, we can accelerate innovation for our customers.”
Custom AI Infrastructure and NVLink Fusion:
Yuanta identified MediaTek’s participation in Nvidia’s NVLink Fusion as the most consequential element of the arrangement. Through that linkage, MediaTek could support the design of custom AI chips for clients while connecting them directly into Nvidia’s AI infrastructure — a complementary rather than competing role alongside the GPU vendor’s proprietary interconnect fabric.
The scale of the bond purchase also signals Nvidia’s long-term commitment to the relationship. Nvidia, currently the world’s most valuable company at roughly a $5 trillion market capitalization, has been deploying capital aggressively across both upstream and downstream partners. This latest deal, however, inevitably risks overlapping with other Nvidia investments — notably its $2 billion partnership with Marvell, which is also heavily oriented around NVLink Fusion and pitch-for the RAN with a one-chip architecture.
NVLink Fusion vs. Ethernet Scale-Out Interconnect:
NVLink Fusion and Ethernet-based scale-out interconnects target fundamentally different layers of the AI fabric, and the distinction turns on memory semantics rather than raw link rate. NVLink Fusion sits in the scale-up domain: it delivers memory-coherent, tightly coupled attachment for custom XPUs into Nvidia’s rack-scale NVLink fabric, with point-to-point GPU/XPU-to-GPU/XPU connectivity and an integrated high-bandwidth-memory interface (NVHBM) that trades die area and power for latency and bandwidth. It is proprietary, closed, and full-coherency by design — precisely the properties that make it hard for open standards to clone. Ethernet scale-out, by contrast, is the scale-out workhorse governing traffic between racks, pods, and clusters; it is loss-based, coarser-grained, and standardized through Ultra Ethernet and the broader IEEE 802.3 ecosystem.
The real contest is not NVLink against Ethernet so much as how much of the memory-coherent scale-up domain Ethernet-based challengers can capture. Standards-backed alternatives — UALink 2.0, Ultra Ethernet, and Broadcom’s Scale-Up Ethernet (SUE) — are pressing into scale-up territory, but they lack NVLink’s memory coherency and direct XPU-to-XPU connectivity, and SUE explicitly does not wire GPUs directly to GPUs. Meanwhile Nvidia’s strategy with NVLink Fusion is to co-opt custom XPUs (AWS Trainium4, and now MediaTek- and Marvell-designed silicon) into its fabric rather than let them migrate to those open scale-up paths. The practical outcome in the data center is a layered model — Ethernet surging in scale-out, and NVLink Fusion consolidating the latency-sensitive, memory-coherent scale-up spine — with Nvidia working to keep its proprietary layer the mandatory gateway that custom accelerators must pass through to reach the wider Ethernet-based world.
The $3.5 billion convertible-bond investment is best read not as a portfolio decision but as a defensive move within the AI rack-scale interconnect stack. The technical centerpiece is MediaTek’s adoption of the NVLink Fusion platform. Nvidia positions it as the connectivity technology and intellectual property that lets hyperscalers and AI-native operators drop custom accelerators (“XPUs”) and CPUs directly into its NVLink scale-up fabric.
MediaTek will now build against the three subsystems that define NVLink Fusion: the NVLink Fusion chiplet, which bridges a customer’s custom XPU to the NVLink scale-up domain over photonic or electrical interconnects (the chiplet itself carries up to ~1.8 TB/s of bidirectional bandwidth); NVLink-C2C, the high-bandwidth die-to-die link between XPUs, Nvidia’s Vera/Rosa CPUs, and other compatible processors; and NVHBM, a customized high-bandwidth-memory interface that trades off die area and power efficiency. The strategic significance is that Nvidia is effectively licensing its interconnect and memory layer as a licensable subsystem rather than enforcing a closed, all-Nvidia island — which lets it absorb “frenemy” silicon into the fabric instead of fighting it.
Two implications stand out for interconnect strategy:
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A hedge on hyperscaler in-house silicon. AWS has already integrated its Trainium4 custom ASICs with NVLink Fusion for rack-scale deployment, and Google, Meta, Microsoft, and OpenAI are all advancing private accelerators. By standardizing the interface (NVLink Fusion, NVLink-C2C, NVHBM, chiplets, and rack-scale integration) that these custom XPUs must speak, Nvidia keeps chiplets, packages, memory, and racks—the connective infrastructure itself—inside its own revenue funnel. MediaTek, in turn, becomes the design conduit that helps hyperscalers and frontier model firms get to that tape-out without building an independent scale-up stack.
