Good idea? Artificial intelligence on credit: The transformation of the tech industry through massive debt.

The AI ​​boom on credit: How tech giants are taking a trillion-dollar risk and Nvidia's clever game – How one corporation profits from another's AI debt frenzy

An unprecedented race for dominance in artificial intelligence has gripped the tech industry. Giants like Meta, Microsoft, Google, and Amazon are investing sums that previously seemed unimaginable to create the infrastructure for the next technological revolution. But behind the dazzling promises of superintelligence and limitless growth lies a new, risky reality: the entire sector is financing its future on credit. It's a colossal gamble, fueled by a mountain of debt of historic proportions, that is shaking the foundations of the industry and potentially the stability of the financial markets.

The transformation is fundamental: Traditional investments, financed by operating profits, are being replaced by aggressive debt financing. In just two months of 2025, $75 billion in new debt flowed into AI-focused tech companies—more than double the previous annual average. The central dilemma: Spending on data centers and chips is exploding, while the resulting revenues are lagging behind. The gap between CEOs' technological optimism and economic reality is widening and becoming the new normal.

But the real danger lies deeper than the corporate balance sheets. An opaque market for private loans is growing in secret, financing a significant portion of the boom outside of public scrutiny. At the same time, disturbing patterns of circular financing are emerging, in which companies like Nvidia and OpenAI lend each other money to buy their own products – a fragile house of cards that will only stand as long as stock prices rise. The parallels to the dot-com bubble are becoming louder and more compelling.

This article analyzes the different strategies of the tech giants—from Meta's high-risk all-in to Microsoft's more solid position—unmasks the players pulling the strings behind the scenes, and examines the systemic risks arising from this debt-driven race. Is it a necessary investment in a groundbreaking future or the biggest speculative bubble of our time?

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Why billion-dollar bets without guaranteed returns are becoming the new standard

The technology sector is undergoing an unprecedented financial transformation. Companies like Meta, Microsoft, Google, and Amazon have abandoned their traditional fundamenta financing patterns and are turning massively to the debt market. This development not only marks a cyclical upswing but also signals profound structural changes in how the world's most valuable companies finance their future. The scale is already impressive: In September and October 2025 alone, $75 billion in investment-grade debt was issued by artificial intelligence-focused tech companies, more than double the average annual sector value of $32 billion between 2015 and 2024.

These figures highlight a key dilemma of our time: Investments in AI infrastructure are growing faster than the revenues they generate. Technological optimism is colliding head-on with economic reality. OpenAI, for example, announced investment plans totaling $1.4 trillion, while simultaneously incurring billions in backlogs. This discrepancy between expenditures and revenues is not pathological or absurd in its exceptional circumstances, but rather is becoming the new normal in the leading technology sector.

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Meta: The prime example of the debt financing paradigm

Meta Platforms embodies the new financing logic of the AI ​​age like no other company. In the fall of 2025, the Facebook-owned company announced the issuance of $30 billion in new bonds, the largest bond issue in its corporate history. The structure of this bond package spans six tranches with maturities ranging from five to forty years, underscoring the fundamental future-oriented nature of this financing strategy. Simultaneously, Meta plans to invest between $70 and $72 billion in capital expenditures for 2025 alone. For the following year, the company announced its intention to increase this figure by up to 24 percent. This equates to an implicit total investment of up to $90 billion annually.

Meta's financing structure reveals an innovative yet questionable financing model. The company has raised $27 billion from private lenders such as PIMCO, Blue Owl Capital, and Apollo Global Management. These arrangements fall under the growing segment of so-called private credit solutions. The advantage of this structure lies in its accounting architecture: the debt is not fully disclosed on the company's public balance sheet but is partially treated off-balance-sheet through complex structures. This allows Meta to mobilize large amounts of capital without fully disclosing the financing burden in its financial statements.

