Alphabet Plunges 12% After Earnings: The Great AI Bubble Debate and Lessons from History

The Great AI Bubble Bull-Bear Debate: The Root of This Crash and Lessons from History

Jinduan Download the App for Exclusive Early Access November 03, 2026 07:48:00 From Beijing Updated November 03, 2026 08:09:18

On October 28, Alphabet (Google's parent company) released its third-quarter report. Revenue grew 25% year over year, cloud business revenue doubled, and operating profit rose 30%. Looking at just these figures, this is a report card that could not possibly be better.

But at the close that day, the stock price had fallen 12% overnight.

The same company, the same set of numbers, produced enormous disagreement in the market: some focused on the cloud business's 103% growth rate, others on negative free cash flow of $9.07 billion; some calculated revenue, others extrapolated debt; the bears said "the bubble is starting to burst," the bulls said "the market is badly mistaken."

Almost no AI participant denies that AI is rewriting the way software, advertising, industry, and organizational production work. Yet the disagreement has become abruptly real in the present: can the money required for this round of buildout ultimately be earned back from the new revenue AI creates; and before the answer arrives, who has the right to keep spending shareholders' money?

01

Why does one earnings report read as two different worlds?

The most confusing thing about Alphabet's earnings report is that it simultaneously contains four mutually contradictory facts.

First, the growth is real.

Quarterly revenue was $127.577 billion, up 25% year over year; Google Cloud revenue was $30.709 billion, up 103% year over year, cloud business operating profit was $11.1 billion, more than triple the prior-year period, and the margin rose to 36.2%. Gemini app users reached 1 billion, and first-party model APIs process nearly 32 billion tokens per minute, more than four times a year earlier. AI is not a story stuck in a slide deck; it is already driving real usage.

Second, the pressure on the traditional cash cow is also real.

Google Search & other revenue was $63.350 billion, up 12% year over year. Management acknowledged on the earnings call that AI Mode queries are growing rapidly, but monetization remains relatively low. Search query volume growth hit a five-year high, yet monetization lagged behind.

Third, the pressure on cash flow is still real.

Operating cash flow for the quarter was about $42.3 billion, capital expenditure about $51.4 billion, free cash flow was negative $9.07 billion, and capital expenditure grew about 114% year over year. The earnings call disclosed that the company's long-term debt had risen from $21.6 billion a year earlier to $125.1 billion. The cash cow is still producing milk, but the bill for building the ranch has, for the first time, grown large enough to change the company's constitution.

Quarterly net profit was $26.170 billion, lower than the prior-year period; other income swung from positive to negative, with the equity investment portfolio recording a loss of about $8.66 billion. Earnings per share missed expectations, and this was not entirely "compute investment dragging down profit"—investment gains and losses accounted for an important share. Therefore, directly equating $2.12 per share with "the failure of the AI business model" is not rigorous.

The bear camp's leaders summarized this set of numbers as: free cash flow turned negative, buybacks halted, reliance on financing, and RPO may not be honored.

The bulls seized on another point: cloud revenue doubled, profit tripled, and negative cash flow is merely an accounting timing difference in which investment precedes return. Some bulls even estimate that the return on newly invested capital is about 20%, clearly above the cost of capital.

Neither side fabricated facts. They simply chose different stretches of the same river.

In fact, free cash flow is the most easily abused metric. Its accounting relationship is simple: free cash flow ≈ operating cash flow − capital expenditure. Operating cash flow shows how much money the existing business can collect today; capital expenditure shows how much the company has invested for the future. When the two subtract to a negative number, it may mean the company is spending more than it earns, or it may mean it is using today's strong profits to build tomorrow's larger capacity. Looking only at whether the number is positive or negative proves nothing.

What really needs to be explored are two other questions: what assets are formed after this money is invested; and for every one yuan invested in these assets, how many dimes can be earned back each year in the future? This is precisely the relationship between return on capital and cost of capital.

If the after-tax operating profit brought in each year by new data centers, chips, and networks, divided by cumulative invested capital, can remain above the financing cost over the long term, then today's negative free cash flow is a prelude to value creation; otherwise, it is turning a good business into a bad balance sheet.

There is also a measurement point that is easily overlooked: ROIC should look at the return on new investment, not the average profit margin of the old business. Alphabet's past high returns from search advertising do not prove that today's new AI capital expenditure will naturally have the same returns. When an advertising company becomes an asset-heavy infrastructure company, it means valuation can no longer rely only on the price-to-earnings ratio; it must also look at how much sustainable cash each yuan of new capital can ultimately leave behind.

The bulls have grounds here: cloud business operating profit is rising rapidly, and the earnings call disclosed cloud backlog of about $561 billion, with slightly more than 55% of that revenue expected to be recognized over the next 24 months.

