AI Bubble
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
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
AI Data Center Boom Carries Huge Default and Demand Risks
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
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
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
FT: Scale of AI private company valuations dwarfs dot-com boom
AI spending boom accelerates: Big tech to invest an aggregate of $400 billion in 2025; much more in 2026!
- Meta Platforms says it continues to experience capacity constraints as it simultaneously trains new AI models and supports existing product infrastructure. Meta CEO Mark Zuckerberg described an unsatiated appetite for more computing resources that Meta must work to fulfill to ensure it’s a leader in a fast-moving AI race. “We want to make sure we’re not underinvesting,” he said on an earnings call with analysts Wednesday after posting third-quarter results. Meta signaled in the earnings report that capital expenditures would be “notably larger” next year than in 2025, during which Meta expects to spend as much as $72 billion. He indicated that the company’s existing advertising business and platforms are operating in a “compute-starved state.” This condition persists because Meta is allocating more resources toward AI research and development efforts rather than bolstering existing operations.
- Microsoft reported substantial customer demand for its data-center-driven services, prompting plans to double its data center footprint over the next two years. Concurrently, Amazon is working aggressively to deploy additional cloud capacity to meet demand. Amy Hood, Microsoft’s chief financial officer, said: “We’ve been short [on computing power] now for many quarters. I thought we were going to catch up. We are not. Demand is increasing.” She further elaborated, “When you see these kinds of demand signals and we know we’re behind, we do need to spend.”
- Alphabet (Google’s parent company) reported that capital expenditures will jump from $85 billion to between $91 billion and $93 billion. Google CFO Anat Ashkenazi said the investments are already yielding returns: “We already are generating billions of dollars from AI in the quarter. But then across the board, we have a rigorous framework and approach by which we evaluate these long-term investments.”
- Amazon has not provided a specific total dollar figure for its planned AI investment in 2026. However, the company has announced it expects its total capital expenditures (capex) in 2026 to be even higher than its 2025 projection of $125 billion, with the vast majority of this spending dedicated to AI and related infrastructure for Amazon Web Services (AWS).
- Apple: Announced it is also increasing its AI investments, though its overall spending remains smaller in comparison to the other tech giants.
As big as the spending projections were this week, they look pedestrian compared with OpenAI, which has announced roughly $1 trillion worth of AI infrastructure deals of late with partners including Nvidia , Oracle and Broadcom.
Despite the big capex tax write-offs (due to the 2025 GOP tax act) there is a large degree of uncertainty regarding the eventual outcomes of this substantial AI infrastructure spending. The companies themselves, along with numerous AI proponents, assert that these investments are essential for machine-learning systems to achieve artificial general intelligence (AGI), a state where they surpass human intelligence.
Yet skeptics question whether investing billions in large-language models (LLMs), the most prevalent AI system, will ultimately achieve that objective. They also highlight the limited number of paying users for existing technology and the prolonged training period required before a global workforce can effectively utilize it.
During investor calls following the earnings announcements, analysts directed incisive questions at company executives. On Microsoft’s call, one analyst voiced a central market concern, asking: “Are we in a bubble?” Similarly, on the call for Google’s parent company, Alphabet, another analyst questioned: “What early signs are you seeing that gives you confidence that the spending is really driving better returns longer term?”
Bank of America (BofA) credit strategists Yuri Seliger and Sohyun Marie Lee write in a client note that capital spending by five of the Magnificent Seven megacap tech companies (Amazon.com, Alphabet, and Microsoft, along with Meta and Oracle) has been growing even faster than their prodigious cash flows. “These companies collectively may be reaching a limit to how much AI capex they are willing to fund purely from cash flows,” they write. Consensus estimates of AI capex suggest will climb to 94% of operating cash flows, minus dividends and share repurchases, in 2025 and 2026, up from 76% in 2024. That’s still less than 100% of cash flows, so they don’t need to borrow to fund spending, “but it’s getting close,” they add.
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Google, which projected a rise in its full-year capital expenditures from $85 billion to a range of $91 billion to $93 billion, indicated that these investments were already proving profitable. Google’s Ashkenazi stated: “We already are generating billions of dollars from AI in the quarter. But then across the board, we have a rigorous framework and approach by which we evaluate these long-term investments.”