Meta CEO Mark Zuckerberg justifies this aggressive investment strategy by arguing that the company must make the leap to artificial superintelligence and thus build the necessary infrastructure. This argument contains a fundamental promise: that today's investments will generate tomorrow's enormously profitable business models. Wall Street initially reacted skeptically to this announcement. Meta's share price fell by as much as 13.5 percent, and the company temporarily lost over $220 billion in market capitalization. This reaction illustrates the central dilemma between management optimism and investor uncertainty.

The profitability of Meta's previous AI investments remains in doubt. While Meta boasts robust operating cash flows with a net profit margin in the high 30s, the return on its specific AI infrastructure investments is unknown. Bernstein analysts warn that Meta's grace period for demonstrating progress in AI beyond its core business is rapidly coming to an end. The company has made massive investments and committed significant personnel resources, but now it must deliver results.

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Microsoft: The solid capitalist in the AI ​​arms race

Microsoft represents the antithesis to Meta's aggressive bet. While the company also invests massive sums, it finances these investments from a significantly stronger balance sheet. In the first quarter of fiscal year 2026, Microsoft spent a record-breaking $34.9 billion on investments, roughly 75 percent more than in the same quarter of the previous year. This equates to an annual investment rate of well over $130 billion. A large portion of these funds went toward expanding its Azure cloud infrastructure and partnerships such as the one with OpenAI.

Microsoft's balance sheet is impressive. The company boasts a net income of $102 billion in the past fiscal year and current equity of $363 billion. Net debt stands at a mere $18 billion, a virtually negligible figure for a company of its size. Net operating margins consistently range between 35 and 37 percent. This means Microsoft is able to fund the majority of its AI infrastructure investments from operating cash flows, without relying on external debt financing. Despite this, Microsoft nearly tripled its finance lease liabilities, a form of debt largely associated with data centers, from 2023 to 2024, increasing them to $46 billion.

Microsoft's strategy is to act quickly but finance cautiously. The company recently joined a consortium of investors to acquire 50 data centers in the US and Latin America for a total of $40 billion. This demonstrates that Microsoft is not primarily reliant on short-term debt financing but is instead able to grow through various channels such as syndicated loans and equity. Microsoft also made an early investment in OpenAI and leased Azure infrastructure to OpenAI. This arrangement has proven highly profitable for Microsoft, as OpenAI then rents computing power from Microsoft for its AI models, thus becoming one of Microsoft's growing revenue streams.

Google and Alphabet: Impressive growth figures meet increased financing needs

Alphabet, Google's parent company, presents a more positive picture than Meta in many respects. The company achieved revenues exceeding $100 billion for the first time in the third quarter of 2025, specifically $102.3 billion, representing a 33 percent increase. CEO Sundar Pichai identified artificial intelligence as the key growth driver and announced plans to increase investments for 2025 to up to $93 billion. This represents a rise from the previous forecast of $85 billion. The majority of these investments will go toward expanding data centers and AI infrastructure.

Approximately 60 percent of Google's capital expenditures are allocated to GPUs and servers, while roughly 40 percent goes toward tools and data center equipment. Google announced a $15 billion data center project in India, its largest outside the US, underscoring the global expansion of its AI infrastructure. The stock market reacted much more positively to Alphabet's increased investment announcement than to Meta's, given Google's proven track record of monetizing its AI products. Google's search business has benefited from the integration of artificial intelligence, and the company has already demonstrated documented revenue growth.

Unlike Meta, Alphabet has been more cautious with debt financing. The company issued bonds for the first time since 2020 in April 2025, but overall its debt-to-equity ratio is significantly less aggressive. This is because Google has massive operating cash flows. Its established business models in advertising and cloud infrastructure are considerably more profitable than those of Meta, whose core application, Facebook, is being revitalized after years of stagnation.

Amazon: The silent giant of AI infrastructure

Amazon is often overlooked in discussions about the debt-financing boom, even though the company makes some of the highest investments worldwide. CEO Andy Jassy raised the investment forecast for 2025 to $125 billion, pointing out that Amazon added 3.8 gigawatts of data center capacity in the last twelve months alone. These figures are staggering. For comparison, Microsoft invests roughly $34.9 billion per quarter, and Meta invests roughly $18 to $20 billion. Amazon's investment rate of $125 billion per year is thus many times higher than that of most of its competitors.

