Holding massive AI infrastructure-related orders is no longer enough to 'protect' a company.

Oracle holds $500 billion in orders, yet its stock price has plummeted 40% from its peak in September. Broadcom currently has a backlog of AI product orders of about $73 billion, and its stock price turned from gains to losses after its latest earnings report.

Dubbed 'Nvidia's favorite child,' CoreWave generates quarterly revenues in the billions of dollars but managed to secure over $36 billion in orders from OpenAI and Meta in just one week. Over the past month, the company's stock has fallen 17%.

While there are concerns about whether these companies have sufficient capacity (and money) to meet client demands, there is also growing skepticism about the reliability of the clients themselves.

Peeling back the layers of the AI infrastructure onion reveals the usual suspects: Meta, Google parent Alphabet, Microsoft, Amazon, Apple, Nvidia, and a few other giants, along with star AI startups like OpenAI and Anthropic.

The star startups are still immature; building their infrastructure almost entirely relies on external financing, making the risks obvious.

The giants should be the stabilizing anchors—they are financially robust, cash-rich, and are filling the next few years with疯狂 (frantic) infrastructure plans worth trillions of dollars.

But the returns from AI, which sits at the center of this spending, remain minuscule. Whether using their 'core business' profits to fuel this new dream will drag down the giants depends entirely on whether the dream is realized quickly enough.

Success means joy for all; failure could mean losing everything.

01

Holding the 'future' card, Oracle experienced great joy and great sorrow within just a few months.

When the joy arrived, Oracle's stock price soared 40% in a single day, and founder and CEO Larry Ellison briefly surpassed Musk to become the world's richest person.

At that time, Ellison proclaimed: 'Artificial intelligence is everything!'

Artificial intelligence is indeed everything. For Oracle, it was the reason for all this joy—OpenAI had then reached a five-year, $300 billion computing power procurement agreement with Oracle, which was the match that ignited Oracle's stock price.

Yet, just three months later, Oracle holds even more orders, but the 'magic' has disappeared.

Oracle recently released its fiscal 2026 Q2 (corresponding to September-November 2025) earnings report. Revenue increased 14% year-over-year, and the company stated its backlog had reached $523 billion.

This figure is $68 billion higher than the previous quarter.

Upon the earnings release, the stock fell 11% that day, marking the largest single-day drop for the company since January. From its peak in September, Oracle's stock price has fallen 40%.

Future orders, amid the current skepticism of an 'AI bubble,' have transformed from a hopeful promise into a heavy burden.

Oracle appears somewhat strained—the financial report shows Oracle's cash flow was negative $10 billion, and quarterly capital expenditure (CapEx) reached $12 billion, nearly $3.7 billion higher than analysts' predictions.

Oracle's CFO revealed that the company's annual expenditure guidance was also raised by $15 billion, reaching a level of $50 billion.

The market's greatest fear is: Does Oracle even have the ability to raise that much money to support this super-large-scale AI infrastructure build-out?

Some analysts predict Oracle will need to borrow $100 billion to complete the construction. In Q2, the company raised $18 billion in debt, one of the largest debt issuances on record for a tech company.

On the earnings call, Oracle vigorously defended itself, explicitly opposing the 'need to borrow $100 billion' prediction, stating the actual financing required would be significantly less. The trick lies in Oracle's adoption of a 'customer brings their own chips' cooperation model.

In other words, it's not Oracle buying chips and renting them to customers, but customers bringing their own chips—something unprecedented in the cloud services industry.

Additionally, Oracle emphasized that some suppliers are willing to lease rather than sell chips to them, allowing Oracle to synchronize payments and receipts.

If Oracle's claims hold true, it could significantly reduce its upfront investment and greatly increase its return rate.

But for the market, the risk hasn't disappeared; it has shifted: from Oracle itself to Oracle's customers. Meta or OpenAI and other clients purchase the expensive GPUs themselves and install them in Oracle's data centers.

Whether Oracle's hundreds of billions in future revenue materializes depends not only on its ability to 'deliver' but also on the customers' ability to 'pay.' Nearly two-thirds of Oracle's nearly $500 billion undelivered order backlog comes from yet-to-be-profitable OpenAI, with another known $20 billion coming from a new agreement with Meta.