So-called circular financing can be explained with one example: a chip company invests in an AI lab, the lab uses the money to purchase compute from a cloud provider, and the cloud provider then buys GPUs from the chip company. All three parties can announce cooperation, with revenue, orders, and valuations rising together, but how much real money external customers actually paid becomes hard to identify.

History has already rehearsed this scene.

A hundred years ago, in 1920s America, the electric utility industry used layered pyramid holding structures to hold stakes in one another and guarantee one another's obligations, packing the same pool of capital repeatedly into different companies' financial statements. Samuel Insull's utility empire once magnified the cash flow of a single power grid into the valuations of dozens of holding companies. After the 1929 crash, the Public Utility Holding Company Act of 1935 was enacted specifically to dismantle this layer of structure.

Previously, the Bank for International Settlements flagged the risk in its Annual Economic Report 2026, noting that the opacity of AI industry financing intensifies financial fragility, with circular financing particularly warranting vigilance; several types of arrangements already publicly discussed in the market—for example, Nvidia investing in OpenAI and discussing guarantees for its data center leases, Google providing support for Anthropic-related data centers, and AMD pairing chip deployment agreements with warrants—are in essence the same ancient impulse as the electric power holding pyramid: using structure to cash in the future in advance.

But it must be pointed out that these transactions are not entirely identical in scale, constraints, and execution, and it cannot be said indiscriminately that they are "all fake revenue." Circular financing itself is not a sin. In the history of railway, telecommunications, aviation, and energy construction, it is not rare for suppliers to help customers finance: external demand genuinely exists, and it can accelerate infrastructure formation; if demand relies mainly on the financier continuously pumping in support, then it is delaying the demand test.

The market panics one moment and buys back the next, and at bottom it is also fighting over this point: it is not only revaluing infrastructure companies, but also rearranging "who ultimately gets the value of AI": who can survive the capex peak, who can turn usage into sustainable charges, and who will be replaced by large models.

One moment the market fears CapEx is too large, the next it fears AI is not fast enough; this is not actually contradictory—it is simultaneously pricing "overinvestment" and "being replaced." What is truly dangerous is using only one of these answers to explain all companies.

02

Technology can win, and valuations can still lose

When talking about the AI bubble, the easiest case to cite is the Nasdaq in 2000: the index fell about 78% from its high, and countless .com companies disappeared. But comparing only index declines yields almost no useful conclusions. Today's large technology companies have profits, users, and global cash flow; they are a different species from the companies back then that had only page views and no revenue.

A more reasonable reference is the infrastructure bubble. And before the telecom bubble, there was an even older sample: railways.

In the 19th century, the United States paid capital far beyond contemporary imagination to build railways. By 2000, annual capital expenditure on U.S. telecom equipment was about $120 billion, construction relied heavily on debt, and total industry debt reached about $1 trillion by 2002. Railways had previewed all of this 150 years earlier at even greater cost: duplicate construction, competitive track laying, land speculation, multiple waves of bankruptcies, until the Panic of 1893, when J.P. Morgan stepped in to restructure. Railways did ultimately reshape America, but most early investors did not live to see the harvest.

The telecom bubble merely performed this script again. Demand did not arrive at the imagined speed; WorldCom and Global Crossing fell one after another, and only a very small proportion of the fiber laid during the boom was ever lit. The Cisco story is especially painful: in March 2000, its share price was $80.06 and its market value about $500 billion; by October 2002, the share price was about $8.60, a decline of about 88%. Over the same period, the company's revenue did not collapse by the same proportion, and later the internet did change the world as expected.

A correct technology judgment and heavy stock losses can absolutely happen at the same time.

This history offers three lessons for today:

First, the return on infrastructure depends not only on whether the technology is useful, but also on the time gap between the pace of construction and the arrival of demand. If capacity arrives first while customers are still on the way, financing costs will blow up.

Second, who the profits belong to. Early telecom builders were not necessarily the final winners of the internet; equipment makers could be repriced because of customer debt problems, while later platform companies took more value instead. The same applies to AI: chipmakers, cloud providers, model companies, and application companies growing at the same time does not mean they will share final profits in the same proportion.

Third, asset specificity. Once fiber is laid, it is hard to repurpose; general-purpose compute, specialized chips, and power facilities in data centers have the same attribute. The faster chips iterate, the more likely depreciation is to become a hidden examiner of profits. AI assets with high book profit margins today may, if their efficiency falls behind and prices decline in three to five years, have actual economic lives shorter than their accounting depreciation periods.

From an economics perspective, this is a typical marginal return proposition. A large future AI market does not mean all of today's investment will pay off; aggregate demand is one thing, and the marginal return of an additional data center, an additional chip, or an industry large model is another. Capital cycles are most likely to spiral out of control within the consensus that "the market is huge," because every company believes that if it does not invest, it will lose the next round.

