AI Order Backlog Reaches $17 Trillion: U.S. Stock Market Long-Term Investment Logic Shifts from "Burning Cash" to "Making Money"

In the first trading week of August 2026, US stocks responded to the preceding two months of intense volatility in a remarkable way: the S&P 500 reached a new all-time high, the Nasdaq 100 surged over 3% in a single day, the "Magnificent Seven" stocks rose nearly 10% over four days, Amazon's market cap returned to $3 trillion, and Nvidia's market cap regained $5 trillion. However, for long-term investors who truly care about "why to buy US stocks," a change more significant than index points is quietly unfolding—AI investment is shifting from a purely "burn rate story" to a "profitability narrative supported by orders," and the positive feedback loop between the US economy and the stock market is being reestablished. Order Backlog Outpaces Capital Expenditure: A Shift in AI Return Logic For the past year, the core question suppressing the valuation of US tech stocks was not whether tech giants had the capacity to invest hundreds of billions, but whether these investments could truly translate into sustainable revenue and profit. "High investment, low return" was once the most potent weapon for short sellers. Today, this skepticism is gradually being resolved by new data. Industry data shows that the cloud business backlog of hyperscale cloud service providers has grown by over 150% year-on-year, reaching approximately $1.7 trillion, while capital expenditure growth in the same period was about 80%. The backlog represents the scale of services already committed by customers, while capital expenditure reflects how much companies are investing—the former is growing significantly faster than the latter, indicating that the addition of computing power has not fallen into oversupply but is being rapidly absorbed by strong demand. In other words, tech giants are not merely betting on a distant future with massive investments; they are building infrastructure that has already been validated by commercial demand. JPMorgan believes that the market is re-evaluating the return capabilities of AI infrastructure. Previously, investors focused on "how much money tech giants spent"; the more important question now is: how much long-term revenue can these investments drive? As order growth begins to outpace capital expenditure, the logic of AI investment is transitioning from a valuation burden to a profit support, and the most difficult valuation digestion phase for tech giants may be nearing its end. Several Wall Street institutions have also noted that despite continuously rising earnings expectations, tech stock valuations have temporarily fallen to the lower end of historical ranges, with forward P/E ratios below the ten-year average, providing a rare entry window for long-term capital. A Change in Financing Structure: From Equity-Driven to Debt-Driven Another structural change worth noting is the method of financing. The annual capital expenditure of tech giants has reached the hundreds of billions of dollars, and they are increasingly financing AI spending by issuing corporate bonds. Google Cloud's contract backlog has exceeded $500 billion, and the market expects the combined capital expenditure guidance for the four major cloud providers in North America in 2026 to be around $720 billion to $745 billion. This shift means the AI boom is gradually transitioning from "equity-driven" to "debt-driven"—the rise in leverage both amplifies the entire economy's sensitivity to the realization of the AI narrative and indirectly confirms the growing confidence of companies in the AI return prospects. AI Spillover Effect: From Data Centers to Manufacturing and Household Wealth The impact of AI investment has long extended beyond the technology sector itself, widely transmitting to the real economy. The US Institute for Supply Management's (ISM) July Manufacturing Index rose from 53.3 in June to 55.6, reaching a high point in about four years since May 2022, and marking the seventh consecutive month in the expansion range. The construction of artificial intelligence infrastructure is accelerating its transmission to equipment, components, and industrial capacity, becoming a major driving force for US manufacturing growth. The data from the construction sector is equally striking: in June, US data center construction spending reached $68.3 billion annually, an increase of $21.5 billion year-over-year, while all other private construction spending decreased by over $100 billion in the same period. The Oxford Economics Institute estimates that AI investment alone contributed nearly a quarter of the recent US GDP growth. Industrial giants like Caterpillar also confirmed in their financial reports that equipment demand remains strong as US AI data center construction continues to advance, leading them to raise full-year revenue growth guidance. The stock market's boom has also fueled consumption through the "wealth effect." Federal Reserve data shows that US household net worth reached $174 trillion in the first quarter, up $13 trillion year-over-year, mainly due to stock market gains. Economists generally believe that for every $1 increase in stock market wealth, households spend a few cents more, with high-income groups holding most stock assets seeing the most significant consumption boost. A Barclays economist stated, "The current US economy is largely AI-driven; it would be hard to imagine the economy having such strong resilience without this momentum." Fundamental Validation: Over 80% of Companies Beat Earnings Expectations The foundation of this market rally is not solely narrative. FactSet data shows that over 84% of S&P 500 constituents that have reported earnings posted profits above market expectations; Bloomberg data indicates that S&P 500 constituents saw a 21.1% sequential increase in second-quarter earnings, a 53.5% year-over-year increase. The S&P 500 total weighting index has set record highs for several consecutive weeks, indicating that the rise is not driven only by a few tech giants but is built on widespread corporate earnings improvement—and for the stock market, sustained and strong earnings growth is always the most solid support. Implications for Asian Investors: The Logic of Long-Term Allocation Has Not Changed For Asian investors with a long-term perspective, the core appeal of US stocks right now lies in the fundamental shift of AI investment from a "vision" to "orders" and from a "cost" to "revenue." The following points are worth noting: * Earnings are the ultimate anchor: Regardless of short-term market volatility, strong and sustained corporate earnings growth remains the core source of long-term returns for US stocks. The race between cloud vendors' orders and capital expenditure warrants continuous tracking as a key indicator of whether the AI narrative can materialize. * Diversified allocation and patience: Recent market fluctuations show that rallies driven by a single narrative are volatile. Investors should diversify risk through a combination of index funds and quality individual stocks, avoiding speculative buying and selling based on short-term sentiment. * Focus on the transmission chain: The transmission of AI investment to manufacturing, construction, energy, and consumption means that the long-term logic of US stocks has moved beyond the technology sector itself to become a broader economic growth story, offering a new dimension for judging market breadth. Of course, risks cannot be ignored. The AI craze is shifting from equity-driven to debt-driven, and rising leverage means that the intensity of a pullback could be amplified if returns do not meet expectations. Geopolitical instability, the progress of trade negotiations, and the uncertainty of the Federal Reserve's policy path remain potential variables suppressing valuations. However, for allocation-focused capital with a long-term view, the appeal of US stocks is not in short-term price points but in the profit-generating capacity of global leaders and the structural growth dividends driven by AI—this is the most solid long-term answer to "why buy US stocks."