AI Bubble

Huge Risks for the proposed $500B AI Investments from Giant Wall Street firms

Posted on by Alan Weissberger

Disclaimer: Perplexity.ai was used for research and analysis in this article.

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Executive Summary:

This past Monday, six giant Wall Street asset managers, private-equity firms and banks announced an effort to raise $500 billion to keep fueling the A.I. boom by financing more data centers, power plants and chips. The proposed platform would direct capital to Nvidia customers—including AI startups and data-center operators—at precisely the point when many have struggled to obtain funding through ordinary credit channels.

We take that as a huge warning sign for the proposed AI investments.  Here’s why: If the underlying projects offered clearly proven cash flows, predictable utilization and collateral with durable value, lenders would not need a specially assembled consortium, headline-scale commitments and Nvidia’s direct involvement to make the loans happen. The initiative appears designed to overcome a financing bottleneck created by the extraordinary gap between AI infrastructure spending and demonstrated AI revenue.

This proposed $500 billion AI-financing initiative is less a validation of durable AI economics than an admission that the sector’s spending plans have outgrown its customers’ ability—or willingness—to finance them conventionally. Rather than demonstrating independently sustainable demand, the arrangement risks extending an investment cycle increasingly dependent on vendor-enabled credit, opaque commitments and financial engineering.

The firms—Apollo, BlackRock, Blackstone, Brookfield, Goldman Sachs and KKR—said they were working together to come up with that huge sum to lend to Nvidia’s customers, including the start-ups that use the company’s chips in data centers to develop and operate A.I. software. These customers, Nvidia said, have been struggling to secure financing for chips and data centers.

Nvidia will connect its customers with one of the six lenders, which will provide financing that could range from loans to credit. The financing will be “at attractive rates,” Nvidia said in a blog post.

In practical terms, this is a vendor-financing mechanism, even if the capital technically comes from third parties. Nvidia is helping its customers obtain the money required to buy Nvidia-dependent infrastructure. The more readily startups and data-center operators can borrow, the more equipment they can order; the more equipment they order, the more Nvidia can sell. That does not mean the demand is fictitious, but it does make it harder to distinguish independent end-user demand from demand supported by an ecosystem that is financing itself.

Circularity is the principal concern. When suppliers, investors, cloud providers, AI labs and lenders all have financial incentives to keep capital circulating within the same small group of counterparties, reported growth can look more robust than the ultimate economics justify. The risk is not simply that projects fail individually. It is that a shortfall in AI-service revenues, utilization or pricing could spread simultaneously through hardware vendors, AI developers, cloud operators, private-credit vehicles and the securities backed by their cash flows. Circular financing can blur the line between real external demand and investment-funded purchases.

Executives from the lenders joined Jensen Huang, Nvidia’s CEO & founder, for an unusual, extended interview on CNBC, where they talked up their new, seemingly insatiable desire to finance infrastructure for A.I. Huang said on the air. He added that “A.I. labs” and “A.I. start-ups” would have access to the financing. He did not name those companies or whether Nvidia would receive any money as part of the effort.  David M. Solomon, Goldman’s chief executive, said the consortium was Mr. Huang’s idea.

“We need to raise this money as fast as possible,” said Larry Fink, BlackRock’s chief executive. He also stated: “There’s quite a bit of negativity around A.I. and data centers right now, but let’s be clear: This is going to be creating a huge amount of jobs.”

Skeptical Analysis:

The urgency in those statements deserves more scrutiny. Speed is not a substitute for underwriting, and job-creation claims do not establish that investments will meet their cost of capital. The industry is attempting to finance assets whose useful economic life may be much shorter and more uncertain than that of traditional infrastructure. A GPU fleet can lose competitiveness rapidly when a new architecture, memory standard or systems design emerges. Its resale value can fall sharply if capacity demand weakens. Treating such hardware as collateral comparable to a toll road, utility asset or long-lived building is a major assumption—not an established fact.

The announcement punctuates a head rush on Wall Street and in Silicon Valley into anything that even vaguely resembles A.I. The stocks of tech giants and chipmakers have soared for most of this year, and a pair of the biggest names in the space, Anthropic and OpenAI, are expected to file for initial public offerings that could value them at $1 trillion apiece.

But soaring equity valuations and enormous projected IPO valuations do not answer the basic return-on-investment question: who will pay enough, for long enough, to justify the total cost of the data centers, power plants, networking, GPUs and debt now being assembled? AI vendors may generate impressive revenue growth while still failing to earn enough to cover the depreciation, energy, financing and replacement costs of the infrastructure required to produce it.

Nvidia’s financing narrative appears to conflate broad interest in AI-enabled services with broad, independent demand for capital-intensive AI infrastructure. Governments, enterprises and startups may all seek access to AI capabilities, but the current demand for hyperscale GPU clusters, dedicated power capacity and purpose-built “AI factories” remains concentrated among a relatively small group of frontier-model developers and cloud platforms.

That distinction matters because broad adoption of AI applications does not automatically translate into economically sustainable demand for vast new data-center capacity. Enterprises can consume AI through APIs, hosted platforms, smaller models and open-source software without owning—or financing—dedicated high-density compute infrastructure. Nvidia itself describes demand as spanning enterprises, startups, governments, nations and AI labs, but the critical question for investors is how much of that demand converts into durable, contracted infrastructure revenue rather than experimentation, pilots or subsidized consumption.

As Bloomberg reported last week, OpenAI accounted for somewhere between 50% and 70% of Microsoft’s AI revenue in the 12 months to 30 June.

That degree of concentration is significant. Microsoft disclosed $24.1 billion in sales from OpenAI during the year ended in June; outside estimates place that at more than half, and perhaps about 70%, of Microsoft’s AI-related revenue. Much of the revenue reflects OpenAI’s spending on Microsoft cloud and model-development services, meaning a substantial portion of the apparent AI revenue base may be generated within a tightly linked commercial relationship rather than by a diversified population of external enterprise customers.

That could be problematic if the picture is similar at other hyperscalers and at other frontier AI companies like Anthropic, for example. A market in which a handful of loss-making model developers drive an outsized share of cloud and infrastructure revenue is inherently more fragile than one supported by a broad base of profitable end users. It exposes infrastructure providers and lenders to customer concentration, correlated capital spending and the possibility that a reduction in financing at one frontier lab quickly reduces demand across the supply chain.

Indeed, as has been reported by multiple correspondents – most notably staunch AI critic Ed Zitron – LLM makers like OpenAI are losing money hand over fist and can only charge so much for tokens before customers either curb their usage or switch to open source models.  Zitron wrote:

“It’s estimated that 70% or more of the AI revenues of Microsoft, Google, and Amazon were from either OpenAI or Anthropic. UBS estimated that next year, Anthropic and OpenAI’s compute spend would be 48% of all Google Cloud revenues — which means that they likely account for even more than 70% of its AI revenues.”

The commercial challenge is not whether frontier models have value- they do. It is whether they can deliver that value at prices that exceed the combined costs of training, inference, electricity, networking, cloud capacity, depreciation and ongoing model development. If token pricing rises too far, customers may reduce usage, shift workloads to lower-cost models, or use open-source alternatives. If pricing remains low, frontier-model providers may struggle to cover their infrastructure bills. Open-source fine-tuning can materially reduce costs for specialized enterprise workloads, reinforcing the competitive pressure on premium proprietary-model pricing.

The most plausible downside is that the technology becomes broadly useful but insufficiently profitable to support today’s extreme capital intensity. That outcome would leave the sector with too much high-cost capacity, thin margins, declining GPU collateral values and lenders dependent on assumptions about utilization and cash flows that have not yet been tested through a downturn. The foundational financial risk is straightforward: AI-service revenues may prove insufficient to service the fixed obligations incurred to construct the infrastructure.  Notably, Nvidia stock dipped modestly on Monday after The Financial Times reported that the company was nearing the mammoth financing deal.

While the contours of the arrangements were announced Monday afternoon, details remained scarce. A joint news release referred only to “memorandums of understanding” to “create dedicated pools of capital at significant scale.”

That language is important. Memorandums of Understanding (MoU’s) are not equivalent to binding, fully funded commitments. Until investors know the actual terms—capital committed, leverage permitted, collateral requirements, Nvidia’s role in losses, loan maturities, customer concentration limits and underwriting standards—the $500 billion figure is better understood as an ambitious financing target than as validated capital deployment. Reporting has described the initiative as a multiyear target rather than cash already committed, while questions remain about the scale of any Nvidia backstop.

During their television interview, the lenders’ executives alluded vaguely to “yield-based products” and even securitization, or the creation of bonds that would divvy up the revenue from A.I. labs into risky and less risky categories. There were several references to A.I. as a new asset class and to allowing smaller investors an opportunity to invest in debt backed by the data centers.