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The scale-up vs. scale-out contest sharpens. NVLink remains essentially the only mature, memory-coherent scale-up fabric in production, whereas Ethernet is consolidating its dominance in scale-out (front-end and increasingly AI back-end) fabrics, and standards like Ultra Ethernet, UALink, and Broadcom’s Scale-Up Ethernet (SUE) are pressing into the scale-up domain. UALink and SUE, notably, lack NVLink’s memory coherency and point-to-point GPU-to-GPU connectivity, so NVLink Fusion’s positioning — co-opting custom XPUs rather than competing with them — lowers the attack surface those open standards could otherwise exploit.
The buy-in therefore consolidates Nvidia’s grip on the two layers that are hardest to commoditize: the latency-sensitive scale-up interconnect and the custom-silicon design flow that feeds it. What operators and chip partners give up is architectural independence — the bond structure and NVLink Fusion licensing effectively bind MediaTek into Nvidia’s ecosystem rather than allowing it to become an independent AI data-center silicon house.
What It Means for AI-RAN:
For the RAN, this is arguably a more consequential signal than the data-center economics. Nvidia’s AI-RAN play, via the AI Aerial software-defined accelerated-computing platform and its founding role in the AI-RAN Alliance, is built on having vRAN and AI workloads share the same GPU-accelerated infrastructure — what the community variously calls “AI for RAN,” “AI on RAN,” and “AI-and-RAN” on common hardware.nvidia+1
Here the MediaTek deal intersects directly with Nvidia’s earlier $2 billion Marvell partnership, which last year wired Marvell into the same NVLink Fusion platform for both the AI factory and AI-RAN ecosystem — Marvell supplying custom XPUs and NVLink Fusion-compatible scale-up networking, while Nvidia contributes the Vera CPU, ConnectX NICs, BlueField DPUs, Spectrum-X switches, and the rack-scale AI compute. The two arrangements are complementary in practice but overlapping in intent: Marvell owns the scale-up networking and the “one-chip-to-rule-the-RAN” custom silicon angle, while MediaTek brings SoC design, advanced packaging, and stronger reach into edge, automotive, and device-grade silicon. Together they give Nvidia two design houses capable of producing custom XPUs that plug into both data-center racks and AI-RAN infrastructure.
The operative question for IEEE readers is whether this broadens the AI-RAN supplier base going forward. Ericsson and Samsung have indicated their vRAN software built for Intel could be ported to AMD or Arm silicon with relatively modest effort, and Dell’Oro projections already have AI-RAN taking a significant share of the broader RAN market. Nvidia’s strategy is to ensure that, however the RAN silicon shakes out, the scale-up fabric and the custom-XPU design path remain NVLink Fusion-based — extending its data-center interconnect franchise into the radio access domain itself. The risk, flagged in the news report, is over-concentration: stacking Marvell and MediaTek onto the same NVLink Fusion foundation concentrates architectural leverage in Nvidia’s hands and could crowd out the open, multi-vendor fabric choices that O-RAN advocates would prefer to see served by standard Ethernet and UALink-style paths.
References:
https://www.lightreading.com/finance/nvidia-tips-3-5b-into-mediatek-in-latest-tech-partnership
Analysis: Nvidia’s $2 billion investment in Marvell; NVLink Fusion ecosystem & RAN vendor silicon strategy
Ericsson and MediaTek Demonstrate 3GPP-Based GNSS RTK Positioning with Sub-30cm Accuracy Over a Commercial 5G Network
5G infrastructure moves from coverage and speeds to cloud-native, orchestration, automation and AI-assisted networks
MediaTek overtakes Qualcomm in 5G smartphone chip market
AI Compute Has a Switchboard Problem: Orchestration & Data Center Fabric Explained
by Gaurav Sharma with Alan J Weissberger
Introduction:
Anthropic [1.] earned more revenue in the second quarter of 2026 than it did in all of 2025. The Claude AI maker company raked in $11.5 billion between April and June 2026, up from $787 million in the year-earlier quarter, $4.73 billion in Q1-2026 and up from ~ $10 billion for the entire previous year. CEO Dario Amodei told CNBC that the growth had been “just crazy” and “too hard to handle,” with demand far outstripping the company’s ability to build infrastructure with demand racing ahead of the company’s ability to scale infrastructure.
