AI Bubble
Ookla: AI platform reliability decreases as outages surge
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 his tail: relationship between U.S. hyperscalers and private Gen AI companies
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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- 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).
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- 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.
- 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.
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- 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.
- 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.
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- 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.
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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
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.
“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.”
References:
China vs U.S.: Race to Generate Power for AI Data Centers as Electricity Demand Soars
Fiber Optic Boost: Corning and Meta in multiyear $6 billion deal to accelerate U.S data center buildout
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
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!
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.
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.
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.
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?
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:.
- 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.
- 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:
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AI Compute Servers: about $105 billion to $111 billion.
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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?
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
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:
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
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Superclusters of Nvidia GPU/AI chips combined with end-to-end network platforms to create next generation data centers
184K global tech layoffs in 2025 to date; ~27.3% related to AI replacing workers
Analysis: SpaceX FCC filing to launch up to 1M LEO satellites for solar powered AI data centers in space
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.
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.
“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.
- 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.
- 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:
Google’s Project Suncatcher: a moonshot project to power ML/AI compute from space
Blue Origin announces TeraWave – satellite internet rival for Starlink and Amazon Leo
China ITU filing to put ~200K satellites in low earth orbit while FCC authorizes 7.5K additional Starlink LEO satellites
Amazon Leo (formerly Project Kuiper) unveils satellite broadband for enterprises; Competitive analysis with Starlink
Telecoms.com’s survey: 5G NTNs to highlight service reliability and network redundancy
Huge significance of EchoStar’s AWS-4 spectrum sale to SpaceX
U.S. BEAD overhaul to benefit Starlink/SpaceX at the expense of fiber broadband providers
Telstra selects SpaceX’s Starlink to bring Satellite-to-Mobile text messaging to its customers in Australia
SpaceX launches first set of Starlink satellites with direct-to-cell capabilities
AST SpaceMobile to deliver U.S. nationwide LEO satellite services in 2026
GEO satellite internet from HughesNet and Viasat can’t compete with LEO Starlink in speed or latency
How will fiber and equipment vendors meet the increased demand for fiber optics in 2026 due to AI data center buildouts?
Subsea cable systems: the new high-capacity, high-resilience backbone of the AI-driven global network
Hyperscaler capex > $600 bn in 2026 a 36% increase over 2025 while global spending on cloud infrastructure services skyrockets
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.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
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
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
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
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)
Canalys & Gartner: AI investments drive growth in cloud infrastructure spending
Sovereign AI infrastructure for telecom companies: implementation and challenges
AI Echo Chamber: “Upstream AI” companies huge spending fuels profit growth for “Downstream AI” firms
Custom AI Chips: Powering the next wave of Intelligent Computing
AI infrastructure spending boom: a path towards AGI or speculative bubble?
by Rahul Sharma, Indxx with Alan J Weissberger, IEEE Techblog
Introduction:
The ongoing wave of artificial intelligence (AI) infrastructure investment by U.S. mega-cap tech firms marks one of the largest corporate spending cycles in history. Aggregate annual AI investments, mostly for cloud resident mega-data centers, are expected to exceed $400 billion in 2025, potentially surpassing $500 billion by 2026 — the scale of this buildout rivals that of past industrial revolutions — from railroads to the internet era.[1]
At its core, this spending surge represents a strategic arms race for computational dominance. Meta, Alphabet, Amazon and Microsoft are racing to secure leadership in artificial intelligence capabilities — a contest where access to data, energy, and compute capacity are the new determinants of market power.
AI Spending & Debt Financing:
Leading technology firms are racing to secure dominance in compute capacity — the new cornerstone of digital power:
- Meta plans to spend $72 billion on AI infrastructure in 2025.
- Alphabet (Google) has expanded its capex guidance to $91–93 billion.[3]
- Microsoft and Amazon are doubling data center capacity, while AWS will drive most of Amazon’s $125 billion 2026 investment.[4]
- Even Apple, typically conservative in R&D, has accelerated AI infrastructure spending.
