Who Is Really Eating the AI Dividend: Computing Power Investment and Profit Distribution as Seen from Silicon Valley Financial Reports

Starting with Meta, Silicon Valley giants have ushered in a particularly bleak earnings week.

On October 29, Amazon released its third-quarter earnings report. Revenue for its AWS division grew 39% year-over-year, the fastest growth rate in nearly five years, but the group's free cash flow over the past 12 months turned negative at $42.4 billion, and the stock price fell by more than 5% in after-hours trading at one point.

Google's experience was even more brutal: despite Google Cloud revenue growing 103% year-over-year, Alphabet's stock price still plunged 12.64% on October 29, falling by more than 13% at one point during the session, with market value evaporating by more than $500 billion, setting a record in the company's history.

Earlier, Meta's third-quarter revenue grew 26% year-over-year, and its stock price fell nearly 7% on the first trading day after the earnings report was released.

To some extent, this is a rebuttal to earlier remarks by Torsten Slok, chief economist at Apollo Global Management. Slok pointed out that over the past few years, per-capita revenue and profit margins of the U.S. Magnificent Seven tech giants have continued to improve, but the other 493 companies in the S&P 500 index have not shown similar improvement.

With a technological revolution, people always expect that the companies creating the technology can earn substantial profits, the enterprises adopting the technology can improve operational efficiency, and ordinary people can also enjoy cheaper services, higher incomes and more leisure.

Leading AI companies still need to invest enormous amounts of capital to maintain technological competition; upstream suppliers bear high expenditures and uncertainty about future demand. As for the enterprises actually using AI, efficiency has improved somewhat, but profits have not generally improved; ordinary people, meanwhile, face the risks of higher job requirements, job displacement and lower incomes.

Revenue at leading AI companies is growing rapidly. In the first half of 2026, Anthropic achieved revenue of $16.286 billion, a year-over-year increase of 1,300%; OpenAI's annualized revenue as of the end of September was also approaching $50 billion.

Frontier models have brought considerable revenue, but the pressure should not be underestimated either.

On October 26, Anthropic officially made public its IPO prospectus, disclosing the company's financial condition. In the first half of 2026, Anthropic's computing power and infrastructure costs alone reached $8.945 billion, accounting for 55% of revenue in the same period, a year-over-year increase of 283%.

Over the next decade, the company still has a series of computing power procurement and equipment leasing agreements to fulfill.

The prospectus shows that Anthropic's minimum purchase amount with Google reached $114.5 billion, with Amazon $110 billion, and with Microsoft $44.9 billion; in addition, the company also reached a computing power procurement agreement with Musk's SpaceXAI, with a payment cap of $120.5 billion through 2029, and entered into an equipment finance lease arrangement with Broadcom, with total payments during the lease term of approximately $161.2 billion, excluding interest.

Among them, the minimum purchase commitments under the three agreements with Google, Amazon and Microsoft total $269.4 billion, with terms extending all the way to 2033 to 2036. According to the contract terms, even if the corresponding computing power is not actually used, Anthropic in principle still needs to pay the minimum commitment amount. SpaceXAI's $120.5 billion is an agreement cap, not all of which constitutes non-cancellable payment obligations. After the initial lock-up period ends in early 2027, the two parties can terminate the contract with 90 days' notice.

As the agreements are gradually fulfilled, Anthropic needs to continuously expand revenue in order to cover the corresponding computing power procurement costs.

OpenAI likewise faces enormous spending pressure. According to financial documents exposed in June this year, OpenAI's research and development expenses in 2025 reached $19.18 billion, with total annual costs and expenses of about $34 billion, ultimately recording an operating loss of $20.92 billion.

According to foreign media reports, OpenAI is advancing a new round of financing of about $30 billion, but the target valuation in the financing negotiations has dropped from $1.4 trillion to $1.25 trillion, a reduction of $150 billion. The willingness of some investors to subscribe fell short of expectations, and the ongoing financing needs and the issue of returns on AI infrastructure investment are coming under market scrutiny.

Although computing power procurement has been locked in years in advance, customers' future spending does not come with equally clear guarantees.

Anthropic disclosed in its prospectus that in the third quarter of 2026, more than 80% of revenue came from Claude model versions released in the past six months. Claude Opus 5.5, released in September, reduced the cost for customers to complete similar tasks by about 40%. Anthropic said efficiency improvements enable the company to maintain profit margins while lowering customer costs, but to maintain this competitiveness, continuous model research and development and optimization remain essential.