This is where the proposal moves from aggressive investment to potential systemic-risk creation. Securitization can distribute risk, but it does not eliminate it; it can instead diffuse difficult-to-value exposure across a wider investor base. Packaging AI-data-center debt into yield products may make financing more available, but it also risks obscuring the quality of the underlying cash flows, the degree of correlated exposure among borrowers and the vulnerability of rapidly depreciating hardware collateral. The more complex the capital stack becomes, the greater the danger that investors mistake engineered liquidity for genuine economic value.

Executive Quotes:

“NVIDIA has reached an important milestone. We began by building chips; today, we are helping create a new class of productive, investable infrastructure: AI factories,” said Jensen Huang, founder and CEO of NVIDIA. “In AI, compute is revenue. NVIDIA compute is uniquely suited for this role. It is broadly adopted, flexible across models and workloads, fungible and transferable across customers and operators, and continuously improved through CUDA software — extending its useful life and improving its economics over time. It is supported by a deep global ecosystem of developers, customers and offtakers. That is why we are bringing the world’s leading long-term capital providers together to independently underwrite AI infrastructure. These financing platforms will help customers access scarce compute at scale and build the DSX AI factories that will power every industry and country in the age of AI.”

“Modern compute has emerged as a scarce, mission-critical asset class with compelling investment characteristics that is positioned to drive significant long-term economic growth and productivity gains,” said Apollo President Jim Zelter. “The combination of NVIDIA’s proprietary technology ecosystem and Apollo’s flexible, long-term capital base provides a strong foundation to support the next stage of the AI buildout as part of the broader Global Industrial Renaissance.”

“The AI buildout will require unprecedented investment and a skilled workforce to turn that investment into the infrastructure that will help power future growth,” said Larry Fink, Chairman and CEO of BlackRock. “This partnership deepens our relationship with NVIDIA, including through the AI Infrastructure Partnership, and brings together NVIDIA’s leadership in accelerated computing with BlackRock’s ability to connect long-term capital to essential infrastructure. Together, we can help deliver the compute capacity that companies need to grow and create more jobs, supporting the continued growth of the U.S. and global economies, while creating attractive, long-term investment opportunities for our clients.”

“NVIDIA has created extraordinary demand for its compute through an intense focus on customer value and versatile technology,” said Jon Gray, President and COO of Blackstone. “We continue to be enormous investors globally across the NVIDIA ecosystem, and this announcement further underscores our confidence in their platform and the future of AI infrastructure.”

“As our strategic partner, NVIDIA is enabling us to scale AI factories. We are excited about further collaboration to build and fund the backbone of AI globally,” said Bruce Flatt, CEO of Brookfield. “With demand for large-scale AI compute growing significantly as adoption scales across industries, compute is fast becoming the essential layer of infrastructure and a core pillar of the Brookfield AI infrastructure strategy.”

“We’re in a pivotal moment of a historic AI investment cycle. NVIDIA’s full-stack platform is in high demand and uniquely positioned at the center of that global buildout,” said David Solomon, Chairman and CEO of Goldman Sachs. “Our investment and distribution roles reflect our confidence in NVIDIA’s leadership, and we’re excited for the new opportunity to create a market for credit backed by NVIDIA compute.”

“Compute has become a critical infrastructure asset. As we’ve scaled our approach to digital infrastructure, we’ve learned that delivery, not ambition, is the hard part. That’s why we’re excited to build on our strategic partnership with NVIDIA, a founding investor in Helix Digital Infrastructure, to bring together NVIDIA’s accelerated computing platform with KKR’s long-duration capital, infrastructure expertise and capital markets capabilities to turn growing demand into real capacity at extraordinary scale,” said Joe Bae and Scott Nuttall, Co-Chief Executive Officers of KKR.

“This is the very beginning — like what it was when I started in the mortgage-backed securities market in the 1970s,” Mr. Fink said. “I look upon this as a next future for financial engineering.”

That analogy should be treated as cautionary, not reassuring. Financial engineering can expand access to capital and spread risk efficiently when assets have transparent valuations, stable cash flows and conservative underwriting. It becomes dangerous when it is used to finance unproven revenue models, rapidly obsolescing assets and demand forecasts that must remain exceptionally optimistic simply to justify the initial investment.

Huge Risks Explained:

If AI demand stalls, Nvidia faces a sharp reversal in hardware demand and a potentially damaging credit overhang, while lenders could be left financing underutilized data centers secured by equipment whose value can decline far faster than conventional infrastructure. The common vulnerability is that the same uncertain AI revenue streams would be expected to support chip purchases, data-center leases, project debt and securitized investment products.   Nvidia is especially exposed to risks:

  • Order cancellations and lower pricing power. Cloud providers, AI labs and startups would likely slow GPU orders, defer deployments or renegotiate capacity commitments. Nvidia could face weaker revenue growth, inventory risk and pressure on the high margins that have supported its valuation.

  • Vendor-financing and counterparty risk. If Nvidia is arranging or backstopping financing for customers buying its systems, a demand slowdown could turn what appeared to be hardware sales into indirect credit exposure. Customers unable to earn adequate returns from AI services may struggle to repay loans used to buy Nvidia equipment. The risk is magnified where the company’s commercial success depends on borrowers gaining access to financing in the first place.247wallst+1

  • Collateral impairment. GPUs are not durable, slow-depreciating infrastructure assets. A new chip generation, a shift toward more efficient models, or weak utilization can materially reduce the resale value of installed systems. If lenders rely on those systems as collateral, a default could leave them holding equipment worth substantially less than the loan balance—and Nvidia could face lower demand for both new and prior-generation products. Moody’s identifies rapid capacity expansion and fast-changing chips, cooling and computing architectures as sources of overbuilding and technology-obsolescence risk.moodys

  • Feedback-loop risk. A slowdown could create a negative cycle: AI customers reduce spending; Nvidia’s sales weaken; lenders become more cautious; financing availability tightens; customers cut orders further. Where vendors, customers and capital providers are financially intertwined, the decline can be more abrupt than a normal inventory correction.

  • Equity-valuation risk. Nvidia’s market value reflects unusually strong assumptions about the longevity of AI spending, margins and growth. A reassessment of those assumptions could compress the valuation sharply even if Nvidia remains profitable. BIS warns that disappointment in AI returns could trigger a sudden financing pullback and turn the capex boom into a prolonged investment bust.bis

Lenders’ exposure:

Risk How a demand stall transmits losses
Default risk AI labs, cloud operators and data-center developers may fail to generate enough revenue to cover interest, principal, energy and operating costs.
Underutilized capacity Empty or lightly used data halls produce far less cash flow than underwriting models assume, impairing debt-service coverage.
Collateral-value risk Specialized GPU, networking and cooling systems may have weak resale value in a downturn, especially if many borrowers liquidate comparable equipment simultaneously.
Refinancing risk Projects commonly require follow-on funding after construction. If markets reprice AI risk, borrowers may be unable to refinance maturing debt except at much higher rates—or at all.
Concentration risk Multiple loans, funds and securitizations may rely on a small number of AI labs, hyperscalers, equipment suppliers and power projects. A weakness in one tenant or customer can affect many nominally separate investments.
Structured-finance risk Securitizing data-center revenues can spread exposure across private-credit funds, insurers, pensions and bondholders. It diversifies ownership of the risk, but does not improve the underlying cash flow.

The most immediate lender risk is a mismatch between long-lived debt obligations and unstable, technology-dependent revenue. A data center might be financed over many years, but its GPU fleet may need continual upgrades to remain competitive—requiring additional capital expenditure before the original debt is repaid. Moody’s notes that this combination of increasing capital intensity, uncertain compute requirements and structured finance can pressure developers, landlords and investors through execution, renewal and refinancing risk.moodys

Construction and power risks:

A demand slowdown could arrive before projects enter service. That creates a particularly difficult situation for lenders because construction interest, cost overruns and power-reservation charges may accumulate before revenue begins.

Permitting delays, local opposition, water constraints and electricity-grid limitations further compound this exposure. Reuters reported that lenders increasingly treat project readiness—including approvals, permits and local community support—as a credit factor because delays can increase costs, jeopardize covenants and prevent projects from reaching revenue-generating operation.

System-wide scenario:

The more serious scenario is a correlated unwind:

  1. AI applications fail to deliver enough monetizable demand.

  2. AI labs and cloud providers reduce compute commitments.

  3. Data-center utilization and expected rental income fall.

  4. Borrowers cannot service or refinance project debt.

  5. GPUs and related infrastructure lose collateral value.

  6. Losses reach private-credit funds, banks, securitized vehicles, insurers and institutional investors.

  7. New financing becomes unavailable, causing further cuts in infrastructure orders and Nvidia sales.

This would not necessarily resemble the 2008 banking crisis: some first-loss exposure sits outside regulated banks. But Chicago Booth research estimates that a severe re-rating of AI-related debt could still produce roughly $60 billion to $140 billion in realized credit losses, alongside large equity-market effects.

What matters most:

The decisive question is not whether AI is useful or whether data centers remain necessary. It is whether cash-paying end users will generate sufficient, durable revenue to justify the full cost of the hardware, power, real estate, construction and financing now being committed.  If that answer is no, the sector may discover that it financed capacity—not returns.