Amazon's strategy is significantly more diversified. The company is not only pursuing an AI infrastructure program, but is also investing in cloud computing via AWS, logistics automation, the development of its own chips like Trainium2, and partnerships such as the one with the AI ​​startup Anthropic. Amazon acquired a stake in Anthropic and generated an extraordinary profit contribution of $9.5 billion from this investment in the last quarter alone.

Unlike Meta and OpenAI, Amazon has a diversified business model with established profitability. The company's e-commerce, cloud, and advertising divisions are already highly profitable. Net revenue grew by approximately 11 percent to $158.9 billion, while profit increased by nearly 39 percent to just over $21 billion. This means that Amazon can finance its AI investments from robust cash flow without relying on aggressive debt-market strategies.

Amazon's funding strategy benefits from a strategic partnership with OpenAI. The company agreed to a deal with OpenAI worth approximately $38 billion, granting OpenAI access to AWS infrastructure with hundreds of thousands of Nvidia GPUs and EC2 Ultra servers. This represents a classic customer-supplier relationship, guaranteeing Amazon the utilization of its data centers while providing OpenAI with short-term computing capacity.

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Oracle: From database king to AI infrastructure player

Oracle presents a fascinating case. The company, long known as a stable, non-volatile software firm, suddenly became an aggressive player in the AI ​​infrastructure race. The explanation lies in a strategic partnership with OpenAI and the Japanese SoftBank Group for the so-called Stargate project. This mega-project plans to build data centers with a total capacity of ten gigawatts for an estimated 500 billion US dollars.

Oracle secured $38 billion in financing from a banking consortium led by JPMorgan Chase and Mitsubishi UFJ. This is the largest financing ever raised for AI infrastructure. The structure of this financing illustrates the complexity of modern infrastructure deals: The $38 billion is split into two senior secured credit facilities. A $23.25 billion package funds a data center in Texas, while a $14.75 billion facility supports a project in Wisconsin. The maturities are four years, and the interest rates are approximately 2.5 percentage points above benchmark rates.

Vantage Data Centers Development is responsible for the construction and operation of both facilities. This structure reveals a fascinating pattern: Oracle itself is less the actual operator of the data centers and more a borrower and customer of the infrastructure. Under the Stargate arrangement, the company commits to paying OpenAI $300 billion over the next five years for the use of this computing power. Oracle thus becomes the financier of an infrastructure that will primarily be used by another company. The chips for these data centers, in turn, are purchased from Nvidia.

Oracle's strategy reveals a deep structural problem: The company has burdened itself with an enormous concentration risk, as two-thirds of all future Oracle revenue depends on just one customer, namely OpenAI. This is an extreme concentration characteristic that carries significant risks.

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Nvidia: The real winner of the funding boom

While companies like Meta, Google, and Amazon are scrambling to secure debt financing for their data centers, Nvidia is in a far more comfortable position. The chipmaker, whose GPU technology is central to all AI infrastructure investments, has become the true financier of the AI ​​boom. Nvidia announced plans to invest up to $100 billion in OpenAI. This is no ordinary investment, but rather a clever financial arrangement that serves multiple purposes.

The structure of the Nvidia-OpenAI deal reveals the circularity of modern AI financing: Nvidia's money is used to build new data centers, which are then equipped with Nvidia's chips. According to the chip manufacturer, Nvidia's chips account for 60 to 70 percent of the total cost of a new data center. The practical calculation is as follows: If OpenAI wants to build one gigawatt of computing power, it needs chips worth approximately 35 billion US dollars. Nvidia contributes roughly ten billion US dollars as equity for every additional gigawatt of computing power. This means that OpenAI only has to pay for just under three-quarters of its chips in cash and receives the rest in exchange for equity. Nvidia, in turn, directly finances the demand for its own chips with this investment.

