AI
Broadcom lending Anthropic up to $42 billion in yet another AI circular financing deal
Backgrounder:
According to Reuters, which obtained Anthropic’s IPO prospectus, the Big Tech giants are depicted in multiple ways in the IPO filing- as distribution partners, financial backers, computer suppliers, and competitors, all at once. They uncovered the clearest view yet of how circular the AI world really is, and how much an AI lab’s success depends on the giants it’s also competing against.
Reuters was able to calculate that Anthropic pays roughly 16% of every dollar it earns from its cloud partners (Amazon, Google, Microsoft) back to those same big tech partners. It counts the full value of contracts sold through a cloud marketplace as revenue, then treats the cloud platform’s cut of revenue as a marketing cost.
OpenAI does the opposite by only counting what it keeps after the cloud partner takes its share. That difference matters for understanding the big numbers floating around, and how to actually compare the two rivals’ toplines as they both eye the public market.
Broadcom’s relationship with Anthropic spans compute supply, equipment leasing and financing — giving the semiconductor company a central role in Anthropic’s infrastructure buildout. That differentiates it from other major partners and investors such as Amazon which primarily provide cloud infrastructure and distribution for Anthropic’s AI model Claude.
As part of that complex relationship, revealed in the IPO filing, Broadcom has agreed to lend Anthropic up to $42 billion to finance infrastructure spending. In turn, Anthropic stands to become the largest customer in Broadcom’s entire chip design business next year, making their relationship a prime example of the reciprocal spending that has animated AI skeptics on Wall Street, even as the AI lab readies a public offering that could see it valued at $2 trillion.
“It feels that there’s quite a concentrated bet right now on two companies being able to generate enough revenues to support all the financing that’s happened,” said Robert Leitao, managing partner of Rothschild & Co.
Anthropic disclosed that Broadcom’s role in supplying hardware and acting as a financing partner creates “potential conflicts of interest” that might affect Anthropic’s ability to access the computing power needed for its work, according to the prospectus.
The AI lab also warned that Broadcom’s decisions around pricing and hardware could affect its ability to procure enough computing infrastructure. Broadcom did not comment. Anthropic declined to comment to Reuters.
In April, Anthropic announced it was teaming up with Broadcom and Google for a deal that would see Google provide its Tensor Processing Unit (TPU) chip capacity to Anthropic, with the supply coming online in 2027. Broadcom designs Google’s TPUs that are manufactured by TSMC in Taiwan.
A Broadcom circuit board for chip testing is pictured during a lab tour as Broadcom prepares to launch new optical chip tech to fend off Nvidia in San Jose, California, U.S., September 5, 2025. Brittany Hosea-Small · REUTERS via Yahoo Finance.
Anthropic is also relying on Broadcom for equipment leasing and financing. Broadcom could designate a financing partner, and the debt instruments could be converted into Anthropic shares. Anthropic said in its filing it doesn’t expect any notes to be sold before it completes its IPO. The convertible note Anthropic would issue could finance about a third of the $125.2 billion commitment the AI lab has made for a five-year lease of tensor processing unit (TPU) computing capacity.
Other Players:
Broadcom rivals Nvidia and AMD have also provided funding to their own customers, including OpenAI and Anthropic, which the labs then used to pay for access to the companies’ high-powered chips. The concern about such circular financial schemes is that if one domino in the row falls, it will cause a chain reaction that will decimate the AI trade and, as a result, the global equities markets that have benefited from and come to rely on AI firms and hyperscalers.
As to the circularity of all of this financing, the BIS notes (via FT Alphaville):
“… circular relationships make reported demand partly endogenous to firms’ own financing decisions. For example, when a supplier finances a customer, part of the supplier’s revenue growth reflects its own capital investment, rather than organic final demand. This makes it harder for investors, lenders and supervisors to gauge what part of the current AI boom is based on organic demand.”
“The parallel with the telecom boom of the late 1990s is instructive: upstream equipment vendors financed network operators so they could buy the vendors’ equipment. This meant that part of the equipment vendors’ reported sales was being funded by the vendors themselves. For a time, as operators expanded their networks, equipment orders also expanded and vendors booked both the sales and loans as assets. However, when operators’ own revenues failed to materialize or slowed, they could neither repay the loans nor sustain the equipment purchases. Equipment vendors then sustained both financial losses and a loss of sales. Such dynamics may also play out in AI if revenue growth and end user demand fall short of firms’ expectations.”
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- 2026 Debt Issuance: Projected between $220 billion and $250+ billion for major US hyperscalers (Alphabet / Google, Amazon, Meta, Microsoft, and Oracle), with broader AI-related market debt reaching up to $570 billion.
- 2027 Forecast: Goldman Sachs projects direct hyperscaler debt issuance to nearly double to around $400 billion (Jeff Pu estimates $419 billion) as companies finance over a third of their infrastructure needs.
- Aggregate Capex: Combined capital expenditures are expected to hit roughly $940 billion in 2026 and scale past $1.3 trillion in 2027.
- Market Share: Hyperscaler investment-grade bond sales have jumped from roughly 2% of total US supply (2022–2024) to roughly 9% in 2026.