Their capex is shown in the chart below:
Analysts estimate that AI could add up to 0.5% to U.S. GDP annually over the next several years. Reflecting this optimism, Morgan Stanley forecasts $2.9 trillion in AI-related investments between 2025 and 2028. The scale of commitment from Big Tech is reshaping expectations across financial markets, enterprise strategies, and public policy, marking one of the most intense capital spending cycles in corporate history.[2]
Meanwhile, OpenAI’s trillion-dollar partnerships with Nvidia, Oracle, and Broadcom have redefined the scale of ambition, turning compute infrastructure into a strategic asset comparable to energy independence or semiconductor sovereignty.[5]
Growth Engine or Speculative Bubble?
As Big Tech pours hundreds of billions of dollars into AI infrastructure, analysts and investors remain divided — some view it as a rational, long-term investment cycle, while others warn of a potential speculative bubble. Yet uncertainty remains — especially around Meta’s long-term monetization of AGI-related efforts.[8]
Some analysts view this huge AI spending as a necessary step towards achieving Artificial General Intelligence (AGI) – an unrealized type of AI that possesses human-level cognitive abilities, allowing it to understand, learn, and adapt to any intellectual task a human can. Unlike narrow AI, which is designed for specific functions like playing chess or image recognition, AGI could apply its knowledge to a wide range of different situations and problems without needing to be explicitly programmed for each one.
Other analysts believe this is a speculative bubble, fueled by debt that can never be repaid. Tech sector valuations have soared to dot-com era levels – and, based on price-to-sales ratios, are well beyond them. Some of AI’s biggest proponents acknowledge the fact that valuations are overinflated, including OpenAI chairman Bret Taylor: “AI will transform the economy… and create huge amounts of economic value in the future,” Taylor told The Verge. “I think we’re also in a bubble, and a lot of people will lose a lot of money,” he added.
Here are a few AI bubble points and charts:
- AI-related capex is projected to consume up to 94% of operating cash flows by 2026, according to Bank of America.[6]
- Over $75 billion in AI-linked corporate bonds have been issued in just two months — a signal of mounting leverage. Still, strong revenue growth from AI services (particularly cloud and enterprise AI) keeps optimism alive.[7]
- Meta, Google, Microsoft, Amazon and xAI (Elon Musk’s company) are all using off-balance-sheet debt vehicles, including special-purpose vehicles (SPVs) to fund part of their AI investments. A slowdown in AI demand could render the debt tied to these SPVs worthless, potentially triggering another financial crisis.
- Alphabet’s (Google’s parent company) CEO Sundar Pichai sees “elements of irrationality” in the current scale of AI investing which is much more than excessive investments during the dot-com/fiber optic built-out boom of the late 1990s. If the AI bubble bursts, Pichai said that no company will be immune, including Alphabet, despite its breakthrough technology, Gemini, fueling gains in the company’s stock price.
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From Infrastructure to Intelligence:
Executives justify the massive spend by citing acute compute shortages and exponential demand growth:
- Microsoft’s CFO Amy Hood admitted, “We’ve been short on capacity for many quarters” and confirmed that the company will increase its spending on GPUs and CPUs in 2026 to meet surging demand.
- Amazon’s Andy Jassy noted that “every new tranche of capacity is immediately monetized”, underscoring strong and sustained demand for AI and cloud services.
- Google reported billions in quarterly AI revenue, offering early evidence of commercial payoff.
Macro Ripple Effects – Industrializing Intelligence:
AI data centers have become the factories of the digital age, fueling demand for:
- Semiconductors, especially GPUs (Nvidia, AMD, Broadcom)
- Cloud and networking infrastructure (Oracle, Cisco)
- Energy and advanced cooling systems for AI data centers (Vertiv, Schneider Electric, Johnson Controls, and other specialists such as Liquid Stack and Green Revolution Cooling).
| Company Name | Core Expertise | Key Solutions for AI Data Centers |
|---|---|---|
| Vertiv | Critical infrastructure (power & cooling) | Offers full-stack solutions with air and liquid cooling, power distribution units (PDUs), and monitoring systems, including the AI-ready Vertiv 360AI portfolio. |
| Schneider Electric | Energy management & automation | Provides integrated power and thermal management via its EcoStruxure platform, specializing in modular and liquid cooling solutions for HPC and AI applications. |
| Johnson Controls | HVAC & building solutions | Offers integrated, energy-efficient solutions from design to maintenance, including Silent-Aire cooling and YORK chillers, with a focus on large-scale operations. |
| Eaton | Power management | Specializes in electrical distribution systems, uninterruptible power supplies (UPS), and switchgear, which are crucial for reliable energy delivery to high-density AI racks. |
- LiquidStack: A leader in two-phase and modular immersion cooling and direct-to-chip systems, trusted by large cloud and hardware providers.