This arrangement is both ingenious and problematic. It guarantees Nvidia exponential sales volumes while simultaneously strengthening the debt network of OpenAI, Oracle, and other players. Nvidia has also acquired a seven percent stake in CoreWeave, another AI-specialized cloud provider. Interestingly, Nvidia is committed to purchasing any excess capacity that CoreWeave cannot bring to market itself until 2032. This is effectively a blank check for its customers. Nvidia also invested five billion US dollars in Intel and is jointly developing new chips with its biggest rival.

Nvidia's stock has risen by approximately 54 percent in 2025 and is on track for its strongest annual rally since 1999. This reflects Nvidia's position as the true beneficiary of the AI ​​boom. While other companies are taking on debt to buy chips, Nvidia is receiving equity and strategic stakes in the world's most valuable AI companies.

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The private credit segment: The blind spot of financial stability

An often overlooked aspect of the AI ​​financing wave is the rapid growth of the so-called private credit market. This rapidly expanding segment of private loans, issued by investment firms, pension funds, and other non-banks, is increasingly financing AI data centers, according to UBS. UBS estimates that AI-related private loans could nearly double in the twelve months leading up to the beginning of 2025.

The problem lies in the lack of transparency and liquidity of these instruments. While private loans offer more flexibility in terms of contract conditions than traditional bank loans, they are difficult to trade during times of crisis. They could therefore cause additional stress in the financial markets should the economic situation worsen. Morgan Stanley estimates that private credit markets could provide more than half of the $1.5 trillion needed to expand data centers by 2028.

Meta is a prime example of this development. The company has raised between 27 and 29 billion US dollars in private capital from firms such as PIMCO, Blue Owl Capital, and Apollo Global Management. These transactions allow Meta to raise billions without having to report the full amount on its balance sheet. The complex structures make it possible to reduce technically speaking debt on the balance sheet, while the economic increase in debt still occurs.

Junk bonds and the rise of speculative debt

Another striking feature is the growth of low-rated bonds in the AI ​​sector. According to Bank of America, the issuance of so-called junk bonds by AI-related companies has increased significantly. These bonds carry credit ratings below investment grade and offer higher yields, but are associated with a correspondingly higher risk of default. The signal is clear: The AI ​​financing boom is also attracting more speculative investors who seek higher returns and are therefore willing to accept correspondingly higher risks.

JP Morgan's analysis shows that AI-related companies now account for 14 percent of the investment-grade index, overtaking US banks as the dominant sector. This illustrates the alarming concentration of systemic risks in the AI ​​sector. A collapse in AI valuations or profitability would therefore directly impact large parts of the credit market.

The funding gap and the illusion of availability

Morgan Stanley sees a potential funding gap of $1.5 trillion for AI infrastructure expansion over the next three years. This is a staggering sum. For comparison, Germany's gross domestic product is approximately €4 trillion, or $4.3 trillion. The amount needed for AI infrastructure is therefore roughly equivalent to one-third of Germany's total economic output, concentrated over three years. The Bain study estimates that annual investment spending will reach $500 billion by 2030 to meet the computing needs of industry.

Whether these funds will actually be available is an open question. While traditional banks are becoming increasingly cautious, the private equity and private credit sectors are stepping in. However, this raises liquidity concerns and increases the system's vulnerability to shocks. If speculative fervor subsides or initial losses occur in this sector, lenders could quickly revert to more rational valuations.

The profitability puzzle: Where are the revenues?

The central puzzle of the entire AI funding wave remains profitability. While the investments are measured and spectacular, the revenues from AI are far less well documented. OpenAI, the most valuable AI startup, earned approximately $13 billion in 2024, but subsequently experienced significant losses. These figures stand in stark contrast to the planned infrastructure investments of one hundred billion dollars or more.