- Credit Impact: Credit spreads on a 10-year hyperscaler credit basket have widened from historical 40–75bp ranges toward 90bp+, driven by leverage concerns.
- Cash Flow Outlook: S&P Global Ratings expects all major hyperscalers to run negative free operating cash flow through 2026 and 2027, with a cash-flow inflection point not projected until 2028–2029.
Source: Google Gemini
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References:
https://www.ft.com/content/87875b20-4081-4511-9afe-4ee389409742
Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23)
Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
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?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Telcos don’t have an AI problem; they have a voice estate visibility problem
By Satish Barot, Co-founder and CTO, Klearcom with Shazia Hasnie
AI is increasing telecom interdependence
This author has spent around twenty years building telephony products, and the last few watching what happens when AI gets added to them. The models are usually fine, but the voice estates around them are not.
Explanation: The voice estate is everything a voice call touches: the number dialed, the carriers delivering it, the menu that answers, the systems behind it, and in every country/location served.
NVIDIA’s February 2026 survey of roughly one thousand telecom respondents found that sixty percent of organizations are using or evaluating generative AI, up from forty nine percent in its 2024 edition. The voice call that once met a gateway and a queue now meets speech recognition, a language model, a synthetic voice, and also identity checks.
Every one of those can be healthy while the call goes wrong. So the useful test is whether you can prove, from outside your own network, that a real customer’s call still ends the way it should.
Why AI amplifies operational complexity
Old fashioned faults announce themselves: a line drops and something logs it. AI is quieter, and it fails in ways worth naming. It gets worse quietly: a model loses accuracy on one kind of request and nothing reports an error. It’s also confidently wrong. WildASR tested seven speech recognition systems on recordings degraded the way phone audio is. When a caller is cut off mid word, by a network delay or by the system deciding they had finished, the models fill in words nobody said. Further down the line that looks like a clean answer. The same benchmark makes a broader point: robustness measured in one language can substantially mispredict behavior in another, so a release validated in one market tells you little about the next.
Speed fails at the edges, not the average. A system that usually answers in under a second, but takes two and a half seconds on one call in ten, feels broken to those callers and healthy on every voice testing team chart. Nor is the result repeatable: EVA-Bench tested twelve systems and found a wide gap between passing once and passing every time, which undoes testing against a single expected answer.
Then the ones nobody looks at: the fallback menu that takes over when the AI gives up is usually the least maintained thing you own, and it runs exactly when things go wrong. None of this turns a light red on a contact center voice dashboard.
The other exposure is a number that anybody can dial. OWASP ranks prompt injection first among risks to applications built on language models, and a voice channel is an open microphone into the instruction path itself: a caller talks the model out of what it was built and told to do. Mitigations exist and they are worth naming. Treat caller speech as untrusted input and keep the transcript out of the context that carries instructions, constrain what the model can emit to a defined set of intents and slots rather than free text, and require confirmation for anything that moves money, changes credentials, or reads account data back.
The customer journey as the real test environment
Take a composite example, assembled from patterns that recur across multi country voice estates rather than drawn from one operator. An operator runs support numbers in fourteen countries behind one AI system. On Friday the team delivers an updated model and a new synthetic voice. Both pass in testing, no issues. By Monday, the share of calls handled without an agent is up by six points, complaints are up in three countries where the release went live first, and every dashboard is green. Nobody knows why.
The new voice made the menus two seconds longer, and nobody adjusted the window in which the system listens for a caller to stop talking. Callers spoke too early, the system heard fragments, filled the gaps, and guessed just confidently enough to keep a person out of the call.
The headline number improved because failures had stopped escalating. Every part did its job, so nothing raised an alarm. What broke was the outcome, and no single part owns the outcome. People given those same recordings transcribed them easily.
Those failures are not hypothetical. Test calls run by my own team over the past two years have found a global biopharmaceutical company whose menu prompts ran into each other faster than callers could answer, a financial services provider whose speech recognition intermittently failed on the word “agent”, so the route to a human worked on some calls and failed on others, and a healthcare data provider whose misrouted number reached an AI assistant that was never meant to answer it. In every case the systems carrying the call reported nothing wrong.
From system monitoring to outcome validation
Monitoring looks inward: are my systems up? Validation looks outward. It places a real call in country and checks the result against what should have happened: is a customer in this country, on this network, right now, getting the right answer?
Monitoring only sees what you own, and these failures hide inside the part causing them. So the two work together: validation tells you something is wrong and where, monitoring tells you why. Three changes follow. Report three numbers where you currently report one: calls the AI resolved correctly, calls it escalated correctly, and calls it failed. Report the slower calls alongside the average ones, and report by country and carrier instead of one global figure.
Figure 1. A single call crosses the voice estate an operator owns and systems it does not. Monitoring covers only the shaded stage. Validation traverses the whole path from the caller’s side.
Continuous testing as an assurance layer
The industry already has vocabulary for this. TM Forum’s autonomous network levels give operators a shared scale for how much of the “operate, assure and optimize” loop runs without people, and the same NVIDIA survey places 88% of organizations at Levels 1 to 3 on a scale of 0 to 5. Moving up that scale rests on evidence about delivered outcomes.