- Green Revolution Cooling (GRC): Pioneers in single-phase immersion cooling solutions that help simplify thermal management and improve energy efficiency.
- Iceotope: Focuses on chassis-level precision liquid cooling, delivering dielectric fluid directly to components for maximum efficiency and reduced operational costs.
- Asetek: Specializes in direct-to-chip (D2C) liquid cooling solutions and rack-level Coolant Distribution Units (CDUs) for high-performance computing.
- CoolIT Systems: Known for its custom direct liquid cooling technologies, working closely with server OEMs (Original Equipment Manufacturers) to integrate cold plates and CDUs for AI and HPC workloads.
–>This new AI ecosystem is reshaping global supply chains — but also straining local energy and water resources. For example, Meta’s massive data center in Georgia has already triggered environmental concerns over energy and water usage.
Global Spending Outlook:
- According to UBS, global AI capex will reach $423 billion in 2025, $571 billion by 2026 and $1.3 trillion by 2030, growing at a 25% CAGR during the period 2025-2030.
Compute demand is outpacing expectations, with Google’s Gemini saw 130 times rise in AI token usage over the past 18 months, highlighting soaring compute and Meta’s infrastructure needs expanding sharply.[9]
Conclusions:
The AI infrastructure boom reflects a bold, forward-looking strategy by Big Tech, built on the belief that compute capacity will define the next decade’s leaders. If Artificial General Intelligence (AGI) or large-scale AI monetization unfolds as expected, today’s investments will be seen as visionary and transformative. Either way, the AI era is well underway — and the race for computational excellence is reshaping the future of global markets and innovation.
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Footnotes:
[1] https://www.investing.com/news/stock-market-news/ai-capex-to-exceed-half-a-trillion-in-2026-ubs-4343520?utm_medium=feed&utm_source=yahoo&utm_campaign=yahoo-www
[2] https://www.venturepulsemag.com/2025/08/01/big-techs-400-billion-ai-bet-the-race-thats-reshaping-global-technology/#:~:text=Big%20Tech’s%20$400%20Billion%20AI%20Bet:%20The%20Race%20That’s%20Reshaping%20Global%20Technology,-3%20months%20ago&text=The%20world’s%20largest%20technology%20companies,enterprise%20strategy%2C%20and%20public%20policy.
[3] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[4] https://www.investing.com/analysis/meta-plunged-12-amazon-jumped-11–same-ai-race-different-economics-200669410
[5] https://www.cnbc.com/2025/10/15/a-guide-to-1-trillion-worth-of-ai-deals-between-openai-nvidia.html
[6] https://finance.yahoo.com/news/bank-america-just-issued-stark-152422714.html
[7] https://news.futunn.com/en/post/64706046/from-cash-rich-to-collective-debt-how-does-wall-street?level=1&data_ticket=1763038546393561
[8] https://www.businessinsider.com/big-tech-capex-spending-ai-earnings-2025-10?
[9] https://finance.yahoo.com/news/ai-capex-exceed-half-trillion-093015889.html
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About the Author:
Rahul Sharma is President & Co-Chief Executive Officer at Indxx – a provider of end-to-end indexing services, data and technology products. He has been instrumental in leading the firm’s growth since 2011. Raul manages Indxx’s Sales, Client Engagement, Marketing and Branding teams while also helping to set the firm’s overall strategic objectives and vision.
Rahul holds a BS from Boston College and an MBA with Beta Gamma Sigma honors from Georgetown University’s McDonough School of Business.
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References:
Curmudgeon/Sperandeo: New AI Era Thinking and Circular Financing Deals
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?
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
FT: Scale of AI private company valuations dwarfs dot-com boom
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
AI Data Center Boom Carries Huge Default and Demand Risks
Will billions of dollars big tech is spending on Gen AI data centers produce a decent ROI?