Similarly, Broadcom, which also holds a massive order backlog, received negative market feedback.

Broadcom also released new earnings. For Q4 of fiscal year 2025, ended November 2, it achieved core performance with both revenue and profit exceeding expectations. AI semiconductor-related revenue grew 74% year-over-year.

On the earnings call, Broadcom CEO Hock Tan stated the company's current backlog of AI product orders is approximately $73 billion, to be delivered over the next six quarters. He emphasized this is a 'floor,' and as new orders continue to pour in, the backlog size is expected to expand further.

However, Broadcom declined to provide clear guidance for full-year 2026 AI revenue, citing uncertainty in customer deployment schedules and potential quarterly fluctuations.

After the earnings release, Broadcom's stock initially rose about 3% but then turned negative, falling over 4% in after-hours trading.

Compared to Oracle's dramatic swings, Broadcom only experienced minor turbulence, but the underlying market sentiment is similar—people are no longer optimistic about that 'future' of大兴 (greatly building) AI infrastructure.

Broadcom's customer base is also relatively concentrated, with its AI-related orders primarily coming from OpenAI, Anthropic, Google parent Alphabet, and Meta, among others.

02

Peel the AI infrastructure onion, and you'll always find those familiar companies at the core—the Magnificent Seven and OpenAI, Anthropic.

Another AI cloud infrastructure startup that has garnered much attention this year is CoreWeave. CoreWeave went public in March this year, marking the largest tech startup listing since 2021. Its stock price subsequently more than doubled, even surpassing the 'Big Seven tech giants'.

Its customer concentration is also extremely high, essentially surviving on orders from Microsoft, OpenAI, Nvidia, and Meta.

Just this Monday (December 9), CoreWeave issued another $2 billion in convertible bonds, while its total debt as of the end of September had already reached $14 billion. Market concerns intensified; its stock price has fallen 17% over the past month.

Again, the market has deep-seated doubts about the AI industry as a whole, extending beyond whether these AI infrastructure-related companies can deliver services as planned to whether the big clients making疯狂 (frantic) deals can actually pay their bills.

And the complex web of circular transactions between all related parties has formed a tight yet opaque network, making everything even less clear.

If we look by client type, startups like OpenAI and Anthropic were the first to raise concerns.

The reason is simple: neither has stable profitability, at least far from sufficient for their膨胀 (expansive) infrastructure plans. They need to rely on external financing, making uncertainty obvious.

The giants, meanwhile, are more like the bellwethers and backstops on the playing field.

The giants spend hundreds of billions of dollars annually on capital expenditure, a significant portion of which goes toward expanding data centers. Their combined capital expenditure for 2026 will be more than 4 times the amount the U.S. publicly traded energy industry spends on drilling exploration wells, extracting oil and gas, transporting gasoline to gas stations, and operating large chemical plants. Amazon alone has capital expenditures bill that exceeds the total for the entire U.S. energy sector.

Compared to the稚嫩 (immature) startups, the giants are obviously wealthy and powerful—they are financially stable and have ample cash flow. At least for now, the spending hasn't exceeded their capacity to bear it.

For example, Microsoft, Google, and Amazon together will spend over $600 billion from 2023 to this year, with expected revenues of $750 billion.

Looking at their recent performance reports, they appear quite strong, with 'exceeding expectations' being the baseline. It seems there's no need to worry—in other words, they can afford this great AI infrastructure build-out.

But upon closer inspection, none have fundamentally changed their revenue structure. AI has indeed begun to generate returns, but its share of total revenue often remains a supporting role, while it takes center stage in spending.

For instance, regarding Microsoft's quarterly earnings at the end of July, TheCUBE Research estimated that AI services contributed about 19% of Azure cloud growth, over $3 billion, but this accounted for less than a tenth of Microsoft's total revenue.

Over half of Google's revenue still comes from advertising and search, while Amazon's e-commerce and advertising still account for over 70% of its revenue.

In other words, the giants are using their mature businesses to nourish the future of AI.

The question is, for how long can they keep nourishing it?

03

The giants have already begun a 'debt frenzy.'

In September, Meta issued $30 billion in bonds. Alphabet recently also announced plans to issue approximately $17.5 billion in bonds in the U.S. market and about $3.5 billion in bonds in the European market.