If the journey is what breaks, the journey is what you test. That means dialing the real public number, from a real device, on a real carrier in the country the customer is calling from, rather than looping a call back inside your own data center. It means testing from the outside in, and it means doing it on a schedule, across every country and carrier your customers use, fixed and mobile.
Figure 2. Testing the journey from the outside in. A scheduled call dialed from a real device in the country the customer calls from, scored on the delivered outcome.
Check outcomes, not connections: audio clear both ways, the right menu, key presses registered, the caller reaching the right team with their details intact. Score it across many calls with a library of realistic phrases, accepting a pass rate rather than an exact match, so decline shows as a trend. Treat a model update like a network change: try it on a few numbers first, then pull it back if the pass rate drops.
Several approaches are in this category. Synthetic transaction monitoring from operator owned probes, carrier side test call generation and crowdsourced testing on real handsets all produce outside in evidence, and they trade off differently on country coverage, cost and how closely the test resembles a real customer’s call. The common requirement is that the evidence originates outside the systems being assured, on a call placed in the country where the customer is dialing from.
Practical implications
Keep human escalation as a safety valve, not a number to drive down: cutting it without checking correctness makes the metric look better while the service gets worse. Then give the outcome an owner. Gartner’s June 2025 forecast that over 40% of agentic AI projects will be canceled by the end of 2027 attributes those cancellations to escalating costs, unclear business value and inadequate risk controls. Model capability doesn’t appear anywhere on that list. This is organizational before it’s technical.
These failures are seams between systems that break without producing an error, and monitoring your own equipment cannot see them: the gap sits between what your systems report and what your customer gets. Networks have been tested end to end for decades rather than trusted part by part. Voice estates deserve the same.
References
- NVIDIA (2026) State of AI in Telecommunications: 2026 Trends. Fourth annual survey report, published 19 February 2026, based on responses from 1,038 telecom professionals worldwide. Summary of findings: Survey Reveals AI Advances in Telecom.
- Tay, G., Ma, W., Lee, J., Tang, Y., Lee, D., Yin, W., Shen, D., Meng, S., Zhu, Y., Li, M. and Smola, A. (2026) Back to Basics: Revisiting ASR in the Age of Voice Agents. Boson AI. arXiv preprint arXiv:2603.25727, 26 March 2026. Introduces the WildASR benchmark. Dataset and code: bosonai/WildASR.
- Bogavelli, T., Gauthier Melançon, G., Stankiewicz, K., Bamgbose, O., Riols, F., Nguyen, H.H., Mehndiratta, R., Brin, L.D., Marinier, J., Subramani, H., Madamala, A., Nemala, S.K. and Sunkara, S. (2026) EVA-Bench: A New End-to-end Framework for Evaluating Voice Agents. arXiv preprint arXiv:2605.13841, 13 May 2026, revised 27 May 2026. DOI: 10.48550/arXiv.2605.13841.
- OWASP GenAI Security Project (2025) OWASP Top 10 for LLM Applications 2025. OWASP Foundation. Prompt injection is listed first, as LLM01.
- Gartner (2025) Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027. Press release, Sydney, 25 June 2025.
- TM Forum (2025) Autonomous Networks Framework v2.0.0 (IG1218F). Introductory Guide, published 20 March 2025, TM Forum Approved 9 May 2025. Levels of autonomy 0 to 5. Evaluation criteria for assigning a level are in Autonomous Network Levels Evaluation Methodology (IG1252).
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Bain & Co: AI Infrastructure Buildout Will Require $6 Trillion Revenue by 2031 to Support Massive CAPEX
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About the author:
Satish Barot is co-founder and CTO of Klearcom. He has spent around twenty years building telephony products and leads the engineering team behind Klearcom’s voice testing platform.
Verizon offers free online AI training tailored to your interests!
Verizon has launched an online portal that curates AI training content from several tech giants, including IBM, Google, Microsoft, Anthropic, OpenAI, and online course provider Coursera. The U.S.’ largest wireless carrier by subscriber count says that the type of courses on offer normally cost in the region of $700 per year, but it is making them available for free.
“Strengthening the American economy starts with making sure every individual has the opportunity to adapt and succeed in a rapidly changing world. AI isn’t just a technological shift – it will change the face of every workforce around the world,” said Verizon CEO Dan Schulman.
“Companies, working closely together and with the public sector, have a responsibility to invest in people with the same urgency they invest in technology. By giving people and small businesses free access to the best AI training, we are helping workers retain their jobs, navigate transitions, support their families, and help small businesses grow – while building confidence in our American economy. When you empower people to embrace change rather than fear it, you create a ripple effect that builds healthier communities and a stronger and more resilient national economy.”
Linked to this initiative is a separate $1 million grant that Verizon has awarded to the Liberty Science Center (LSC) in Jersey City, New Jersey. The funds will be spend on providing practical AI skills to individuals and small businesses.