Google and Microsoft have already achieved initial successes in AI monetization. Google has integrated its AI capabilities into its search function, thereby improving advertising business efficiency. Microsoft sells AI capabilities through its Azure cloud offering and Copilot products. Meta, on the other hand, has not yet defined clear profitability pathways for its AI infrastructure.

The problem lies in a classic mismatch between capital expenditures and their amortization. Data centers and chips have relatively short lifecycles. A GPU of this generation can become obsolete in three to four years if technological breakthroughs occur more rapidly. This means that investments with short amortization horizons must be financed, especially when returns on equity exceeding 15-20 percent are expected.

Deutsche Bank and the risk management dilemma

A recent case vividly illustrates the risks of this financing wave. Deutsche Bank has generously granted loans for the construction of AI data centers. This represents concentrated risk for the bank. According to the Financial Times, Deutsche Bank managers are discussing betting on falling share prices of AI companies, as falling prices could indicate financial difficulties within the sector, jeopardizing the loans.

The bank is considering two strategies: First, using short selling of AI stocks to offset loan losses with speculative gains. Second, structuring so-called synthetic risk transfer (SRT) transactions, in which third parties assume a portion of the credit risk. In this process, SRT buyers purchase securities linked to specific loans and provide the lender with funds. In return, they receive comparatively high interest rates. Deutsche Bank would either have to add entirely different loans or offer higher interest rates to sell the SRT securities.

This reveals a deep systemic problem: banks are forced to diversify their risk concentrations because individual concentrations in AI infrastructure loans are becoming too large. This, in turn, increases the complexity of the financial system.

The structural vicious cycle: Circular financing and dependencies

The German news channel n-tv and the Financial Times have pointed to a fascinating yet disturbing pattern: the AI ​​funding boom is increasingly operating through circular financing. Companies lend each other money to buy each other's products. OpenAI buys chips from Nvidia for up to $100 billion and receives shares in Nvidia in return. OpenAI buys chips from AMD for up to $100 billion and receives an option on ten percent of AMD's shares.

Oracle is building $300 billion worth of data centers for OpenAI and has agreed that OpenAI will pay exactly that amount in billing fees over the next five years. Oracle is buying the chips for these data centers from Nvidia. The deal represents a gigantic concentration risk: two-thirds of all future Oracle revenue now depends on just one customer.

These circular financing arrangements work as long as the shares of the participating companies rise. But they are fundamentally fragile. If OpenAI fails to demonstrate its profitability, or if revenue expectations decline, a downward spiral could ensue. Nvidia might choose not to exercise its options, Oracle might not generate revenue from OpenAI, and the entire financing chain could collapse.

According to calculations by the Financial Times, OpenAI has purchased 20 gigawatts of computing power worth one trillion US dollars through the Circle deals. This is roughly equivalent to the electricity produced by 20 nuclear reactors. Despite this, the AI ​​company is incurring billions in losses. An anonymous analyst warns in the British newspaper that OpenAI is “by no means able” to meet even one of these commitments.

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The bubble debate: Comparisons to the dot-com era

Market observers and analysts are intensely debating whether the current wave of AI funding represents a bubble, comparable to the dot-com bubble of the late 1990s. Bank of America published a study in which 54 percent of surveyed fund managers stated that a bubble had formed in AI stocks. This is an alarming percentage and suggests that even professional investors harbor significant doubts about the valuation logic.

JPMorgan CEO Jamie Dimon warned that high asset prices are “a category of concern” and that “many assets” could enter bubble territory. Bank of America’s Global Fund Manager Survey identified, for the first time, an “AI stock market bubble” as the most significant global downside risk for fund managers overseeing nearly $500 billion.

Michael O'Rourke, chief strategist at JonesTrading, makes a compelling argument that there is an AI bubble, based on megadeals such as Google's $15 billion investment in data centers in India and OpenAI's estimated $1.5 trillion plan to expand AI infrastructure, which stand in stark contrast to OpenAI's $13 billion in annual revenue and lack of profitability.