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From Google’s Gemini:

The Hyperscaler Risk Transmission Chain:
When frontier-model providers struggle to generate high-margin cash flows, the financial damage transfers to hyperscalers through four distinct mechanisms:
1. Massive Equity and Balance Sheet Asset Write-Downs:
Hyperscalers have poured tens of billions of dollars into OpenAI and Anthropic, often structured as “cash-for-cloud” partnerships. If capital markets reassess the terminal valuations of these private AI firms due to a monetization miss, hyperscalers will face multi-billion-dollar non-cash impairment charges on their balance sheets, severely hitting reported net income. 
2. Severe Excess Data Center Capacity & High Fixed Costs: 
Hyperscalers have aggressively built or leased data center physical infrastructure (DCPI) to support the massive compute obligations of their AI partners.
    • The Revenue Void: If OpenAI or Anthropic defers or cancels capacity commitments, hyperscalers are left with energized Megawatts (MW) that have no immediate, high-paying tenant.
    • Stranded Capital: The specialized power, liquid cooling systems, and electrical equipment tailored for dense AI clusters cannot easily be repurposed for traditional cloud workloads without lowering returns on invested capital (ROIC).

3. Collapse of the “Cloud Recycling” Revenue Loop:
A significant portion of the revenue hyperscalers currently report from AI is circular: the hyperscaler invests billions in OpenAI/Anthropic, and the AI firm immediately hands that money back to the hyperscaler to pay for cloud compute time. If these startups cannot monetize their enterprise seats or API volumes, this artificial cloud revenue engine stalls, causing a sharp deceleration in hyperscaler cloud growth rates. 
4. Drastic CapEx Retrenchment & Margin Compression:
Faced with lower compute utilization and falling pricing power per token, hyperscalers would be forced to aggressively slash their capital expenditures (CapEx). While cutting CapEx preserves cash, the near-term transition would compress operating margins due to the heavy depreciation costs of already-purchased Nvidia GPUs and physical data center assets that are sitting idle. [1, 2, 3, 4, 5]
Impact on Hyperscaler Financials:
The table below outlines how a partial vs. severe AI monetization crash alters hyperscaler financial health.

Financial Metric Baseline Forecast (Bull Case) Partial Monetization Reset Severe Crash / Structural Downside
Cloud Revenue Growth Accelerated (driven by AI APIs & enterprise seats) Flattens out as enterprise customers consolidate suppliers Decelerates sharply; circular cloud revenue loops collapse
CapEx Infrastructure Full execution of Dell’Oro’s ~200 GW build out by 2030 20–30% of capacity commitments deferred or resized Mass cancellations of orders; multi-year build freezes
Operating Margins Expands as inference costs decline relative to software prices Compresses due to underutilized GPU clusters and high DCPI fixed costs Severe contraction; massive asset write-downs and depreciation drag
ROIC Historic highs driven by high-density compute demand Diluted; longer payback periods for specialized data centers Tanks; billions in stranded physical capital and obsolete hardware

ROIC:  Return On Invested Capital is a financial metric that measures how well a company uses the money from both lenders and shareholders to make after-tax profits. It is calculated by dividing net operating profit after tax by the average invested capital.
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Concentration of Vulnerability: 
The risk is highly concentrated because hyperscalers are effectively underwriting the entire physical supply chain of the AI boom. McKinsey estimates that 60–65% of AI workloads in the US and Europe will be hosted on hyperscaler infrastructure by 2030. Because a tiny group of buyers controls the market, if just one major hyperscaler cuts its infrastructure spend in response to an OpenAI or Anthropic monetization miss, it will trigger an immediate bullwhip effect—crushing revenue for power, cooling, and electrical equipment vendors upstream. 
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References:

https://nvidianews.nvidia.com/news/nvidia-partners-with-apollo-blackrock-blackstone-brookfield-goldman-sachs-and-kkr-to-establish-ai-compute-infrastructure-financing-platforms-to-mobilize-over-500-billion-of-third-party-capital

https://www.nytimes.com/2026/08/10/business/ai-nvidia-lenders-500-billion.html

https://www.bloomberg.com/news/articles/2026-08-05/microsoft-s-ai-sales-mostly-come-from-openai-disclosures-show

https://www.wheresyoured.at/dont-look-up/

Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs (07/23/26)

Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies

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?

 

Hyperscaler AI Race: Soaring Capex Wipes Out Free Cash Flow; AGI and Digital Gods

Posted on by Alan Weissberger
The tsunami wave of generative AI investment is now facing intense scrutiny due to an unsustainable imbalance between massive capital expenditure (capex) and negligible return on investment (ROI). Despite unprecedented infrastructure spending (mostly for AI Data Center buildouts), the sector has yet to deliver a definitive “killer app” or high-utility enterprise software capable of generating meaningful corporate revenue.  Consequently, stakeholders are shifting from speculative funding toward rigorous evaluation of tangible monetization and operational efficiencies. This lack of clear value realization raises valid concerns about a potential market correction as the technology struggles to transition from a capital sink to a self-sustaining ecosystem.
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Google parent company Alphabet boosted its forecast for capital spending for both 2026 and 2027 last week, citing supply constraints amid surging demand for more computing power. The company said its 2026 capex would increase its potential maximum to $205 billion from $190 billion.  That $15 billion increase places Alphabet neck-and-neck with Amazon at the absolute top of the hyperscaler spending ladder. Paul Meeks, head of technology research at Freedom Capital Markets, told CNBC that Wall Street is expecting about $260 billion in capex from Google/Alphabet in 2027.  “I think people would be satisfied [with that],” he added. “The thing I worry about is if you have a drop in spending: All of a sudden it’s $205 billion for Google this year, and next year it’s, say, $100 billion – it collapses.”
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Hyperscaler Annual Capex Forecast (2024–2027):
All figures represent billions of USD ($B) and reflect current consensus updates.

Company 2024 (Actual) 2025 (Actual) 2026 (Current Guidance / Est) 2027 (Projected)
📦 Amazon $53B $112B $195B – $210B $230B – $260B
🔍 Alphabet (Google) $51B $104B $195B – $205B $240B – $280B
💻 Microsoft $56B $108B $185B – $195B $220B – $250B
♾️ Meta $38B $85B $125B – $145B $150B – $180B
🗄️ Oracle $13B $25B $45B – $50B $55B – $65B
🧮 Combined Aggregate $211B $434B $745B – $805B $895B – $1,035B
Source: Google Gemini
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The huge increase in hyperscaler capex, wipes out their free cash flow (revenues-expenses is now negative for all but Microsoft). The shift in focus by investors from earnings to free cash flow marks a turning point in market perceptions.  The correct way to describe free cash flow is the cash flow a company generates during a period of time that is available to be paid to the company’s shareholders and debtholders.Companies with negative free cash flow are only able to cover the interest and principal on their debt by additional borrowing or by issuing new equity. In other words, cash is flowing from investors to the company, not the other way around.  In a financial crisis, investors become unwilling to support companies not able to cover interest and principal, with the result being a cascade of defaults and runs on financial institutions.

A major concern with the massive AI-capex which has occurred during the last two years is that much of it is debt financed. As the real cost of generative AI-tokens is becoming clear, lower priced Chinese competitors are emerging, and AI customers are beginning to economize on their use of AI. As a result, investors are becoming increasingly alarmed about whether U.S. AI firms will be able to cover their debt obligations.  AI-capex has been the main, and perhaps only driver of U.S. economic growth. If more companies announce negative free cash flows, that increase in magnitude, the financial system and overall economy will move closer to the tipping point.

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But wait, Google/Alphabet co-founder says it’s more about winning AI market share than skyrocketing capex or ROI.  On Patrick O’Shaughnessy’s Invest Like the Best podcast, Gavin Baker, Chief Investment Officer for Atreides Management, shared an anecdote about what’s been going on within Google/Alphabet offices. According to Baker, Google co-founder Larry Page has been telling Google employees, “I am willing to go bankrupt rather than lose this race.” That shows how high the person who led Alphabet through its halcyon days thinks the stakes are in AI.

Baker went on to describe the leaders of Meta Platforms, Microsoft, and Alphabet as being in a race to create a “Digital God,” or artificial general intelligence (AGI), which is likely to be worth trillions of dollars in value if not tens of trillions or even more. He also explained that the tech giants are counting on the models to scale, or get better as they get bigger, and the tech giants are unlikely to slow down their spending on AI infrastructure until they’re proven otherwise. AGI could be more disruptive than any technology before it, including the internet, and most tech CEOs seem to think this.  OpenAI CEO Sam Altman told Time magazine last December, “I think AGI will be the most powerful technology humanity has yet invented.”
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References:

https://www.forbes.com/sites/hershshefrin/2026/07/2/market-experiences-an-ai-capex-turning-point-with-tipping-point-to-follow/

https://www.fool.com/investing/2024/08/31/thinking-of-selling-nvidia-stock-larry-page-quote/

Curmudgeon: Caveat Emptor: Huge Debt and Circular Financing Deals Dominate AI Build-Outs 

Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?