“As a company with a strong footprint in New Jersey, Verizon is deeply committed to supporting the communities we call home, and our longstanding partnership with Liberty Science Centre is a cornerstone of that commitment,” said Donna Epps, chief responsible business officer of Verizon. “Verizon and LSC have a shared vision of empowering the learners and leaders of tomorrow, and we’re excited to work together to create programming that evokes curiosity for new technologies for educators, families and students from across the state.”
The non-profit “Centre for Humane Technology (CHT)” – co-founded by ex-Googler Tristan Harris – has published a report (PDF) explaining how AI could damage everything from personal relationships to governments, and -most significantly- the workplace.
References:
https://www.verizon.com/ai-skills/home
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
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?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
The AI Infrastructure Build-Out: A $10 Trillion Bet on Compute, Power, and Networks
Introduction:
According to the Wall Street Journal, the AI build-out is rapidly becoming the largest concentrated infrastructure investment cycle in modern American economic history. Unlike earlier national build-outs—railroads, interstate highways, electrification, or the commercial internet—this cycle is being driven largely by a small group of cloud platforms deploying highly specialized compute, networking, power, cooling, and semiconductor infrastructure at unprecedented speed. Economist Stijn van Nieuwerburgh estimates that U.S. spending on data centers and related AI infrastructure could reach $10.3 trillion [1.] between 2025 and 2032, equivalent to an average of 3.6% of annual GDP. The estimate encompasses far more than conventional enterprise data centers: it reflects the industrial-scale infrastructure needed to train and serve frontier AI models, including GPU and accelerator clusters, high-bandwidth memory, advanced packaging, optical interconnects, high-capacity Ethernet and InfiniBand fabrics, grid interconnection, substations, backup generation, liquid cooling, and long-haul fiber connectivity.
Note 1. The $10.3 trillion number is a scenario-based estimate of U.S. AI infrastructure investment during 2025–2032—not a forecast of announced corporate spending. The Brookings analysis behind it assumes that about 183 GW of new data-center capacity will be completed through 2032, versus a 509-GW announced/planned pipeline. A representative 200-MW AI campus is estimated to cost about $8.2 billion: $5.6 billion for IT equipment, $2.2 billion for the facility, and $0.4 billion for power infrastructure. Thus, most of the investment is in compute and networking hardware rather than buildings.
The scale creates a major financing challenge: the five largest hyperscalers are projected to spend about $800 billion on capex in 2026, exceeding their combined operating cash flow. Under the Brookings assumptions, the resulting infrastructure would need roughly $3.7 trillion of annual revenue by 2032 to produce a 10% unlevered return. The key economic issue, therefore, is whether future AI revenue and utilization can justify the enormous capital investment.
From Cloud Data Centers to AI Factories:
The defining characteristic of this AI buildout investment cycle is its concentration. The five U.S. hyperscalers (Alphabet, Amazon, Meta, Microsoft, and Oracle) are collectively expected to invest roughly $4.2 trillion in capital expenditures during the four years ending in 2029, according to FactSet estimates cited in the source material. Increasingly, this capital is directed toward AI-optimized facilities: campuses designed around megawatt-scale accelerator pods, dense GPU clusters, high-radix network fabrics, and power delivery systems capable of supporting workloads whose energy and cooling profiles differ sharply from those of traditional cloud computing.
Those five major hyperscalers increased combined capital expenditures from approximately $97 billion in 2020 to more than $400 billion in 2025, with the paper projecting approximately $800.5 billion in 2026. That 2026 figure is significant because it exceeds their combined operating cash flow of approximately $707.1 billion. In other words, projected capex is about 113% of operating cash flow. Pacific Software Ventures That creates a MAJOR financing problem: AI infrastructure investment is becoming too large to be financed entirely from hyperscaler internally generated cash.
AI infrastructure is not simply an expansion of conventional cloud capacity. Large-model training and inference create a distinct systems-engineering problem. Training AI frontier foundation models requires thousands to hundreds of thousands of tightly coupled accelerators. Those accelerators must exchange model parameters, activation data, and gradients at extremely high rates. Network performance therefore becomes a first-order determinant of usable compute capacity. A GPU cluster can deliver poor economics if its fabric introduces congestion, latency, packet loss, or inadequate bisection bandwidth during distributed training.
That requirement is accelerating deployment of:
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GPU- and AI-accelerator servers with high-bandwidth memory and advanced semiconductor packaging.
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High-speed scale-up interconnects within accelerator nodes and scale-out fabrics across clusters.
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400 GbE, 800 GbE, and emerging 1.6 TbE Ethernet architectures, along with InfiniBand deployments for tightly coupled training environments.
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Optical transceivers, co-packaged optics research, photonic switching, and expanded fiber density within and between data-center campuses.
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AI-aware workload scheduling, distributed storage, data pipelines, checkpointing systems, and network telemetry.
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Direct-to-chip liquid cooling, rear-door heat exchangers, chilled-water systems, and other thermal-management systems required by high-density AI racks.
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New transmission lines, substations, transformers, gas generation, battery systems, and other power infrastructure needed to support multi-hundred-megawatt and gigawatt-scale campuses.
In effect, hyperscalers are building what are increasingly described as AI factories: integrated physical and digital production systems that convert electricity, capital equipment, data, and semiconductor capacity into trained models, inference tokens, and AI-enabled cloud services.
