By the end of 2025 and January 2026, the seven tech giants of the U.S. stock market experienced a shift in sentiment from three years of leading gains to a collective pullback. Previously, this group had long served as the "engine" of the U.S. bull market, with the index tracking the seven giants reaching a record high on October 29, 2025, as the market assigned almost unconditional premiums to the AI dividend. However, as 2025 came to a close, with accumulated valuations and falling stock prices, a new round of questioning centered around "when will the hundreds of billions in AI investments pay off" began to press during the earnings season. Short-term financial statements have yet to reflect the full potential of AI dividends, while funds have already started to calculate the rhythm of returns. The earnings reports, set to be released in late January, are likely to become a key gateway for the valuation reset of the tech sector and the repricing of global risk appetite.

From Faith Frenzy to Pullback Chill: The Valuation Myth of the Seven Giants is Paused

● Timeline of the index peak: The index tracking the seven giants reached a record level on October 29, 2025, marking a peak in the narrative of "AI + tech leaders" over the past three years. At this point, the market assigned a high imaginative space to the future penetration rates of cloud computing, AI chips, and smart terminals, with valuations based more on long-term stories than recent cash flows. The seven giants were once seen as "irreplaceable beta."

● Symbol of narrowing gains and differentiation: By the end of 2025, this index experienced a phase pullback from its high, with significant differentiation in the stock performance of the seven companies. Currently, only Alphabet and Amazon maintain approximately 20% gains, while the other components have significantly retraced. This situation indicates that the market still recognizes the medium to long-term profitability of search + advertising and e-commerce + cloud, while also highlighting that the AI narratives of the other companies are beginning to face stricter tests of realization.

● Sentiment shift from fervor to wait-and-see: During the peak phase of the index, funds exhibited an almost "unconditional buying" behavior towards the seven giants, viewing any pullback as an opportunity to increase positions. As pullbacks and differentiation emerged, concerns over high valuations and performance realization quickly intensified, leading funds to shift from a singular bet on the seven giants to a more diversified defensive and rotational approach. Buying pressure transitioned from being faith-driven to calculating risk-reward ratios, with wait-and-see sentiment gradually rising before January 2026.

Hundreds of Billions in AI Bets: Wall Street Shifts from Celebration to Accounting

● Emotional inflection point of skepticism about returns: By the end of 2025, "Wall Street began to doubt when the hundreds of billions spent by the seven giants on AI development would yield returns," a sentiment frequently appearing in media and institutional commentary. The AI arms race, once seen as a "must-follow," began to be re-evaluated by some investors, shifting the question from "should we invest" to "how long until we see returns," marking a narrative shift from emotion-driven to return-focused scrutiny.

● Outline of massive capital expenditures: Over the past three years, the seven giants have continuously invested huge amounts of capital in large model training, cloud computing infrastructure, dedicated chips, and terminal hardware products, with the total scale commonly described as "hundreds of billions." Although the briefings did not disclose specific amounts and project details at the company level, it is confirmed that whether it is data center expansions, GPU purchases, or self-developed AI chips and smart device iterations, they are continuously eroding short-term profit statements, becoming the most scrutinized investment items in earnings reports.

● Valuation models retreating from long-term to near-term: The commercialization of AI often requires a long incubation period, while financial statements focus more on visible cash flows over the next one to two years. At the current stage, the revenue and profit pull from AI-related products and services is still on the eve of testing and scaling, leading valuation models to shrink from ten-year visions to the visibility of profits over the next few years. The result is that the market begins to re-discount "how much premium the story provided and how much the books can support in valuation," with sentiment shifting from imaginative premiums to cash flow constraints.

Cloud, Hardware, and Advertising Flex Their Muscles: Earnings Reports as a Major Checkup for AI Infrastructure

● Cloud computing as the cornerstone of the AI era: With the trend of AI applications fully moving to the cloud, the growth quality of the cloud businesses of Microsoft, Amazon, and Alphabet is seen as a core observation window for this earnings season. Investors will be more sensitive to dissect whether AI-related workloads bring incremental revenue, whether profit margins are pressured by high computing costs, and the structural changes between long-term contracts and short-term projects, to determine whether AI cloud businesses are "burning money to gain a foothold" or beginning to enter a healthy cycle of "using the cloud to support AI."

● Profit elasticity challenges of AI chips and terminals: Companies like Nvidia, Tesla, and Apple, with their layouts in AI chips, in-vehicle computing platforms, and smart terminals, will also be tested through shipment rhythms and profit performance. The market is concerned not only with hardware sales but also whether high-value-added AI features bring higher unit profits and whether new replacement and upgrade cycles are forming. If earnings reports reveal a slowdown in order rhythms or cost pressures not covered by pricing power, valuations originally based on high growth expectations may be forced to adjust downward.

