Author: Chen Le ｜ Editor: Ye Jinyan ｜ Production: Deep Web · Tencent News Xiao Man Studio

After the US midterm elections, Wall Street's optimism about AI took a sharp downturn in just two days.

On November 5, the market value of the US stock market's "Seven Sisters" evaporated by about $1.04 trillion in a single day, but the decline varied significantly among companies: Apple fell slightly by 1.53%, while Tesla dropped sharply by 10.77%; as the biggest winner of the AI wave, NVIDIA plummeted 9.26%, with a single-day market value loss exceeding $440 billion, amounting to 2 to 4 times the drop of its chip buyers (Microsoft, Google, Amazon).

The trigger for this crash was the US midterm elections. After the Democrats won control of both houses of Congress on November 3, the US stock market initially rose the next day, but by the 5th, investors began to worry that the new Congress might tighten AI regulations and slow down data center construction, causing the Nasdaq to plummet 5.52%. On the 6th, technology stocks rebounded, with the Nasdaq rising 1.09%, NVIDIA up 2.78%, and AMD soaring about 11%, but Apple and Microsoft continued to decline. The Nasdaq still posted a cumulative drop of 3.4% for the week.

In fact, except for Tesla, the fundamentals of the giants have not deteriorated: NVIDIA's revenue doubled year-on-year, Apple reached a new high for the same period, and the cloud businesses of Microsoft, Google, Amazon, and Meta's advertising business all continued to grow.

Economist Song Qinghui analyzed that policy uncertainty and the market's anxiety about AI investment returns resonated with each other. The valuations of technology giants have already heavily drawn on the high growth potential of AI, but massive investments have yet to translate to commensurate profits. In the third quarter of this year, Microsoft, Google, Amazon, and Meta continued to increase their investments in data center and other infrastructure, with the growth rate of capital expenditures far exceeding operating cash flow.

Every hefty investment from the giants translates into revenue for NVIDIA. Once buyers can no longer "spend", how long can NVIDIA's high growth be sustained?

Image: Capital expenditure on infrastructure by tech giants and revenue trends of NVIDIA

If data centers stop operating, those who "sell shovels" will suffer the most

The answer to this question primarily depends on whether data centers can be constructed as planned. After the elections, in addition to concerns over recovering AI investments, the market gained an additional layer of worry: Will the regulatory policies of the new Congress and local governments slow down data center construction? Compared to cloud giants, NVIDIA is more sensitive to this change due to its reliance on downstream procurement.

Image: Status of restrictive proposals on US data center regulation and distribution of computing power planning

NVIDIA's net profit in the recent financial quarter approached $60 billion, with an estimated revenue growth of 12% sequentially for the next quarter. However, over 90% of its revenue heavily relies on data centers. In its customer structure, cloud giants and AI startup companies, as well as government and enterprise clients each account for half, with the latter growing at a faster pace. This model determines that NVIDIA's performance is deeply tied to the pace of downstream infrastructure: if data centers are built quickly, shipments will increase; if progress is hindered, orders will only be delayed.

This election happened to introduce variables at this juncture. On Election Day, among the 41 local proposals related to data centers across 10 states in the US, 40 tended to restrict data center development, with 27 restrictive or oppositional proposals passing. The newly elected Democratic governor of Texas, Inohosa, even promised during the campaign to fully suspend data center construction until dedicated legislation is passed.

SemiAnalysis, a semiconductor research organization, calculated that most of the data centers scheduled for delivery by 2027 have already started construction, and the real impact of this election is on the planning for 2028, with Texas alone accounting for nearly 30% of the newly added computing capacity in the US for 2028. The policy changes brought by the election may primarily affect NVIDIA's growth expectations for 2028 and beyond.

If projects are postponed, the impacts on both ends of the supply chain differ: buyers can pause certain capital expenditures, while NVIDIA may face the risk of delayed chip order deliveries. The market on November 4 already showed this differentiation: according to foreign media reports, while investors bet on the giants cutting back on AI expenditures on the same day, Microsoft and Amazon surged by about 6%, while NVIDIA alone fell.

On the 5th, a wave of sell-offs spread throughout the entire AI supply chain. Amid Qualcomm's weak performance and market concerns about policy slowing AI infrastructure construction, the Philadelphia semiconductor index plunged by 12% in a single day, with losses far exceeding those of cloud computing companies.