A Historically Large Capital Concentration:
AI investment is projected to reach 1.9% of U.S. GDP in 2026, according to Goldman Sachs estimates cited in the source material. The late-19th-century railroad boom was the last period in which a single new infrastructure category represented a larger share of the U.S. economy.
The comparison is useful, but incomplete. Railroads connected physical markets over decades. The AI build-out is being deployed on a far more compressed timetable and is dependent on global supply chains for leading-edge accelerators, high-bandwidth memory, advanced substrates, optical components, power equipment, and data-center construction capacity.
This creates a reinforcing investment loop:
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Foundation-model developers require more compute to train larger or more capable models.
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Cloud providers build additional accelerator capacity to support training and inference demand.
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Semiconductor vendors, memory suppliers, networking companies, optical-component manufacturers, and power-equipment suppliers expand production.
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Data-center developers secure land, power contracts, grid interconnections, fiber routes, water or cooling capacity, and financing.
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Enterprises adopt AI services, increasing inference demand and reinforcing hyperscaler investment.
The strategic question is whether revenue from AI applications, enterprise subscriptions, API usage, advertising optimization, software agents, automation, and industry-specific deployments will scale fast enough to justify the capital intensity of the underlying infrastructure.
Financial and Infrastructure Risks:
The scale of investment introduces material financial-system risk. A growing portion of AI-related infrastructure is being financed through debt, including special-purpose entities and off-balance-sheet structures that may have limited public disclosure. These structures can allow technology companies and infrastructure developers to finance data-center construction, equipment purchases, and long-term capacity commitments without placing all obligations directly on corporate balance sheets.
That can be economically rational when capacity utilization is high and long-term AI demand is durable. However, it also creates exposure if expected AI revenues, cloud bookings, or accelerator utilization fail to materialize.
The central risk is not merely that an individual model underperforms. It is that a synchronized reduction in AI capital expenditure could affect multiple interconnected sectors at once:
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Data-center developers and construction firms.
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Semiconductor, memory, storage, and server suppliers.
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Optical networking and switching vendors.
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Utilities, independent power producers, and grid-equipment manufacturers.
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Banks, private-credit funds, infrastructure lenders, and equipment-finance providers.
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Commercial real-estate markets in data-center-heavy regions.
A sudden pause in hyperscaler spending would therefore have broader consequences than a typical technology downcycle. It could reduce orders across a deeply interdependent industrial supply chain while exposing leveraged infrastructure vehicles to weaker cash flows.
IT Product Inflation, Power, and Network Capacity:
The AI build-out is also creating supply-side pressure in strategic technology markets. Demand for data-center equipment—especially memory, advanced semiconductors, servers, optics, and power-delivery equipment—has tightened supply and raised costs. The source material notes that prices paid by importers for computers, peripherals, and semiconductors were 20% higher in August than a year earlier.
That inflation can propagate beyond the data center. Higher component prices can increase the cost of consumer electronics, including smartphones, PCs, gaming systems, and storage products. Enterprises may also face higher prices for servers, networking equipment, cloud services, and AI-enabled software.
Power is an equally important constraint. AI data centers concentrate demand geographically, often creating large and relatively inflexible new loads on regional grids. A single large campus may require hundreds of megawatts, while the next generation of AI campuses could require gigawatt-scale capacity. This is driving demand for new generation, transmission capacity, substations, transformers, energy storage, and grid-management technologies.
The result is a collision between digital infrastructure planning and energy-system planning. Data-center capacity is no longer determined primarily by real estate, fiber connectivity, or server availability. In many markets, the gating factor is now the ability to obtain firm power, complete interconnection studies, procure transformers and switchgear, and finance new grid infrastructure.
Conclusions:
For IEEE Techblog readers, the central issue is not whether AI demand is real. It is whether the industry can build an economically sustainable, energy-efficient, resilient, and interoperable infrastructure stack at the required scale.
That challenge spans multiple engineering domains:
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Semiconductor architecture, packaging, memory bandwidth, and energy efficiency.
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Data-center electrical design, cooling, rack density, and operational resiliency.
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High-performance networking, congestion control, optical interconnects, and distributed-system design.
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AI software optimization, including model efficiency, quantization, sparsity, scheduling, and inference optimization.
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Grid integration, power electronics, demand response, and energy-aware workload placement.
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Security, supply-chain assurance, and operational management across increasingly autonomous infrastructure.
The AI boom may indeed become the defining infrastructure investment cycle of this era. Its long-term success, however, will depend less on headline capital-expenditure totals than on whether the industry can translate massive spending on accelerators and data centers into durable productivity gains, commercially viable AI services, and infrastructure that does not impose unsustainable costs on power systems, supply chains, consumers, or the financial sector.
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References:
Dell’Oro: Data Center capex grew 92% in 2Q-2026 (caveats galore)
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
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?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Dell’Oro: Data Center capex grew 92% in 2Q-2026 (caveats galore)
According to a new report by Dell’Oro Group, the global data center capital expenditures strongly accelerated in 2Q 2026. Continued AI infrastructure investment supported growth across compute, storage, networking, and physical infrastructure, while rising memory and storage prices significantly increased server average selling prices.