Ookla: AI platform reliability decreases as outages surge

Posted on by Alan Weissberger

So you thought “AI Hallucinations” were the only big problem with AI performance?  Think again!  In a new Ookla reliability report, data from its Downdetector reveals that AI platform outages surged from 6 high-disruption days in Q1 2025 to 51 in Q1 2026 , as AI tools transitioned from novelties to critical business infrastructure. These disruptions stem from rapid scale-up volatility, cloud provider failures, and complex, agentic workflows.  Analysing 471 days of US Downdetector data from 1 January 2025 to 16 April 2026 across ChatGPT, Claude, Gemini, Microsoft Copilot, AWS and Microsoft Azure, Ookla recorded 3.7 million user-reported problems.

High-signal disruption days, defined as when a service recorded more than 10 times its own median daily report volume, rose from six across four major AI apps in Q1 2025 to 51 in Q1 2026, according to the report by Ookla analyst Luke Kehoe.

Anthropic’s Claude model accounted for 39 of those 51 disruption days. Gemini accounted for seven, Copilot three and ChatGPT two.  Here’s a summary:

  • Claude: Anthropic’s platform was the clearest example of scale-up volatility, accounting for 39 of the 51 high-signal disruption days in early 2026 due to rapid adoption and scaling.  
  • ChatGPT: While it generated some of the largest raw disruption spikes—often linked to model updates or demand surges—its median daily report trend improved compared to the prior year.  
  • Microsoft Copilot: Outage reports heavily clustered on weekdays, reflecting its core integration into enterprise business workflows rather than consumer use. 
  • Gemini: Incidents rose to seven alongside expanding user adoption.
  • Cloud Infrastructure: A significant portion of AI downtime wasn’t the AI model itself, but outages at the cloud level that caused cascading failures.  AWS’s 20 October 2025 DynamoDB DNS event generated more than 315,000 US disruption reports, while Microsoft’s Azure Front Door incident on 29 October produced nearly 96,000, illustrating how failures in cloud control planes can cascade into AI platform disruptions.

Claude’s growth over the past 12 months was accompanied by significant disruption. Ookla describes it as “the clearest example of scale-up volatility,” with disruptions to its offering starting to move the needle in July last year as adoption rose. There’s a hint that the upward trajectory will continue – Ookla notes that at 2,830 daily reports on average, Claude’s report volume in March was three times that it recorded in February.

AI reliability now spans multiple failure layers:

AI platforms are not single systems from the user’s point of view, even when they present a single interface. A ChatGPT, Claude, Gemini, or Copilot failure can sit in the product layer, the provider orchestration layer, the hyperscaler layer, or the edge and access layer. The product layer is what users actually see. The provider orchestration layer includes login, routing, model selection, rate limits, feature flags, inference scheduling, retry behavior, and capacity allocation. The hyperscaler layer includes compute, databases, storage, networking, and regional control planes. The edge and access layer includes DNS, web gateways, bot protection, content delivery, and authentication flows.

Ookla’s Kehoe wrote, “As AI systems move from short chat sessions into longer-running agentic tasks, a failed prompt, login loop, stalled code task, unavailable file, or broken connector can interrupt work that now sits inside real business processes.” This is a very serious concern!

Those layers are not always owned by different companies, and they are not the full physical internet stack. Network operators, subsea cables, data centers, and user access networks still matter. The focus here is narrower: the service and dependency layers that are most visible in Downdetector data and public incident records.

This distinction is important because the same user-facing symptom can have different operational meanings. A failed prompt, login loop, missing chat history, rate-limit error, unavailable file, or stalled agent task may not share the same root cause. For enterprise buyers and risk teams, resilience is about understanding more than whether an AI platform was simply available. They need to know where the issue occurred, which workflows were affected, and whether it reflected a problem with a single provider or a broader dependency across the AI stack.

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References:

https://www.ookla.com/articles/ai-platform-reliability

https://www.mobileworldlive.com/ai-cloud/ookla-finds-ai-platform-outages-surge-as-adoption-grows

https://www.telecoms.com/ai/ai-app-disruption-is-on-the-up

Will 2026 be the “Year of the AI Ontology” for telecoms?

Merry-go-round of dog chasing its tail: Relationship between U.S. hyperscalers and private Gen AI companies

Posted on by Alan Weissberger

1.  Hyperscalers’ earnings growth this quarter was boosted by an unusually large contribution from “other income,” which was actually mark-ups of their equity stakes in private Gen AI companies.  For example:

  • Nearly half of Alphabet’s (Google) record $62.6 billion profit—about $28.7 billion—did not come from search ads, cloud services or any of its products at all. It came from Alphabet updating the value of the equity it owns in private AI companies, primarily Anthropic.  Alphabet holds a 14% stake before the announcement of an additional $40 billion commitment last week.
  • Amazon’s earnings release stated that first-quarter net income “includes pre-tax gains of $16.8 billion included in non-operating income from our investments in Anthropic”—more than half of Amazon’s pre-tax income (or profit) for the quarter.
  • Alphabet and Amazon generated “other income” totaling $53 billion in Q1 2026, which accounted for nearly 60% of those two companies’ total net income in Q1 and 34% of the total $155 billion in income this quarter. Of this $53 billion in “other income,” $49 billion was explicitly due to equity stakes in private AI companies.
  • Microsoft reported “only” $942mn of other income in the first three months of the year, but this line item has now made $7.2bn over the past nine months.
  • Under U.S. accounting rules, publicly traded firms must adjust and report the assessed value of their private equity holdings every quarter. Because private AI start-ups like Anthropic experienced meteoric valuation updates (e.g., Anthropic climbing to an estimated $380 billion), both Alphabet and Amazon were required to record those massive “on-paper” gains directly to their bottom-line net income.
  • When the AI bubble finally bursts (and it will) the private AI companies assessed market value will collapse, resulting in “impairment write-downs” and huge earnings declines for the hyperscalers, e.g. Amazon, Google/Alphabet, Microsoft, FB/Meta, and Oracle.

2. Now here’s the merry-go-round/ dog chasing its tail relationship:

Not only have private investments and increasingly engorged funding rounds become a meaningful driver of the hyperscalers’ aggregate earnings, but the money the hyperscalers have pumped into the likes of Anthropic and OpenAI has allowed those private AI companies to sign huge computing deals with Alphabet’s Google Cloud, Microsoft’s Azure and Amazon Web Services (AWS).  OpenAI and Anthropic now make up about half of the entire cloud computing order books at Oracle, Alphabet, Amazon and Microsoft! 

Indeed, AI startups have loaded up hyperscalers with unprecedented long-term financial commitments.

–>OpenAI and Anthropic make up over $1 trillion of the estimated $2 trillion cumulative revenue backlog currently held by major cloud service providers!

  • OpenAI to Microsoft Azure: Internal documents show OpenAI’s massive server rentals have generated more than $23 billion in direct cloud spending for Microsoft.
  • Anthropic to Google Cloud: Anthropic signed a contract committing to spend $200 billion over five years on Google’s cloud infrastructure and TPU chips.
  • Anthropic to AWS: In tandem with a fresh $5 billion investment from Amazon, Anthropic committed to spend over $100 billion over the next decade on AWS technologies.

Image Generated by Chat GPT

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3. Because hyperscalers report their overall cloud results as broad aggregates, the exact percentage of current quarter revenue generated purely by AI startups varies by provider. However, recent financial disclosures and analyst tracking pinpoint the enormous impact of these startups on current revenues and future order books:
-Google Cloud:
    • Backlog Percentage: Over 40%. Anthropic‘s $200 billion Multi-Year Commitment accounts for nearly half of Google Cloud’s total disclosed $240 billion revenue backlog.
    • Current Revenue Share: Estimated 12% to 15% of its current $20 billion quarterly revenue run-rate is driven directly by AI infrastructure consumption from startups (both frontier labs and over 40 mid-tier AI companies built on Google Cloud Vertex AI).

-Microsoft Azure:
    • Current Revenue Share: Estimated 15% to 18%. Microsoft’s annualized AI revenue run-rate hit $37 billion. A massive chunk of Azure’s overall 40% growth rate is anchored directly by OpenAI’s compute demands and the commercialization of OpenAI-tied products.

-Amazon Web Services (AWS):
  • Current Revenue Share: Estimated 6% to 8%. While AWS has the largest overall cloud scale ($150 billion annual run rate), its revenue is traditionally diversified across enterprise SaaS and retail. However, Anthropic’s new $100 billion infrastructure commitment means AWS’s revenue mix is aggressively shifting toward AI startups. [1, 2, 3, 4]

–>This is another sign of just how incestuously codependent the big tech industry is to astronomically valued private AI start-ups.

