AI supply chains are becoming more complex: Why is advanced packaging more critical than chips?

Over the past few years, the core narrative of the AI industry has almost entirely revolved around "chip performance improvement." The market has focused on GPU computing power, model capabilities, and training efficiency. NVIDIA has become the most central pricing anchor in this phase, with almost all AI asset valuations expanding around chip capabilities.

But entering 2026, an increasingly obvious change has begun to emerge: pure chip performance improvements alone can no longer explain the expansion speed of AI systems. Even as GPUs continue to evolve, the bottlenecks for AI training and inference are shifting to more fundamental layers—how data flows, how chips coordinate, and how systems are packaged.

In other words, AI competition is moving from a "single chip performance race" to a competition of "how the entire system operates collaboratively." And advanced packaging is the core node of this shift.

The Essence of Advanced Packaging: AI Moves from the "Chip Era" to the "System Era"

Advanced packaging was not the most attention-grabbing segment in the traditional semiconductor industry; it was more like a backend step in the manufacturing process. But in the AI era, its importance has been dramatically amplified because AI chips are no longer single computing units—they are complex systems composed of GPUs, HBM, and high-speed interconnection modules.

The core role of advanced packaging is not to make chips smaller, but to enable multiple chips with different functions to work together with higher efficiency. It determines how data flows between chips, whether latency is controllable, and whether the entire system can operate stably.

Against the backdrop of ever-expanding AI models and growing parameter scales, the importance of system efficiency has begun to outweigh single-point performance improvements. Even if the performance of a single GPU continues to improve, if data cannot quickly enter the computing units, the overall system will still be constrained. This means that packaging capability is shifting from a "supporting role" to "core infrastructure."

Why the AI Bottleneck Is Moving from Chips to Packaging

In the past, the market believed the AI bottleneck was in GPUs, but the reality is that when GPU performance reaches a certain level, system bottlenecks begin to expand upstream and laterally.

On one hand, AI training requires a large number of GPUs to work collaboratively, which imposes higher demands on data transmission efficiency. On the other hand, high-bandwidth memory like HBM improves the speed of data supply, but if packaging and interconnection capabilities are insufficient, data still cannot efficiently enter the computing units.

Thus, the market gradually discovers a structural problem: chips are getting stronger, but system efficiency is not improving at the same pace.

This leads to a very critical change: the AI bottleneck is no longer "insufficient computing power," but "computing power cannot be fully utilized." And the core solution to this problem is no longer designing more powerful chips, but improving packaging and system integration capabilities.

CoWoS and HBM: The "Dual Core Structure" of AI Systems

In the current AI supply chain, two keywords are becoming increasingly important: one is CoWoS, and the other is HBM.

CoWoS represents advanced packaging capability—it determines how multiple chips are integrated into an efficient system. HBM represents high-bandwidth memory—it determines how data enters the GPU at high speed. Together, they form the foundational structure of the AI chip system.

But the problem is that both are becoming supply bottlenecks. With the explosive growth of AI demand, packaging capacity and high-end memory capacity are both under tension, limiting the actual output capability of AI chips.

This brings an important market change: the upper limit of AI is no longer determined by design capability, but by the collaborative capability of packaging and memory. In other words, the speed of AI expansion is being controlled by "system capability" rather than "single-point performance."

Supply Chain Restructuring: Moving from Chip-Centric to Packaging-Centric

In the traditional semiconductor cycle, the industry core revolved around chip design—whoever could design a stronger chip could gain a higher market share. But in the AI era, this logic is being rewritten.

Three key changes are occurring in the current industry structure. First, the capacity bottleneck is shifting from wafer manufacturing to the packaging stage. Second, industry value is beginning to concentrate on supply chain bottlenecks rather than the design side. Third, system-level integration capability is beginning to replace single-performance advantages.

This change implies a long-term trend is forming: the AI industry is no longer a "design-driven industry" but a "supply chain-driven industry." Packaging capability is no longer a backend process but a key variable determining the pace of the entire industry.

From a Capital Perspective: Why the Market Is Beginning to Reprice Packaging Capability

From the capital market perspective, the rising importance of advanced packaging essentially means a change in valuation systems.

In the past, the market valued semiconductor companies mainly based on three dimensions: chip performance, market share, and technological leadership. But at the current stage, these factors are giving way to a more fundamental indicator: whether a company holds system-level bottleneck capabilities.

If a company can control packaging capacity or key supply chain nodes, it is not just a manufacturing participant but the pace-setter for the expansion of the entire AI system. This role change directly affects the market's long-term pricing logic for it.

Therefore, packaging capability is shifting from a "cost center" to a "value center" and is beginning to command a premium in the capital market.

Structural Changes in the AI Industry Chain: From Single-Point Competition to System Competition

The most important change in the AI industry today is not the rise or fall of a particular stock, but the migration of the industry structure itself.

The previous AI logic was single-point driven, such as the explosive rise of GPUs or HBM. But now, the market is entering a more complex structure: GPUs, HBM, packaging, data centers, and interconnection networks all participate in pricing, forming a multi-level rotation structure.

This structure means that the duration of AI trends may be longer, but the volatility will also be greater. A single asset no longer dominates the market; instead, multiple bottleneck segments jointly drive the overall cycle.

Gate Stock Trading: AI Supply Chain Opportunities from a Cross-Market Perspective

As the complexity of the AI supply chain increases, related assets are distributed across different markets, such as computing power and equipment companies in US stocks, memory manufacturers in Korean stocks, and Asian manufacturing chain companies. No single market can fully reflect the structural changes in the AI industry.

In this context, Gate stock trading supports 24/7 trading of US stocks, Hong Kong stocks, and Korean stocks, enabling investors to flexibly switch between different markets and track the complete industrial chain changes from computing power to memory to packaging. This cross-market capability makes capturing AI supply chain rotations more efficient.

Conclusion: AI Competition Has Entered the "System-Level Era"

The rise of advanced packaging marks a new phase for the AI industry. Competition is no longer purely about chip performance; it is about how efficiently the entire system operates. From GPUs to HBM, to packaging and interconnection, AI is becoming a systems engineering project highly dependent on supply chain collaboration.

The future core of AI is not just computing power improvement, but system efficiency optimization. Whoever can control the bottleneck segment will determine the expansion pace of the entire industry.

FAQs

• 1. Why has advanced packaging become important in the AI era? Because AI has shifted from single-chip computing to multi-chip system collaboration, and packaging determines overall efficiency.

• 2. What is the relationship between CoWoS and HBM? CoWoS handles system integration, while HBM handles high-speed memory; together they form the foundation of AI performance.

• 3. Why is the AI bottleneck moving from chips to packaging? Because after computing power improves, data flow and system coordination capability become new limiting factors.

• 4. What does this mean for the semiconductor industry? Industry value is shifting from the design side to manufacturing and packaging segments, increasing the importance of the supply chain.

• 5. What role does Gate stock trading play in this trend? It helps investors track different segments of the AI supply chain across markets, improving the efficiency of capturing rotations.