Anthropic is not alone. Bank of America projects that AI compute demand will outstrip supply through 2029. GPU lead times now stretch to 36–52 weeks.
Note 1. Anthropic is an American artificial intelligence startup founded by former OpenAI members that focuses on developing safety-oriented, steerable, and interpretable large language models like the Claude AI assistant.
This is happening even as the industry throws historic capital at capacity. Global data-center capital expenditure (capex) is on track to surpass $1 trillion in 2026, according to Dell’Oro Group. Yet the teams building with AI still cannot procure the compute they need, when they need it, at a price that lets them survive long enough to validate their thesis. The AI compute market has a switchboard problem — and fixing it calls for the same kind of thinking that transformed telecommunications.
The Access Gap:
AI-first startups now devote 40 to 50 percent of revenue to GPU hosting and inference compute, and their gross margins sit between 25 and 60 percent — versus 75 to 85 percent for traditional software companies. Compute has become the single largest cost line for most AI businesses, and it dictates what a team can afford to build.
Meanwhile, meaningful enterprise GPU capacity sits dormant. Teams hoard hardware for fear of losing access, locking accelerators into long-term reservations that sit underused overnight and between training runs. The capacity exists; the coordination does not.
The burden lands hardest on those who cannot afford the reservation game. Founders step down to cheaper hardware that slows their research. Teams cut experiments because they cannot secure enough accelerators. Projects stall while usable compute sits idle behind someone else’s contract. In this environment, access — not merit — decides which ideas reach the market and which never get tested.
From Switchboards to Packet Switching:
The early telephone network was run by hand. Every call required an operator to connect the subscriber — and each call claimed a dedicated circuit for its entire duration, even during silence. It worked, but it was slow, labor-intensive and wasteful.
GPU procurement works the same way today. An AI team identifies the hardware it wants, negotiates a reservation with a hyperscaler — AWS, Microsoft Azure or Google Cloud — and waits for capacity to become available. Each commitment locks a slice of the fleet to a single customer. The process is manual, slow and wasteful.
Telecommunications escaped this model in stages. Automated switching removed the operator; packet switching removed the dedicated circuit. Instead of reserving an entire line for one conversation, the network segmented each message into packets and routed them over whatever path had spare capacity, allowing many conversations to share a single trunk through statistical multiplexing. The same physical infrastructure carried far more traffic because capacity was allocated dynamically rather than reserved in advance.
AI compute needs an analogous shift: an orchestration plane that discovers available accelerators across multiple sources and routes each workload to suitable hardware — without the customer negotiating each connection individually.
It is already taking shape. Vendors are building orchestration systems that aggregate capacity from owned infrastructure, data centres and distributed GPU providers, then present it to the customer as a single service. The customer submits a job; the orchestration layer selects suitable hardware, assembles a cluster and delivers it.
Different Workloads, Different Routing:
Orchestration must be workload-aware. Pre-training the largest frontier models requires thousands of accelerators coupled over low-latency fabrics with precise topology; these jobs will continue to demand dense, purpose-built clusters.
Inference, fine-tuning and research are far more elastic. They tolerate geographic spread and run across a wider, more heterogeneous hardware pool. This mirrors how packet-switched networks treat traffic types differently while carrying them on shared infrastructure: a voice call needs bounded latency and continuity, while an email is routed over whatever path has spare capacity.
That differentiation opens the door for network operators. Data-centre operators and carriers already own much of the physical connectivity — fibre, points of presence, interconnection — needed to knit scattered compute into a coherent supply system. Rather than letting AI infrastructure consolidate into a small number of hyperscalers, the industry can use existing transport and edge assets to link regional data centres and GPU providers into a broader, more liquid market.
Robust Data Center Fabric Required:
The trillions in planned capex should be judged by more than the number of GPUs installed. If new capacity flows mainly to customers who can lock in multi-year reservations, the supply of compute grows even as the population of companies able to use it shrinks — fewer experiments, fewer competing hypotheses, a smaller set of teams shaping what AI becomes.
A healthier market would let AI teams reach compute from multiple providers through a single, well-connected service, with the network doing the work of matching each job to the right hardware — the telecoms discipline of statistical multiplexing applied to the GPU fleet.