Microsoft reported that it expects to face capacity shortages that will affect its ability to power both its current businesses and AI research needs until at least the first half of the next year. The company noted that its cloud computing division, Azure, is absorbing “most of the revenue impact.”
Amazon informed investors of its expedited efforts to bring new capacity online, citing its ability to immediately monetize these investments.
“You’re going to see us continue to be very aggressive in investing capacity because we see the demand,” said Amazon Chief Executive Andy Jassy. “As fast as we’re adding capacity right now, we’re monetizing it.”
Meta’s chief financial officer, Susan Li, stated that the company’s capital expenditures—which have already nearly doubled from last year to $72 billion this year—will grow “notably larger” in 2026, though specific figures were not provided. Meta brought this year’s biggest investment-grade corporate bond deal to market, totaling some $30 billion, the latest in a parade of recent data-center borrowing.
Apple confirmed during its earnings call it is also increasing investments in AI . However, its total spending levels remain significantly lower compared to the outlays planned by the other major technology firms.
Skepticism and Risk:
While proponents argue the investments are necessary for AGI and offer a competitive advantage, skeptics question if huge spending (capex) on AI infrastructure and large-language models will achieve this goal and point to limited paying users for current AI technology. Meta CEO Zuckerberg addressed this by telling investors the company would “simply pivot” if its AGI spending strategy proves incorrect.
The mad scramble by mega tech companies and Open AI to build AI data centers is largely relying on debt markets, with a slew of public and private mega deals since September. Hyperscalers would have to spend 94% of operating cash flow to pay for their AI buildouts so are turning to debt financing to help defray some of that cost, according to Bank of America. Unlike earnings per share, cash flow can’t be manipulated by companies. If they spend more on AI than they generate internally, they have to finance the difference.
Hyperscaler debt taken on so far this year have raised almost as much money as all debt financings done between 2020 and 2024, the BofA research said. BofA calculates $75 billion of AI-related public debt offerings just in the past two months!
In bubbles, everyone gets caught up in the idea that spending on the hot theme will deliver vast profits — eventually. When the bubble is big enough, it shifts the focus of the market as a whole from disliking capital expenditure, and hating speculative capital spending in particular, to loving it. That certainly seems the case today with surging AI spending. For much more, please check-out the References below.
Postscript: November 23, 2025:
In this new AI era, consumers and workers are not what drives the economy anymore. Instead, it’s spending on all things AI, mostly with borrowed money or circular financing deals.
BofA Research noted that Meta and Oracle issued $75 billion in bonds and loans in September and October 2025 alone to fund AI data center build outs, an amount more than double the annual average over the past decade. They warned that “The AI boom is hitting a money wall” as capital expenditures consume a large portion of free cash flow. Separately, a recent Bank of America Global Fund Manager Survey found that 53% of participating fund managers felt that AI stocks had reached bubble proportions. This marked a slight decrease from a record 54% in the prior month’s survey, but the concern has grown over time, with the “AI bubble” cited as the top “tail risk” by 45% of respondents in the November 2025 poll.
JP Morgan Chase estimates up to $7 trillion of AI spending will be with borrowed money. “The question is not ‘which market will finance the AI-boom?’ Rather, the question is ‘how will financings be structured to access every capital market?’ according to strategists at the bank led by Tarek Hamid.
As an example of AI debt financing, Meta did a $27 billion bond offering. It wasn’t on their balance sheet. They paid 100 basis points over what it would cost to put it on their balance sheet. Special purpose vehicles happen at the tail end of the cycle, not the early part of the cycle, notes Rajiv Jain of GQG Partners.
References:
IBM and Groq Partner to Accelerate Enterprise AI Inference Capabilities
IBM and Groq [1.] today announced a strategic market and technology partnership designed to give clients immediate access to Groq’s inference technology — GroqCloud, on watsonx Orchestrate – providing clients high-speed AI inference capabilities at a cost that helps accelerate agentic AI deployment. As part of the partnership, Groq and IBM plan to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture. IBM Granite models are also planned to be supported on GroqCloud for IBM clients.