“Data center capex growth broadened in the second quarter as investment accelerated across both established Cloud Service Providers and emerging AI infrastructure customers,” said Baron Fung, Vice President of Research at Dell’Oro Group.
“Spending remained concentrated in NVIDIA Blackwell Ultra and hyperscaler custom accelerators, while agentic AI created incremental demand for general-purpose compute, storage, and complementary networking. Neocloud providers and AI model builders are also becoming increasingly important contributors to infrastructure investment. These companies are rapidly expanding their own capacity while deepening partnerships with cloud service providers.”
“Looking ahead, ongoing accelerator deployments and emerging agentic AI and AI-related storage workloads should sustain strong capex growth through the remainder of 2026 and beyond, although supply constraints could limit the pace at which planned infrastructure is deployed,” explained Fung.
Additional highlights from the 2Q 2026 Data Center IT Capex Quarterly Report:
- Neocloud and AI Model Builder capex grew the fastest among the customer segments, reflecting the early stages of their infrastructure buildouts.
- Higher memory and storage prices provided an additional lift to capex by driving server average selling prices higher.
- Dell led server OEM revenue, followed by SuperMicro and Lenovo, while white-box server revenue reached a record high.
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On August 18th Dell’Oro Group forecasted that worldwide data center capital capex is to maintain growth momentum and surpass $3 trillion by 2030. High-end accelerators powering accelerated servers optimized for AI are expected to represent the largest share of data center capex and remain the primary driver of capex growth over the forecast period.
“Our 2030 data center capex outlook has nearly doubled since the January 2026 forecast, reflecting higher hyperscale capex guidance, increased projections for global data center power capacity, and higher commodity costs,” said Baron Fung, Vice President of Research at Dell’Oro Group. “High-end accelerators powering AI-optimized servers are expected to account for the largest share of data center capex and remain the primary driver of growth over the forecast period.
“However, the pace of growth will depend on the sustainability of investment, power availability, and supply chain conditions. Accelerated and heterogeneous computing, along with innovations in server efficiency, could help mitigate the rising cost and infrastructure demands of AI. The Top 4 US hyperscalers alone could represent about half of global capex, while enterprise investment remains constrained by uncertain AI returns,” according to Fung.
Additional highlights from the Data Center IT Capex 5-Year July 2026 Forecast Report:
- High-end accelerators are expected to account for the largest share of data center capex and remain the primary driver of spending growth through 2030.
- General-purpose server demand is expected to benefit from growing inference, agentic AI, and storage workloads, broadening infrastructure growth beyond accelerated computing.
- The newly added AI-specialized cloud segment, comprising AI model builders and neocloud service providers, is projected to grow at nearly a 60 percent CAGR, outpacing the growth of other customer segments.
Additional highlights from the Data Center IT Capex 5-Year July 2026 Forecast Report:
- High-end accelerators are expected to account for the largest share of data center capex and remain the primary driver of spending growth through 2030.
- General-purpose server demand is expected to benefit from growing inference, agentic AI, and storage workloads, broadening infrastructure growth beyond accelerated computing.
- The newly added AI-specialized cloud segment, comprising AI model builders and neocloud service providers, is projected to grow at nearly a 60 percent CAGR, outpacing the growth of other customer segments.
IEEE Techblog Analysis:
- Dell’Oro had already raised its 2026 global data-center capex outlook to more than $1 trillion in its June 2026 report. It also said 2H26 growth was expected to accelerate because of NVIDIA Rubin deployments and hyperscaler custom-accelerator refreshes. That makes the new 92% 2Q figure much more significant: this isn’t simply a strong quarter; it is occurring within a $1-trillion-plus annual investment cycle.
- Epoch AI’s tracking shows combined hyperscaler quarterly capex has been increasing at an average 72% annual rate since 2Q23 and projects approximately $770 billion for 2026 if the trend continues.
- Another estimate from Moody’s put 2026 hyperscaler capex at $785 billion, including Microsoft, Amazon, Meta, Alphabet, Oracle and CoreWeave.
- Dell’Oro’s separate 2Q semiconductor/component report says data-center component revenue increased 182% YoY, while DRAM and storage-drive average selling prices per bit more than doubled. Therefore, some of the 92% increase in data-center capex is clearly inflation in the cost of the equipment, not necessarily an equivalent increase in physical infrastructure.
- Therefore, the 92% increase in capex should not be interpreted as a 92% increase in deployed computing capacity, because sharply higher DRAM, NAND/storage and other component prices are inflating server system costs.
What’s Missing from this Report:
The press release for this report notes that worldwide data center capital expenditures grew 92% in 2Q02026, driven by surging AI demand and memory costs. However, it omits precise spending figures for individual hyperscalers (Alphabet, Amazon, Meta, Microsoft and Oracle) as well as OEM market share details. Moreover, YoY growth can conceal the current trajectory. Sequential growth would show whether the AI infrastructure spending acceleration actually intensified during 2Q of 2026.