Telecommunications offers the template. Every forward step it took made the same physical infrastructure serve more users. AI compute looks ready for the same move. The hardware is there, but is the network that connects it robust enough?
The physical network that connects AI compute servers (GPUs/TPUs) to each other and to high-performance storage is collectively called the Data Center Network (DCN) Fabric. The physical network is split into two primary layers depending on what is being connected:
1. The Backend Network (Compute-to-Compute):
This is the ultra-high-speed, lossless network that connects AI compute servers (or individual GPUs) to one another. It handles “East-West” traffic—such as gradient exchanges and parameter updates—during massive parallel AI training.InfiniBand: Long considered the gold standard for high-performance computing (HPC). It relies on dedicated, high-speed physical switches and host channel adapters (pioneered largely by NVIDIA/Mellanox). It features native Remote Direct Memory Access (RDMA), allowing systems to exchange data directly from memory to memory without involving the host CPU.AI-Optimized Ethernet (RoCEv2): A highly popular open alternative that uses traditional physical Ethernet cabling and switches but runs RDMA over Converged Ethernet (RoCEv2).
Platforms like NVIDIA Spectrum-X utilize optimized Ethernet hardware to achieve lossless, low-latency performance comparable to InfiniBand.Ultra Ethernet: Driven by the Ultra Ethernet Consortium (UEC), this next-generation physical transport standard optimizes Ethernet specifically for massive scale-out AI environments.
2. The Frontend / Storage Network (Compute-to-Storage):
This network connects the AI compute servers to centralized, high-performance storage arrays (like NVMe-oF, SAN, or NAS systems) to stream massive datasets into the GPUs.High-Speed Ethernet: The physical storage network is predominantly built on high-bandwidth Ethernet (moving rapidly up to 400G and 800G per port).Storage Protocols: It leverages protocols like NVMe-oF (NVMe over Fabrics) or RoCEv2 to pull unstructured data (images, text corpuses) from storage units into the compute cluster at lightning speeds without stalling the GPUs.
| Feature | InfiniBand Fabric | AI Ethernet (RoCEv2 / UEC) |
|---|---|---|
| Primary Use Case | Tightly coupled GPU-to-GPU training | Compute-to-Storage & open scale-out clusters |
| Physical Hardware | Dedicated, specialized switches & optics | Standard, widely available Ethernet switches |
| Data Flow Style | Lossless by design (Credit-based flow control) | Lossless via configuration (PFC / ECN mechanisms) |
| Ecosystem | Proprietary / Closed ecosystem | Open standard, multi-vendor interoperability |
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References:
Telecom data centers must be redesigned for the AI era with rack scale architectures, enhanced power & cooling requirements
Analysis: Ethernet gains on InfiniBand in data center connectivity market; White Box/ODM vendors top choice for AI hyperscalers
Will AI clusters be interconnected via Infiniband or Ethernet: NVIDIA doesn’t care, but Broadcom sure does!
Cisco’s Silicon One G300 as the dominant AI networking fabric, competing with Broadcom’s Tomahawk 6 series
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
After Bell Labs: Telecom Industry Funds Only a Fraction of the Innovation Needed
Many telecom analysts have noted former Bell Labs CTO and President Marcus Weldon scathing linkedin post, sharply criticizing deep staff cuts and warnings of “erasure” at the iconic research division. Weldon said he believes Bell Labs staffing has been cut to nearly half of the 1,200 strong workforce that was in place during his tenure (2013-to-2021). While he acknowledged that restructuring could account for some of those changes, he argued a 50% reduction in force in five years “is both shocking and unprecedented.”
An unidentified Nokia spokesperson told Fierce that Bell Labs “remains a deeply important part of Nokia, with a long track record of turning world-class research into technologies that deliver commercial impact and move our industry forward.” However, the company acknowledged that the hundred-year-old Bell Labs is “entering a new chapter.”
It’s important to recognize that Bell Labs is not the only big research house that’s disappeared. There’s also Bellcore/Telcordia, Nortel Networks R&D (Bay Street Labs), Xerox PARC, HP Labs, Telco labs (e.g. Pac Bell/SBC, Ameritech, Bell South, Bell Northern Research, GTE Labs, Sprint Labs, and many more).
Meanwhile, telecom analyst Sebastian Barros states “the $1.3 trillion telecom industry is funding only a fraction of the innovation it will need for whatever comes after 6G.” It appears to us that the industry’s economic model is badly failing to fund future innovation needed for growth.