However, there are also more nuanced opinions. Lale Akoner, global market analyst at eToro, argues that the rally is based on strong conviction rather than mere complacency. She describes the market as being in the “pricing to perfection” stage, where investors are focusing more on potential success stories than on actual implementation. She notes that many tech companies have solid balance sheets, which suggests a “pricing to perfection” situation rather than a classic bubble.

This is an important distinction. A true bubble is characterized by massive speculation on companies lacking operational substance. Big Tech, on the other hand, does have operational substance: Microsoft earns $102 billion annually, Google over $70 billion, and Meta over $50 billion. The question is not whether these companies are profitable, but whether their AI-specific investments will pay off.

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The energy infrastructure bottlenecks

An often overlooked but critical problem lies in the energy infrastructure. The planned data centers require colossal amounts of energy. OpenAI plans to build ten gigawatts of computing power, roughly equivalent to the output of ten nuclear power plants. Microsoft and Google are planning similarly massive expansions. The Bank of England warned that material bottlenecks in electricity, data, or raw material supply chains could damage AI's valuations.

These energy problems are not trivial. They require massive investments in power grid infrastructure, energy generation, and cooling systems. These investments must be made in parallel with data center investments, leading to even higher overall capital requirements.

Who else is going into debt? The extended analysis

Besides the Big Tech companies, a second wave of players is also taking on massive debt for AI. These are primarily specialized cloud providers and AI infrastructure startups. CoreWeave, an AI-focused cloud provider, has borrowed heavily from private credit funds and bond investors to purchase chips from Nvidia. The company, which went public in March, has raised approximately $25 billion in public debt and issued shares since last year.

Fluidstack, another cloud computing startup, is also borrowing large sums of money, using its chips as collateral. This is a risky arrangement, as the chips could quickly lose value.

SoftBank, the Japanese tech conglomerate, is also financing its share of a multi-billion-dollar partnership with OpenAI through debt. Following Elon Musk's critical remark in January that SoftBank "didn't actually have" the money, SoftBank attempted to improve its public image. Nevertheless, the financing structure remains fragile.

According to media reports, Elon Musk's own startup xAI is set to raise $12 billion in new debt financing, following a $5 billion funding round earlier this year. Nvidia also reportedly plans to participate in xAI's latest funding round with a $2 billion investment, and the new funds are expected to be used to order $20 billion worth of chips from Nvidia.

The regulatory dimension

The Bank of England warned in a report that risk zones are forming in parts of the financial system characterized by opaque, difficult-to-trade, and illiquid assets. This is a clear critique of the growing private credit sector. Regulators worldwide will be forced to monitor these risks more closely.

Basel III banking regulations could also play a role. While traditional banks operate under stricter capital requirements, private equity funds and other non-bank lenders can take more risks. This creates regulatory arbitrage opportunities.

The long-term perspective: investment or speculation?

The central question at the end of this analysis is: Is the current wave of AI funding a legitimate investment in infrastructure for a transformative technology, or is it speculative overreaction? The answer is probably: both.

There are undoubtedly fundamental, non-speculative reasons for massive investments in AI infrastructure. AI technology is transformative and will massively increase productivity. The necessary computing infrastructure does not yet exist and must be built. This is legitimate from a long-term perspective.

At the same time, short-term financing patterns, and especially circular financing, are alarming. If OpenAI cannot meet its obligations, if the return on infrastructure investments is lower than expected, or if technological breakthroughs render the planned investments obsolete, a massive crash could occur.

The likely future scenario is not an abrupt crash, but rather a gradual reduction in the level of euphoria. Companies will lower growth rates if profitability falls short of expectations. This could lead to a slower, but longer-lasting, adjustment phase. Some players, particularly those with weak funding positions like OpenAI, could face significant financial difficulties.

For analysts, this is a crucial period of observation. The following two to three years will reveal whether AI infrastructure investments prove transformative or whether they turn out to be a massive overinvestment in a technology that is not yet ready for market.

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