The market value of the "seven giants" of US technology stocks evaporated by about $797 billion in a single trading day, marking the largest single-day decline since April 2025. Compared to the historical high hit in late May 2025, the total shrinkage is nearly $2 trillion. The pressure from accumulated valuations and return periods over the past few months was concentrated and released on this day. Leading stocks became the core of the sell-off, with Tesla's stock price plummeting about 15%, and Alphabet falling about 7.1%. The capital expenditure guidance disclosed during the earnings window was quickly interpreted as an increased burning of cash for AI, sparking market concerns that these investments would drag down profits and cash flow in the short term. On the same trading day, the South Korean KOSPI index dropped by as much as 5.61%, with Samsung falling over 6% and SK Hynix down 5.52%, putting pressure on both the technology and semiconductor sectors, and global risk appetite significantly retreated. On one side of the panic selling, investors rapidly opposed the model of "continuously spending money to buy computing power and data centers" due to the sharp contraction in market value; on the other side, institutional investors, represented by JPMorgan, cited data showing an 82% year-on-year growth in Google's cloud business, over $1 billion in TPU revenue, and a significant increase in order backlog, emphasizing that the monetization of AI has begun to show signs of acceleration, advocating for a buy-on-dip stance. Therefore, this sharp drop of the seven technology giants is not only a rare price adjustment but also pushes the disputes surrounding "AI cash burning" and "AI monetization" to the forefront. The current stage is entering a game that needs to be verified with data and time, determining whether it is a necessary investment for long-term returns or an aggressive expansion with cash flow inconsistencies.

$800 billion evaporated: A single-day plunge of the seven technology giants

In this latest round of intense market fluctuations, the technology "seven giants," which once drove US stocks higher, collectively lost steam, with a total market value evaporating by about $797 billion in a single day, close to $800 billion, marking the largest single-day decline since April 2025. On this day, Tesla's stock price dropped about 15%, and Alphabet (Google's parent company) fell about 7.1%. The rare and significant decline in these leading stocks directly dragged down index performance, with the technology weight concentrated on one side, causing a chain reaction in the sector from individual stocks to indices.

If we rewind time to late May 2025, after that round of historical highs, the overall valuation of the seven giants had already begun to face pressure under the combination of "high valuation + uncertainty in AI return cycles." From the peak to this plunge, the cumulative market value of the seven giants shrank by about $2 trillion, meaning that this nearly $800 billion single-day evaporation is not an isolated event but rather a concentrated release of pressures built up over the previous months: after signals emerged from earnings reports showing rising capital expenditures and increased AI investments, the market's optimistic expectations for future profit paths were forced to downgrade, while risk premiums quickly adjusted upwards. In such a high-valuation environment, investors are not only reassessing whether these companies can continue to deliver high growth but are also repricing the question of "how much drawdown they are willing to endure for long-term AI dividends." This day's plunge is essentially a concentrated reflection of the high-valuation technology leaders repricing future growth paths and drawdown risks after the AI narrative enters a validation stage.

AI cash burning anxiety: Capital expenditure becomes the trigger for selling

The direct signal that triggered this intense adjustment was the revision of capital expenditure paths during the earnings season. The market clearly expressed concerns that Alphabet raised its capital expenditure guidance in its latest earnings report, which was interpreted as an indication that the proportion of investments related to AI infrastructure continues to increase. At the same time, Tesla also plans to increase capital expenditures, naturally linking its funding to a series of projects relying on AI and computing power, such as autonomous driving and robotics. On the same trading day, both companies recorded single-day declines of about 7.1% and 15%, respectively. Within the high-range context, this synchronized combination of "increasing investment + sharp decline in stock prices" was quickly summarized with a keyword: AI cash burning.

From a financial perspective, the problem lies not only in "how much money is spent," but in "how long it will take to break even." Briefing information indicates that investors generally view such high capital expenditures as a strong bet on future AI dividends, but they also worry that the related expenditure return cycles are too long and that short-term profitability and free cash flow are significantly squeezed. Given that the technology "seven giants" were already in a high-valuation range and had cumulatively retraced about $2 trillion in market value since the peak in late May 2025, once the return prospects are called into question, the rational choice is to cash out gains while high by concentrating sell-offs and repricing risk. This means that the market narrative of AI investment is shifting from a "story period," where long periods without looking at accounts and only listening to stories are tolerated, to a "cash flow verification period," where the effective use of investments must be continuously confirmed with revenue, profit, and cash flow data.