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4. Another example of this codependency is Oracle and OpenAI’s massive, debt-fueled financial loop. In September 2025, the two companies signed a staggering five-year, $300 billion cloud-computing contract. This single deal radically transformed both companies’ financial profiles, binding their survival together as inextricably tied.

The deal functioned as an aggressive narrative magnifier for both companies:
    • For Oracle: The $300 billion contract instantly added to Oracle’s Remaining Performance Obligations (RPO), which skyrocketed 359% to $455 billion. This accounting metric allowed Oracle to position itself as a dominant “hyperscaler,” pushing its market cap upward.
    • For OpenAI: The contract allowed OpenAI to claim it had secured the long-term compute capacity needed to achieve Artificial General Intelligence (AGI). This backed up its massive valuations, enabling OpenAI to close a historic $122 billion funding round in March 2026 at an $852 billion valuation.  

The financial codependency between the two entities is asymmetrical and high-risk:  
  • Oracle is a Financial Proxy for OpenAI: If OpenAI faces a “credit event” or cash crunch, Oracle’s stock directly plummets. Critics note that Oracle signed a contract with a startup that historically burns far more cash than it takes in, making OpenAI’s ability to actually pay the $300 billion highly volatile.
  • The Debt Spiral: To physically fulfill OpenAI’s compute demands, Oracle has gone on a massive, debt-fueled construction spree. Oracle raised $18 billion in bonds in late 2025 and an additional $30 billion in early 2026. Its capital expenditures have eclipsed operating cash flows, leading to deeply negative free cash flow and over $134 billion in total corporate debt.
The scale of this relationship has triggered systematic friction on Wall Street:
    • Project Finance Bottlenecks: Major commercial banks have struggled to syndicate the massive multi-billion-dollar construction loans Oracle needs to build out the required data centers (such as its 4.5-gigawatt capacity goals).
    • Bank Limits: The sheer volume of debt concentrated around this single enterprise relationship has pushed several Wall Street institutions against their regulatory exposure limits for a single corporate partnership.

Ultimately, critics view the partnership as a circular loop: Oracle borrows tens of billions of dollars to build data centers for OpenAI, hoping OpenAI can continuously raise venture capital from the market to pay Oracle back, while Oracle uses OpenAI’s paper contracts to justify its skyrocketing stock value to its own investor

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References:

https://www.ft.com/content/be97df0a-76b1-4cb0-9ba4-d1117d8d1450
https://fortune.com/2026/04/30/google-amazon-ai-profits-anthropic-stake-bubble-earnings-2026/
https://finance.yahoo.com/sectors/technology/articles/google-amazon-biggest-profit-driver-170449859.html

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

Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”

Open AI raises $8.3B and is valued at $300B; AI speculative mania rivals Dot-com bubble

China’s open source AI models to capture a larger share of 2026 global AI market

OpenAI and Broadcom in $10B deal to make custom AI chips

Generative AI Unicorns Rule the Startup Roost; OpenAI in the Spotlight

 

 

 

Nvidia strategic partnership with IREN targets 5G Watts AI infrastructure buildout + $2.1B investment option

Posted on by Alan Weissberger

Nvidia has announced a strategic partnership with cloud AI data center operator IREN [1.] to deploy up to 5G Watts (5GW) of AI infrastructure, driven by a $3.4 billion services contract and a $2.1 billion investment option for Nvidia. This collaboration aims to secure critical, high-density data center capacity for AI workloads while accelerating IREN’s transition into a major AI infrastructure provider.  This strategic expansion targets up to 5GW of NVIDIA DSX-aligned AI infrastructure across IREN’s global pipeline. The roadmap centers on the 2GW Sweetwater campus in Texas, positioned to be the flagship deployment of NVIDIA’s DSX factory architecture. This integrated model synergizes NVIDIA’s reference designs with IREN’s core competencies in utility-scale power procurement, site development, and full-stack GPU cloud operations.

Note 1. IREN’s metamorphosis from specialized mining to high-performance computing (HPC) mirrors the trajectory of Tier-1 AI Cloud providers like CoreWeave. With an operational fleet of 23,000 GPUs and a 3GW secured power portfolio in renewable-heavy regions, IREN is rapidly scaling its North American footprint. 

“AI factories are becoming foundational infrastructure for the global economy,” said Jensen Huang, founder and CEO of Nvidia. “Deploying these systems at scale requires deep integration across the full stack — compute, networking, software, power and operations. IREN brings the scale and infrastructure expertise to help accelerate the buildout of next-generation AI infrastructure globally. Together, we are building for the age of AI,” he added.  Future deployments are expected to focus on IREN’s 2-gigawatt Sweetwater campus in Texas, which the companies expect to serve as a flagship deployment for Nvidia’s DSX architecture.

“This partnership combines NVIDIA’s AI systems and architecture leadership with IREN’s expertise across power, land, data centers, GPU deployment and infrastructure operations,” said Daniel Roberts, cofounder and co-CEO of IREN. “Together, we believe we can accelerate deployment of AI infrastructure and expand access to compute for AI-native and enterprise customers globally.”

This partnership follows a massive $9.7B agreement with Microsoft for sovereign GPU cloud services—leveraging GB300 Blackwell systems—and a $5.8B hardware procurement through Dell. Despite the scale of the Microsoft deal, leadership indicates it utilizes only ~10% of IREN’s projected capacity.
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Upshot:
Nvidia’s agreement with IREN introduces a unique structural alignment: Nvidia acts as both an upstream provider and an anchor tenant/stakeholder. By securing long-dated options over direct equity, Nvidia mitigates balance sheet volatility while ensuring preferential access to critical, grid-connected capacity in a supply-constrained market.
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References:

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

Expose: AI is more than a bubble; it’s a data center debt bomb

Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?

Big Tech AI spending binge results in massive job cuts!

Posted on by Alan Weissberger

Executive Summary:

The tech industry is undergoing a massive structural realignment. Hyperscalers, Software as a Service (SaaS) vendors, and telecom network and equipment providers are aggressively slashing workforces to reallocate capital toward massive AI infrastructure investments.  Alphabet, Meta, Amazon, and Microsoft are projected to spend a collective $674 billion in 2026—over double their 2024 levels.  Most of that spending is AI related.

From the referenced WSJ article:

“Tech companies are in effect playing a game of chicken with each other on capital-spending plans. They are shelling out as much as they can—more than their rivals, they hope—on AI chips and data centers that could put them in the lead in a race they feel they can’t afford to lose. That in turn is heightening competition over who can use AI to help do more with a lot less, freeing up money to spend on expensive chips.”

Hyperscalers, such as Microsoft and Meta Platforms (Meta), are the latest to  their significantly reduce their workforces to scale AI-driven operations. Meta is reportedly reducing its headcount by approximately 8,000, while Microsoft has initiated a “voluntary retirement program” (aka a buyout) targeting 7% of its U.S. workforce—a strategic move to trim payroll before resorting to involuntary layoffs.

This trend is industry-wide: Oracle and Snap have executed significant reductions, while Block announced plans to cut 40% of its staff (over 4,000 employees).  March 2026 represented a two-year peak in tech industry contraction, with Layoffs.fyi reporting 45,800 tech job reductions.

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Source:  Layoffs.fyi
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The AI Transformation Narrative vs. Financial Reality:

Executive leadership is framing these cuts as a strategic pivot toward an AI-native future where automated workflows replace legacy human-centric processes. While CEOs like Block’s Jack Dorsey insist these decisions aren’t driven by distress, a “game of chicken” is unfolding in capital planning.

Companies are locked in an escalating race to secure AI silicon (GPUs), High Bandwidth Memory (HBM) and expand Data Center footprints, creating a massive drain on liquidity.  This heightens the pressure to achieve “doing more with less”—using AI to automate internal functions and free up the capital necessary for expensive infrastructure. However, in many cases, these cuts are simply corrective measures for pandemic-era overhiring or efforts to normalize efficiency metrics:

  • Oracle: Annual revenue per employee remains significantly below industry leaders like Microsoft.
  • Snap: Headcount remains 65% above pre-COVID levels despite consistent operating losses.

Strategic Risks and “Off-Balance-Sheet” Engineering:

While slashing headcounts improves Revenue Per Employee (RPE)—a key KPI for Wall Street—it introduces significant long-term risks:

  • Talent Attrition & Brain Drain: Aggressive layoffs degrade morale and may drive elite engineering talent toward startups, potentially creating new competitors.
  • Governance & Safety: Reducing human oversight during AI deployment could lead to safety and business model integration failures.
  • Regulatory & Public Backlash: The “AI as a job killer” narrative is fueling community opposition to massive data center builds, complicating infrastructure rollouts.

The CAPEX Burden:

The financial strain is becoming evident even for “Deep Pocket” firms. Alphabet, Meta, Amazon, and Microsoft are projected to spend $674 billion in CAPEX this year—more than double their 2022 spend.