In addition, the prospectus disclosed that in the first nine months of 2026, Anthropic's top five customers contributed about 25% of revenue. But many large customers have not signed long-term contracts, or their contracts are about to expire, and they can reduce or even stop spending at any time.

Compliance costs are likewise not to be ignored. In 2025, Anthropic accrued $1.5 billion in settlement expenses due to the Bartz copyright lawsuit, equivalent to about 33% of that year's revenue; OpenAI disclosed on October 23 that after an internal research model broke through network isolation, the company suspended training and evaluation of its most advanced models, and the relevant training did not resume until 11 days later.

As model capabilities improve, safeguarding the security of the research and development environment has also become work in which the company must continuously invest resources.

From computing power procurement to model research and development, from customer competition to security and compliance, the investments of leading AI companies continue. They need to constantly develop more powerful models, win more customers, and rely on future revenue growth to cover the costs they have already borne today.

In fact, the benefits created by technology do not necessarily remain in the hands of the technology providers.

In 2004, economist William Nordhaus studied technological innovation in the United States from 1948 to 2001 and estimated that the excess profits obtained by innovators amounted to only about 2.2% of the present value of the social benefits created by technological innovation. Most of the benefits flowed to consumers through means such as lower prices.

Upstream companies that have won orders are under equally obvious pressure

NVIDIA's revenue in the second quarter of fiscal 2027 was $96.2 billion, up 106% year on year, and net profit was $59.7 billion, up 126% year on year; TSMC's third-quarter revenue was $46.7 billion, up 41.2% year on year, and net profit was NT$763.1 billion, up 68.7% year on year, setting a profit record for the sixth consecutive quarter; on October 28, SK Hynix announced its third-quarter financial report, with revenue of KRW 104.62 trillion, up 328% year on year, operating profit of KRW 83.33 trillion, up 632% year on year, and an operating margin of 80%.

But these profits have not fully dispelled doubts in the capital market.

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TSMC's capital expenditure budget for this year has reached $60 billion to $64 billion. Management expects cumulative capital expenditure from 2026 to 2028 to significantly exceed that of the previous three years. Although the company still maintains strong cash generation capacity, building new fabs and expanding advanced process and packaging capacity both require investment years in advance. Whether future customer demand can continue to support these expansions determines the ultimate return on investment.

NVIDIA's risks have even extended to the customer side. Financial documents disclosed in August this year show that the company's supply chain and capacity commitments increased from $119 billion in the previous quarter to $279 billion. At the same time, it has also committed to purchasing up to about $36 billion of cloud services over the next several years from some AI cloud service providers that buy NVIDIA GPUs, and to providing up to $105 billion in conditional credit guarantees for data center projects built by SB Energy and leased by OpenAI.

On October 29, Amazon announced its third-quarter financial report, with AWS revenue reaching $46 billion and operating profit reaching $17.8 billion. But the group's free cash flow over the past 12 months turned from positive $14.8 billion in the same period last year to negative $42.4 billion. The company maintained its plan for about $220 billion in cash capital expenditure in 2026, most of which is for AI infrastructure.

Google is also facing cash flow pressure from massive investment. Financial results announced on October 28 show that although Google Cloud has achieved considerable profits, Alphabet's capital expenditure for the quarter reached $51.4 billion, and free cash flow was negative $9.07 billion. The company maintained its capital expenditure guidance of $195 billion to $205 billion for 2026 and expects it to increase further in 2027.

Microsoft's quarterly free cash flow was still $18.6 billion, but capital expenditure also reached $45.2 billion and is expected to exceed $50 billion next quarter.

As a representative of large-scale expansion of AI data centers, as of the end of August this year, Oracle's capital expenditure over the past 12 months totaled about $75.66 billion, and free cash flow was negative $28.72 billion.

Also on October 28, Oracle announced its financing and cash flow plan for the next four years: capital expenditure is expected to peak in fiscal 2027 to 2028, free cash flow is expected to turn positive in the second half of fiscal 2028, and free cash flow is expected to reach at least $30 billion in fiscal 2029, when it will begin to rely on its own cash flow to support business growth.

To complete this transition, Oracle has committed to raising no more than $20 billion in additional financing through the capital market by fiscal 2030, planning to raise $10 billion each through equity financing and new debt, excluding the previously completed $20 billion equity financing and refinancing of existing debt.

For Neocloud companies such as CoreWeave and Nebius that specialize in renting out AI computing power, funding pressure is even more direct: taking CoreWeave as an example, in the second quarter of 2026, the company's revenue was $2.575 billion, up 112% year on year, while its net loss in the same period reached $626 million. Of that, net interest expense alone was $640 million, already equivalent to about a quarter of quarterly revenue.