Image Credit: Sebastian Barros
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Our Analysis:
The core problem — R&D was outsourced and never replaced. After the 1984 Bell System breakup and liberalization in Europe and Asia, operators pivoted to customers, spectrum, deployment, and operations, while Ericsson, Nokia, Huawei, Qualcomm, ZTE, Samsung, and a long tail of suppliers took over most technology development. Operators became buyers of innovation rather than creators of it.
The Bell Labs model that produced the transistor, information theory, Unix, and modern AI is gone. That institution ran on an economic engine that no longer exists: in 1974 AT&T booked about 1.4% of US GDP, with Bell Labs alone spending roughly 2% of revenue on nonmilitary R&D — over four cents of every AT&T dollar. That stable, massive funding let researchers pursue problems that wouldn’t become products for fifteen years. Expecting a vendor like Nokia (€19.9B annual sales) to recreate that under today’s competitive economics ignores the financial logic of modern telecom. The contraction is visible: Marcus Weldon estimates Bell Labs research staff has fallen from over 1,200 to roughly 600 since he left the labs.
The industry is capex-heavy but R&D-light. Telecom invests more in capital expenditures than almost any other sector — over $350 billion per year — yet only the top 10 technology providers collectively spend around $50 billion annually on R&D. The capex money flows into deploying networks, not inventing or researching new technologies.
–>Yet in 2025, Huawei invested $27.5 billion in R&D. That was ~22% of its total revenue for that year.
The next cycle looks even more disciplined. Analysts expect 6G RAN capex to ramp only toward the end of the decade, with cumulative 6G RAN revenue in the first six years projected 10–20% lower than the comparable 5G period. Nearly 400 organizations are investing in 6G R&D, but venture-backed startups barely participate in a material way, leaving innovation concentrated among incumbents.
The takeaway: a $1.3 trillion industry funds only a fraction of the innovation it needs because its institutional R&D engine was dismantled decades ago and never rebuilt — operators spend on capex, vendors own the R&D, and the pipeline of disruptive new entrants is thin. That’s why “whatever comes after 6G” may arrive with far less foundational research behind it than the generations that preceded it.
Telecom Capex vs. AI Hyperscaler Capex:
The headline shift is quite stark. In 2026, the AI hyperscalers alone are on track to outspend the entire global telecom industry on capital investment — roughly doubling their own 2025 figures while telecom capex flattens or edges down. Here are the numbers side by side:
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Hyperscalers are sprinting. Amazon alone plans ~$200B in 2026 capex (up from ~$125–132B), Alphabet $175–185B, Meta $115–135B, Microsoft $110–120B, and Oracle ~$50B. The vast majority goes to AI compute, data centers, and networking.
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Telecom is grinding. Analysts see global operator capex edging down slightly by 2026, with spending holding near 20% of sales as fiber completion and 5G Standalone upgrades wind down. US telco capex was $80.5B in 2024.
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Capital intensity is extreme. 2026 hyperscaler capex runs at roughly 86% of revenue for Oracle, 54% for Meta, and 46–47% for Microsoft and Alphabet.
Why this matters for the Barros argument: This is the flip side of the underinvestment thesis. Hyperscalers are channeling unprecedented capital into AI infrastructure — funded increasingly by debt, with incremental borrowing as a share of hyperscaler capex rising from ~9% in FY-2024 to ~32% by mid-2026 — while telecom operators, the sector that historically built the networks, are cutting back. The investment gravity has shifted from connectivity infrastructure to AI models andcompute, which is exactly why a $1.3 trillion industry funds only a fraction of the innovation it will need after 6G.
References:
https://sebastianbarros.substack.com/p/telecom-is-massively-underinvesting
https://www.fierce-network.com/wireless/nokia-defends-bell-labs-future-after-ex-chief-blasts-cuts
Dell’Oro: 6G RAN Capex to reach $500 billion by 2034 + Counterpoint
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
Hyperscaler AI Race: Soaring Capex Wipes Out Free Cash Flow; AGI and Digital Gods
China’s state owned telcos slash CAPEX to the lowest in decades!