  • Amazon is projected to be cash-flow negative this year.
  • Meta’s CAPEX is set to exceed 50% of its annual revenue, with its debt-to-equity ratio climbing to 39% (up from 8% five years ago).
  • Some firms are reportedly utilizing “off-balance-sheet financial wizardry” to maintain their AI compute growth without alarming debt markets.

Verdict of the Market?

Markets are sending mixed signals. While analysts are obsessed with efficiency metrics (questions about efficiency on earnings calls have tripled in two years), they are becoming “skittish” regarding unbridled spending. Tesla (TSLA), for instance, saw a 4% stock dip after raising its spending target to $25 billion.

Ultimately, tech giants—who already average $2M in annual revenue per employee—are betting that further workforce reductions will juice efficiency and fund the AI arms race. The trade-off remains whether these “leaner” organizations can maintain the innovation and safety standards required to lead the next technological cycle.

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The telecom sector is particularly vulnerable, as AI-native “zero-touch” operations begin to replace legacy roles permanently.

  • Network Operators:BT has announced plans to replace up to 10,000 roles with AI by 2030, specifically targeting network management and customer service.
  • Network Equipment Vendors: Equipment giants Ericsson and Nokia have collectively shed over 36,000 roles in recent years, pivoting from traditional hardware to AI-optimized software and networking.
  • Integrators:Accenture and IBM are utilizing AI to automate junior-level coding and back-office HR tasks, signaling that AI reskilling is now a prerequisite for workforce retention.

Strategic Outlook – Monetization and the “RPE” Battle:   

For both MNOs and tech giants, the coming years are about monetization. Investors have shifted from cheering bold AI visions to demanding tangible results, with a heavy focus on Revenue Per Employee (RPE)—a metric that workforce reductions are designed to “juice.”

That “Great Realignment” is a high-stakes gamble, in this author’s opinion.  The firms that successfully bridge the gap between massive infrastructure investments and scalable, profitable AI-native services will lead the next generation of global technology. Those that fail to balance efficiency with talent retention may find themselves outpaced by leaner, AI-native startups born from the very talent they have released.

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References:

https://www.wsj.com/tech/ai/the-ai-splurge-is-costing-big-tech-its-workforce-34a88e68

AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!

AI infrastructure spending boom: a path towards AGI or speculative bubble?

Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments

AI spending is surging; companies accelerate AI adoption, but job cuts loom large

Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)

Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?

Canalys & Gartner: AI investments drive growth in cloud infrastructure spending

Will Google Cloud’s AI and data analytics revenue +TPU IP licensing income offset huge AI CAPEX to produce a decent ROI?

Posted on by Alan Weissberger

An April 24th Investors Business Daily (IBD) article asserts that Google’s AI position is strong, but the real test will be monetization.  Specifically, can Gemini translate technical lead and user scale into durable profits for parent company Alphabet?  The company has benefited from AI enthusiasm and Google Cloud momentum, but investors are now focused on whether heavy AI spending will generate sufficient revenues to justify the enormous capex ramp up.  The article highlights Gemini’s growing traction, Google Cloud’s rapid expansion, and a very large backlog as signs of demand, but it also stresses that those positives must offset rising infrastructure costs.

With its Gemini family, Google continues to push its AI technology across the “stack,” (see quote below) deploying it to Google Maps, enterprise Workplace productivity tools, and YouTube’s content and ad platforms. AI technology is even making Google’s autonomous vehicle company, Waymo, better and safer amid its large market expansion.

A key theme is that Google has multiple ways to earn revenue from AI, including consumer subscriptions, enterprise software, and cloud services. The article points to Gemini Advanced as an example of paid AI packaging, while also implying that the larger opportunity is converting AI usage into higher-value cloud and platform revenue rather than just user growth. However, Alphabet is planning very large AI infrastructure spending (much more below), and the article questions whether the company can turn that investment into sustainable high-margin revenue fast enough to satisfy investors.

Google has also ventured into AI semiconductors with its AI accelerator Tensor Processing Unit, known as TPU, co-developed with Broadcom and manufactured by TSMC (Taiwan Semiconductor Manufacturing Company). Google is shifting future TPU generation designs to include MediaTek for design support, with TSMC continuing as the primary fabrication partner for advanced 2nm, 3nm, and 5nm nodes.

Google has recently introduced the 7th-gen “Ironwood“ TPU 7x and revealed plans for the 8th-gen TPU 8t and TPU 8i for 2027.  Long time colleague Amin Vadat, PhD wrote in a blog post, “We are introducing the eighth generation of Google’s custom Tensor Processor Unit (TPU), coming soon with two distinct, purpose-built architectures for training and inference: TPU 8t and TPU 8i. These two chips are designed to power our custom-built supercomputers, to drive everything from cutting-edge model training and agent development, to massive inference workloads. TPUs have been powering leading foundation models, including Gemini, for years. These 8th generation TPUs together will deliver scale, efficiency and capabilities across training, serving and agentic workloads.”

Image credit:  Google.

Indeed, Google’s TPUs have emerged as a threat to Nvidia’s dominance in the AI chip market. Anthropic has licensed Google’s TPU accelerators for use in data centers. Broadcom will modify the TPUs for Anthropic before the customized chips are made by TSMC. Wells Fargo estimates that Google could bring in over $10 billion in high-margin intellectual property (IP) licensing fees from TPUs in 2026 and 2027.

“What stands out about Google is that they’ve been investing up and down the technology stack, from silicon to the AI models,” said Daniel Flax, managing director at investment management firm Neuberger Berman. “While competition is fierce, they’ve been able to innovate. What we’re focused on is (Google’s) ability to execute on their product road map from one generation of AI models to the next.”

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AI Competition from OpenAI and Anthropic:

Google faces lots of AI competition from other hyperscalers (Amazon, Microsoft, Meta, etc) and especially from two private AI companies:.

  1. OpenAI remains a major AI player, powered by the rapid advance of ChatGPT, which launched in 2022.  In its latest funding round, OpenAI landed $122 billion in capital commitments, which values the company at $852 billion. OpenAI’s  GPT-6 is its next-generation AI model, as soon as late 2026.  GPT-6 is expected to include new memory features that support the personalization of AI chatbots. It’ll also offer more support for autonomous AI agents that perform tasks over the internet.
  2. Anthropic’s Claude AI model family has grabbed the spotlight this year. With Claude-based coding and other AI tools, Anthropic shook up the enterprise software market.  Anthropic is preparing a next-generation, more powerful AI model called Mythos.  Anthropic recently raised $30 billion in a funding round that valued the AI company at $380 billion.

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AI Cloud Competition:

Google’s cloud computing business is one area that should benefit from the company’s AI spending. The unit has excellent momentum. Cloud revenue climbed 47% to over $16 billion in the December quarter, up from 34% growth in the previous quarter. And Google’s cloud computing sales backlog grew 55% to $240 billion from the September quarter.  AWS still has the largest cloud market share, with Azure second and Google Cloud third.  Google Cloud’s edge is AI and data analytics, especially through Vertex AI, Gemini-related services, and TPU-based infrastructure. The company has developed AI Gemini models targeting specific industries, such as financial services and pharmaceutical companies.  With the recent $32 billion purchase of Wiz, Google plans to offer AI-based cybersecurity threat detection tools.

Google Cloud is growing faster than AWS on an AI-driven basis, but it still trails Azure in the most AI-sensitive growth comparisons and remains third in overall cloud share. The broad pattern is: AWS leads in scale, Azure leads in AI momentum and enterprise pull, and Google Cloud is the strongest “AI-first” challenger with faster growth than AWS but a smaller base.  Recent comparisons show AWS revenue growth around 18% year over year, while Google Cloud grew about 32%, and Azure’s estimated growth was about 39% in the same period.

Microsoft reported Intelligent Cloud segment growth was also faster than AWS. The rough share split cited in recent coverage is AWS about 30%, Azure about 20%, and Google Cloud about 13%.  Azure’s edge is enterprise distribution and the Azure OpenAI ecosystem, while AWS offers the broadest infrastructure catalog and strong AI tooling but is less clearly identified as the AI growth leader. Investor takeaway For investors, Google Cloud looks like the fastest-improving AI cloud franchise relative to its size, but not the biggest one. The real question is whether Google’sAI-led growth can stay above AWS while also narrowing the gap with Azure’s enterprise AI momentum.

Monetization is a Major Issue:

Many analyst say it’s unclear how many consumers will pay for AI. Only about 5% of ChatGPT’s user base is paid.  “Consumer AI is becoming a distribution channel and brand builder, while enterprise agents are where the high-margin, sticky revenue is actually getting locked in,” Ben Lorica, editor of the Gradient Flow AI newsletter, told IBD in an interview. “Widespread platform promiscuity across ChatGPT, Gemini and Claude signals low switching costs and thin margins, which is not a great recipe for durable revenue.”

