Analysis of the U.S. Equity AI Industry Chain Investment Logic
2026-06-24Beginner
2026-06-24Beginner
The AI‑Driven Capital Expenditure Super‑Cycle
Goldman Sachs’ latest report points out that large language models and AI technologies are pushing enterprises to massively increase infrastructure and compute spending, and a private‑sector‑investment‑driven CapEx super‑cycle has officially begun. Goldman estimates that 2026 capital expenditure by major cloud service operators will reach approximately $770 billion, close to 100% of their operating cash flow. J.P. Morgan also confirms that the five hyperscale tech giants (Microsoft, Meta, Oracle, Google, and Amazon) now expect over $650 billion in CapEx for 2026, an upward revision of $130 billion from the previous earnings season. In 2025, AI‑related investment already contributed 25 basis points to U.S. real GDP growth.
Going further, the pricing logic in the current U.S. equity market is also undergoing a material shift. The four largest North American cloud vendors have raised their 2026 AI CapEx guidance to $710 billion, locking in earnings performance for the next 12–24 months, with hardware companies’ full‑year net profit growth median exceeding 80%. Against this backdrop, continued earnings beats are expected to offset valuation headwinds from rising rates, positioning the AI industry chain as a core investment theme for U.S. equities to navigate volatility. J.P. Morgan, in its mid‑2026 outlook, similarly emphasizes that AI has moved from concept to reality and is driving a real and massive CapEx cycle; however, the risk also comes from AI itself – if bullish sentiment reverses, the most crowded AI segments will face the greatest selling pressure.
Three‑Tier Structure of the AI Industry Chain: Infrastructure, Models, and Applications
Translating the above macro narrative to the industry level, the first step is to clarify the structural layering within the chain. As of June 2026, the total market cap of AI‑related companies in the S&P 500 exceeded $10 trillion, but the business models, risk profiles, and growth drivers vary enormously across different segments. Specifically, they can be divided into three layers:
Compute Infrastructure Layer: The Most Certain "Pick‑and‑Shovel" Play
This layer sits at the very upstream of the chain, with demand directly derived from the rigid spending on compute by mid‑ and downstream players. Revenue is verifiable – every chip sold has a clear customer and price. Among all segments, this layer offers the highest certainty, truly the "pick‑and‑shovel" provider of the AI era.
Nvidia (NVDA) is the undisputed leader, dominating the training and inference markets for GPUs, with data‑center revenue growing over 300% in the past six quarters. In Q1 FY2026, Nvidia’s revenue reached $44.1 billion, up 69% YoY, with data‑center contributing $39.1 billion, up 73% YoY. Blackwell architecture penetration has far exceeded expectations, accounting for 70% of data‑center revenue. Nvidia has announced an annual cadence for new‑generation chips: Blackwell in 2024, Blackwell Ultra in 2025, and a new platform featuring Vera CPU and Rubin GPU in 2026. CEO Jensen Huang called this “the largest infrastructure expansion in human history.” Goldman Sachs believes chipmakers are the biggest beneficiaries of the AI spending boom, with semiconductor industry net profit margins nearing 50%.
Broadcom (AVGO) takes a differentiated route – custom ASICs (Application‑Specific Integrated Circuits). In Q2 FY2026, Broadcom’s AI semiconductor revenue reached $10.8 billion, up 143% YoY, serving six core custom‑chip clients including Google, Meta, Anthropic, and OpenAI. AMD (AMD), with its MI series accelerators, is gradually gaining share in inference scenarios, while Micron Technology (MU) benefits from surging demand for HBM (High‑Bandwidth Memory).
Foundation Model Layer: A High‑Uncertainty Track
Moving upstream from infrastructure brings us to companies that develop large language models, multimodal models, and directly offer APIs or products. However, this track contrasts sharply with the certainty of the compute layer – training frontier models costs tens to hundreds of millions of dollars, and inference gross margins are under pressure from compute costs and pricing competition. As of June 2026, only a very few leading model companies have achieved overall profitability. Open‑source models continue to narrow the gap with closed‑source ones, further eroding closed‑source pricing power. For U.S. equity investors, direct investment opportunities are limited – OpenAI and Anthropic are both private, and Google’s and Meta’s AI revenues are not separately disclosed.
Application Software Layer: Dual Optimization of Revenue and Costs
One level above are software companies that integrate AI into specific work scenarios. These fall into two categories: incumbent giants (Microsoft, Salesforce, Adobe, etc.) that embed AI into existing product suites to gain incremental revenue through price increases or customer acquisition; and AI‑native startups that build AI‑first workflows from scratch. The core logic of this layer is the superposition of revenue growth and cost control – higher pricing on the revenue side, and reduced labor costs through automation on the cost side. The margin improvement potential here is more elastic than in the previous two layers.
The Beneficiary Chain and Diffusion Logic of AI CapEx
Having clarified the three‑layer structure, the next question is: where is the money being spent, and where are opportunities spreading? Investment opportunities in AI infrastructure are not simply about buying GPU leaders; they spread sequentially along the “compute – storage – connectivity – optics – power” chain. Compute chips (Nvidia, AMD, Broadcom) are the starting point; high‑bandwidth memory (Micron, SK Hynix) follows closely, as HBM is a necessary complement for GPU performance; network connectivity and optical communications gain incremental demand from rising data‑center interconnection complexity; power and cooling also benefit from ever‑increasing server power consumption. McKinsey estimates that by 2030, global data centers may require about $6.7 trillion in CapEx, of which approximately $5.2 trillion is AI‑workload‑related, implying that opportunities along this chain will persist for years. Goldman expects AI gains to broaden from mega‑cap companies to a wider universe, and the catch‑up potential in small‑ and mid‑cap segments is worth watching.
Conclusion
Taken together, the investment logic for the U.S. equity AI industry chain in 2026 is at a critical inflection point, shifting from “concept‑driven” to “earnings‑driven.” The unprecedented CapEx of hyperscale tech giants provides a solid order anchor for the chain, while sustained earnings validation remains the fundamental prerequisite for maintaining elevated valuations. As Goldman Sachs emphasizes, in a high‑borrowing‑cost environment, robust earnings growth will become an even more important driver of stock performance. For investors, selecting individual stocks along the beneficiary chain and seeking under‑priced opportunities in the most certain segments may offer a viable path to capturing excess returns in this AI wave.
Risk Disclaimer: This article is for objective analysis of investment logic only and does not constitute any investment advice. Market risks exist; invest with caution.
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