US stocks are entering the "Distributed Pricing Era": a structural restructuring from Magnificent Seven dominance to the diffusion of the AI industry chain.

Over the past few years, one of the most notable features of the U.S. stock market has been its highly concentrated index structure. A small number of large tech companies dominate index movements and are also the core driving force behind the AI trend. During this phase, whether it was the rise of the index or the spread of the AI narrative, it essentially relied on the valuation expansion of a few leading companies.

However, after entering 2026, this structure is beginning to change. The market still revolves around AI, but the flow of capital is no longer concentrated; instead, it is spreading along the industrial chain. This change is not reflected in daily ups and downs, but in the declining correlation between sectors, accelerating rotation, and deepening internal structural divergence.

In other words, the U.S. stock market is gradually shifting from a "centralized pricing system" to a "distributed pricing system."

1. The Logic Behind the Formation of the Magnificent Seven Era: How Centralized Pricing Was Established

The so-called Magnificent Seven era is essentially a highly centralized market structure. During this phase, the rise of the index almost entirely depended on a few large tech companies. These companies are not only key participants in AI infrastructure but also core gateways to multiple growth areas such as cloud computing, advertising, and consumer technology.

Three conditions laid the foundation for this structure. First, the tech industry is highly concentrated, with leading companies controlling the vast majority of computing power, data, and platform resources. Second, the explosive demand for computing power in the early stages of AI made GPUs and cloud providers the only clear growth outlets. Third, ample liquidity in the capital market led funds to favor betting on the most certain leading assets.

During this phase, the market logic was very simple: the rise of the index meant the rise of a few companies, and the AI trend meant the expansion of GPUs and cloud computing.

2. The Starting Point of Structural Change: The AI Industry Chain Begins to Lengthen

When AI entered the stage of large model training and inference expansion, a key change emerged: the industry chain lengthened.

Early AI growth was mainly concentrated on the computing power side, but as model scales continued to grow, bottlenecks gradually spread outward, including multiple links such as storage bandwidth, data transmission efficiency, network interconnectivity, and data center energy consumption.

This means AI is no longer a single technical issue but a systems engineering problem. As system complexity increases, a single company can no longer cover all growth gains, and the industry chain is split into multiple value nodes. When growth sources become dispersed, the capital structure naturally changes, shifting from concentrated bets on leaders to distributed allocation.

3. Capital Migration Path: From Leader Concentration to Chain Rotation

The current capital structure of the U.S. stock market is undergoing a very critical transformation, shifting from single-point concentration to chain rotation. In the early stage, the capital path was: large tech companies → GPU leader → cloud providers. This structure was very concentrated, with capital primarily pricing around computing power expansion.

But in the current stage, the capital path has evolved into a more complex structure: GPU → HBM → network chips → data centers → power and infrastructure. The essence of this change is the migration of AI bottlenecks. As GPU supply gradually expands, the market begins to focus on how data moves, how it is stored, and how it is efficiently distributed. When computing power is no longer the only constraint, the importance of storage and interconnectivity rises rapidly.

This shift transforms the market from a single main line to a multi-node rotation structure.

4. Why the Influence of the Magnificent Seven Is Declining: Not Weakening, But Diluting

The influence of the Magnificent Seven has not absolutely declined; it has been relatively diluted. This dilution comes from two aspects.

First, AI growth is no longer concentrated in a single link but distributed across multiple industry nodes.

Second, the scale of capital expenditure has significantly increased, splitting growth gains across the entire supply chain system.

Under this structure, even if a single company grows strongly, it cannot fully represent the expansion speed of the entire AI industry. The market is beginning to realize that AI is not a story driven by a single company, but a story driven by a system.

Therefore, pricing power is gradually shifting from the company level to the industry chain level.