Positive data: JPMorgan bets on AI monetization

During the same window of time when the market sold off Alphabet due to the raised capital expenditure guidance, JPMorgan provided a completely different interpretative framework. Research reports pointed out that Google's cloud business revenue surged by 82% year-on-year in the second quarter, reaching $24.77 billion, a figure that far exceeds the usual growth rates of cloud services and is clearly attributed to increased demand for computing power and services related to AI. In more detailed structures, TPU revenue separately exceeded $1 billion for the first time, indicating that dedicated AI chips are no longer just a cost item but are beginning to form a measurable pool of product revenues. More importantly, the order backlog for Google Cloud and computing services increased by $52 billion quarter-on-quarter, totaling $514 billion, indicating that the locked-in demand for the future is not just "existing" but is rapidly expanding.

Based on this, JPMorgan suggested that the market currently focuses on the "cash burning" aspect of capital expenditures but overlooks the accelerating signals of AI monetization already emerging on the revenue side. The research report particularly emphasized that Google's search business revenue still maintains a 17% year-on-year growth, providing continuous cash flow for the advertising and search ecosystem, objectively offering a cushion for high-intensity AI investments. Based on the three sets of data from cloud revenue, TPU revenue, and order backlog, JPMorgan's conclusion is that computing power and AI services have not yet peaked but are rather at the early stages of an upward cycle; thus, the stock price correction caused by current panic is more like a "buy-on-dip" allocation window. This directly counters the seller sentiment that views AI as a short-term drag on profitability, shifting the controversy regarding AI investment prospects from "should we invest?" to "how to repricing risks between high investment and accelerated monetization."

Korean stocks drop 5%: Global technology sentiment spilling over

On the same trading day, the spillover of sentiment first affected Korea in Asia. According to the same source, the Korean KOSPI index briefly expanded its decline to 5.61%, which is a highly significant single-day pullback in the historical samples of that market; at the heavyweight stock level, Samsung’s stock price dropped over 6%, and SK Hynix fell 5.52%, with the technology and semiconductor sectors almost experiencing synchronized pressure. Due to these two companies' positions in the global storage, chip manufacturing, and hardware supply chain, their price fluctuations are often seen as a sensitive reflection of the global technology climate and capital expenditure expectations.

In the current narrative, the semiconductor and hardware chains are the physical foundation of the AI investment story: from TPU to data center expansion to high-bandwidth storage, all depend on large-scale investments in computing chips and supporting devices and are therefore more easily concentrated in pricing amid fluctuating emotions over "should AI continue to burn money?" and "can returns be realized?" The briefing assesses that the synchronous decline of the Korean stock market on that day "may have been dragged down by global technology stock sentiment," although it did not provide specific evidence of capital flows or trading structure causality. Therefore, a more prudent interpretation is that in the context of US tech giants plummeting and rising disputes over AI capital expenditures, the amplified declines in the KOSPI, Samsung, and SK Hynix constituted a synchronized and quantifiable market signal for the phase downturn of global technology risk appetite.

Short pain vs. long gain: The game of AI investment return periods

From a pricing logic perspective, this round of intense adjustments is essentially a battle of two perspectives surrounding the same AI capital expenditures: from a short-term perspective, Alphabet raising capital expenditure guidance and Tesla increasing investment were directly calculated into pressures on profit margins and free cash flow, resulting in the cumulative market value of the technology "seven giants" shrinking by about $2 trillion since the late May 2025 peak, with a further evaporation of about $797 billion in a single day; this is precisely a concentrated realization of the short pain pricing of "cost first." From a long-term perspective, the same investments are seen as laying down moats for capturing computing power, data, and application ecosystems, with the key being whether sufficiently strong revenue and order growth can hedge against this. The latest earnings reporting window can be regarded as the starting point of a “return verification period”: sellers are no longer willing to pay simply for an “AI vision,” but instead use the 82% year-on-year growth in Google Cloud, TPU revenue exceeding $1 billion for the first time, an increase in order backlog of $52 billion to $514 billion, and a 17% year-on-year growth in search business revenue as cash flow support to test the actual monetization capability of AI projects. Against the backdrop of similar pressures on the Korean KOSPI and Samsung and SK Hynix, once similar data is consistently verified across more tech giants, the valuation premium of tech stocks is more likely to shrink from the storytelling imaginary space to a pricing framework driven by performance, cash flow, and AI return cycles.

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