The result is a prisoner's dilemma: if rivals expand and I do not, I may lose the market; if everyone expands, the industry may end up with overcapacity. The rationality of individual companies adds up to industry overinvestment.

To break it, three indicators must be put into one pool and observed continuously:

Incremental return on invested capital (operating profit from new investment divided by newly invested capital, rather than the entire company's historical profit margin);

Price and utilization rate (if revenue growth comes from price increases, it must be sustained by higher utilization and lower costs; a smart computing center with a high idle rate is merely expensive fixed assets no matter how large its scale);

Cash payback period (long-term contracts can ease uncertainty, but cannot eliminate it; the longer the payback period, the more important customer credit, technology substitution, and financing costs become).

This is precisely why the bulls emphasize cloud business profits and vertical integration, while the bears emphasize end demand and financing structure; in fact, both sides can be partly right at the same time: the self-built computing power of leading companies may yield reasonable returns, while highly leveraged marginal projects may not. A bubble does not have to occur evenly across the entire industry; it often bursts first in the most fragile projects and the most optimistic valuations.

Putting financial reports, transaction structures, and history together, we do not agree with the two extreme views: "AI is just a scam" cannot explain Google Cloud's revenue doubling, model invocation volume growing severalfold, or AI's entry into advertising, office work, and industrial processes; "as long as AI is useful, all investment is reasonable" evades capital costs, depreciation, and payback periods.

A more precise perspective may be: which revenue comes from external customers paying repeatedly, and which revenue mainly comes from capital and contracts confirming each other. The more of the former, the more construction can weather cycles; the higher the share of the latter, the more one must guard against chain reactions caused by changes in financing conditions.

Of course, for a company like Alphabet, another layer must be added: its core search business is still generating enormous cash, so the key question is not "will it collapse," but whether new businesses can earn returns no lower than the old business, and under what framework the market is willing to price it.

03

China mapping: the same capital cycle, two balance sheets

America's contradiction lies in the relationship among massive capital expenditure, complex financing arrangements, and the realization of end demand; China's computing power construction is also expanding rapidly, but the driving forces are also layered with the national computing network, East Data West Computing, telecom operator investment, local industrial policy, and domestic substitution, and the funding sources, project types, and assessment methods are all different.

East Data West Computing has already formed 8 major hub nodes and 10 data center clusters, and the intelligent computing scale built in the 8 major hubs exceeds 80% of the national total; during the "15th Five-Year Plan" period, new direct investment in computing network construction is expected to reach 4 trillion yuan; in the first half of 2026, China's intelligent computing scale reached 2,185 EFLOPS, up 177% year on year.

These figures can show the intensity of construction, but cannot automatically explain usage efficiency. What China truly needs to guard against, besides "borrowing to build computing power," are three more local types of mismatch:

Mismatch between construction and scenarios (projects are obtained first, models and applications are sought later; smart computing centers become investment-promotion scorecards but lack enough workload to fill them);

Mismatch between hardware and software (growth in domestic chip shipments is important, but if frameworks, toolchains, model adaptation, and developer services cannot keep up, procurement scale does not equal usable computing power);

Mismatch between general capabilities and industry needs (what factories may need is not a larger general model, but specialized models that can connect to quality inspection, production scheduling, energy management, and after-sales service; parameter scale does not directly create cash flow).

Here a meaningful contrast is hidden:

In the 19th century, American railways were driven by private capital, expanded through land grants and bonds, and ultimately consolidated after bankruptcy due to duplicated construction and debt; China's high-speed rail, power grids, and computing networks, by contrast, follow a state-led, unified planning path. The driving mechanisms differ, but the capital cycle poses the same test: after construction, is there enough demand to use it and earn the money back?

Historically, state-led infrastructure has also had stories of capacity preceding demand, except the risk is not called "bubble bursting" but "insufficient utilization" and "hidden debt."

Therefore, China's question cannot be simplified to "whether to follow the investment"; what needs to be assessed is: for each additional unit of computing power investment, can local, billable, renewable, and exportable industry capabilities be formed?

Power, equipment, and engineering get paid first. The expansion of smart computing centers first brings orders for servers, networking, cooling, power, and construction; this layer most resembles the "shovel sellers" of infrastructure cycles, with high order visibility and the greatest sensitivity to changes in construction pace. It earns money from capital expenditure, not from application renewals.

Chips and computing power operations come next. Domestic substitution provides additional demand, but assessment cannot stop at procurement and shipments; what truly determines asset quality is utilization, unit computing cost, software compatibility, and customer structure.