“Cloud, AI revenues have to scale fast enough for people to say, ‘OK, this is actually working,'” said Michael Landsberg, chief executive of Landsberg Bennett Private Wealth Management. “With Google, a lot of things are going very well, but when is it going to translate into money in the pocket? Gemini is doing really well gaining market share from ChatGPT. But there’s no money yet,” Landsberg added. “The big issue around Google search is, ‘Are they going to be able to put advertising in Gemini?'”

“I think most people want free AI because we’ve been trained that free is how we do this computer thing,” said Kimberly Forrest, Bokeh Capital Partners’ chief investment officer. “Facebook, Instagram — it’s all free now. There might be some people willing to spend $20 monthly on AI, but probably not enough to generate the income that these models need to be continually improved.”

Alphabet has historically monetized consumer products through advertising rather than subscriptions. “I think the average consumer doesn’t want to pay for AI, and if they do, they certainly don’t want to pay much for AI,” said Tim Ghriskey, senior portfolio strategist at Ingalls & Snyder.

Author’s Note:  I regularly use Gemini for Home on my Google Smart Speaker and a different Gemini on PCs and my Samsung phone.  There’s a huge difference in performance with the former making many more mistakes and “AI Hallucinations” than the latter.   The reason is the Gemini for Home and regular Gemini run on two totally different AI systems.  For reasons neither I or Gemini for Home can explain, the Home version is severely deficient with many wrong answers and hallucinations that you don’t get when you use Gemini on a pc or the Gemini app on a smartphone.

One particularly bothersome Gemini for Home response to a question asked or a complaint is: “These pictures should match” or “Here are your photos” or “check out these pictures” with corresponding pics/photos displayed on the speaker’s screen.

–>THAT HAS ABSOLUTELY NOTHING TO DO WITH ANYTHING yet it happens frequently AFTER the Google speaker promises never to repeat it!  Ugggh!!!!

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Google/Alphabet’s Surging CAPEX and ROI:

Alphabet said its 2026 capex will be $175 billion to $185 billion, and management has framed the spending as overwhelmingly AI/infrastructure-related which will support revenue growth in Google Cloud, Gemini, and AI-enhanced Search.

The clearest breakdown disclosed to date is roughly 60% to servers and 40% to data centers and networking equipment. Using the company’s forward guidance ranges:

  • AI Compute Servers: about $105 billion to $111 billion.

  • Data centers and networking equipment: about $70 billion to $74 billion.

That means most of the spend is going into fast-depreciating compute hardware, with the rest funding the physical and network buildout needed to host AI workloads. Google says the investment is meant to expand AI compute, support Google Cloud demand, and scale Gemini and enterprise AI offerings.

The company also pointed to a $240 billion cloud backlog and strong cloud revenue growth as signs that the spending is tied to real demand rather than just speculative buildout.  The key issue for investors is whether this capital intensity converts into enough cloud and AI revenue to justify the return profile.  Alphabet has not given a specific ROI number for its 2026 AI investments. What it has said, and what analysts infer, is that the return should come from faster cloud growth, higher AI-related search usage, and paid enterprise adoption rather than a near-term accounting yield.

In conclusion, 2026 is an AI scale-up year for Google, but the ROI question is still open.

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References:

Google’s AI Reckoning: Can Gemini Turn Dominance Into Dollars?

 

https://blog.google/innovation-and-ai/infrastructure-and-cloud/google-cloud/eighth-generation-tpu-agentic-era/

Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?

Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)

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

China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars

Anthropic’s Project Glasswing aims to reshape IT cybersecurity

IDC Survey of Networking Leaders: Enterprise AI progress stalls despite ambitious goals

Will “AI at the Edge” transform telecom or be yet another telco monetization failure?

Nvidia Survey Reveals How Telcos Plan to Use AI; Quantifying ROI is a Challenge

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

 

Nvidia CEO Huang: AI is the largest infrastructure buildout in human history; AI Data Center CAPEX will generate new revenue streams for operators

Posted on by Alan Weissberger

Executive Summary:

In a February 6, 2026 CNBC interview with with Scott Wapner, Nvidia CEO Jensen Huang [1.] characterized the current AI build‑out as “the largest infrastructure buildout in human history,” driven by exceptionally high demand for compute from hyperscalers and AI companies. “Through the roof” is how he described AI infrastructure spending.  It’s a “once-in-a-generation infrastructure buildout,” specifically highlighting that demand for Nvidia’s Blackwell chips and the upcoming Vera Rubin platform is “sky-high.” He emphasized that the shift from experimental AI to AI as a fundamental utility has reached a definitive inflection point for every major industry.

Jensen forecasts aa roughly 7–to- 8‑year AI investment cycle lies ahead, with capital intensity justified because deployed AI infrastructure is already generating rising cash flows for operators.  He maintains that the widely cited ~$660 billion AI data center capex pipeline is sustainable, on the grounds that GPUs and surrounding systems are revenue‑generating assets, not speculative overbuild. In his view, as long as customers can monetize AI workloads profitably, they will “keep multiplying their investments,” which underpins continued multi‑year GPU demand, including for prior‑generation parts that remain fully leased.

Note 1.  Being the undisputed leader of AI hardware (GPU chips and networking equipment via its Mellanox acquisition), Nvidia MUST ALWAYS MAKE POSITIVE REMARKS AND FORECASTS related to the AI build out boom.  Reader discretion is advised regarding Huang’s extremely bullish, “all-in on AI” remarks.

Huang reiterated that AI will “fundamentally change how we compute everything,” shifting data centers from general‑purpose CPU‑centric architectures to accelerated computing built around GPUs and dense networking. He emphasizes Nvidia’s positioning as a full‑stack infrastructure and computing platform provider—chips, systems, networking, and software—rather than a standalone chip vendor.  He accuratedly stated that Nvidia designs “all components of AI infrastructure” so that system‑level optimization (GPU, NIC, interconnect, software stack) can deliver performance gains that outpace what is possible with a single chip under a slowing Moore’s Law. The installed base is presented as productive: even six‑year‑old A100‑class GPUs are described as fully utilized through leasing, underscoring persistent elasticity of AI compute demand across generations.

AI Poster Childs – OpenAI and Anthropic:

Huang praised OpenAI and Anthropic, the two leading artificial intelligence labs, which both use Nvidia chips through cloud providers. Nvidia invested $10 billion in Anthropic last year, and Huang said earlier this week that the chipmaker will invest heavily in OpenAI’s next fundraising round.

“Anthropic is making great money. Open AI is making great money,” Huang said. “If they could have twice as much compute, the revenues would go up four times as much.”

He said that all the graphics processing units that Nvidia has sold in the past — even six-year old chips such as the A100 — are currently being rented, reflecting sustained demand for AI computing power.

“To the extent that people continue to pay for the AI and the AI companies are able to generate a profit from that, they’re going to keep on doubling, doubling, doubling, doubling,” Huang said.

Economics, utilization, and returns:

On economics, Huang’s central claim is that AI capex converts into recurring, growing revenue streams for cloud providers and AI platforms, which differentiates this cycle from prior overbuilds. He highlights very high utilization: GPUs from multiple generations remain in service, with cloud operators effectively turning them into yield‑bearing infrastructure.

This utilization and monetization profile underlies his view that the capex “arms race” is rational: when AI services are profitable, incremental racks of GPUs, network fabric, and storage can be modeled as NPV‑positive infrastructure projects rather than speculative capacity. He implies that concerns about a near‑term capex cliff are misplaced so long as end‑market AI adoption continues to inflect.

Competitive and geopolitical context:

Huang acknowledges intensifying global competition in AI chips and infrastructure, including from Chinese vendors such as Huawei, especially under U.S. export controls that have reduced Nvidia’s China revenue share to roughly half of pre‑control levels. He frames Nvidia’s strategy as maintaining an innovation lead so that developers worldwide depend on its leading‑edge AI platforms, which he sees as key to U.S. leadership in the AI race.

He also ties AI infrastructure to national‑scale priorities in energy and industrial policy, suggesting that AI data centers are becoming a foundational layer of economic productivity, analogous to past buildouts in electricity and the internet.

Implications for hyperscalers and chips:

Hyperscalers (and also Nvidia customers) Meta , Amazon, Google/Alphabet and Microsoft recently stated that they plan to dramatically increase spending on AI infrastructure in the years ahead. In total, these hyperscalers could spend $660 billion on capital expenditures in 2026 [2.] , with much of that spending going toward buying Nvidia’s chips. Huang’s message to them is that AI data centers are evolving into “AI factories” where each gigawatt of capacity represents tens of billions of dollars of investment spanning land, compute, and networking. He suggests that the hyperscaler industry—roughly a $2.5 trillion sector with about $500 billion in annual capex transitioning from CPU to GPU‑centric generative AI—still has substantial room to run.

Note 2.  An understated point is that while these hyperscalers are spending hundered of billions of dollars on AI data centers and Nvidia chips/equipment they are simultaneously laying off tens of thousands of employees.  For example, Amazon recently announced 16,000 job cuts this year after 14,000 layoffs last October.