Conclusions:
The 92% year-over-year increase in 2Q26 data-center capex needs to be viewed against a much larger AI infrastructure investment cycle. Dell’Oro had already raised its 2026 global data-center capex forecast to more than $1 trillion, with 2H26 spending expected to accelerate further as NVIDIA’s Rubin systems and hyperscaler custom accelerators ramp. At the same time, Dell’Oro reported that data-center semiconductor and component revenue surged 182% in 2Q26, with DRAM and storage-drive prices per bit more than doubling year over year. Consequently, a significant portion of the reported capex growth reflects higher equipment prices rather than a comparable increase in physical computing capacity. Meanwhile, hyperscaler capex is increasingly being supplemented by debt-financed Neocloud and AI-model-builder infrastructure, broadening the investment cycle beyond the traditional cloud giants.
Dell’Oro Group’s Data Center IT Capex Quarterly Report details the data center infrastructure capital expenditures of the largest hyperscale cloud service providers, AI Model Builders, Neocloud, Rest of Cloud, Telco, and Enterprise customer segments. It provides the allocation of data center infrastructure capex for general-purpose and accelerated servers, storage systems, and other auxiliary data center equipment. The report also discusses market trends, drivers of the leading cloud service providers’ capex growth during the quarter, and the outlook for the next year. To purchase this report, please contact us at [email protected].
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References:
Dell’Oro: 2H2026 Data Center Capex to Accelerate due to massive AI Deployments
PwC: Global AI data center spending to hit $31.6tn by 2050; Role of full stack orchestration layer explained
Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
AI risks and backlash increase; Recap of the circular loop of fake AI profits and hyperscaler markups of private AI companies
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Analysis: Cisco, HPE/Juniper, and Nvidia network equipment for AI data centers
Networking chips and modules for AI data centers: Infiniband, Ultra Ethernet, Optical Connections
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?
Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms
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?
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
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)
Structured Error Analysis: The Missing Pre-Deployment Step for Machine Learning in Telecom Operations
By Priyank Jain with Alan J Weissberger
Introduction:
Machine learning now sits inside telecom operations. Models predict equipment failures before they become outages, forecast traffic for capacity planning, flag telemetry anomalies, and estimate quality of experience from indirect signals—decisions that carry real financial weight. Yet most teams still approve a model for deployment on a single number: accuracy, AUC, or a concordance index computed once on a held-out set. That number is a poor predictor of production behavior. This post argues that structured error analysis belongs before deployment, and walks through five failure modes a single aggregate metric reliably hides.
Terms:
Training fits parameters on historical data; inference applies the fitted model to new data in production. These are two different software environments, and that difference causes more production failures than bad modeling does. Train-serve skew is any gap between training and serving conditions [10]. A fault-prediction model trained on offline-cleaned, gap-filled, five-minute telemetry may encounter a streaming collector that handles missing samples differently and closes its window a few seconds early. A feature named mean_utilization_5m exists in both places and means something slightly different in each. Nothing errors; accuracy just quietly drops.
Censoring matters whenever a model predicts time-to-event. An asset that has not failed yet is censored: we know it survived to now, not how long it will last. Censored observations are legitimate training data; mislabeled ones are poison [4], [5]. Data drift is a change in the model’s inputs—a firmware release that changes how a counter is reported [1], [2]. Concept drift is a change in the underlying relationship—say, a topology change that makes a predictive signal meaningless [3]. Both degrade a validated model, and neither appears in the original evaluation. The residual—the gap between prediction and outcome for one case—is the raw material of error analysis.
Five Failure Modes:
Environment mismatch. Models rarely fail because the mathematics was wrong; they fail because training misrepresented production. A feature computed one way offline and another online, a timezone off by an hour, a normalization constant recomputed over the wrong window—each is trivial, and together they are the most common reason validated performance does not survive deployment. The defense is computing features once and consuming them in both paths. Where that is impossible, run an automated comparison: sample cases, compute features through both paths, and assert agreement within tolerance. Sharing one feature pipeline and persisting trained preprocessors prevents this.
Wrong population. A time-to-failure model over a fleet defines the population by a rule such as “decommission date is empty.” That looks reasonable but is wrong whenever a status field and a date field disagree. An asset marked failed that never had a decommission date written satisfies the rule; the model treats it as healthy and learns that dead things are alive. Survival estimates come out optimistic, worst where data hygiene is worst. Every model-side metric looks fine because the model faithfully learns its labels. When a result contradicts domain knowledge, interrogate the population definition before touching the model.
Metric too good. If a feature table is assembled alongside the outcome, the outcome—or a near-copy such as observed duration—can end up in the feature set, and the model reports near-perfect discrimination. The same happens with fields populated only after the event, such as a diagnostic code set once a fault is confirmed. These are not predictive features; they are the answer arriving late. Treat an unexpectedly excellent score as a bug report and audit whether each feature would be available at scoring time and could have been influenced by the outcome.
The average hid the failure. A single metric over an evaluation set is an average, and averages destroy structure. An anomaly detector at 94% accuracy nationally is consistent with 97% on dense urban sites and 61% on rural sites with sparse telemetry. The aggregate is not wrong; it just does not answer where the model can be trusted. Networks are heterogeneous—multiple hardware generations, uneven telemetry coverage—and model performance is almost never uniform across that variation. The weakest segments usually have the least training data and matter most operationally.