Dell’Oro: Global telecom CAPEX declined 10% YoY in 1st half of 2024
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
Gates warns of “turbulent AI era;” OpenAI calls for collective action on AI cybersecurity
Introduction:
The AI risks are very real and growing each day. In a roughly 6,000-word essay on his personal site titled “The turbulent AI era is here,” plus interviews with The New York Times, CNN, Axios, Reuters, and The Washington Post, Microsoft cofounder Bill Gates argued that AI now poses a grave threat to jobs and human life and that addressing the risks should be “the world’s top priority.” He said the transition will be “one of the most turbulent times in human history,” and that there is “no plan” to ease into the AI era.
Image Credit: Telecoms.com
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Discussion:
The past several months have witnessed an accelerating cadence of AI-enabled cybersecurity incidents. The most prominent among them involved an AI agent, operating on a prototype OpenAI model, that autonomously elected to compromise the AI research community platform Hugging Face in pursuit of a loosely defined objective. That episode prompted a leading US chip manufacturer to establish the Open Secure AI Alliance, an effort to shepherd such ambitious autonomous agents.
Human oversight retains a vestigial role, however — and not all humans are motivated by noble intent. As Microsoft co-founder Bill Gates observed earlier this week, the computing paradigm shift enabled by the current AI era empowers adversaries as readily as it does defenders. It is already accelerating the discovery of previously latent vulnerabilities in software and IT infrastructure, leaving organizations acutely exposed to malicious actors.
“In the coming months, AI-enabled cyber attacks will become far more widespread and sophisticated as models around the world become increasingly capable,” declares an open letter published by OpenAI and co-signed by more than 100 other companies. “The companies and public services our communities depend on—from hospitals to water treatment plants to the infrastructure that powers the internet—are at risk.”
Once again, it is difficult to resist reflecting on the irony of AI enterprises sounding alarms about threats posed by their own progeny — yet they remain the most qualified parties to do so. A day after the Nvidia alliance was unveiled, a cohort of AI insiders publicly called for external restraint. This latest initiative suggests that plea went unanswered.
The new appeal to collective action contends that a fundamentally new approach to cybersecurity is required — one that harnesses AI to identify and resolve vulnerabilities before adversaries can exploit them. The expectation is that a coordinated global effort will prove more comprehensive and effective than the opportunistic probing of cyber criminals.
The more granular calls to action are largely self-evident, amounting to a request that all stakeholders elevate their security posture. “Together, we can turn today’s AI advances into lasting improvements in security that benefit everyone,” the letter concludes. Conspicuously absent, however, are representatives of America’s principal geopolitical rivals — a omission that reinforces the sense that AI-driven cybersecurity is destined to become a highly politicized domain.
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Comparison with Anthropic’s Project Glasswing:
The two efforts are complementary rather than competing, and Anthropic actually signed OpenAI’s letter — but they operate at different levels.
OpenAI’s Collective Cyber Defense:
A policy and advocacy coalition. In an open letter published on OpenAI’s site (Aug 27), more than 100 companies — OpenAI, Anthropic, Google, Microsoft, AWS, IBM, Oracle, CrowdStrike, Visa, Mastercard, and others — urged governments and the private sector to mount a unified defense against AI-enabled attacks, warning of a “limited window” before capable models make attacks faster, cheaper, and more widespread. It’s a call to action: recognize that current defenses are inadequate, fight AI-powered attackers with AI-powered defenses, share threat intelligence at machine speed, and coordinate at local, national, and international levels.
Anthropic’s Project Glasswing:
A concrete defensive-security program. Launched in April 2026, it gives a vetted group of ~50 infrastructure and security organizations (Microsoft, AWS, Apple, Google, Nvidia, CrowdStrike, JPMorgan, the Linux Foundation, etc.) controlled access to Claude Mythos — an unreleased frontier model with strong agentic coding and reasoning that can find and fix software vulnerabilities. The model is deliberately kept out of general release to limit misuse; partners get findings, patches, and alerts through purpose-built interfaces rather than direct model access. Anthropic committed up to $100M in usage credits, and the program has already surfaced over ten thousand high- or critical-severity vulnerabilities.
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References:
https://openai.com/collective-cyberdefense/
https://www.telecoms.com/security/tech-consortium-rings-the-ai-cyber-attack-alarm-bell-once-again
Anthropic’s Project Glasswing aims to reshape IT cybersecurity
SHIELD-6G with AI-native cyber threat intelligence platform to enhance cybersecurity for Europe’s future 6G networks
Cybersecurity threats in telecoms require protection of network infrastructure and availability