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Note 1. Groq is a privately held company founded by Jonathan Ross in 2016. As a startup, its ownership is distributed among its founders, employees, and a variety of venture capital and institutional investors including BlackRock Private Equity Partners. Groq developed the LPU and GroqCloud to make compute faster and more affordable. The company says it is trusted by over two million developers and teams worldwide and is a core part of the American AI Stack.
NOTE that Grok, a conversational AI assistant developed by Elon Musk’s xAI is a completely different entity.
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Enterprises moving AI agents from pilot to production still face challenges with speed, cost, and reliability, especially in mission-critical sectors like healthcare, finance, government, retail, and manufacturing. This partnership combines Groq’s inference speed, cost efficiency, and access to the latest open-source models with IBM’s agentic AI orchestration to deliver the infrastructure needed to help enterprises scale.
Powered by its custom LPU, GroqCloud delivers over 5X faster and more cost-efficient inference than traditional GPU systems. The result is consistently low latency and dependable performance, even as workloads scale globally. This is especially powerful for agentic AI in regulated industries.
For example, IBM’s healthcare clients receive thousands of complex patient questions simultaneously. With Groq, IBM’s AI agents can analyze information in real-time and deliver accurate answers immediately to enhance customer experiences and allow organizations to make faster, smarter decisions.
This technology is also being applied in non-regulated industries. IBM clients across retail and consumer packaged goods are using Groq for HR agents to help enhance automation of HR processes and increase employee productivity.
“Many large enterprise organizations have a range of options with AI inferencing when they’re experimenting, but when they want to go into production, they must ensure complex workflows can be deployed successfully to ensure high-quality experiences,” said Rob Thomas, SVP, Software and Chief Commercial Officer at IBM. “Our partnership with Groq underscores IBM’s commitment to providing clients with the most advanced technologies to achieve AI deployment and drive business value.”
“With Groq’s speed and IBM’s enterprise expertise, we’re making agentic AI real for business. Together, we’re enabling organizations to unlock the full potential of AI-driven responses with the performance needed to scale,” said Jonathan Ross, CEO & Founder at Groq. “Beyond speed and resilience, this partnership is about transforming how enterprises work with AI, moving from experimentation to enterprise-wide adoption with confidence, and opening the door to new patterns where AI can act instantly and learn continuously.”
IBM will offer access to GroqCloud’s capabilities starting immediately and the joint teams will focus on delivering the following capabilities to IBM clients, including:
- High speed and high-performance inference that unlocks the full potential of AI models and agentic AI, powering use cases such as customer care, employee support and productivity enhancement.
- Security and privacy-focused AI deployment designed to support the most stringent regulatory and security requirements, enabling effective execution of complex workflows.
- Seamless integration with IBM’s agentic product, watsonx Orchestrate, providing clients flexibility to adopt purpose-built agentic patterns tailored to diverse use cases.
The partnership also plans to integrate and enhance RedHat open source vLLM technology with Groq’s LPU architecture to offer different approaches to common AI challenges developers face during inference. The solution is expected to enable watsonx to leverage capabilities in a familiar way and let customers stay in their preferred tools while accelerating inference with GroqCloud. This integration will address key AI developer needs, including inference orchestration, load balancing, and hardware acceleration, ultimately streamlining the inference process.
Together, IBM and Groq provide enhanced access to the full potential of enterprise AI, one that is fast, intelligent, and built for real-world impact.
References:
FT: Scale of AI private company valuations dwarfs dot-com boom
AI adoption to accelerate growth in the $215 billion Data Center market
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?
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
FT: Scale of AI private company valuations dwarfs dot-com boom
The Financial Times reports that ten loss making artificial intelligence (AI) start-ups have gained close to $1 trillion in private market valuation in the past 12 months, fuelling fears about a bubble in private markets that is much greater than the dot com bubble at the end of the 20th century. OpenAI leads the pack with a $500 billion valuation, but Anthropic and xAI have also seen their values march higher amid a mad scramble to buy into emerging AI companies. Smaller firms building AI applications have also surged, while more established businesses, like Databricks, have soared after embracing the technology.