Wrong objective. A model minimizes a loss that stands in for the outcome you care about. When the two drift apart, the model optimizes the stand-in faithfully and fails the objective—Goodhart’s law inside a training loop [6], [7]. In operations the common form is class imbalance: a maintenance model drives aggregate loss down by predicting no failure for a rarely failing class, learning to ignore the cases it was deployed to find [8], [9]. A subtler form ignores cost asymmetry—a missed fault causing an outage and a false alarm wasting a site visit are not equally expensive, but a symmetric loss treats them as equal. State the operational objective in plain language before training, then ask whether the loss actually rewards it.
The Procedure:
Structured error analysis characterizes where and how a model is wrong, not how often. The output is a map of competence. Five steps: build an evaluation set that looks like production, deliberately including every hardware generation, site class, and traffic regime; score and keep everything—prediction, outcome, residual, and full feature vector; stratify by operationally meaningful dimensions and recompute performance within each segment; cluster the residuals to find signatures in data quality, configuration, or thinly sampled regions; and write a disposition for each cluster—fix, accept and document, or exclude from operating scope. That document, the explicit statement of where the model can and cannot be trusted, is the actual artifact of validation.
The artifact also builds operator trust. Engineers dispatching a crew want to know when to believe the model; an aggregate figure is not actionable. A ranked risk score with its two or three drivers attached becomes a claim network operators can check, agree with, or push back on. Models are ignored far more often for being unexplainable than for being inaccurate.
Figure 1. Validation architecture. Features are defined once and consumed by both the training and inference paths (the train-serve control point). Model scoring feeds structured error analysis, whose results decide whether a model clears the deployment gate.
Conclusions:
If a model reports a quantity over a horizon longer than the observed data, part is measurement and part is assumption—report the split, test against several tail assumptions, and decline horizons where extrapolation dominates. That omission matters because the two parts carry very different risk: a precise-looking figure can be almost entirely assumption, and any decision anchored to it—capital planning, replacement schedules, contractual commitments—inherits a fragility nobody has accounted for. Reporting the split is the difference between acting on evidence and acting on a guess wearing a decimal point.
Figure 2. Reporting beyond the observed window, shown for an equipment time-to-failure (survival) model. Up to the last observed point the fraction of assets still in service rests on data (solid); beyond it, the curve rests on a chosen tail assumption (dashed). When such a model reports remaining useful life over a long horizon, the assumed portion can dominate the reported number.
Second, validation expires: because of drift, a model is only validated as of a date. Repeat the error analysis on a fixed cadence against recent data, compare segment-level results to baseline, and give every deployed model a named owner, a review date, and documented dependencies [10], [11]. The absence of a decommissioning plan turns a stale model into a permanent liability.
For telecom operations, where networks are heterogeneous and the consequential cases are usually the rare ones, the recommendation is blunt: require the error map, not the accuracy number, before deployment.
About the Author:
Priyank Jain is a Data Scientist II at Boost Mobile, where he builds production machine learning across retail and telecom, including subscriber survival and churn models, demand and traffic forecasting, and location recommendation systems. He has over seven years in applied machine learning, is an IEEE graduate student member, and has authored peer-reviewed journal and conference papers.
LinkedIn: https://www.linkedin.com/in/priyankjn7/
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References:
[1] “What is data drift in ML, and how to detect and handle it,” Evidently AI, Jan. 9, 2025. [Online]. Available: https://www.evidentlyai.com/ml-in-production/data-drift
[2] “What is data drift in machine learning,” Chalk AI, Apr. 9, 2026. [Online]. Available: https://chalk.ai/blog/data-drift
[3] “Concept drift,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Concept_drift
[4] “Survival analysis and interpretation of time-to-event data,” PMC/NIH, Jul. 13, 2018. [Online]. Available: https://pmc.ncbi.nlm.nih.gov/articles/PMC6110618/
[5] “Survival analysis,” Wikipedia. [Online]. Available: https://en.wikipedia.org/wiki/Survival_analysis
[6] “Measuring Goodhart’s law,” OpenAI, Apr. 13, 2022. [Online]. Available: https://openai.com/index/measuring-goodharts-law/
[7] “On Goodhart’s law, with an application to value alignment,” arXiv:2410.09638, Oct. 12, 2024.
[8] “Class-imbalanced datasets,” Google Machine Learning Crash Course. [Online]. Available: https://developers.google.com/machine-learning/crash-course/overfitting/imbalanced-datasets
[9] “5 effective ways to handle imbalanced data in machine learning,” MachineLearningMastery, Apr. 20, 2025. [Online]. Available: https://machinelearningmastery.com/5-effective-ways-to-handle-imbalanced-data-in-machine-learning/
[10] “MLOps: Continuous delivery and automation pipelines in machine learning,” Google Cloud, Aug. 28, 2024. [Online]. Available: https://docs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
[11] “MLOps lifecycle: Stages, workflow, and best practices,” LaunchDarkly, May 30, 2026. [Online]. Available: https://launchdarkly.com/blog/mlops-lifecycle/
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