Models and platforms come later still. Growth in invocation volume does not equal platform profitability; inference costs, the proportion of free users, enterprise contract terms, and the speed of price declines determine whether this layer can form stable gross margins; open source can expand usage, but open source itself is not a business model; hosting, enterprise services, and industry delivery determine the source of cash.

The place where industrial applications in China are most likely to form differentiated returns may be: China has a complete range of manufacturing categories, dense industrial belts, and large amounts of real production data; in quality inspection, equipment maintenance, supply chains, drug R&D support, government and financial back offices, there are clear labor costs and error costs. Whether AI can reduce rework, shorten downtime, and lower inventory answers the value question better than model benchmark scores.

Robots and intelligent terminals connect software capabilities to the physical world, offering the greatest imaginative space while also bringing higher hardware investment, after-sales costs, and safety requirements; one cannot skip the unit economics just because the space is large.

Laid out this way, one finds: if AI policy rewards only "building it," resources will be pushed toward the first two layers; if it begins to assess "using it" and "earning it back," returns will remain in the last three layers. The true maturity of an industrial chain is not that every link is hot, but that cash can flow all the way back from the application end to the computing power and chip ends.

In the 2026 policy language on new quality productive forces, "fully implementing the 'AI+' initiative" has already been placed together with "not to become detached from the real economy and turn to the virtual, not to seek excessive scale and completeness, and not to create bubbles." The essence of this combination is precisely to shift assessment from investment scale to industrial results.

For local governments, intelligent computing centers should shift from "project attraction" to "asset management," and new projects should at least answer these questions: Are there real local customers? What is the expected utilization rate? Can electricity and network costs be covered? Can domestic software and hardware be used by enterprises at a low threshold? The significance of Shanghai and Zhuhai issuing computing vouchers is not to hand out one more subsidy, but to channel the real demand of small and medium-sized enterprises to existing computing power, avoiding idle capacity on one side while new capacity is built on the other.

For traditional companies in industrial belts, what is most worth doing may not yet be striving for a larger general-purpose model, but rather organizing industry data, process knowledge, and on-site problems into reusable specialized capabilities. Only when one factory proves that AI reduces defect rates, shortens line-change times, and makes veteran workers' experience replicable will ten factories possibly be willing to renew. The premise for the common prosperity of an industrial belt is not that everyone buys the same expensive system, but that trial costs are lowered, validation results are shared, and specialized models can migrate between similar processes.

For tech companies that are themselves driving forces, independent controllability cannot be accepted merely on the basis of "whether there are domestic chips," but must also be assessed by migration cost: how much code an enterprise must change to move from one chip to another, whether domestic frameworks can retain developers, and how much electricity each effective task consumes after model deployment. China's core AI industry scale has exceeded 1.2 trillion yuan, intelligent computing power has reached 2,592 EFLOPS, and there are more than 6,600 AI enterprises. Scale has already been formed; the harder task in the next stage is to turn this scale into stable returns.

For investors, it is most important to distinguish the "three kinds of benefit": order increases, domestic substitution, and operational improvement. The first two can bring revenue; only the third shows that AI has truly entered corporate income statements. Looking only at project wins and computing power scale makes it easy to misread "construction benefits" as "long-term profitability."

04

This debate will not be ended by some grand essay on a "bursting bubble" or by a single stock price rebound. It has observable verification paths:

Watch the external realization of RPO: in the coming quarters, the speed at which cloud backlog converts into revenue, customer concentration, and collection status are more important than total contract value.

Watch whether the gap between capital expenditure and operating cash flow narrows: if AI investment begins to generate returns, free cash flow will improve before grand narratives do; if the negative value continues to expand, the market will demand clearer evidence of ROIC.

Watch inference prices and utilization: falling prices are not inherently bad news; if costs fall faster and utilization is higher, profits can still grow; if prices, utilization, and depreciation deteriorate simultaneously, revenue growth cannot save returns.

Watch renewal rates on China's application side: whether manufacturing enterprises continue paying in the second year, and whether industrial belts move from pilots to regular procurement, are more persuasive than the number of launch events.

Watch power and software constraints: data center electricity use, domestic framework adaptation, and cross-chip migration costs will determine whether China's computing power investment ultimately becomes an asset or a burden.

The lesson left by 2000 has never been "do not invest in new technology," but that technological prospects, capital returns, and valuations must be answered separately. AI can continue to change the world while also eliminating some projects with excessively high valuations and excessively low returns. What truly must be guarded against is by no means technology moving too fast, but rather booking the revenue of the next decade on today's ledger in advance—the root cause of this round of stampede-style plunges in global AI concept stocks is precisely this kind of "full pricing."

One hundred and fifty years ago, railway investors were also betting on the same future. They were wrong in only one thing: that future arrived late, and they could not wait for it to arrive. We may need to take this as a lesson.

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