Dell’Oro: Analysis of the Nokia-NVIDIA-partnership on AI RAN
RAN silicon rethink – from purpose built products & ASICs to general purpose processors or GPUs for vRAN & AI RAN
Nokia in major pivot from traditional telecom to AI, cloud infrastructure, data center networking and 6G
Reuters: US Department of Energy forms $1 billion AI supercomputer partnership with AMD
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Expose: AI is more than a bubble; it’s a data center debt bomb
We’ve previously described the tremendous debt that AI companies have assumed, expressing serious doubts that it will ever be repaid. This article expands on that by pointing out the huge losses incurred by the AI startup darlings and that AI poster child Open AI won’t have the cash to cover its costs 9which are greater than most analysts assume). Also, we quote from the Wall Street Journal, Financial Times, Barron’s, along with a dire forecast from the Center for Public Enterprise.
In Saturday’s print edition, The Wall Street Journal notes:
OpenAI and Anthropic are the two largest suppliers of generative AI with their chatbots ChatGPT and Claude, respectively, and founders Sam Altman and Dario Amodei have become tech celebrities.
What’s only starting to become clear is that those companies are also sinkholes for AI losses that are the flip side of chunks of the public-company profits.
OpenAI hopes to turn profitable only in 2030, while Anthropic is targeting 2028. Meanwhile, the amounts of money being lost are extraordinary.
It’s impossible to quantify how much cash flowed from OpenAI to big tech companies. But OpenAI’s loss in the quarter equates to 65% of the rise in underlying earnings of Microsoft, Nvidia, Alphabet, Amazon and Meta together. That ignores Anthropic, from which Amazon recorded a profit of $9.5B from its holding in the loss-making company in the quarter.
OpenAI committed to spend $250 billion more on Microsoft’s cloud and has signed a $300 billion deal with Oracle, $22 billion with CoreWeave and $38 billion with Amazon, which is a big investor in rival Anthropic.
OpenAI doesn’t have the income to cover its costs. It expects revenue of $13 billion this year to more than double to $30 billion next year, then to double again in 2027, according to figures provided to shareholders. Costs are expected to rise even faster, and losses are predicted to roughly triple to more than $40 billion by 2027. Things don’t come back into balance even in OpenAI’s own forecasts until total computing costs finally level off in 2029, allowing it to scrape into profit in 2030.
The losses at OpenAI that has helped boost the profits of Big Tech may, in fact, understate the true nature of the problem. According to the Financial Times:
OpenAI’s running costs may be a lot more than previously thought, and that its main backer Microsoft is doing very nicely out of their revenue share agreement.
OpenAI appears to have spent more than $12.4bn at Azure on inference compute alone in the last seven calendar quarters. Its implied revenue for the period was a minimum of $6.8bn. Even allowing for some fudging between annualised run rates and period-end totals, the apparent gap between revenues and running costs is a lot more than has been reported previously.
The apparent gap between revenues and running costs is a lot more than has been reported previously. If the data is accurate, then it would call into question the business model of OpenAI and nearly every other general-purpose LLM vendor.
Also, the financing needed to build out the data centers at the heart of the AI boom is increasingly becoming an exercise in creative accounting. The Wall Street Journal reports:
The Hyperion deal is a Frankenstein financing that combines elements of private-equity, project finance and investment-grade bonds. Meta needed such financial wizardry because it already issued a $30B bond in October that roughly doubled its debt load overnight.
Enter Morgan Stanley, with a plan to have someone else borrow the money for Hyperion. Blue Owl invested about $3 billion for an 80% private-equity stake in the data center, while Meta retained 20% for the $1.3 billion it had already spent. The joint venture, named Beignet Investor after the New Orleans pastry, got another $27 billion by issuing bonds that pay off in 2049, $18 billion of which Pimco purchased. That debt is on Beignet’s balance sheet, not Meta’s.
Dan Fuss, vice chairman of Loomis Sayles told Barrons: “We are good at taking credit risk,” Dan said, cheerfully admitting to having the scars to show for it. That is, he added, if they know the credit. But that’s become less clear with the recent spate of mind-bendingly complex megadeals, with myriad entities funding multibillion-dollar data centers. Fuss thinks current data-center deals are too speculative. The risk is too great and future revenue too uncertain. And yields aren’t enough to compensate, he concluded.
Increased wariness about monster hyper-scaler borrowings has sent the cost of insuring their debt against default soaring. Credit default swaps (CDS) more than doubled for Oracle since September, after it issued $18 billion in public bonds and took out a $38 billion private loan. CoreWeave’s CDS gapped higher this past week, mirroring the slide of the data-center company’s stock.