● AI's hedging effect on advertising business: Alphabet and Meta already hold a dominant share in the digital advertising space, with AI expected to improve ad matching efficiency and conversion rates, thus providing profit hedges during macroeconomic slowdowns. If earnings reports can reflect stable or slightly increased ad prices and improved ROI without relying on broad volume expansion, the market will tend to recognize the logic of "AI-driven efficiency dividends." Conversely, it may be believed that AI technology is not yet sufficient to offset the pressures from the overall macro environment and regulation.

Key Time Points Approaching: A Week of Earnings Reports Condensing All Market Anxiety

● Highly concentrated earnings report timing: According to current arrangements, Microsoft, Tesla, and Meta's earnings reports are scheduled for January 28, and Apple's for January 29. This concentration of timing makes the short week almost a "global tech pricing week." Multiple main lines of cloud, AI hardware, advertising, and ecosystem devices will be submitted within days, and the sentiment and valuation anchors of the tech sector may be rapidly reshaped during this period.

● Typical traps of expectation gap trading: Before the earnings reports, some companies have already been pushed to extremely high expectation ranges by the market. Even if their performance exceeds current profit levels, any deviation from "story version" expectations may lead to scenarios where "good performance also declines." Conversely, some companies that have had their expectations significantly lowered during the pullback may become targets for funds seeking "low expectation surprises" as long as they deliver not-so-bad results, with volatility stemming more from expectation adjustments than absolute numbers.

● Funds re-pricing the track based on earnings reports: Institutional funds are likely to collectively reassess the valuations of the AI hardware chain, cloud computing, and advertising sectors around these key earnings reports. If cloud businesses confirm sustained growth in AI demand, the valuation premium of cloud leaders will be solidified; if AI hardware orders show signs of slowing, the hardware chain may face systemic downgrades; if the advertising business demonstrates AI-driven resilience, it may lead to a re-rating of the entire "AI cost reduction and efficiency increase" logic, and these chain re-pricings will spread to a broader range of tech growth stocks.

If Performance Shines, Will Funds Flow Back? Tech and Crypto on the Same Risk Chain

● Path to warming risk appetite: The market generally believes that "if performance shines, funds may flow back into the tech sector." Once the seven giants collectively deliver better-than-expected results, the pressure of high valuations will be partially alleviated, and actively managed funds may flow back from defensive positions to growth stocks, rebuilding risk exposure by increasing holdings in leading tech stocks. Overall market risk appetite is also expected to rise from a wait-and-see range to a stage of tentative accumulation.

● Indirect resonance between the seven giants and crypto: The stock performance of the seven giants is highly correlated with the Nasdaq and growth stock factors, which are key barometers of global risk asset sentiment. When tech leaders strengthen and investors' wealth effects increase, risk appetite often spills over into higher volatility assets. The crypto market has repeatedly shown characteristics of amplifying volatility in line with U.S. growth stocks over the past several cycles, and this emotional resonance may continue after this round of earnings reports.

● Chain pressure if performance disappoints: Conversely, if multiple giants' earnings reports fall short of expectations, the adjustment of tech stocks may further intensify, forcing investors to reduce holdings in high-beta assets to control portfolio volatility, leading to tighter liquidity. In this scenario, not only traditional growth stocks will be suppressed, but crypto assets may also face pressure in "de-risk" trading, encountering dual pressures of valuation compression and capital outflows, with risk assets overall entering a passive deleveraging phase.

The AI Story Reaches the Moment of Exam Paper Distribution

● Transitioning from the story era to the accounting era: Over the past three years, the seven giants have led the rise of the U.S. stock market with AI narratives, but now, before the earnings season, they face doubts about "the story being told too quickly and the performance realization being too slow." The core contradiction lies in the market's shift from being willing to pay premiums for long-term imaginings to demanding more solid support in profit and cash flow statements. This marks a transition of AI from the "story era" to the "accounting era," with valuation anchors shifting from emotion to data.

● Asset paths under three scenarios: If AI returns significantly exceed expectations, the seven giants will regain valuation premiums, and the tech and broader growth sectors may welcome a new round of upward momentum, synchronously boosting the overall sentiment of risk assets; if performance generally meets expectations, the market may enter a structurally differentiated trend, making finer distinctions between sub-sectors and business models, with the index maintaining high-level fluctuations; if performance is significantly below expectations, tech stocks may experience deeper valuation compressions, leading global risk assets into a defensive phase of "re-pricing + deleveraging."

● Viewing long cycles amid volatility: For investors, the upcoming earnings reports will amplify short-term price fluctuations and emotional swings, but what truly determines the long-term value of AI remains the gradual increase in penetration rates and the process of business models transitioning from experimentation to scalable profitability. While paying attention to the realization of each quarter's performance, it is also essential to be cautious of being swept up by short-term emotions and to maintain a calm judgment on the long-term evolution paths of AI infrastructure and applications.

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