In August 2026, after experiencing consolidation and volatility in early summer, the US stock market is quietly ushering in a clear main theme—the accelerated iteration and implementation of AI computing infrastructure. As the absolute leader in global AI chips, Nvidia's capacity expansion and demand matching for its latest Blackwell architecture chips have become a barometer affecting the Nasdaq index and the sentiment of the entire US tech sector. High-frequency data from the supply chain this week shows that mass production of Blackwell chips is not only proceeding as scheduled, but order backlogs from downstream cloud service providers far exceed the market's previous neutral expectations. This fundamental industry movement has injected a strong stimulant into US tech stocks for the second half of the year.
Blackwell Architecture Enters Capacity Ramp-up, AI Computing Demand Shows Non-linear Growth
Entering the second half of 2026, the development of AI large models has shifted from a pure parameter scale race to deep waters involving multimodal processing, complex logical reasoning, and edge-side application implementation. The evolution of this technological paradigm places exponentially escalating stringent demands on underlying computing power. Nvidia's B-series AI accelerator cards based on the Blackwell architecture, with their leap in tensor computing performance, more efficient memory bandwidth, and energy efficiency, have naturally succeeded the Hopper architecture to become the hard currency that top global tech companies are vying to purchase.
From the latest cross-validation of the industry chain, TSMC, as the core foundry, has seen a significant ramp-up in advanced packaging capacity (such as CoWoS) for Nvidia's high-end chips in the third quarter. Although the market generally worried earlier this year that yield bottlenecks in advanced packaging might cause shipment delays, supply chain yields have so far stabilized at a level suitable for large-scale commercial delivery. According to industry analyst estimates, Nvidia's Blackwell architecture chip shipments in Q3 2026 are expected to increase by over 60% quarter-over-quarter, and the full-year revenue contribution expectation has been further revised upward.
What is even more exciting for the market is that this computing demand is not a short-term pulse-like outbreak, but shows significant non-linear growth characteristics. As generative AI gradually closes its commercial loop in vertical fields such as medical R&D, financial quantitative research, and autonomous driving simulation, enterprise customers' computing consumption models are transitioning from training-dominated to an equal emphasis on training and large-scale inference. The computing demand on the inference side, characterized by high frequency and continuity, directly locks in cloud vendors' capital expenditure expectations for the next few years, providing extremely high visibility for Nvidia's performance.
Cloud Vendors' Capex Continues to Increase, Computing Arms Race Enters a New Phase
The capital expenditure trends of US tech giants (such as Microsoft, Google, Amazon, and Meta) are the most intuitive macro indicators for measuring the prosperity of the AI industry. Entering the end of Q2 2026, these super cloud service providers released clear signals in their earnings reports: AI infrastructure investment has not hit a ceiling, but is instead in a new phase of accelerated deepening.
This round of capex increases is highly targeted, meaning the vast majority of incremental budgets are flowing to next-generation computing clusters represented by Nvidia's Blackwell architecture. The core logic behind this is that against the backdrop of converging large model capabilities, the depth and breadth of computing scale have become the only moat for tech giants to build competitive barriers. Owning larger, more efficient computing clusters means being able to train smarter multimodal models and serve hundreds of millions of C-end users with lower latency, thereby occupying a dominant position in the battle for traffic distribution rights in the AI era.
From the perspective of capital flow monitoring data, the US stock market has keenly captured this trend. On multiple trading days since August, funds have been continuously and steadily flowing out of traditional defensive sectors and some consumer goods sectors with peaked valuations, and re-entering high-elasticity growth tracks such as semiconductors, cloud computing servers, and AI infrastructure software. Driven by core tech stocks, the Nasdaq index has shown strong resilience and an upward willingness, and the market's voting with their feet once again confirms the absolute core position of the AI main theme in US stock investment logic.
Upstream and Downstream Resonance: Storage, Optical Modules, and Liquid Cooling Usher in a Davis Double Play
The large-scale volume production of Nvidia's Blackwell chips is not an isolated stock event; its powerful industry spillover effect is creating a resonance wave throughout the US tech sector. For professional US stock investors, merely staring at Nvidia's earnings is clearly insufficient to cover the entire AI computing dividend. Around the capillaries of computing infrastructure—the upstream and downstream industry chains—a large number of high cost-performance investment opportunities are emerging.
First is the High Bandwidth Memory (HBM) track. The Blackwell architecture's demand for video memory bandwidth has reached unprecedented heights, directly triggering explosive demand for HBM3e and even next-generation HBM4. Storage chip giants in the US stock market, such as Micron Technology, are enjoying a super industry cycle of both volume and price increases. Because the ASP (Average Selling Price) of HBM products is much higher than traditional DRAM, and capacity allocation squeezes out some traditional storage shares, the overall storage market is experiencing structural shortages. This supply-demand pattern has not only significantly improved the gross margins of storage manufacturers but also caused their earnings data to repeatedly exceed market expectations.