According to the Bank Credit Analyst (BCA), capex busts weigh on the economy, which further hits asset prices, the firm says. Following the dot-com bust, a housing bubble grew, which burst in the 2008-09 financial crisis. “It is far from certain that a new bubble will emerge (after the AI bubble bursts) this time around, in which case the resulting recession could be more severe than the one in 2001,” BCA notes.
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The widening gap between the expenditures needed to build out AI data centers and the cash flows generated by the products they enable creates a colossal risk which could crash asset values of AI companies. The Center for Public Enterprise reports that it’s “Bubble or Nothing.”
Should economic conditions in the tech sector sour, the burgeoning artificial intelligence (AI) boom may evaporate—and, with it, the economic activity associated with the boom in data center development.
Circular financing, or “roundabouting,” among so-called hyperscaler tenants—the leading tech companies and AI service providers—create an interlocking liability structure across the sector. These tenants comprise an incredibly large share of the market and are financing each others’ expansion, creating concentration risks for lenders and shareholders.
Debt is playing an increasingly large role in the financing of data centers. While debt is a quotidian aspect of project finance, and while it seems like hyperscaler tech companies can self-finance their growth through equity and cash, the lack of transparency in some recent debt-financed transactions and the interlocked liability structure of the sector are cause for concern.
If there is a sudden stop in new lending to data centers, Ponzi finance units ‘with cash flow shortfalls will be forced to try to make position by selling out position’—in other words to force a fire sale—which is ‘likely to lead to a collapse of asset values.’
The fact that the data center boom is threatened by, at its core, a lack of consumer demand and the resulting unstable investment pathways, is itself an ironic miniature of the U.S. economy as a whole. Just as stable investment demand is the linchpin of sectoral planning, stable aggregate demand is the keystone in national economic planning. Without it, capital investment crumbles.
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Postscript (November 23, 2025):
In addition to cloud/hyperscaler AI spending, AI start-ups (especially OpenAI) and newer IT infrastructure companies (like Oracle) play a prominent role. It’s often a “scratch my back and I’ll scratch yours” type of deal. Let’s look at the “circular financing” arrangement between Nvidia and OpenAI where capital flows from Nvidia to OpenAI and then back to Nvidia. That ensures Nvidia a massive, long-term customer and providing OpenAI with the necessary capital and guaranteed access to critical, high-demand hardware. Here’s the scoop:
- Nvidia has agreed to invest up to $100 billion in OpenAI over time. This investment will be in cash, likely for non-voting equity shares, and will be made in stages as specific data center deployment milestones are met.
- OpenAIhas committed to building and deploying at least 10 gigawatts of AI data center capacity using Nvidia’s silicon and equipment, which will involve purchasing millions of Nvidia expensive GPU chips.
Here’s the Circular Flow of this deal:
- Nvidia provides a cash investment to OpenAI.
- OpenAI uses that capital (and potentially raises additional debt using the commitment as collateral) to build new data centers.
- OpenAI then uses the funds to purchase Nvidia GPUs and other data center infrastructure.
- The revenue from these massive sales flows back to Nvidia, helping to justify its soaring stock price and funding further investments.
What’s wrong with such an arrangement you ask? Anyone remember the dot-com/fiber optic boom and bust? Critics have drawn parallels to the “vendor financing” practices of the dot-com era, arguing these interconnected deals could create a “mirage of growth” and potentially an AI bubble, as the actual organic demand for the products is difficult to assess when companies are essentially funding their own sales.
However, supporters note that, unlike the dot-com bubble, these deals involve the creation of tangible physical assets (data centers and chips) and reflect genuine, booming demand for AI compute capacity although it’s not at all certain how they’ll be paid for.
There’s a similar cozy relationship with the $1B Nvidia invested in Nokia with the Finnish company now planning to ditch Marvell’s silicon and replace it by buying the more expensive, power hungry Nvidia GPUs for its wireless network equipment. Nokia, has only now become a strong supporter of Nvidia’s AI RAN (Radio Access Network), which has many telco skeptics.
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References:
https://www.wsj.com/tech/ai/big-techs-soaring-profits-have-an-ugly-underside-openais-losses-fe7e3184
https://www.ft.com/content/fce77ba4-6231-4920-9e99-693a6c38e7d5
https://www.wsj.com/tech/ai/three-ai-megadeals-are-breaking-new-ground-on-wall-street-896e0023
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