However, this does not mean that the demand for computing power has peaked. Song Qinghui reminds us that programmatic trading, leveraged funds, and options hedging can significantly amplify short-term volatility; a day-long plunge does not represent a proportionate loss of intrinsic value for companies. On the 6th, NVIDIA rebounded 2.78%, but still had not recovered most of the previous day’s 9.26% drop. The new quarterly financial report to be released on November 17 will become an important window for the market to assess its growth prospects.

In contrast, Apple and Tesla are at opposite ends of this logical chain. Song Qinghui points out that the growth logic of the two companies is entirely different from that of AI infrastructure companies: Apple's profit base relies on smartphones and services, with less dependence on data center construction, making it relatively resilient; Tesla experienced the largest drop, impacted by Musk's political investments. Previously, Musk had invested over $400 million to support the Republican Party, and after the Democrats won, its business landscape could face more regulatory scrutiny. Within two days after the election, the combined market value of Tesla and SpaceX evaporated by over $400 billion.

Who will foot the next hefty bill?

Whether the data centers can be established ultimately depends on funding. This huge expense is primarily borne by NVIDIA's core buyers, Microsoft, Google, Amazon, and Meta. The latest financial reports indicate that the four companies are still ramping up AI investments, but they can no longer fully rely on self-generated cash flow to support them.

Image: Comparison of operating cash flow and capital expenditure gaps for Microsoft, Google, Amazon, and Meta

A year ago, the combined operating cash flow of these four companies exceeded fixed asset expenditures by about $61.7 billion; in the third quarter of this year, this surplus turned into a gap of about $26.4 billion.

This is not because AI is unprofitable. Google's cloud operating profit in the third quarter was three times that of the same period last year, AWS operating profit increased from $11.4 billion to $17.8 billion, and Microsoft Azure revenue grew by 48%.

The gap arises from the time lag: money for building data centers needs to be paid upfront, while revenues may take years to recover. For example, Google has $561 billion in cloud orders, with over 55% to be recognized in revenue within two years, yet its signed future purchase commitments amount to $924 billion, 1.65 times its orders.

Moreover, the upfront payments are increasingly reliant on borrowing. Google raised its long-term debt from about $20 billion to $125 billion within a year and launched a stock issuance plan exceeding $80 billion; Amazon's free cash flow turned from a net inflow of $14.8 billion to a net outflow of $42.4 billion over the past 12 months, mainly supplemented by bond issuance. Meta's free cash flow fell into the negative for the first time in over a decade, with trading levels of long-term bonds comparable to many BBB-rated companies.

Aviva Investors believes this reflects investor concerns over excessive spending and the uncertainty of AI monetization. Among the four, only Microsoft still has about $18.6 billion in free cash flow and has not undertaken major bond issuance in recent years. According to Bank of America statistics, the five major cloud providers are projected to issue about $121 billion in dollar bonds by 2025, over four times the average level of the previous five years.

As the scale of external financing expands, financing costs have also begun to become an important factor affecting the pace of data center expansion. Song Qinghui notes that persistent inflation and high interest rates will drive up financing costs, and projects that rely on external financing with long return cycles should moderately slow their expansion. At present, the 10-year US Treasury yield remains above 4.8%. Approvals are another hurdle. SemiAnalysis points out that while the giants can afford the costs brought by new regulations, this does not mean they can deliver computing power on time, and projects that have been halted are also difficult to quickly relocate to other states.

The most pressure is on the batch of NVIDIA's fastest-growing clients. Pan Helin, a member of the Information Communication Economic Expert Committee of the Ministry of Industry and Information Technology, points out that unlisted large model companies like OpenAI and Anthropic highly rely on external financing. If interest rates remain high and regulations tighten, their subsequent financing difficulties will greatly increase.

This is precisely why NVIDIA has dropped more steeply than its buyers. Nigel Green, CEO of deVere Group, believes that investors will next scrutinize each company to see who can truly convert AI investments into revenue.

On November 6, NVIDIA did experience a rebound, but still did not recover most of the previous day's losses. Compared to Apple's relatively independent profit model from AI infrastructure, NVIDIA's high growth is more dependent on continued investment from downstream. How quickly buyers can build data centers will directly impact this "shovel seller's" next round of growth.

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