U.S. venture capitalists (VCs) have poured $161 billion into artificial intelligence startups this year — roughly two-thirds of all venture spending, according to PitchBook — even as the technology’s commercial payoff remains elusive. VCs are on track to spend well over $200bn on AI companies this year.
Most of that money has gone to just 10 companies, including OpenAI, Anthropic, Databricks, xAI, Perplexity, Scale AI, and Figure AI, whose combined valuations have swelled by nearly $1 trillion, Financial Times calculations show. Those AI start-ups are all burning cash with no profits forecasted for many years.
Start-ups with about $5mn in annual recurring revenue, a metric used by fast-growing young businesses to provide a snapshot of their earnings, are seeking valuations of more than $500mn, according to a senior Silicon Valley venture capitalist.
Valuing unproven businesses at 100 times their earnings or more dwarfs the excesses of 2021, he added: “Even during peak Zirp [zero-interest rate policies], these would have been $250mn-$300mn valuations.”
“The market is investing as if all these companies are outliers. That’s generally not the way it works out,” he said. VCs typically expect to lose money on most of their bets, but see one or two pay the rest off many times over.
“There will be casualties. Just like there always will be, just like there always is in the tech industry,” said Marc Benioff, co-founder and chief executive of Salesforce, which has invested heavily in AI. He estimates $1tn of investment on AI might be wasted, but that the technology will ultimately yield 10 times that in new value.
“The only way we know how to build great technology is to throw as much against the wall as possible, see what sticks, and then focus on the winners,” he added.
“Of course there’s a bubble,” said Hemant Taneja, chief executive of General Catalyst, which raised an $8 billion fund last year and has backed Anthropic and Mistral. “Bubbles align capital and talent around new trends. There’s always some destruction, but they also produce lasting innovation.”
Venture investors have weathered cycles of boom and bust before — from the dot-com crash in 2000 to the software downturn in 2022 — but the current wave of AI funding is unprecedented. In 2000, VCs invested $10.5 billion in internet startups; in 2021, they deployed $135 billion into software firms. This year, they are on pace to exceed $200 billion in AI. “We’ve gone from the doldrums to full-on FOMO,” said one investment executive.
OpenAI and its start-up peers are competing with Meta, Google, Microsoft, Amazon, IBM, and others in a hugely capital-intensive race to train ever-better models, meaning the path to profitability is also likely to be longer than for previous generations of start-ups.
Backers are betting that AI will open multi-trillion-dollar markets, from automated coding to AI friends or companionship. Yet some valuations are testing credulity. Startups generating about $5 million in annual recurring revenue are seeking valuations above $500 million, a Silicon Valley investor said — 100 times revenue, surpassing even the excesses of 2021. “The market is behaving as if every company will be an outlier,” he said. “That’s rarely how it works.”
The enthusiasm has spilled into public markets. Shares of Nvidia, AMD, Broadcom, and Oracle have collectively gained hundreds of billions in market value from their ties to OpenAI. But those gains could unwind quickly if questions about the startup’s mounting losses and financial sustainability persist.
Sebastian Mallaby, author of The Power Law, summed it up beautifully:
“The logic among investors is simple — if we get AGI (Artificial General Intelligence, which would match or exceed human thinking), it’s all worth it. If we don’t, it isn’t…. “It comes down to these articles of faith about Sam’s (Sam Altman of OpenAI) ability to work it out.”
References:
https://www.ft.com/content/59baba74-c039-4fa7-9d63-b14f8b2bb9e2
Big tech spending on AI data centers and infrastructure vs the fiber optic buildout during the dot-com boom (& bust)
Can the debt fueling the new wave of AI infrastructure buildouts ever be repaid?
Amazon’s Jeff Bezos at Italian Tech Week: “AI is a kind of industrial bubble”
Gartner: AI spending >$2 trillion in 2026 driven by hyperscalers data center investments
AI Data Center Boom Carries Huge Default and Demand Risks
Canalys & Gartner: AI investments drive growth in cloud infrastructure spending