Secondly, the optical module and network communication track cannot be ignored either. When tens of thousands of Blackwell chips form ultra-large intelligent computing clusters, inter-node data transmission latency becomes the biggest bottleneck restricting computing performance. Therefore, the penetration rate of 800G and even 1.6T high-speed optical modules has seen an explosive increase in the second half of 2026. The order visibility of related US communication component suppliers is lined up to 2027, and the industry's fundamentals are experiencing a Davis Double Play phase from expectation realization to significant earnings upward revisions.
In addition, as the computing density per cabinet skyrockets, traditional air cooling can no longer meet heat dissipation needs, making liquid cooling technology a rigid demand. The evolution from cold plate liquid cooling to immersion liquid cooling has spawned a number of US segment leaders specializing in heat dissipation technology. With their deep technical accumulation in thermal management, these companies are rapidly entering the supply chain systems of leading manufacturers such as Nvidia and Super Micro Computer, showing extremely high growth elasticity in their performance.
US Tech Sector Valuation Reassessment: Can High Valuations Be Digested by High Growth?
Facing the continuous rise in stock prices of Nvidia and a host of AI industry chain companies, the market has inevitably generated a familiar concern: has the current valuation already overdrawn future growth space? Is there a risk of bubbling in the US tech sector?
To answer this question, we must return to the essence of corporate profitability. Taking Nvidia as an example, although its dynamic P/E ratio seems to be at a historical high in absolute terms, if considering the compound growth rate of its net profit over the next two years, its PEG (Price/Earnings to Growth) ratio is actually still at a reasonable or even low level. More importantly, the productivity improvement brought by the AI computing revolution is substantively reshaping the profit models of tech giants.
Previously, market skepticism about AI mainly focused on how to monetize it. But entering 2026, with the steady increase in Microsoft Copilot enterprise subscription rates, the accelerated growth of Amazon AWS AI cloud service revenue, and the soaring conversion rates brought by Meta introducing AI large models into its ad targeting algorithms, investors have clearly seen the dawn of AI transitioning from R&D investment to a commercial closed loop. This substantive improvement on the profit side provides solid financial support for tech giants to maintain high-intensity capital expenditures, and also provides the logical basis for current high valuations to be digested by rapidly growing EPS (Earnings Per Share).
From the perspective of sector rotation observation, the current rise of the US tech sector is not a liquidity-driven bull market driven by blind sentiment, but an earnings-driven bull market with solid fundamental support. Unlike the landscape during the 2000 dot-com bubble where many companies only had concepts but no profits, the core companies driving the US tech market today all have extremely abundant free cash flow, monopolistic market positions, and clear commercial monetization paths. Therefore, as long as AI technology iteration does not stagnate and enterprise computing demand does not dry up, there is reason for the tech sector's valuation center to maintain a high level.
US Market Open Preview: Grasp the Computing Main Theme, Balance Structural Risks
Looking ahead to the US stock market trend in the second half of 2026, the capacity release and performance realization of Nvidia's Blackwell architecture will undoubtedly become the core variable affecting the Nasdaq index's fluctuations. At this point in time, for US stock investors, strategically maintaining an overweight position in the AI computing main theme remains the choice with the highest win rate, but tactically, it is necessary to more finely grasp the market rhythm and structural risks.
In terms of allocation strategy, it is recommended that investors adopt a barbell strategy of core plus satellite. For core positions, continue to firmly hold tech giants with deep moats and clear AI monetization paths, such as Nvidia, Microsoft, and Google, to share the Beta dividend of AI industry development. For satellite positions, one can appropriately tilt towards highly elastic computing industry chain segment leaders such as optical modules, HBM storage, and liquid cooling, to capture Alpha returns that exceed the market.
At the same time, it is also necessary to be alert to potential structural risks. On one hand, close attention needs to be paid to the Federal Reserve's monetary policy direction in the second half of the year. Although the market has already priced in some rate cut expectations, if inflation data fluctuates and leads to a tightening of monetary policy again, the rise in risk-free interest rates will exert phased pressure on high-valuation tech stocks. On the other hand, it is necessary to guard against the risk of global supply chain disruptions caused by geopolitical factors, especially in the East Asian region where high-end semiconductor manufacturing is highly dependent; any disturbance could trigger violent fluctuations in chip stocks.
In summary, the US stock market in August 2026 is standing at a new starting point of surging AI computing demand. The full rollout of Nvidia's Blackwell architecture is not only a chip technology iteration, but also a reconstruction of the global digital economy's foundation. At this intersection full of opportunities and challenges, BGU FinBK suggests that investors pierce through the fog of short-term market volatility, anchor the vast sea of stars in the long-term development of the AI industry, move forward steadily in the tide of the computing era, and grasp the historic opportunity of the US tech sector's valuation reassessment.
