Over the past few years, one of the most prominent features of the US stock market has been the highly concentrated structure of its major indexes. A handful of large technology companies have dominated index performance and served as the driving force behind the AI rally. During this period, both index gains and the spread of the AI narrative have fundamentally relied on the valuation expansion of a few leading companies.
However, after 2026, this structure is beginning to shift. While the market still revolves around AI, capital flows are no longer as concentrated—they’re starting to spread across the entire industry chain. This change doesn’t manifest in single-day price swings, but rather in declining correlations between sectors, faster sector rotations, and deeper internal structural differentiation.
In other words, the US stock market is gradually moving from a "concentrated pricing system" to a "distributed pricing system."
1. The Formation Logic of the Magnificent Seven Era: How Concentrated Pricing Emerged
The so-called Magnificent Seven era is essentially a highly concentrated market structure. During this phase, index gains depended almost entirely on a few large technology companies. These firms were not only key players in AI infrastructure, but also served as core gateways for growth in cloud computing, advertising, consumer tech, and other sectors.
Three foundational conditions enabled this structure. First, the tech sector was highly concentrated, with leading firms controlling the majority of computing power, data, and platform resources. Second, the early stage of AI saw explosive demand for computing power, making GPUs and cloud providers the only clear avenues for growth. Third, abundant market liquidity led capital to focus on the most certain blue-chip assets.
During this stage, market logic was straightforward: index gains equaled gains for a few companies, and the AI rally was synonymous with GPU and cloud computing expansion.
2. The Starting Point of Structural Change: The AI Value Chain Lengthens
As AI entered the era of large model training and expanded inference, a key shift began: the value chain lengthened.
Early AI growth was concentrated on the computing side, but as model sizes continued to scale, bottlenecks started to emerge further down the chain—spanning storage bandwidth, data transmission efficiency, network interconnectivity, and data center energy consumption.
This means AI is no longer a single technical challenge, but a complex systems engineering problem. As system complexity rises, no single company can capture all the growth. The value chain breaks into multiple high-value nodes. As growth sources diversify, capital allocation naturally shifts from concentrated bets on leaders to distributed positioning across the chain.
3. Capital Migration Pathways: From Leader Concentration to Value Chain Rotation
Currently, the US equity capital structure is undergoing a critical transition—from single-point concentration to rotational flows along the value chain. In the earlier phase, the capital path was: large tech companies → leading GPU makers → cloud providers. This was a highly concentrated structure, with capital pricing focused on computing power expansion.
Now, the capital path has evolved into a more complex structure: GPU → HBM (High Bandwidth Memory) → network chips → data centers → power and infrastructure. The essence of this shift is the migration of AI bottlenecks. As GPU supply expands, the market’s attention moves to how data is moved, stored, and efficiently distributed. Once computing power is no longer the sole constraint, the importance of storage and interconnection rises rapidly.
This evolution is transforming the market from a single-track focus to a multi-node rotational structure.
4. Why the Magnificent Seven’s Influence Is Waning: Not Weakening, but Diluting
The influence of the Magnificent Seven hasn’t absolutely declined—it’s been relatively diluted. This dilution stems from two factors.
First, AI growth is no longer concentrated in a single segment, but is spread across multiple nodes in the value chain.
Second, a significant increase in capital expenditures means growth returns are distributed throughout the entire supply chain.
In this structure, even if a single company grows rapidly, it can no longer fully represent the expansion pace of the entire AI industry. The market is beginning to realize that AI is not a company-driven story, but a system-driven one.
As a result, pricing power is gradually shifting from the company level to the industry value chain level.
5. Multi-Center Driven Structure: The US Market Is Rebuilding Its Pricing Model
The US market is now forming a new structural model—a multi-center driven system. In this system, there is no longer a single core asset. Instead, several driving centers coexist, including computing power, storage, networking, and infrastructure. The relationships among these centers are no longer linear but mutually influential. For example, GPUs drive HBM demand, but HBM constraints, in turn, limit GPU expansion; network chips boost data flow efficiency, which impacts computing power utilization.
This complex interplay means the market is no longer driven by a single trend, but by multidimensional rotations.
6. Changing Market Behavior: From Trend Trading to Structural Trading
During the Magnificent Seven-dominated phase, the market favored trend trading—capital was concentrated and volatility was relatively predictable. But as the market enters the distributed pricing era, behavior is shifting noticeably.
- Correlations between sectors are declining; industries no longer rise and fall in sync.
- Rotation is accelerating, with capital quickly moving among different AI value chain segments.
- The divergence between index levels and internal structure is widening—indexes stay elevated, but internal volatility rises sharply.
This shift means trading is becoming more challenging, but structural opportunities are increasing.
7. AI Is Evolving from a Thematic Rally to a Structural Cycle
The fundamental change in the current AI rally is its transition from a theme-driven surge to a structural cycle. Thematic rallies are marked by explosive, concentrated inflows in one direction. Structural cycles, by contrast, feature phased rotations, with growth driven by multiple segments working together. That’s why, even though the market remains focused on AI, the experience has changed—volatility is higher, but the underlying trend persists.
Essentially, the AI rally hasn’t disappeared. It has simply entered a more complex stage of development.
8. Cross-Market Linkages: The US Is No Longer the Sole AI Pricing Center
As the AI value chain globalizes, the US is no longer the only pricing center. Korean equities (storage), Hong Kong equities (technology), and US equities (computing power) now form a complementary structure, with different markets covering different parts of the value chain.
This globally distributed setup further reinforces capital rotation and makes the AI rally more globally interconnected.
Against this backdrop, cross-market analysis has become essential for understanding AI trends.
9. Gate Stock Trading: A Tool for Tracking AI Structural Shifts Across Markets
As the AI value chain expands across computing, storage, networking, and energy, no single market can fully reflect industry changes. US, Hong Kong, and Korean equities now have distinct roles in the industry’s division of labor, making cross-market tracking increasingly important.
Gate stock trading offers 24/7 access to US, Hong Kong, and Korean equities, enabling investors to continuously monitor price movements and capital flows in AI-related assets across different market sessions. From computing chips to storage leaders to infrastructure plays, investors can participate more flexibly in the global AI value chain rotation.
10. Conclusion: The US Stock Market Is Entering a New Era of Distributed Pricing
The US market is undergoing a profound structural transformation—from the concentrated pricing of the Magnificent Seven to distributed pricing along the value chain. The core driver of this shift is the expansion and increasing complexity of the AI value chain.
Looking ahead, the key market question will no longer be whether a single company can keep rising, but which segment of the AI value chain becomes the next bottleneck. Whoever controls the bottleneck holds pricing power.
AI is evolving from an investment theme into a long-term structural cycle, redefining the pricing logic of US equities.
FAQs
Q1: Have the Magnificent Seven truly lost their dominance?
Not absolutely—their relative influence has been diluted as the AI value chain disperses.
Q2: Why is AI driving changes in market structure?
Because AI has shifted from a single-point computing challenge to a systems engineering problem, lengthening the industry value chain.
Q3: Is the market currently in a bull phase or a period of volatility?
It’s more of a structural bull market, but with high internal volatility and rapid rotations.
Q4: What is distributed pricing?
It means the market is no longer priced by a single company, but by the entire industry value chain working together.
Q5: What is the core variable for future AI market trends?
The key variable is the shifting location of bottlenecks, not the performance of any single leader.