To buy GPUs and build data centers, these companies need to continuously borrow or raise funds. Equipment procurement, depreciation, and interest costs have already occurred, while future rental income will only be gradually realized over many years.

On one side are NVIDIA, TSMC, SK Hynix, and others, which have already realized considerable profits but still need to continue investing for future growth; on the other side are cloud computing and data center companies, which need to bear huge construction costs, and some of them also face heavy financing and cash flow pressure.

The upstream appears to have captured the benefits first, but the money already earned cannot guarantee that the next round of investment will continue to generate returns.

Ordinary people have become more efficient, but their lives are not necessarily better

In August this year, McKinsey released its latest global AI survey. 80% of respondents said AI had improved their work efficiency, but only 37% believed that AI had made a positive contribution to their company's EBIT, basically flat compared with last year. Among them, high-performing organizations that can truly attribute at least 5% of EBIT to AI and consider this impact significant account for only about 6% of all respondents.

Another set of data comes from J.P. Morgan Asset Management. On October 16, the institution released an analysis pointing out that in the second-quarter 2026 earnings season, about two-thirds of S&P 500 companies mentioned AI on their earnings calls, but only 17% provided specific application results, and companies that truly quantitatively disclosed AI's contribution to profit accounted for only 2%.

J.P. Morgan gave two examples: American Express used AI to shorten the software development cycle by more than 30%, but chose to have engineers use the saved time to handle backlogged projects; Netflix used AI to improve content production efficiency and reduce costs, and planned to invest the saved funds into more content production.

Efficiency improvements do exist, but companies choose to reinvest the gains into their businesses, and in the short term this may not necessarily be reflected as improved profit margins.

In May this year, the International Labour Organization (ILO) published a dedicated study calling this phenomenon AI's "Aggregation Paradox."

The study pointed out that AI can bring productivity improvements of 10% to 70% on some specific tasks, but these local gains have not yet formed equally obvious growth at the enterprise, industry, and macroeconomic levels. Companies need to redesign workflows, adjust organizational structures, train employees, and also bear the deployment and operating costs of AI systems. Only when the technology is truly integrated into the entire production process can local efficiency gains possibly translate into broader economic benefits.

Compared with corporate profits, the situation of ordinary workers may be more complicated.

On October 6, Gallup released a survey of American workers. Among 7,845 surveyed employees who had used AI at least a few times in the past year, 63% believed that AI made them complete work faster, and 56% believed that AI helped them find more creative solutions.

Similar changes have also appeared in Chinese companies. In September this year, the International Labour Organization (ILO), together with researchers from Renmin University of China, conducted in-depth interviews with 21 Chinese companies and surveyed 1,591 professionals. The study found that AI has already brought obvious efficiency improvements in some business areas: at one insurance company, 300 customer service representatives increased the average number of issues handled per day from 6,000 to 15,000; at one smart manufacturing factory, production efficiency increased by 30%.

But after efficiency improvements, workers may not necessarily receive corresponding returns. The Gallup survey showed that 31% of AI users said their employers required them to take on more work responsibilities because of the application of AI. Among employees who believed that AI helped them speed up their work, this proportion further rose to 37%.

There is also uncertainty regarding income. Among the Chinese professionals surveyed by the ILO, 39% expected that AI might lead to a decline in their income.

In addition, in August this year, Stanford University researchers updated a study based on salary records of millions of U.S. employees: in occupations with higher AI exposure, the employment level of young employees aged 22 to 25 showed a 19% relative gap compared with peers of the same age in groups with lower AI exposure.

It is worth noting that workers are also consumers. Free chatbots, increasingly cheaper AI services, and tasks that used to require paid purchases but can now be completed on their own all mean that consumers have gained real convenience.

But the fact that consumers have gained convenience does not mean that their overall economic situation will necessarily improve. The Organisation for Economic Co-operation and Development (OECD) has long tracked changes in labor productivity and wages and found that many economies have already seen a disconnect between productivity growth and real wage growth. Even if the entire economic system can create more output with less labor, these gains may not necessarily translate proportionally into ordinary people's income.

Not long ago, in an interview hosted by Walter Isaacson, author of Steve Jobs, Musk described the future as he imagined it: superintelligence may be thousands or even millions of times smarter than humans, and just as humans do not care about the daily lives of chimpanzees in the forest, superintelligence may likewise not care about humans.

Such a future cannot yet be verified. But today, when tech giants have already begun to envision unlimited intelligence and unlimited abundance, ordinary people are still waiting for some much more realistic changes—higher income, less work, and genuinely improved lives.

We are still waiting.