5. Multi-Center Driven Structure: The U.S. Stock Market Is Reconstructing Its Pricing System

Currently, the U.S. stock market is forming a new structural model, namely a multi-center driven system. In this system, there is no longer a single core asset, but multiple driving centers coexisting, including computing power centers, storage centers, network centers, and infrastructure centers. These centers no longer have a linear transmission relationship but a cross-influence relationship. For example, GPUs drive demand for HBM growth, but HBM limitations in turn affect the expansion speed of GPUs; network chips improve data flow efficiency while impacting computing power utilization.

This complex interaction structure makes the market no longer a one-way trend but a multi-dimensional rotation.

6. Market Behavior Changes: From Trend Trading to Structural Trading

In the Magnificent Seven-dominated phase, the market leaned more toward trend trading, with concentrated capital and relatively predictable volatility. But after entering the distributed pricing phase, market behavior has changed significantly.

• Correlation between sectors has declined; the rise and fall rhythms of different sectors are no longer synchronized.
• Rotation speed has accelerated, with capital quickly switching between different AI chains.
• The divergence between the index and its internal structure has increased, meaning the index remains high but internal volatility rises significantly.

This change means trading difficulty increases, but structural opportunities increase.

7. AI Is Transforming from a Thematic Trend to a Structural Cycle

The essential change in the current AI trend is that it is shifting from being theme-driven to a structural cycle. Thematic trends are characterized by concentrated bursts, with capital flowing uniformly into a single direction. In contrast, structural cycles are characterized by segmented rotation, with growth driven jointly by multiple links. This is also why, although the market is still within the AI main line, the feel has changed, manifested as increased volatility but the trend not yet over.

In essence, the AI trend has not disappeared; it has entered a more complex stage of development.

8. Cross-Market Linkages: The U.S. Stock Market Is No Longer the Sole AI Pricing Center

As the AI industry chain becomes global, the U.S. stock market is no longer the sole pricing center. South Korean stock storage, Hong Kong stock technology, and U.S. stock computing power form a complementary structure, with different markets bearing different industry links.

This global distributed structure further strengthens the capital rotation characteristics and makes the AI trend more globally interconnected.

In this context, cross-market observation becomes an important way to understand the AI trend.

9. Gate Stock Trading: A Tool for Cross-Market Tracking of AI Structural Changes

As the AI industry chain continues to expand to multiple levels including computing power, storage, network, and energy, a single market can no longer fully reflect industry changes. The U.S. stock market, Hong Kong stock market, and South Korean stock market form a clear industrial division structure, making cross-market tracking increasingly important.

Gate stock trading supports 7×24 hour all-weather trading of U.S., Hong Kong, and South Korean stocks, allowing investors to continuously track price changes and capital flows of AI-related assets across different market sessions—from computing power chips to storage leaders to infrastructure chains—enabling more flexible participation in the global AI industry chain rotation.

10. Conclusion: The U.S. Stock Market Is Entering a New Era of Distributed Pricing

The U.S. stock market is undergoing a deep structural change, moving from centralized pricing by the Magnificent Seven to distributed pricing across the industry chain. The core driver of this change comes from the expansion and complexity of the AI industry chain.

The key question for the future market will no longer be whether a single company continues to rise, but which link in the AI industry chain becomes the new bottleneck. Whoever controls the bottleneck holds pricing power.

AI is evolving from an investment theme into a long-term structural cycle and redefining the pricing logic of the U.S. stock market.

FAQs

Q1: Have the Magnificent Seven truly lost their dominant position?

Not an absolute loss, but their relative influence has been diluted by the spread of the AI industry chain.

Q2: Why does AI lead to market structural changes?

Because AI has shifted from a single-point computing power issue to a systems engineering problem, lengthening the industry chain.

Q3: Is the current market a bull market or a consolidation?

It's more of a structural bull market, but with high-volatility rotation internally.

Q4: What is distributed pricing?

It means the market is no longer priced by a single company, but collectively by the entire industry chain.

Q5: What is the key variable for the future AI trend?

The core variable is the shift in bottleneck positions, not the performance of a single leader.