Data from Bank of America shows that in September and October alone, large tech companies focused on artificial intelligence issued $75 billion in U.S. investment-grade bonds, more than double the industry's average annual issuance of $32 billion between 2015 and 2024.

These companies' revenue growth should currently be able to support expenditure, but keeping pace in the AI field will ultimately require more debt as well.

The Wall Street Journal pointed out sharply in an analysis: AI is making giants weaker.

As of the end of the third quarter of this year, Microsoft's cash and short-term investments accounted for about 16% of total assets, down from about 43% in 2020. The cash reserves of Alphabet and Amazon have also decreased significantly.

Alphabet and Amazon's free cash flow this year is expected to be lower than last year. Although Microsoft's free cash flow over the last four quarters appears to have grown compared to the same period last year, its disclosed capital expenditure does not include spending on long-term leases for data centers and computing equipment. If these expenditures were included, its free cash flow would also decline.

This trend seems destined to continue.

Analysts estimate that Microsoft, including lease expenditures, is expected to spend approximately $159 billion next year; Amazon is预计 (expected) to spend about $145 billion; Alphabet is预计 (expected) to invest $112 billion. If predictions hold, these companies will have cumulatively投入 (invested) $1 trillion within four years, most of it in the AI field.

Overall, these changes—reduced cash balances, reduced cash flow, increased debt—are fundamentally changing the business models of tech companies.

The tech industry is beginning to resemble industries like semiconductor manufacturing, where tens of billions of dollars are invested in building cutting-edge factories that take years to construct but even longer to yield returns.

Deploying trillions of dollars across hundreds of massive data centers presents clear and significant challenges merely from an execution perspective.

Data centers consume enormous amounts of electricity—GPUs require大量 (a lot of) power for computation—and current power grids cannot handle the surge in demand. Secondly, cooling is also a problem. GPUs run very hot and require large amounts of fresh water to keep the equipment running. Some communities have begun opposing the construction of data centers, worried about the impact on water supply.

Nvidia and OpenAI jointly announced a massive new agreement worth up to $100 billion this year, with OpenAI planning to deploy 10 gigawatts of Nvidia systems. But recently, Nvidia's CFO admitted that this plan is still at the letter of intent stage and has not been formally signed.

This, on the one hand, casts a shadow over the 'credibility' of the bustling AI infrastructure deals and, on the other hand, hints at future uncertainty.

The reason for the delay in signing the agreement has not been made public, but the 'risk factors' section of Nvidia's SEC filing can serve as a reference.

In the filing, Nvidia warned that if customers scale back demand, delay financing, or change direction, the company could face risks of 'excess inventory,' 'cancellation fees,' or 'inventory write-downs and impairments.'

Additionally, the availability of 'data center capacity, power, and capital' is key to the deployment of AI systems. The filing stated that power infrastructure construction is a 'process that will take years' and will face 'regulatory, technical, and construction challenges.'

Even if the AI infrastructure eventually progresses smoothly, it is not the end of 'success.'

AI infrastructure ultimately serves AI demand. If the infrastructure is in place but market demand fails to materialize, low utilization rates of the infrastructure will result in huge losses.

Of course, not everyone is frowning with worry. Supporters believe it's a豪赌 (worthwhile gamble) because AI demand will grow at an exponential, not linear, rate.

Analyst Azeem Azhar calculated that the direct revenue from AI services has grown nearly ninefold over the past two years.

In other words, if this growth rate continues, it's only a matter of time before AI companies begin generating record-breaking profits.

'I think people who are纠结于 (stuck on) the specific financing methods for these investments have an old mindset. Everyone assumes this technology will develop at a linear speed. But AI is an exponential growth technology. It's a completely different model,' Azhar said.

But the question is, will the moment when AI begins to bring explosive' profits' arrive, and when will it come?

Ultimately, whether AI infrastructure will drag down the giants is essentially a race of AI market demand catching up to AI infrastructure build-out. If it catches up, the AI infrastructure will be 'worth it.' If it doesn't, the massive data centers will ultimately become like 'ghost towns.' That would be the ultimate proof that the giants' AI bets were incorrect and would have disastrous consequences.