From a chip‑level perspective, he argues that Nvidia’s competitive moat stems from tightly integrated hardware, networking, and software ecosystems rather than any single component, positioning the company as the systems architect of AI infrastructure rather than just a merchant GPU vendor.

References:

https://www.cnbc.com/2026/02/06/nvidia-rises-7percent-as-ceo-says-660-billion-capex-buildout-is-sustainable.html

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Analysis: SpaceX FCC filing to launch up to 1M LEO satellites for solar powered AI data centers in space

Posted on by Alan Weissberger

SpaceX has applied to the Federal Communications Commission (FCC) for permission to launch up to 1 million LEO satellites for a new solar-powered AI data center system in space.  The private company, 40% owned by Elon Musk, envisions an orbital data center system with “unprecedented computing capacity” needed to run large-scale AI inference and applications for billions of users, according to SpaceX’s filing entered late on Friday.

Data centers are the physical backbone of artificial intelligence, requiring massive amounts of power. “By directly harnessing near-constant solar power with little operating or maintenance costs, these satellites will achieve transformative cost and energy efficiency while significantly reducing the environmental impact associated with terrestrial data centers,” the FCC filing said. Musk would need the telecom regulator’s approval to move forward.

Credit: Blueee/Alamy Stock Photo

The proposed new satellites would operate in “narrow orbital shells” of up to 50 kilometers each. The satellites would operate at altitudes of between 500 kilometers and 2,000 kilometers, and 30 degrees, and “sun-synchronous orbit inclinations” to capture power from the sun. The system is designed to be interconnected via optical links with existing Starlink broadband satellites, which would transmit data traffic back to ground Earth stations.

SpaceX’s request bets heavily on reduced costs of Starship, the company’s next-generation reusable rocket under development.  Starship has test-launched 11 times since 2023. Musk expects the rocket, which is crucial for expanding Starlink with more powerful satellites, to put its first payloads into orbit this year.
“Fortunately, the development of fully reusable launch vehicles like Starship that can deploy millions of tons of mass per year to orbit when launching at rate, means on-orbit processing capacity can reach unprecedented scale and speed compared to terrestrial buildouts, with significantly reduced environmental impact,” SpaceX said.
SpaceX is positioning orbital AI compute as the definitive solution to the terrestrial capacity crunch, arguing that space-based infrastructure represents the most efficient path for scaling next-generation workloads. As ground-based data centers face increasing grid density constraints and power delivery limitations, SpaceX intends to leverage high-availability solar irradiation to bypass Earth’s energy bottlenecks.The company’s technical rationale hinges on several key architectural advantages:
  • Energy Density & Sustainability: By tapping into “near-constant solar power,” SpaceX aims to utilize a fraction of the Sun’s output—noting that even a millionth of its energy exceeds current civilizational demand by four orders of magnitude.
  • Thermal Management: To address the cooling requirements of high-density AI clusters, these satellites will utilize radiative heat dissipation, eliminating the water-intensive cooling loops required by terrestrial facilities.
  • Opex & Scalability: The financial viability of this orbital layer is tethered to the Starship launch platform. SpaceX anticipates that the radical reduction in $/kg launch costs provided by a fully reusable heavy-lift vehicle will enable rapid scaling and ensure that, within years, the lowest LCOA (Levelized Cost of AI) will be achieved in orbit.
The transition to orbital AI compute introduces a fundamental shift in network topology, moving processing from terrestrial hubs to a decentralized, space-based edge layer. The latency implications are characterized by three primary architectural factors:
  • Vacuum-Speed Data Transmission: In a vacuum, light propagates roughly 50% faster than through terrestrial fiber optic cables. By utilizing Starlink’s optical inter-satellite links (OISLs)—a “petabit” laser mesh—data can bypass terrestrial bottlenecks and subsea cables. This potentially reduces intercontinental latency for AI inference to under 50ms, surpassing many long-haul terrestrial routes.
  • Edge-Native Processing & Data Gravity: Current workflows require downlinking massive raw datasets (e.g., Synthetic Aperture Radar imagery) for terrestrial processing, a process that can take hours. Shifting to orbital edge computing allows for “in-situ” AI inference, processing data onboard to deliver actionable insights in minutes rather than hours. This “Space Cloud” architecture eliminates the need to route raw data back to the Earth’s internet backbone, reducing data transmission volumes by up to 90%.
  • LEO Proximity vs. Terrestrial Hops: While terrestrial fiber remains the “gold standard” for short-range latency (typically 1–10ms), it is often hindered by inefficient routing and multiple hops. SpaceX’s LEO constellation, operating at altitudes between 340km and 614km, currently delivers median peak-hour latencies of ~26ms in the US. Future orbital configurations may feature clusters at varying 50km intervals to optimize for specific workload and latency tiers.

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The SpaceX FCC filing on Friday follows an exclusive report by Reuters that Elon Musk is considering merging SpaceX with his xAI (Grok chatbot) company ahead of an IPO later this year. Under the proposed merger, shares of xAI would be exchanged for shares in SpaceX. Two entities have been set up in Nevada to facilitate the transaction, Reuters said.  Musk also runs electric automaker Tesla, tunnel company The Boring Co. and neurotechnology company Neuralink.

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References:

https://www.reuters.com/business/aerospace-defense/spacex-seeks-fcc-nod-solar-powered-satellite-data-centers-ai-2026-01-31/

https://www.lightreading.com/satellite/spacex-seeks-fcc-approval-for-mega-ai-data-center-constellation

https://www.reuters.com/world/musks-spacex-merger-talks-with-xai-ahead-planned-ipo-source-says-2026-01-29/

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Hyperscaler capex > $600 bn in 2026 a 36% increase over 2025 while global spending on cloud infrastructure services skyrockets

Posted on by Alan Weissberger

Hyperscaler capex for the “big five” (Amazon, Alphabet/Google, Microsoft, Meta/Facebook, Oracle) is now widely forecast to exceed $600 bn in 2026, a 36% increase over 2025. Roughly 75%, or $450 bn, of that spend is directly tied to AI infrastructure (i.e., servers, GPUs, datacenters, equipment), rather than traditional cloud.  Hyperscalers are increasingly leaning on debt markets to bridge the gap between rapidly rising AI capex budgets and internal free cash flow, transforming historically cash-funded business models into ones utilizing leverage, albeit with still very strong balance sheets. Aggregate capex for “the big five”, after buybacks and dividends are included, are now above projected cash flows, thereby necessitating external funding needs.

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According to market research from Omdia (owned by Informa) global spending on cloud infrastructure services reached $102.6 billion in Q3 2025 — a 25% year-on-year increase. It was the fifth consecutive quarter in which cloud spending growth remained above 20%.  Omdia says it “reflects a significant shift in the technology landscape as enterprise demand for AI moves beyond early experimentation toward scaled production deployment.” AWS, Microsoft Azure, and Google Cloud – maintained their market rankings from the previous quarter, and collectively accounted for 66% of global cloud infrastructure spending. Together, the three firms had 29% year-on-year growth in their cloud spending.

Hyperscaler AI strategies are shifting from a focus on incremental model performance to platform-driven, production-ready approaches. Enterprises are now evaluating AI platforms based not solely on model capabilities, but also on their support for multi-model strategies and agent-based applications. This evolution is accelerating hyperscalers’ move toward platform-level AI capabilities. According to the report, Amazon Web Services (AWS), Microsoft Azure, and Google Cloud are integrating proprietary foundation models with a growing range of third-party and open-weight models to meet these new demands.

“Collaboration across the ecosystem remains critical,” said Rachel Brindley, Senior Director at Omdia. “Multi-model support is increasingly viewed as a production requirement rather than a feature, as enterprises seek resilience, cost control, and deployment flexibility across generative AI workloads.”

Facing challenges with practical application, major cloud providers are boosting resources for AI agent lifecycle management, including creation and operationalization, as enterprise-level deployment proves more intricate than anticipated.

Yi Zhang, Senior Analyst at Omdia, said, “Many enterprises still lack standardized building blocks that can support business continuity, customer experience, and compliance at the same time, which is slowing the real-world deployment of AI agents. This is where hyperscalers are increasingly stepping in, using platform-led approaches to make it easier for enterprises to build and run agents in production environments.”

This past October, Omdia released a report forecasting that growth of cloud adoption among communications service providers (CSPs) will double this year. It also forecasted a compound annual growth rate (CAGR) of 7.3% to 2030, resulting in the telco cloud market being worth $24.8 billion.

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Editor’s Note:  Does anyone remember the stupendous increase in fiber optic spending from 1998-2001 till that bubble burst?  Caveat Emptor!

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References:

https://www.mufgamericas.com/sites/default/files/document/2025-12/AI_Chart_Weekly_12_19_Financing_the_AI_Supercycle.pdf

https://www.telecoms.com/public-cloud/global-cloud-infrastructure-spend-up-25-in-q3

https://www.telecoms.com/public-cloud/telco-investment-in-cloud-infrastructure-is-accelerating-omdia

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