Author: Changan I Biteye Content Team
In November last year, Sun Zhenyu posted a tweet:
Short-term chip shortage, long-term energy shortage, always a storage shortage—BitTorrent’s future is unimaginable.
If you view this statement as an industry assessment rather than a viral catchphrase, you’ll realize upon reflection:
These three lines represent the most realistic profit path for AI market trends.
What would the outcome be if you had bought U.S. stocks related to storage concepts after that tweet was posted?
• Micron: +214%
• Seagate: +180%
• Western Digital: +190%
• SanDisk: +552%
Let’s break this article down along these three lines:
Why will AI first benefit chips, then create an energy bottleneck, and ultimately increase long-term storage demand? Which assets have already emerged in this structural shift?
I. Chip: What will be realized first from the AI boom is not narrative, but orders.
What AI ignited first was not the application layer, but the underlying computing power.
Whether training large models, or performing everyday inference, Agent calls, or multimodal processing, the first step is always to get the computations running—and these computations ultimately depend on GPUs, HBM, high-speed interconnects, and advanced process technologies.
In other words, the growing demand for AI will not first ripple through later stages, but will instead manifest as more immediate realities:
More chips, stronger chips, chips with higher bandwidth.
This is why AI demand is first reflected in the chip sector.
Industry data has clearly outlined this: according to the fiscal year 2026 figures, NVIDIA's revenue increased by 65% year-over-year, indicating that demand for high-performance computing chips continues to grow.
🌟 What assets are available in this direction?
Core Computing Power Layer: NVIDIA (NVDA), AMD, Broadcom (AVGO), TSMC (TSM)
Domestic computing power layer: Hygon Information (688041.SH), Cambricon (688256.SH), and others. Hygon Information is one of China’s leading x86 server CPU companies, with revenue of RMB 9.162 billion in 2024, a 52.4% year-over-year increase.
Semiconductor equipment layer: ASML, Applied Materials (AMAT), Lam Research (LRCX). The stock price of ASML, the leading lithography machine manufacturer, in the form of U.S. ADRs, has reached a new all-time high at the start of 2026, with a single-day gain of over 8% on January 2 and a cumulative rise of 27% since the beginning of the year; Lam Research has risen by as much as 30% since the start of the year; Applied Materials has surged by as much as 28%. The stock prices of these three major semiconductor equipment giants have significantly outperformed the S&P 500 Index.
🌟 Performance Over the Past Year
The chip sector was the earliest to rally and experienced the largest gains in this AI-driven market surge. NVIDIA, as the leader, has seen a cumulative gain of over 1,000% since early 2023. Equipment-related segments continued to hit new highs in early 2026 and remain in a strong upward cycle.
Citigroup released a research report predicting that the global semiconductor equipment sector will enter a "Phase 2 bull market cycle," with the primary focus for chip stocks in 2026 clearly on ASML, Lam Research, and Applied Materials.
II. Energy: As AI grows larger, the bottleneck shifts from chips to electricity
No matter how many chips you have, without power, they won't run.
Buying chips is just the beginning; running large models, data centers, and inference services over the long term requires continuous power supply, along with additional cooling and heat dissipation demands.
Traditional data centers typically have a power capacity of 5 to 15 kilowatts per cabinet, while AI data centers have significantly increased to 50 to 100 kilowatts, creating a completely different scale of power consumption and cooling demands.
The IEA's analysis this year notes that data center electricity consumption is expected to increase to approximately 945 TWh by 2030, roughly doubling from current levels, with AI being the primary driver. The U.S. Department of Energy has also explicitly stated that the growing electricity demand from data centers is placing significant pressure on regional power grids.
🌟 What assets are available in this direction?
Gas turbines: GE Vernova (GEV): Orders for gas turbines have surged, reaching $59 billion for the full year 2025, with backlog growing to $150 billion; management has raised its 2026 revenue guidance to $44–45 billion.
Independent power producer: Constellation Energy (CEG): The largest zero-carbon power operator in the U.S., with nuclear assets directly signing long-term power purchase agreements with tech giants;
Vistra (VST): Combines nuclear and natural gas assets, with a mid-point 2026 EBITDA guidance approximately 30% higher than 2025.
Uranium resources: Cameco (CCJ): The world's largest publicly traded uranium miner and a primary beneficiary of the nuclear power resurgence.
🌟 Performance Over the Past Year
GE Vernova's stock has risen 167% over the past year. The 52-week low was $408, and it reached a high of $1,181, nearly doubling in value within this range.
Constellation Energy reached an all-time high in 2025, then retraced approximately 28% from its peak due to regulatory policy fluctuations, and is currently at a relatively low level.
Vistra remains strong overall, with long-term power supply contracts for data centers continuing to be secured. The energy sector as a whole has been repriced from a traditional defensive position to a core beneficiary of AI infrastructure.
Three: Storage — the most easily overlooked but longest-lasting benefit
The core logic behind beneficial storage is simple: AI is not a one-time call—it is fundamentally a system that continuously ingests, accumulates, and invokes data.
Training requires reading large amounts of data, checkpoints must be saved during training, inference needs to load models and use caching, and RAG and Agent continuously access knowledge bases, logs, and memory.
This way, what AI brings is not just "more data," but:
• More frequent data reading and writing
• Call more real-time
• Manage more complex
• Greater pressure on migration and caching
Looking further down, the more expensive the GPU, the less it can afford to sit idle, so the industry will increasingly focus on how to deliver data faster and more reliably to computing endpoints.
In other words, as AI continues to develop, storage is no longer just a "warehouse for data," but rather the foundational data infrastructure that ensures the entire AI system operates continuously.
🌟 What assets are available in this direction?
Memory chip manufacturers: SK Hynix (000660.KS), Samsung Electronics (005930.KS), Micron Technology (MU)
NAND/SSD/HDD manufacturers: SanDisk (SNDK), Seagate (STX), Western Digital (WDC)
Domestic storage design: GigaDevice, Puyan Semiconductor, Dongxin Semiconductor, Beijing Junzheng, Memblaze, and storage module manufacturers Demingli, Shannon Core, and Jiangbo Long, among others.
🌟 Performance Over the Past Year
Since 2026, the storage sector has been one of the strongest segments in the AI industry chain.
In the U.S. stock market, driven by investments in AI infrastructure and demand for high-capacity storage, Seagate, SanDisk, and Western Digital have all risen significantly this year. According to Reuters at the end of April, Seagate and Western Digital have more than doubled, while SanDisk has risen approximately 350% this year.
The manufacturers of memory chips have also strengthened in tandem; Micron has surged significantly this year, while SK Hynix continues to benefit from HBM shortages and intense demand from major companies for production capacity, reporting a 198% year-over-year increase in first-quarter revenue and a 406% year-over-year increase in operating profit, further enhancing its profitability.
Final note: First come the chips, then the electricity, and finally storage.
The first wave of AI's realization is chips; the second wave's bottleneck is energy; the third wave of long-term beneficiaries is storage.
Logical correctness does not equate to a comfortable entry point. Structural opportunities exist, but don't chase prices blindly.
What truly matters is not the hype itself, but which layer of the industry chain you're on.
Disclaimer: The above is merely a review of the industry chain and does not constitute investment advice. In particular, some assets have experienced extremely significant gains since 2026; even if the logic is sound, it does not mean the entry point is favorable.